Method: Introduced PlotCraft benchmark with 1k challenging visualization tasks across 7 high-level tasks and 48 chart types. Developed SynthVis-30K dataset via collaborative agent framework and created PlotCraftor model for complex data visualization.

Result: Evaluation of 23 leading LLMs revealed performance deficiencies in sophisticated visualization tasks. PlotCraftor achieved performance comparable to leading proprietary approaches, with over 50% improvement on hard tasks across multiple benchmarks.

Conclusion: The work addresses a significant gap in LLM capabilities for complex data visualization and provides a comprehensive benchmark, dataset, and model that substantially improves performance on challenging visualization tasks.

Abstract: Recent Large Language Models (LLMs) have demonstrated remarkable profi- ciency in code generation. However, their ability to create complex visualiza- tions for scaled and structured data remains largely unevaluated and underdevel- oped. To address this gap, we introduce PlotCraft, a new benchmark featuring 1k challenging visualization tasks that cover a wide range of topics, such as fi- nance, scientific research, and sociology. The benchmark is structured around seven high-level visualization tasks and encompasses 48 distinct chart types. Cru- cially, it is the first to systematically evaluate both single-turn generation and multi-turn refinement across a diverse spectrum of task complexities. Our com- prehensive evaluation of 23 leading LLMs on PlotCraft reveals obvious per- formance deficiencies in handling sophisticated visualization tasks. To bridge this performance gap, we develope SynthVis-30K, a large-scale, high-quality dataset of complex visualization code synthesized via a collaborative agent frame- work. Building upon this dataset, we develope PlotCraftor, a novel code gener- ation model that achieves strong capabilities in complex data visualization with a remarkably small size. Across VisEval, PandasPlotBench, and our proposed PlotCraft, PlotCraftor shows performance comparable to that of leading propri- etary approaches. Especially, on hard task, Our model achieves over 50% per- formance improvement. We will release the benchmark, dataset, and code at https://github.com/Speakn0w/PlotCraft-Benchmark.

Method: Uses an LLM-based pipeline with: 1) LLM-guided multi-dimensional corpus augmentation, 2) LoRA-fine-tuned lightweight encoder for embeddings and prototype standardization, 3) Retrieve-reuse-revise-retain inference cycle with prototype voting and continuous prototype library enrichment.

Result: ProtoMBTI outperforms baselines on both MBTI dichotomies and full 16-type tasks across Kaggle and Pandora benchmarks, and shows robust cross-dataset generalization.

Conclusion: Aligning personality inference with psychological prototype reasoning improves accuracy, interpretability, and transfer learning for text-based personality modeling.

Abstract: Personality recognition from text is typically cast as hard-label classification, which obscures the graded, prototype-like nature of human personality judgments. We present ProtoMBTI, a cognitively aligned framework for MBTI inference that operationalizes prototype theory within an LLM-based pipeline. First, we construct a balanced, quality-controlled corpus via LLM-guided multi-dimensional augmentation (semantic, linguistic, sentiment). Next, we LoRA-fine-tune a lightweight (<=2B) encoder to learn discriminative embeddings and to standardize a bank of personality prototypes. At inference, we retrieve top-k prototypes for a query post and perform a retrieve–reuse–revise–retain cycle: the model aggregates prototype evidence via prompt-based voting, revises when inconsistencies arise, and, upon correct prediction, retains the sample to continually enrich the prototype library. Across Kaggle and Pandora benchmarks, ProtoMBTI improves over baselines on both the four MBTI dichotomies and the full 16-type task, and exhibits robust cross-dataset generalization. Our results indicate that aligning the inference process with psychological prototype reasoning yields gains in accuracy, interpretability, and transfer for text-based personality modeling.

Method: Developed a framework of Residual Stream Decoders to probe model activations for paragraph-scale and document-scale plans, testing several decoding methods.

Result: Found that information equivalent to 5+ tokens of future context can be decoded from activations in small models.

Conclusion: These results establish groundwork for improved monitoring of language models and better understanding of how they encode longer-term planning information.

Abstract: Interpretability studies in language models often investigate forward-looking representations of activations. However, as language models become capable of doing ever longer time horizon tasks, methods for understanding activations often remain limited to testing specific concepts or tokens. We develop a framework of Residual Stream Decoders as a method of probing model activations for paragraph-scale and document-scale plans. We test several methods and find information can be decoded equivalent to 5+ tokens of future context in small models. These results lay the groundwork for better monitoring of language models and better understanding how they might encode longer-term planning information.

Method: Proposes predicting information-rich tokens during training instead of standard next-token prediction, evaluated across arithmetic, multi-label text classification, and natural-language generation tasks.

Result: The paper demonstrates improved training effectiveness through selective token prediction strategies, though specific performance metrics are not detailed in the abstract.

Conclusion: Provides a principled approach to LLM training optimization that advances both model performance and theoretical understanding of target-token selection strategies.

Abstract: Optimizing training performance in large language models (LLMs) remains an essential challenge, particularly in improving model performance while maintaining computational costs. This work challenges the conventional approach of training LLMs using next-token prediction (NTP), arguing that by predicting information-rich tokens during training, there is a more effective way to train LLMs. We investigate the impact of the proposed solution in three kinds of tasks for LLMs: arithmetic, multi-label classification of text, and natural-language generation. This work offers a principled approach to optimizing LLM training, advancing both model performance and theoretical understanding of the target-token selection strategies.

Method: Introduced three automatic metrics (prompt-to-line, line-to-line, and Q&A consistency), validated against human annotations, and used them as reward signals for multi-turn reinforcement learning to fine-tune LLMs for patient, student, and social chat partner roles.

Result: The method reduces inconsistency by over 55%, resulting in more coherent and faithful simulated users.

Conclusion: The proposed framework effectively improves persona consistency in LLM-generated dialogue through automatic evaluation metrics and reinforcement learning fine-tuning.

Abstract: Large Language Models (LLMs) are increasingly used to simulate human users in interactive settings such as therapy, education, and social role-play. While these simulations enable scalable training and evaluation of AI agents, off-the-shelf LLMs often drift from their assigned personas, contradict earlier statements, or abandon role-appropriate behavior. We introduce a unified framework for evaluating and improving persona consistency in LLM-generated dialogue. We define three automatic metrics: prompt-to-line consistency, line-to-line consistency, and Q&A consistency, that capture different types of persona drift and validate each against human annotations. Using these metrics as reward signals, we apply multi-turn reinforcement learning to fine-tune LLMs for three user roles: a patient, a student, and a social chat partner. Our method reduces inconsistency by over 55%, resulting in more coherent and faithful simulated users.

Method: The system integrates large language model teammates with a Bloom-aligned, retrieval-augmented copilot (C2D2) that expands curated content into cognitive snippets and uses prompt-engineered agents with scaffolding that fades as learners gain confidence.

Result: In a pilot with four graduate students, participants preferred the agent-based version over physical card decks and viewed it as more scalable, though a ceiling effect was observed on simple knowledge quizzes.

Conclusion: LLM-augmented tabletop exercises can provide lightweight, repeatable cybersecurity practice without traditional logistical burdens, with planned extensions including multi-player modes and larger comparative studies.

Abstract: Traditional cybersecurity tabletop exercises (TTXs) provide valuable training but are often scripted, resource-intensive, and difficult to scale. We introduce AgentBnB, a browser-based re-imagining of the Backdoors & Breaches game that integrates large language model teammates with a Bloom-aligned, retrieval-augmented copilot (C2D2). The system expands a curated corpus into factual, conceptual, procedural, and metacognitive snippets, delivering on-demand, cognitively targeted hints. Prompt-engineered agents employ a scaffolding ladder that gradually fades as learner confidence grows. In a solo-player pilot with four graduate students, participants reported greater intention to use the agent-based version compared to the physical card deck and viewed it as more scalable, though a ceiling effect emerged on a simple knowledge quiz. Despite limitations of small sample size, single-player focus, and narrow corpus, these early findings suggest that large language model augmented TTXs can provide lightweight, repeatable practice without the logistical burden of traditional exercises. Planned extensions include multi-player modes, telemetry-driven coaching, and comparative studies with larger cohorts.

Method: Created IL-PCR corpus for both retrieval tasks, experimented with lexical models, semantic models, GNN-based ensembles, and developed an LLM-based re-ranking approach to exploit task interdependence.

Result: The LLM-based re-ranking approach achieved the best performance by leveraging the dependence between statute retrieval and precedent retrieval tasks.

Conclusion: IL-PCR provides a unified framework for legal retrieval tasks, and exploiting the interdependence between statute and precedent retrieval through LLM-based re-ranking yields superior performance.

Abstract: Identifying/retrieving relevant statutes and prior cases/precedents for a given legal situation are common tasks exercised by law practitioners. Researchers to date have addressed the two tasks independently, thus developing completely different datasets and models for each task; however, both retrieval tasks are inherently related, e.g., similar cases tend to cite similar statutes (due to similar factual situation). In this paper, we address this gap. We propose IL-PCR (Indian Legal corpus for Prior Case and Statute Retrieval), which is a unique corpus that provides a common testbed for developing models for both the tasks (Statute Retrieval and Precedent Retrieval) that can exploit the dependence between the two. We experiment extensively with several baseline models on the tasks, including lexical models, semantic models and ensemble based on GNNs. Further, to exploit the dependence between the two tasks, we develop an LLM-based re-ranking approach that gives the best performance.

Method: Proposes POSESTITCH-SLT, a pre-training scheme inspired by linguistic-template-based sentence generation, using template-generated sentence pairs to train a transformer-based encoder-decoder architecture.

Result: Achieves significant BLEU-4 score improvements: from 1.97 to 4.56 on How2Sign and from 0.55 to 3.43 on iSign, outperforming prior state-of-the-art methods for pose-based gloss-free translation.

Conclusion: Template-driven synthetic supervision is effective for low-resource sign language translation settings, demonstrating the value of synthetic data generation in overcoming data scarcity.

Abstract: Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. Prior arts have focused on various feature extraction and architectural changes to support neural machine translation for sign languages. We propose POSESTITCH-SLT, a novel pre-training scheme that is inspired by linguistic-templates-based sentence generation technique. With translation comparison on two sign language datasets, How2Sign and iSign, we show that a simple transformer-based encoder-decoder architecture outperforms the prior art when considering template-generated sentence pairs in training. We achieve BLEU-4 score improvements from 1.97 to 4.56 on How2Sign and from 0.55 to 3.43 on iSign, surpassing prior state-of-the-art methods for pose-based gloss-free translation. The results demonstrate the effectiveness of template-driven synthetic supervision in low-resource sign language settings.

Method: Introduces PBLBench benchmark with Analytic Hierarchy Process (AHP) for expert-driven pairwise comparisons to establish reliable ground truth and structured evaluation criteria. Evaluates 15 leading MLLMs/LLMs on complex reasoning tasks requiring domain-specific knowledge and long-context understanding.

Result: Even the most advanced models achieve only 59% rank accuracy on PBLBench, highlighting significant challenges in handling complex educational reasoning tasks that resemble those handled by human experts.

Conclusion: PBLBench serves as a catalyst for developing more capable AI agents to alleviate teacher workload and enhance educational productivity, addressing current limitations in MLLM evaluation for educational applications.

Abstract: Project-Based Learning (PBL) involves a variety of highly correlated multimodal data, making it a vital educational approach within STEM disciplines. With the rapid development of multimodal large language models (MLLMs), researchers have begun exploring their potential to enhance tasks such as information retrieval, knowledge comprehension, and data generation in educational settings. However, existing benchmarks fall short in providing both a free-form output structure and a rigorous human expert validation process, limiting their effectiveness in evaluating real-world educational tasks. Additionally, few methods have developed automated pipelines to assist with the complex responsibilities of teachers leveraging MLLMs, largely due to model hallucination and instability, which lead to unreliable implementation. To address this gap, we introduce PBLBench, a novel benchmark designed to evaluate complex reasoning grounded in domain-specific knowledge and long-context understanding, thereby challenging models with tasks that closely resemble those handled by human experts. To establish reliable ground truth, we adopt the Analytic Hierarchy Process (AHP), utilizing expert-driven pairwise comparisons to derive structured and weighted evaluation criteria. We assess the performance of 15 leading MLLMs/LLMs using PBLBench and demonstrate that even the most advanced models achieve only 59% rank accuracy, underscoring the significant challenges presented by this benchmark. We believe PBLBench will serve as a catalyst for the development of more capable AI agents, ultimately aiming to alleviate teacher workload and enhance educational productivity.

Method: Builds upon Mamba-2 architecture with parallel training capabilities and constant inference complexity. Introduces a sparse formulation that updates only a subset of recurrent states while maintaining performance comparable to dense models.

Result: Achieves performance comparable to Transformers on short-context tasks while demonstrating superior generalization in long-context scenarios. The sparse version preserves strong performance while improving efficiency.

Conclusion: Factorization Memory represents the first RNN architecture that successfully combines sparse memory activation with competitive performance across both short and long-context settings, providing an efficient alternative to Transformers.

Abstract: We propose Factorization Memory, an efficient recurrent neural network (RNN) architecture that achieves performance comparable to Transformer models on short-context language modeling tasks while also demonstrating superior generalization in long-context scenarios. Our model builds upon Mamba-2, enabling Factorization Memory to exploit parallel computations during training while preserving constant computational and memory complexity during inference. To further optimize model efficiency and representational capacity, we develop a sparse formulation of Factorization Memory that updates only a subset of recurrent states at each step while preserving the strong performance of its dense counterpart. To our knowledge, this represents the first RNN architecture that successfully combines sparse memory activation with competitive performance across both short and long-context settings. This work provides a systematic empirical analysis of Factorization Memory in comparison to Transformer and Mamba-2 architectures.

Method: Uses automated pipeline to construct Spatial Knowledge Graphs (SKG) capturing human-like spatial cognition (directional and distance relationships), then integrates diffusion models for image generation and MLLMs for text description to create spatially-consistent multimodal data.

Result: Data synthesized from diverse spatial knowledge types (direction and distance) significantly enhances MLLMs’ spatial perception and reasoning abilities, though with slight reduction in general capabilities. The approach enables scalable generation of realistic spatial configurations.

Conclusion: Knowledge-based data synthesis using spatial knowledge graphs can effectively advance spatial intelligence development in MLLMs, providing a systematic framework for generating spatially coherent multimodal data.

Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have significantly enhanced their capabilities; however, their spatial perception abilities remain a notable limitation. To address this challenge, multimodal data synthesis offers a promising solution. Yet, ensuring that synthesized data adhere to spatial common sense is a non-trivial task. Our approach addresses this critical gap by providing a systematic framework for generating spatially coherent data. In this work, we introduce SKG2DATA, a novel multimodal synthesis approach guided by spatial knowledge graphs, grounded in the concept of knowledge-to-data generation. SKG2DATA employs an automated pipeline for constructing Spatial Knowledge Graph (SKG) that effectively captures human-like spatial cognition, including directional and distance relationships. These structured representations then serve as precise guidance for our integrated synthesis pipeline, where a diffusion model generates spatially-consistent images while a MLLM produces corresponding textual descriptions. The automated construction of SKG enables scalable generation of diverse yet realistic spatial configurations, overcoming the limitations of manual data collection and annotation. Extensive experiments demonstrate that data synthesized from diverse types of spatial knowledge, including direction and distance, enhance the spatial perception and reasoning abilities of MLLMs markedly, albeit with a slight cost to their general capabilities. We hope that the idea of knowledge-based data synthesis can advance the development of spatial intelligence. Code is available at https://github.com/zjunlp/Knowledge2Data.

Method: Formal analysis of the causal language modeling objective’s structural properties, showing it assigns identical likelihood to corpora and their reversals.

Result: Demonstrates that standard CLM pretraining is direction-blind due to reversal invariance, explaining comparable performance between forward and reversed text training.

Conclusion: Proposes viewing pretraining through temporal asymmetry lens, suggesting future work on loss functions and architectures that explicitly model language’s directional dependencies while maintaining modeling capacity.

Abstract: We formalize a structural property of the causal (autoregressive) language modeling (CLM) objective: reversal invariance. Formally, the next-token prediction loss assigns identical likelihood to a corpus and its reversal, implying that standard CLM pretraining is direction-blind. This symmetry explains why models trained on reversed text can achieve comparable performance to those trained on forward text, despite the inherently time-asymmetric nature of human language and reasoning. We argue that this invariance represents a limitation of current pretraining objectives rather than a benign artifact. If natural language encodes directional dependencies - phonological, morphological, or causal - a symmetric objective may fail to capture them. We therefore propose viewing pretraining through the lens of temporal asymmetry, motivating future work on loss functions and architectures that explicitly model the arrow of language while retaining standard language modeling capacity.

Method: Uses controlled Word-Gloss Inference task where models infer missing words and glosses using varying levels of linguistic information (glosses, grammatical explanations, translations) from typologically diverse reference grammars.

Result: Incorporating structured linguistic cues leads to consistent improvements in reasoning performance across all tested models.

Conclusion: Highlights both the promise and current limitations of using LLMs for typologically informed linguistic analysis and low-resource language documentation.

Abstract: This paper introduces LingGym, a new benchmark that evaluates LLMs’ capacity for meta-linguistic reasoning using Interlinear Glossed Text (IGT) and grammatical descriptions extracted from 18 typologically diverse reference grammars. Unlike previous work that focuses on specific downstream tasks, we assess whether LLMs can generalize linguistic inference across low-resource languages and structures not seen during training. We present a controlled evaluation task: Word-Gloss Inference, in which the model must infer a missing word and gloss from context using varying levels of linguistic information (e.g., glosses, grammatical explanations, translations). Our results show that incorporating structured linguistic cues leads to consistent improvements in reasoning performance across all models. This work highlights both the promise and current limitations of using LLMs for typologically informed linguistic analysis and low-resource language documentation.

Method: Created MultiMed-ST dataset with 290,000 samples across 5 languages (Vietnamese, English, German, French, Chinese), conducted comprehensive analysis including empirical baselines, bilingual-multilingual comparison, end-to-end vs. cascaded approaches, task-specific vs. multi-task models, code-switch analysis, and error analysis.

Result: MultiMed-ST is the largest medical machine translation dataset and the largest many-to-many multilingual speech translation dataset across all domains. The study provides the most comprehensive speech translation analysis in the field’s history.

Conclusion: The work establishes foundational resources for medical speech translation with publicly available code, data, and models, enabling future research and practical applications in healthcare communication.

Abstract: Multilingual speech translation (ST) and machine translation (MT) in the medical domain enhances patient care by enabling efficient communication across language barriers, alleviating specialized workforce shortages, and facilitating improved diagnosis and treatment, particularly during pandemics. In this work, we present the first systematic study on medical ST, to our best knowledge, by releasing MultiMed-ST, a large-scale ST dataset for the medical domain, spanning all translation directions in five languages: Vietnamese, English, German, French, and Simplified/Traditional Chinese, together with the models. With 290,000 samples, this is the largest medical MT dataset and the largest many-to-many multilingual ST among all domains. Secondly, we present the most comprehensive ST analysis in the field’s history, to our best knowledge, including: empirical baselines, bilingual-multilingual comparative study, end-to-end vs. cascaded comparative study, task-specific vs. multi-task sequence-to-sequence comparative study, code-switch analysis, and quantitative-qualitative error analysis. All code, data, and models are available online: https://github.com/leduckhai/MultiMed-ST

Method: Created a dataset of debugging problems with manually annotated reasoning trajectories, then developed LLM-based solutions to generate reasoning trajectories and Socratic conversations based on them.

Result: Frontier models achieved 91% correct reasoning trajectories and 98.7% valid conversation turns in large-scale LLM-as-judge evaluation.

Conclusion: LLMs can effectively generate reasoning trajectories and Socratic conversations for debugging, enabling students to identify and correct programming misconceptions through guided discovery.

Abstract: In Socratic debugging, instructors guide students towards identifying and fixing a bug on their own, instead of providing the bug fix directly. Most novice programmer bugs are caused by programming misconceptions, namely false beliefs about a programming concept. In this context, Socratic debugging can be formulated as a guided Reasoning Trajectory (RT) leading to a statement about the program behavior that contradicts the bug-causing misconception. Upon reaching this statement, the ensuing cognitive dissonance leads the student to first identify and then update their false belief. In this paper, we introduce the task of reasoning trajectory generation, together with a dataset of debugging problems manually annotated with RTs. We then describe LLM-based solutions for generating RTs and Socratic conversations that are anchored on them. A large-scale LLM-as-judge evaluation shows that frontier models can generate up to 91% correct reasoning trajectories and 98.7% valid conversation turns.

Method: Intrinsic mechanism analysis of iterative paraphrasing effects, creation of PADBen benchmark with five-type text taxonomy and five progressive detection tasks, evaluation of 11 state-of-the-art detectors.

Result: Detectors achieve over 90% accuracy on direct LLM outputs but fail catastrophically against iteratively-paraphrased content. Critical asymmetry found: detectors identify plagiarism evasion but fail at authorship obfuscation.

Conclusion: Current detection approaches cannot handle the intermediate laundering region created by iterative paraphrasing, requiring fundamental advances beyond existing semantic and stylistic discrimination methods.

Abstract: While AI-generated text (AIGT) detectors achieve over 90% accuracy on direct LLM outputs, they fail catastrophically against iteratively-paraphrased content. We investigate why iteratively-paraphrased text – itself AI-generated – evades detection systems designed for AIGT identification. Through intrinsic mechanism analysis, we reveal that iterative paraphrasing creates an intermediate laundering region characterized by semantic displacement with preserved generation patterns, which brings up two attack categories: paraphrasing human-authored text (authorship obfuscation) and paraphrasing LLM-generated text (plagiarism evasion). To address these vulnerabilities, we introduce PADBen, the first benchmark systematically evaluating detector robustness against both paraphrase attack scenarios. PADBen comprises a five-type text taxonomy capturing the full trajectory from original content to deeply laundered text, and five progressive detection tasks across sentence-pair and single-sentence challenges. We evaluate 11 state-of-the-art detectors, revealing critical asymmetry: detectors successfully identify the plagiarism evasion problem but fail for the case of authorship obfuscation. Our findings demonstrate that current detection approaches cannot effectively handle the intermediate laundering region, necessitating fundamental advances in detection architectures beyond existing semantic and stylistic discrimination methods. For detailed code implementation, please see https://github.com/JonathanZha47/PadBen-Paraphrase-Attack-Benchmark.

Method: Built MedRECT benchmark from Japanese Medical Licensing Exams and curated English counterpart, evaluating 9 LLMs across three subtasks: error detection, localization, and correction. Used LoRA fine-tuning for targeted improvements.

Result: Reasoning models showed substantial improvements (up to 13.5% in detection, 51% in extraction). Cross-lingual gaps of 5-10% from English to Japanese. Fine-tuned models achieved asymmetric improvements in correction and exceeded human expert performance.

Conclusion: MedRECT is the first comprehensive cross-lingual benchmark for medical error correction, providing a reproducible framework for developing safer medical LLMs across languages.

Abstract: Large language models (LLMs) show increasing promise in medical applications, but their ability to detect and correct errors in clinical texts – a prerequisite for safe deployment – remains under-evaluated, particularly beyond English. We introduce MedRECT, a cross-lingual benchmark (Japanese/English) that formulates medical error handling as three subtasks: error detection, error localization (sentence extraction), and error correction. MedRECT is built with a scalable, automated pipeline from the Japanese Medical Licensing Examinations (JMLE) and a curated English counterpart, yielding MedRECT-ja (663 texts) and MedRECT-en (458 texts) with comparable error/no-error balance. We evaluate 9 contemporary LLMs spanning proprietary, open-weight, and reasoning families. Key findings: (i) reasoning models substantially outperform standard architectures, with up to 13.5% relative improvement in error detection and 51.0% in sentence extraction; (ii) cross-lingual evaluation reveals 5-10% performance gaps from English to Japanese, with smaller disparities for reasoning models; (iii) targeted LoRA fine-tuning yields asymmetric improvements in error correction performance (Japanese: +0.078, English: +0.168) while preserving reasoning capabilities; and (iv) our fine-tuned model exceeds human expert performance on structured medical error correction tasks. To our knowledge, MedRECT is the first comprehensive cross-lingual benchmark for medical error correction, providing a reproducible framework and resources for developing safer medical LLMs across languages.

Method: G2 employs a base generator with dual Guides that guide generation through decoding-based interventions, encouraging diverse outputs conditioned on the original query without requiring training.

Result: Comprehensive experiments show G2 effectively improves output diversity while maintaining optimal balance between diversity and quality.

Conclusion: G2 successfully addresses LLM output diversity limitations through a training-free approach that preserves generation quality while enhancing diversity.

Abstract: Large Language Models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. However, these models exhibit a critical limitation in output diversity, often generating highly similar content across multiple attempts. This limitation significantly affects tasks requiring diverse outputs, from creative writing to reasoning. Existing solutions, like temperature scaling, enhance diversity by modifying probability distributions but compromise output quality. We propose Guide-to-Generation (G2), a training-free plug-and-play method that enhances output diversity while preserving generation quality. G2 employs a base generator alongside dual Guides, which guide the generation process through decoding-based interventions to encourage more diverse outputs conditioned on the original query. Comprehensive experiments demonstrate that G2 effectively improves output diversity while maintaining an optimal balance between diversity and quality.

Method: The study assesses memorization effects across three LLMs (DeepSeek R1, Llama 4 Scout, Mixtral 8x7B), analyzing how memorization-driven outputs vary across academic disciplines and world regions.

Result: Global analysis shows consistent bias favoring highly cited researchers, but this pattern is not uniform - Clinical Medicine and parts of Africa show more balanced representation, suggesting areas where LLM training data may reflect greater equity.

Conclusion: The findings highlight both risks and opportunities in deploying LLMs for scholarly discovery, emphasizing the need to address memorization-driven biases while recognizing potential for more equitable representation in certain contexts.

Abstract: Ongoing breakthroughs in Large Language Models (LLMs) are reshaping search and recommendation platforms at their core. While this shift unlocks powerful new scientometric tools, it also exposes critical fairness and bias issues that could erode the integrity of the information ecosystem. Additionally, as LLMs become more integrated into web-based searches for scholarly tools, their ability to generate summarized research work based on memorized data introduces new dimensions to these challenges. The extent of memorization in LLMs can impact the accuracy and fairness of the co-authorship networks they produce, potentially reflecting and amplifying existing biases within the scientific community and across different regions. This study critically examines the impact of LLM memorization on the co-authorship networks. To this end, we assess memorization effects across three prominent models, DeepSeek R1, Llama 4 Scout, and Mixtral 8x7B, analyzing how memorization-driven outputs vary across academic disciplines and world regions. While our global analysis reveals a consistent bias favoring highly cited researchers, this pattern is not uniformly observed. Certain disciplines, such as Clinical Medicine, and regions, including parts of Africa, show more balanced representation, pointing to areas where LLM training data may reflect greater equity. These findings underscore both the risks and opportunities in deploying LLMs for scholarly discovery.

Method: Built BHEPC corpus with expert human translators across education, administration, and news domains. Evaluated proprietary and open-source MLLMs on bidirectional translation tasks, including fine-tuning NLLB-200 and testing in-context learning capabilities.

Result: Fine-tuned NLLB-200 distilled 600M variant model outperformed other models in bidirectional translation between English/Hindi and Bhili. Comprehensive evaluation established benchmarks and assessed cross-domain generalization.

Conclusion: This work bridges critical resource gaps for low-resource languages and promotes inclusive NLP technologies, demonstrating multilingual models’ potential in low-resource scenarios.

Abstract: The linguistic diversity of India poses significant machine translation challenges, especially for underrepresented tribal languages like Bhili, which lack high-quality linguistic resources. This paper addresses the gap by introducing Bhili-Hindi-English Parallel Corpus (BHEPC), the first and largest parallel corpus worldwide comprising 110,000 meticulously curated sentences across Bhili, Hindi, and English. The corpus was created with the assistance of expert human translators. BHEPC spans critical domains such as education, administration, and news, establishing a valuable benchmark for research in low resource machine translation. To establish a comprehensive Bhili Machine Translation benchmark, we evaluated a wide range of proprietary and open-source Multilingual Large Language Models (MLLMs) on bidirectional translation tasks between English/Hindi and Bhili. Comprehensive evaluation demonstrates that the fine-tuned NLLB-200 distilled 600M variant model outperforms others, highlighting the potential of multilingual models in low resource scenarios. Furthermore, we investigated the generative translation capabilities of multilingual LLMs on BHEPC using in-context learning, assessing performance under cross-domain generalization and quantifying distributional divergence. This work bridges a critical resource gap and promotes inclusive natural language processing technologies for low-resource and marginalized languages globally.

Method: Designed utility and privacy tests on large-scale datasets with dynamic split sizes, running experiments on datasets of varying sizes (up to 1 million texts) to quantify the effect of increasing dataset size.

Result: Findings reveal that dataset size plays an integral role in evaluating DP text rewriting mechanisms, significantly affecting the privacy-utility trade-off.

Conclusion: The study calls for more rigorous evaluation procedures in DP NLP and provides insights into the future of DP NLP in practice and at scale.

Abstract: Recent work in Differential Privacy with Natural Language Processing (DP NLP) has proposed numerous promising techniques in the form of text rewriting mechanisms. In the evaluation of these mechanisms, an often-ignored aspect is that of dataset size, or rather, the effect of dataset size on a mechanism’s efficacy for utility and privacy preservation. In this work, we are the first to introduce this factor in the evaluation of DP text privatization, where we design utility and privacy tests on large-scale datasets with dynamic split sizes. We run these tests on datasets of varying size with up to one million texts, and we focus on quantifying the effect of increasing dataset size on the privacy-utility trade-off. Our findings reveal that dataset size plays an integral part in evaluating DP text rewriting mechanisms; additionally, these findings call for more rigorous evaluation procedures in DP NLP, as well as shed light on the future of DP NLP in practice and at scale.

Method: ToM constructs a DocTree through hierarchical semantic parsing of document structure, then performs recursive reasoning using Tree MapReduce: Map step generates rationales at child nodes, Reduce step aggregates rationales across siblings to resolve conflicts at parent nodes.

Result: Experimental results on 70B+ LLMs show ToM significantly outperforms existing divide-and-conquer frameworks and retrieval-augmented generation methods, achieving better logical coherence and long-context reasoning.

Conclusion: ToM effectively addresses limitations of current approaches by leveraging document hierarchy for coherent long-context reasoning through recursive bottom-up aggregation.

Abstract: Large Language Models (LLMs), constrained by limited context windows, often face significant performance degradation when reasoning over long contexts. To address this, Retrieval-Augmented Generation (RAG) retrieves and reasons over chunks but frequently sacrifices logical coherence due to its reliance on similarity-based rankings. Similarly, divide-and-conquer frameworks (DCF) split documents into small chunks for independent reasoning and aggregation. While effective for local reasoning, DCF struggles to capture long-range dependencies and risks inducing conflicts by processing chunks in isolation. To overcome these limitations, we propose ToM, a novel Tree-oriented MapReduce framework for long-context reasoning. ToM leverages the inherent hierarchical structure of long documents (e.g., main headings and subheadings) by constructing a DocTree through hierarchical semantic parsing and performing bottom-up aggregation. Using a Tree MapReduce approach, ToM enables recursive reasoning: in the Map step, rationales are generated at child nodes; in the Reduce step, these rationales are aggregated across sibling nodes to resolve conflicts or reach consensus at parent nodes. Experimental results on 70B+ LLMs show that ToM significantly outperforms existing divide-and-conquer frameworks and retrieval-augmented generation methods, achieving better logical coherence and long-context reasoning. Our code is available at https://github.com/gjn12-31/ToM .

Method: Proposes Mastery-Score metric to identify redundant knowledge for corpus pruning, Query Router to avoid irrelevant documents, and Noise-Tolerant Tuning to improve LLM’s internal knowledge utilization with pruned corpus.

Result: Zero-RAG prunes Wikipedia corpus by 30%, accelerates retrieval stage by 22%, and maintains RAG performance while reducing knowledge redundancy.

Conclusion: Zero-RAG effectively addresses knowledge redundancy in RAG systems through corpus pruning and improved internal knowledge utilization, demonstrating practical efficiency gains without compromising performance.

Abstract: Retrieval-Augmented Generation has shown remarkable results to address Large Language Models’ hallucinations, which usually uses a large external corpus to supplement knowledge to LLMs. However, with the development of LLMs, the internal knowledge of LLMs has expanded significantly, thus causing significant knowledge redundancy between the external corpus and LLMs. On the one hand, the indexing cost of dense retrieval is highly related to the corpus size and thus significant redundant knowledge intensifies the dense retrieval’s workload. On the other hand, the redundant knowledge in the external corpus is not helpful to LLMs and our exploratory analysis shows that it instead hurts the RAG performance on those questions which the LLM can answer by itself. To address these issues, we propose Zero-RAG to tackle these challenges. Specifically, we first propose the Mastery-Score metric to identify redundant knowledge in the RAG corpus to prune it. After pruning, answers to “mastered” questions rely primarily on internal knowledge of the LLM. To better harness the internal capacity, we propose Query Router and Noise-Tolerant Tuning to avoid the irrelevant documents’ distraction and thus further improve the LLM’s utilization of internal knowledge with pruned corpus. Experimental results show that Zero-RAG prunes the Wikipedia corpus by 30% and accelerates the retrieval stage by 22%, without compromising RAG’s performance.

Method: Fine-tuned DialoGPT (a lightweight generative dialogue model) on a synthetically constructed dataset of doctor-patient interactions covering ten common diseases prevalent in rural Nepal.

Result: The fine-tuned model produced coherent, contextually relevant, and medically appropriate responses, demonstrating understanding of symptoms, disease context, and empathetic communication.

Conclusion: Compact, offline-capable dialogue models with targeted datasets are effective for domain adaptation in low-resource healthcare environments, offering promising directions for rural medical conversational AI.

Abstract: Conversational agents are increasingly being explored to support healthcare delivery, particularly in resource-constrained settings such as rural Nepal. Large-scale conversational models typically rely on internet connectivity and cloud infrastructure, which may not be accessible in rural areas. In this study, we fine-tuned DialoGPT, a lightweight generative dialogue model that can operate offline, on a synthetically constructed dataset of doctor-patient interactions covering ten common diseases prevalent in rural Nepal, including common cold, seasonal fever, diarrhea, typhoid fever, gastritis, food poisoning, malaria, dengue fever, tuberculosis, and pneumonia. Despite being trained on a limited, domain-specific dataset, the fine-tuned model produced coherent, contextually relevant, and medically appropriate responses, demonstrating an understanding of symptoms, disease context, and empathetic communication. These results highlight the adaptability of compact, offline-capable dialogue models and the effectiveness of targeted datasets for domain adaptation in low-resource healthcare environments, offering promising directions for future rural medical conversational AI.

Method: Introduces MALoR metric to quantify bias based on masked token probabilities, and proposes mitigation through continued pre-training on gender-balanced datasets generated via Counterfactual Data Augmentation.

Result: Significant bias reduction across models: BERT-base “he-she” bias dropped from 1.27 to 0.08, “his-her” from 2.51 to 0.36; BERT-large “male-female” bias decreased from 1.82 to 0.10. Similar improvements observed in other models.

Conclusion: The proposed approach effectively reduces gender bias in transformer models without compromising downstream task performance, providing a practical solution for bias mitigation.

Abstract: Gender bias in language models has gained increasing attention in the field of natural language processing. Encoder-based transformer models, which have achieved state-of-the-art performance in various language tasks, have been shown to exhibit strong gender biases inherited from their training data. This paper investigates gender bias in contextualized word embeddings, a crucial component of transformer-based models. We focus on prominent architectures such as BERT, ALBERT, RoBERTa, and DistilBERT to examine their vulnerability to gender bias. To quantify the degree of bias, we introduce a novel metric, MALoR, which assesses bias based on model probabilities for filling masked tokens. We further propose a mitigation approach involving continued pre-training on a gender-balanced dataset generated via Counterfactual Data Augmentation. Our experiments reveal significant reductions in gender bias scores across different pronoun pairs. For instance, in BERT-base, bias scores for “he-she” dropped from 1.27 to 0.08, and “his-her” from 2.51 to 0.36 following our mitigation approach. We also observed similar improvements across other models, with “male-female” bias decreasing from 1.82 to 0.10 in BERT-large. Our approach effectively reduces gender bias without compromising model performance on downstream tasks.

Method: Detect word salad behavior using a single-layer linear classifier that analyzes hidden states of <\n\n> tokens trailing reasoning chunks. Once detected, apply a simple chop followed by a straightforward regeneration prompt.

Result: Substantial length savings with minimal quality loss. WSC is lightweight, minimally invasive, and only removes semantically redundant tokens.

Conclusion: WSC or similar components are essential for all LRM applications focused on user experience, given their low overhead, strong savings, and the lack of semantic value in word salad tokens.

Abstract: Large Reasoning Models (LRMs) are often bottlenecked by the high cost of output tokens. We show that a significant portion of these tokens are useless self-repetitions - what we call “word salad” - that exhaust the decoding budget without adding value. Interestingly, we observe that LRMs are self-aware when trapped in these loops: the hidden states of <\n\n> tokens trailing each reasoning chunk exhibit patterns that allow us to detect word salad behavior on-the-fly via a single-layer linear classifier. Once detected, a simple chop appended by a straightforward regeneration prompt yields substantial length savings with minimal quality loss. Our work offers WordSaladChopper (WSC) - a lightweight, turnkey component for LRM that is minimally invasive to its reasoning trajectory by only removing semantically redundant tokens. Given its low overhead, strong savings, and the lack of semantic value of word salad tokens, we believe it is not too far-fetched to argue that WSC - or a similar component - is a must-have for all LRM applications with user experience in mind. Our code is publicly available at https://github.com/wenyaxie023/WordSaladChopper.

Method: Proposes CISEA-MRFE framework with three components: Contextual Instruction (CI) for domain-aware sentiment disambiguation, Semantic Enhancement Augmentation (SEA) for sentiment-consistent paraphrastic augmentation, and Multi-Refined Feature Extraction (MRFE) combining Scale-Adaptive Depthwise Encoder (SADE) for multi-scale features and Emotion Evaluator Context Encoder (EECE) for affect-aware modeling.

Result: Outperforms strong baselines on four benchmark datasets with relative accuracy improvements: 4.6% on IMDb, 6.5% on Yelp, 30.3% on Twitter, and 4.1% on Amazon.

Conclusion: CISEA-MRFE demonstrates effective generalization ability for sentiment classification across varied domains, validating the proposed approach’s effectiveness.

Abstract: Sentiment analysis using deep learning and pre-trained language models (PLMs) has gained significant traction due to their ability to capture rich contextual representations. However, existing approaches often underperform in scenarios involving nuanced emotional cues, domain shifts, and imbalanced sentiment distributions. We argue that these limitations stem from inadequate semantic grounding, poor generalization to diverse linguistic patterns, and biases toward dominant sentiment classes. To overcome these challenges, we propose CISEA-MRFE, a novel PLM-based framework integrating Contextual Instruction (CI), Semantic Enhancement Augmentation (SEA), and Multi-Refined Feature Extraction (MRFE). CI injects domain-aware directives to guide sentiment disambiguation; SEA improves robustness through sentiment-consistent paraphrastic augmentation; and MRFE combines a Scale-Adaptive Depthwise Encoder (SADE) for multi-scale feature specialization with an Emotion Evaluator Context Encoder (EECE) for affect-aware sequence modeling. Experimental results on four benchmark datasets demonstrate that CISEA-MRFE consistently outperforms strong baselines, achieving relative improvements in accuracy of up to 4.6% on IMDb, 6.5% on Yelp, 30.3% on Twitter, and 4.1% on Amazon. These results validate the effectiveness and generalization ability of our approach for sentiment classification across varied domains.

Method: ISA establishes a taxonomy of intent transformations to generate attacks that mislead LLMs into perceiving harmful requests as benign information-seeking queries, using minimal natural edits rather than complex tokens or lengthy context.

Result: Extensive experiments show ISA achieves over 70% improvement in attack success rate compared to direct harmful prompts, and fine-tuning models on ISA-reformulated benign data elevates success rates to nearly 100%. Existing defenses prove inadequate against ISA.

Conclusion: ISA reveals fundamental challenges in intent inference for LLM safety and underscores the need for more effective defenses beyond current methods.

Abstract: Large language models (LLMs) remain vulnerable to jailbreaking attacks despite their impressive capabilities. Investigating these weaknesses is crucial for robust safety mechanisms. Existing attacks primarily distract LLMs by introducing additional context or adversarial tokens, leaving the core harmful intent unchanged. In this paper, we introduce ISA (Intent Shift Attack), which obfuscates LLMs about the intent of the attacks. More specifically, we establish a taxonomy of intent transformations and leverage them to generate attacks that may be misperceived by LLMs as benign requests for information. Unlike prior methods relying on complex tokens or lengthy context, our approach only needs minimal edits to the original request, and yields natural, human-readable, and seemingly harmless prompts. Extensive experiments on both open-source and commercial LLMs show that ISA achieves over 70% improvement in attack success rate compared to direct harmful prompts. More critically, fine-tuning models on only benign data reformulated with ISA templates elevates success rates to nearly 100%. For defense, we evaluate existing methods and demonstrate their inadequacy against ISA, while exploring both training-free and training-based mitigation strategies. Our findings reveal fundamental challenges in intent inference for LLMs safety and underscore the need for more effective defenses. Our code and datasets are available at https://github.com/NJUNLP/ISA.

Method: Present FlashEVA, an efficient implementation of EVA (Efficient Attention via Control Variates), and demonstrate how to finetune transformers to adapt to FlashEVA attention with as few as 1.5B tokens.

Result: FlashEVA achieves up to 6.7x higher throughput and 5x lower peak GPU memory usage during inference compared to standard Transformer implementations, while preserving effectiveness across various downstream tasks.

Conclusion: FlashEVA represents a significant step towards more efficient and adaptable Transformer-based models for inference, offering control over throughput-accuracy trade-offs through adjustable hyperparameters, though limitations exist in retrieval-focused tasks.

Abstract: Transformer models have revolutionized natural language processing, achieving state-of-the-art performance and demonstrating remarkable scalability. However, their memory demands, particularly due to maintaining full context in memory, pose significant challenges for inference. In this paper, we present FlashEVA, an efficient implementation of EVA (Efficient Attention via Control Variates), and demonstrate how to finetune transformers to adapt to FlashEVA attention. Our method enables fine-tuning of Transformer models with as few as 1.5B tokens while preserving effectiveness across various downstream tasks. Notably, FlashEVA achieves up to 6.7x higher throughput and 5x lower peak GPU memory usage during inference compared to standard Transformer implementations. Despite these improvements, we observe limitations in retrieval-focused tasks. Our implementation offers control over the trade-off between throughput and accuracy through adjustable hyperparameters, providing flexibility for diverse use cases. This work represents a significant step towards more efficient and adaptable Transformer-based models for inference.

Method: OpenSIR uses alternating teacher-student roles where the LLM generates novel problems optimizing for difficulty and diversity, and solves them without external supervision through self-play.

Result: Substantial improvements: Llama-3.2-3B-Instruct advanced from 73.9 to 78.3 on GSM8K and 28.8 to 34.4 on College Math; Gemma-2-2B-Instruct rose from 38.5 to 58.7 on GSM8K.

Conclusion: OpenSIR achieves open-ended learning through co-evolving teacher-student roles that adaptively calibrate difficulty and drive diverse exploration, enabling autonomous progression from basic to advanced mathematics.

Abstract: Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may limit models’ ability to surpass human-level performance. While self-play offers a promising alternative, existing approaches depend on external verifiers or cannot learn open-endedly. We present Open-Ended Self-Improving Reasoner (OpenSIR), a self-play framework where an LLM learns to generate and solve novel problems by alternating teacher and student roles without external supervision. To generate novel problems, OpenSIR optimises for both difficulty and diversity, rewarding problems that challenge appropriately while exploring distinct concepts, enabling open-ended mathematical discovery. Starting from a single trivial seed problem, OpenSIR substantially improves instruction models: Llama-3.2-3B-Instruct advances from 73.9 to 78.3 on GSM8K, and from 28.8 to 34.4 on College Math, while Gemma-2-2B-Instruct rises from 38.5 to 58.7 on GSM8K. Our analyses reveal that OpenSIR achieves open-ended learning through co-evolving teacher-student roles that adaptively calibrate difficulty and drive diverse exploration, progressing autonomously from basic to advanced mathematics.

Method: Proposes SpecDiff-2 framework that leverages discrete diffusion as a non-autoregressive drafter to address parallelism limitations, and develops novel techniques to calibrate discrete diffusion drafters with autoregressive verifiers to address misalignment issues.

Result: Achieves state-of-the-art performance across reasoning, coding, and mathematical benchmarks with up to +55% improvement in tokens-per-second over previous baselines and up to 5.5x average speed-up over standard decoding, with no accuracy loss.

Conclusion: SpecDiff-2 successfully addresses the fundamental bottlenecks in speculative decoding through non-autoregressive drafting and improved calibration, establishing a new state-of-the-art for LLM inference acceleration.

Abstract: Speculative decoding has become the standard approach for accelerating Large Language Model (LLM) inference. It exploits a lossless draft-then-verify procedure to circumvent the latency of autoregressive decoding, achieving impressive speed-ups. Yet, current speculative decoding approaches remain limited by two fundamental bottlenecks: (1) the autoregressive dependency during drafting which limits parallelism, and (2) frequent rejections of draft tokens caused by misalignment between the draft and verify models. This paper proposes SpecDiff-2, a novel framework to jointly address these two bottlenecks. It leverages discrete diffusion as a non-autoregressive drafter to address bottleneck (1) and develops novel techniques to calibrate discrete diffusion drafters with autoregressive verifiers, addressing bottleneck (2). Experimental results across a comprehensive benchmark suite show that SpecDiff-2 achieves a new state-of-the-art across reasoning, coding, and mathematical benchmarks, improving tokens-per-second by up to an average of +55% over previous baselines and obtaining up to 5.5x average speed-up over standard decoding, without any loss of accuracy.

Method: Evaluated GPT-4.1 and DeepSeek-Chat on ten probabilistic prompts (e.g., rolling dice) with and without explicit probability cues, measuring response validity and alignment between token-level probabilities and theoretical distributions.

Result: Both models achieved perfect response accuracy across all scenarios, but their token-level probability and entropy values consistently diverged from the corresponding theoretical distributions.

Conclusion: Current LLM uncertainty quantification methods using token probabilities may not reliably reflect theoretical probability distributions, even when models produce accurate responses.

Abstract: Reliable uncertainty quantification (UQ) is essential for ensuring trustworthy downstream use of large language models, especially when they are deployed in decision-support and other knowledge-intensive applications. Model certainty can be estimated from token logits, with derived probability and entropy values offering insight into performance on the prompt task. However, this approach may be inadequate for probabilistic scenarios, where the probabilities of token outputs are expected to align with the theoretical probabilities of the possible outcomes. We investigate the relationship between token certainty and alignment with theoretical probability distributions in well-defined probabilistic scenarios. Using GPT-4.1 and DeepSeek-Chat, we evaluate model responses to ten prompts involving probability (e.g., roll a six-sided die), both with and without explicit probability cues in the prompt (e.g., roll a fair six-sided die). We measure two dimensions: (1) response validity with respect to scenario constraints, and (2) alignment between token-level output probabilities and theoretical probabilities. Our results indicate that, while both models achieve perfect in-domain response accuracy across all prompt scenarios, their token-level probability and entropy values consistently diverge from the corresponding theoretical distributions.

Method: Used quantitative methods and character-level embeddings with supervised model to analyze detective characters across French literature from 1866 to 2017.

Result: The model successfully captured the unity of the detective archetype across 150 years, showing evolution from secondary narrative role to central character and “reasoning machine” in classical detective stories, with increased complexity and moral ambiguity after WWII hardboiled influence.

Conclusion: The detective archetype in French fiction follows an evolutionary path from secondary character to complex protagonist, reflecting genre transformations and cultural influences over time.

Abstract: This research explores the evolution of the detective archetype in French detective fiction through computational analysis. Using quantitative methods and character-level embeddings, we show that a supervised model is able to capture the unity of the detective archetype across 150 years of literature, from M. Lecoq (1866) to Commissaire Adamsberg (2017). Building on this finding, the study demonstrates how the detective figure evolves from a secondary narrative role to become the central character and the “reasoning machine” of the classical detective story. In the aftermath of the Second World War, with the importation of the hardboiled tradition into France, the archetype becomes more complex, navigating the genre’s turn toward social violence and moral ambiguity.

Method: Created XNationQA benchmark with questions on 9 countries’ geography, culture, and history in 7 languages, then benchmarked 8 multilingual LLMs using two novel transference metrics.

Result: Models show significant discrepancy in accessing culturally specific facts across languages, often knowing more cultural information in English than the culture’s dominant language. Models perform better in Western languages but aren’t necessarily more literate about Western countries.

Conclusion: Multilingual LLMs have limited ability to transfer knowledge across languages (especially open-source models), and current evaluation methods fail to capture cultural literacy fairly across different regions.

Abstract: Most multilingual question-answering benchmarks, while covering a diverse pool of languages, do not factor in regional diversity in the information they capture and tend to be Western-centric. This introduces a significant gap in fairly evaluating multilingual models’ comprehension of factual information from diverse geographical locations. To address this, we introduce XNationQA for investigating the cultural literacy of multilingual LLMs. XNationQA encompasses a total of 49,280 questions on the geography, culture, and history of nine countries, presented in seven languages. We benchmark eight standard multilingual LLMs on XNationQA and evaluate them using two novel transference metrics. Our analyses uncover a considerable discrepancy in the models' accessibility to culturally specific facts across languages. Notably, we often find that a model demonstrates greater knowledge of cultural information in English than in the dominant language of the respective culture. The models exhibit better performance in Western languages, although this does not necessarily translate to being more literate for Western countries, which is counterintuitive. Furthermore, we observe that models have a very limited ability to transfer knowledge across languages, particularly evident in open-source models.

Method: Systematic multilingual evaluation across 10 languages (high-, medium-, low-resource) using 6 LLMs on HarmBench and AdvBench, assessing logical-expression-based and adversarial-prompt-based jailbreaks.

Result: Attack success and defense robustness vary across languages; high-resource languages are safer under standard queries but more vulnerable to adversarial attacks; simple defenses are effective but language- and model-dependent.

Conclusion: Findings demonstrate the need for language-aware and cross-lingual safety benchmarks for LLMs to address varying safety alignment across different languages.

Abstract: Large language models (LLMs) undergo safety alignment after training and tuning, yet recent work shows that safety can be bypassed through jailbreak attacks. While many jailbreaks and defenses exist, their cross-lingual generalization remains underexplored. This paper presents the first systematic multilingual evaluation of jailbreaks and defenses across ten languages–spanning high-, medium-, and low-resource languages–using six LLMs on HarmBench and AdvBench. We assess two jailbreak types: logical-expression-based and adversarial-prompt-based. For both types, attack success and defense robustness vary across languages: high-resource languages are safer under standard queries but more vulnerable to adversarial ones. Simple defenses can be effective, but are language- and model-dependent. These findings call for language-aware and cross-lingual safety benchmarks for LLMs.

Method: Alternates between local (sliding-window) and global (compression, selective) attention across layers, enhanced with Multi-head Latent Attention for sliding-window and Group-head Latent Attention for compression/selective branches.

Result: Reduces KV-cache memory by 50% versus Native Sparse Attention while improving common-sense reasoning and long-text understanding. Matches or exceeds full attention and native sparse attention in benchmarks.

Conclusion: Alternating attention patterns with latent attention mechanisms effectively improves long-context modeling efficiency and performance across various model sizes.

Abstract: In this work, we conduct a systematic analysis of Native Sparse Attention (NSA) and propose targeted improvements that enhance long-context modeling. A key insight is that alternating between local (sliding-window) and global (compression, selective) attention across layers, rather than using fixed patterns, enables more effective propagation of long-range dependencies and substantially boosts performance on long-sequence tasks. Meanwhile, we further refine NSA’s branches with Latent Attention that the sliding-window branch is enhanced with Multi-head Latent Attention (MLA) while compression and selective branches adopt Group-head Latent Attention (GLA). These changes reduce KV-cache memory by 50% versus NSA while improving the model’s common-sense reasoning and long-text understanding capabilities. Experiments on models from 340M to 1.3B parameters (trained on 15B and 100B tokens) show our method matches or exceeds full attention and native sparse attention in both common-sense reasoning and long-context understanding tasks.

Method: TriCon-Fair uses a decoupled loss combining triplet and language modeling terms. It assigns each anchor an explicitly biased negative and an unbiased positive to decouple push-pull dynamics, while jointly optimizing an LM objective to preserve general capability.

Result: Experimental results show TriCon-Fair reduces discriminatory output beyond existing debiasing baselines while maintaining strong downstream performance.

Conclusion: TriCon-Fair offers a practical and ethical solution for sensitive NLP applications by effectively addressing social bias in LLMs without compromising model performance.

Abstract: The increasing utilization of large language models raises significant concerns about the propagation of social biases, which may result in harmful and unfair outcomes. However, existing debiasing methods treat the biased and unbiased samples independently, thus ignoring their mutual relationship. This oversight enables a hidden negative-positive coupling, where improvements for one group inadvertently compromise the other, allowing residual social bias to persist. In this paper, we introduce TriCon-Fair, a contrastive learning framework that employs a decoupled loss that combines triplet and language modeling terms to eliminate positive-negative coupling. Our TriCon-Fair assigns each anchor an explicitly biased negative and an unbiased positive, decoupling the push-pull dynamics and avoiding positive-negative coupling, and jointly optimizes a language modeling (LM) objective to preserve general capability. Experimental results demonstrate that TriCon-Fair reduces discriminatory output beyond existing debiasing baselines while maintaining strong downstream performance. This suggests that our proposed TriCon-Fair offers a practical and ethical solution for sensitive NLP applications.

Method: Three-component framework: (1) Principal Knowledge Collection - large-scale repository of atomic knowledge; (2) knowledge-grounded evaluation metrics for measuring recall and application of prerequisite knowledge; (3) evaluator LLM for cost-effective metric computation.

Result: The evaluation suite effectively identifies missing or misapplied knowledge elements, revealing fundamental reasoning deficiencies in LLMs.

Conclusion: The framework provides crucial insights into LLM reasoning quality and demonstrates applications beyond evaluation, including integration into preference optimization.

Abstract: Step-by-step reasoning has become a standard approach for large language models (LLMs) to tackle complex tasks. While this paradigm has proven effective, it raises a fundamental question: How can we verify that an LLM’s reasoning is accurately grounded in knowledge? To address this question, we introduce a novel evaluation suite that systematically assesses the knowledge grounding of intermediate reasoning. Our framework comprises three key components. (1) Principal Knowledge Collection, a large-scale repository of atomic knowledge essential for reasoning. Based on the collection, we propose (2) knowledge-grounded evaluation metrics designed to measure how well models recall and apply prerequisite knowledge in reasoning. These metrics are computed by our (3) evaluator LLM, a lightweight model optimized for cost-effective and reliable metric computation. Our evaluation suite demonstrates remarkable effectiveness in identifying missing or misapplied knowledge elements, providing crucial insights for uncovering fundamental reasoning deficiencies in LLMs. Beyond evaluation, we demonstrate how these metrics can be integrated into preference optimization, showcasing further applications of knowledge-grounded evaluation.

Method: Uses OCR-based pretraining objective, self-supervised masked contrastive learning, and late interaction scoring mechanism specifically designed for multimodal document structures.

Result: Achieves 3.61% improvement over existing retrieval models on ViDoRe V2 benchmark and shows stronger generalization to out-of-domain benchmarks.

Conclusion: ColMate successfully bridges the gap between multimodal representation learning and document retrieval, demonstrating superior performance through specialized multimodal techniques.

Abstract: Retrieval-augmented generation has proven practical when models require specialized knowledge or access to the latest data. However, existing methods for multimodal document retrieval often replicate techniques developed for text-only retrieval, whether in how they encode documents, define training objectives, or compute similarity scores. To address these limitations, we present ColMate, a document retrieval model that bridges the gap between multimodal representation learning and document retrieval. ColMate utilizes a novel OCR-based pretraining objective, a self-supervised masked contrastive learning objective, and a late interaction scoring mechanism more relevant to multimodal document structures and visual characteristics. ColMate obtains 3.61% improvements over existing retrieval models on the ViDoRe V2 benchmark, demonstrating stronger generalization to out-of-domain benchmarks.

Method: Assessed two leading LLMs on medical diagnostic scenarios using readability metrics for understandability and LLM-as-a-Judge ratings for empathy, compared with human evaluations.

Result: LLMs adapt explanations to socio-demographic variables and patient conditions but generate overly complex content and display biased affective empathy, leading to uneven accessibility.

Conclusion: Systematic calibration is needed to ensure equitable patient communication with LLMs.

Abstract: Large language models (LLMs) show promise for supporting clinicians in diagnostic communication by generating explanations and guidance for patients. Yet their ability to produce outputs that are both understandable and empathetic remains uncertain. We evaluate two leading LLMs on medical diagnostic scenarios, assessing understandability using readability metrics as a proxy and empathy through LLM-as-a-Judge ratings compared to human evaluations. The results indicate that LLMs adapt explanations to socio-demographic variables and patient conditions. However, they also generate overly complex content and display biased affective empathy, leading to uneven accessibility and support. These patterns underscore the need for systematic calibration to ensure equitable patient communication. The code and data are released: https://github.com/Jeffateth/Biased_Oracle

Method: Created multilingual riddle dataset with traditional and context-reconstructed variants, evaluated 5 LLMs using 7 prompting strategies, and conducted two-stage evaluation (riddle-solving and self-assessment).

Result: Gemini 2.5 Pro performed best overall but showed minimal gains from few-shot learning. Key finding: initial accuracy inversely correlated with self-awareness - top models were overconfident (4.34% TNR) while weaker models were more self-aware (42.09% TNR).

Conclusion: Clear gaps exist in multilingual reasoning, highlighting the need for models that not only reason effectively but also recognize their own limitations across diverse cultural contexts.

Abstract: The extent to which large language models (LLMs) can perform culturally grounded reasoning across non-English languages remains underexplored. This paper examines the reasoning and self-assessment abilities of LLMs across seven major Indian languages-Bengali, Gujarati, Hindi, Kannada, Malayalam, Tamil, and Telugu. We introduce a multilingual riddle dataset combining traditional riddles with context-reconstructed variants and evaluate five LLMs-Gemini 2.5 Pro, Gemini 2.5 Flash, Mistral-Saba, LLaMA 4 Scout, and LLaMA 4 Maverick-under seven prompting strategies. In the first stage, we assess riddle-solving performance and find that while Gemini 2.5 Pro performs best overall, few-shot methods yield only marginal gains, and accuracy varies notably across languages. In the second stage, we conduct a self-evaluation experiment to measure reasoning consistency. The results reveal a key finding: a model’s initial accuracy is inversely correlated with its ability to identify its own mistakes. Top-performing models such as Gemini 2.5 Pro are overconfident (4.34% True Negative Rate), whereas lower-performing models like LLaMA 4 Scout are substantially more self-aware (42.09% True Negative Rate). These results point to clear gaps in multilingual reasoning and highlight the need for models that not only reason effectively but also recognize their own limitations.

Method: Uses an easy supervisor targeting longer-text detection tasks to enhance a target detector. Longer texts alleviate inexact label impact, and structural incorporation models supervisor as lower performance bound for detector.

Result: Extensive experiments across cross-LLM, cross-domain, mixed text, and paraphrase attack scenarios demonstrate significant detection effectiveness.

Conclusion: The framework provides reliable supervision under inexact conditions and effectively approximates underlying golden labels through indirect optimization.

Abstract: Existing machine-generated text (MGT) detection methods implicitly assume labels as the “golden standard”. However, we reveal boundary ambiguity in MGT detection, implying that traditional training paradigms are inexact. Moreover, limitations of human cognition and the superintelligence of detectors make inexact learning widespread and inevitable. To this end, we propose an easy-to-hard enhancement framework to provide reliable supervision under such inexact conditions. Distinct from knowledge distillation, our framework employs an easy supervisor targeting relatively simple longer-text detection tasks (despite weaker capabilities), to enhance the more challenging target detector. Firstly, longer texts targeted by supervisors theoretically alleviate the impact of inexact labels, laying the foundation for reliable supervision. Secondly, by structurally incorporating the detector into the supervisor, we theoretically model the supervisor as a lower performance bound for the detector. Thus, optimizing the supervisor indirectly optimizes the detector, ultimately approximating the underlying “golden” labels. Extensive experiments across diverse practical scenarios, including cross-LLM, cross-domain, mixed text, and paraphrase attacks, demonstrate the framework’s significant detection effectiveness. The code is available at: https://github.com/tmlr-group/Easy2Hard.

Method: Uses three specialized agents: Grounding Agent for schema linking, Generation Agent trained via multi-turn RL policy with ReAct-style loop (Think-Act-Observe), and Validation Agent for trajectory selection. Generates multiple interaction trajectories and selects optimal one using next-token prediction.

Result: Achieves Execution Accuracy of 77.84% on BIRD dev set and 89.75% on Spider test set, demonstrating state-of-the-art performance.

Conclusion: The structured multi-agent workflow combining interactive RL for generation and generative modeling for verification proves highly effective for robust and accurate SQL generation.

Abstract: Translating natural language to SQL remains difficult for complex queries. Such queries often need environmental interaction and self-correction. To address this, we introduce MARS-SQL, a novel multi-agent framework that combines principled task decomposition and interactive reinforcement learning (RL). Our system comprises three specialized agents: a Grounding Agent for schema linking, a Generation Agent for query generation, and a Validation Agent for final selection. The core of our framework is the Generation agent, which is trained via a multi-turn RL policy. Adopting a ReAct-style Think-Act-Observe loop, the agent iteratively generates thoughts, executes SQL actions against a live database, and revises its strategy based on execution feedback, enabling dynamic, stateful reasoning and self-correction. At inference time, we generate multiple interaction trajectories to explore diverse reasoning paths. The Validation agent, then selects the optimal trajectory by modeling verification as a next-token prediction task and choosing the solution with the highest generation probability. This structured workflow pipelines specialized agents. It combines interactive RL for generation with generative modeling for verification. The approach proves highly effective for robust and accurate SQL generation. Experiments show that MARS-SQL achieves state-of-the-art Execution Accuracy of 77.84% on the BIRD dev set and 89.75% on the Spider test set. Our code is available at https://github.com/YangHaolin0526/MARS-SQL.

Method: Develop a checklist generator to decompose instructions into constraint checklists, collect high-quality critique training data through multi-stage critique filtering, and employ constraint-level preference optimization to train IF-CRITIC.

Result: IF-CRITIC beats strong LLM-as-a-Judge baselines including Deepseek-R1 and o4-mini, and enables LLMs to achieve substantial performance gains in instruction-following optimization with lower computational overhead.

Conclusion: IF-CRITIC provides scalable reward signals that significantly improve instruction-following evaluation and optimization while reducing computational costs compared to existing methods.

Abstract: Instruction following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imposed in input instructions. Numerous studies have attempted to enhance this ability through preference optimization or reinforcement learning based on reward signals from LLM-as-a-Judge. However, existing evaluation models for instruction following still possess many deficiencies, such as substantial costs and unreliable assessments. To this end, we propose IF-CRITIC, an LLM critic that can provide efficient and reliable assessments of constraint following in the instructions. We first develop a checklist generator to decompose instructions and generate constraint checklists. With the assistance of the checklists, we collect high-quality critique training data through a multi-stage critique filtering mechanism and employ a constraint-level preference optimization method to train IF-CRITIC. Extensive experiments demonstrate that the evaluation performance of IF-CRITIC can beat strong LLM-as-a-Judge baselines, including Deepseek-R1 and o4-mini. With the scalable reward signals provided by IF-CRITIC, LLMs can achieve substantial performance gains in instruction-following optimization under lower computational overhead compared to strong LLM critic baselines.

Method: End-to-end reinforcement learning framework with small LLM generating prompts and large LLM performing reasoning. Uses multi-turn interaction and dual-constrained rewards for correctness, quality, and accuracy.

Result: Significantly outperforms baseline models across multiple public datasets and tasks. The framework is plug-and-play and supports various large-scale LLMs.

Conclusion: Prompt-R1 effectively addresses the prompt engineering challenge through automated collaboration between small and large LLMs, demonstrating superior performance across diverse tasks.

Abstract: Recently, advanced large language models (LLMs) have emerged at an increasingly rapid pace. However, when faced with complex problems, most users are often unable to provide accurate and effective prompts to interact with LLMs, thus limiting the performance of LLMs. To address this challenge, we propose Prompt-R1, an end-to-end reinforcement learning framework that uses a small-scale LLM to collaborate with large-scale LLMs, replacing user interaction to solve problems better. This collaboration is cast as a multi-turn prompt interaction, where the small-scale LLM thinks and generates prompts, and the large-scale LLM performs complex reasoning. A dual-constrained reward is designed to optimize for correctness, generation quality, and reasoning accuracy. Prompt-R1 provides a plug-and-play framework that supports both inference and training with various large-scale LLMs. Experiments on multiple public datasets show that Prompt-R1 significantly outperforms baseline models across tasks. Our code is publicly available at https://github.com/QwenQKing/Prompt-R1.

Method: Integrates open-source LLMs with real-time API calls to NOAA data streams, automatically identifying, parsing, and synthesizing relevant datasets into natural-language responses and visualizations.

Result: In blind comparison with three other AI chat products, only OceanAI produced NOAA-sourced values with original data references; others either declined or provided unsupported results.

Conclusion: OceanAI advances transparency, reproducibility, and trust by grounding outputs in verifiable observations, offering a scalable framework for AI-enabled decision support in ocean sciences.

Abstract: Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified “hallucinations” undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmospheric Administration (NOAA). Each query such as “What was Boston Harbor’s highest water level in 2024?” triggers real-time API calls that identify, parse, and synthesize relevant datasets into reproducible natural-language responses and data visualizations. In a blind comparison with three widely used AI chat-interface products, only OceanAI produced NOAA-sourced values with original data references; others either declined to answer or provided unsupported results. Designed for extensibility, OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring. By grounding outputs and verifiable observations, OceanAI advances transparency, reproducibility, and trust, offering a scalable framework for AI-enabled decision support within the oceans. A public demonstration is available at https://oceanai.ai4ocean.xyz.

Method: VayuChat integrates data from CPCB monitoring stations, state-level demographics, and NCAP funding records into a unified interface powered by large language models. It answers natural language questions and responds with executable Python code and interactive visualizations.

Result: The system enables users to perform complex environmental analytics through simple conversations, making data science accessible to policymakers, researchers, and citizens. The platform is publicly deployed and includes a live demonstration.

Conclusion: VayuChat provides an accessible conversational interface that transforms dispersed environmental data into actionable insights, bridging the gap between data availability and policy decision-making for air quality management in India.

Abstract: Air pollution causes about 1.6 million premature deaths each year in India, yet decision makers struggle to turn dispersed data into decisions. Existing tools require expertise and provide static dashboards, leaving key policy questions unresolved. We present VayuChat, a conversational system that answers natural language questions on air quality, meteorology, and policy programs, and responds with both executable Python code and interactive visualizations. VayuChat integrates data from Central Pollution Control Board (CPCB) monitoring stations, state-level demographics, and National Clean Air Programme (NCAP) funding records into a unified interface powered by large language models. Our live demonstration will show how users can perform complex environmental analytics through simple conversations, making data science accessible to policymakers, researchers, and citizens. The platform is publicly deployed at https://huggingface.co/spaces/SustainabilityLabIITGN/ VayuChat. For further information check out video uploaded on https://www.youtube.com/watch?v=d6rklL05cs4.

Method: Created a validated dataset from publicly available guidelines using GPT, containing realistic patient scenarios and clinical questions, then benchmarked a range of recent popular LLMs.

Result: The framework supports systematic evaluation of LLMs’ clinical utility and guideline adherence, showcasing the validity of the dataset through benchmarking.

Conclusion: The study provides a validated framework and dataset for systematically evaluating LLMs’ clinical reasoning capabilities and adherence to medical guidelines.

Abstract: Large language models (LLMs) are increasingly used in healthcare, yet standardised benchmarks for evaluating guideline-based clinical reasoning are missing. This study introduces a validated dataset derived from publicly available guidelines across multiple diagnoses. The dataset was created with the help of GPT and contains realistic patient scenarios, as well as clinical questions. We benchmark a range of recent popular LLMs to showcase the validity of our dataset. The framework supports systematic evaluation of LLMs’ clinical utility and guideline adherence.

Method: Derived datasets from web crawls with complete open-source pipeline including document selection, text extraction, language identification, deduplication, annotation (register labels, quality estimates, PII), and filtering. Evaluated through quality probes, manual inspection, and end-to-end model training.

Result: Successfully created 30 trillion token dataset - likely the largest available multilingual collection. Also produced comprehensive evaluation benchmarks for 9 European languages, trained 57 monolingual encoder-decoder models, and generated large parallel text collections.

Conclusion: The initiative provides a foundational resource for multilingual NLP research with high-quality data, processing tools, and evaluation frameworks that can advance language model development across diverse languages.

Abstract: We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 trillion tokens, this is likely the largest generally available multilingual collection of LLM pre-training data. At 30 trillion tokens, this is likely the largest generally available multilingual collection of LLM pre-training data. These datasets are derived from web crawls from different sources and accompanied with a complete, open-source pipeline for document selection from web archives, text extraction from HTML, language identification for noisy texts, exact and near-deduplication, annotation with, among others, register labels, text quality estimates, and personally identifiable information; and final selection and filtering. We report on data quality probes through contrastive and analytical statistics, through manual inspection of samples for 24 languages, and through end-to-end evaluation of various language model architectures trained on this data. For multilingual LLM evaluation, we provide a comprehensive collection of benchmarks for nine European languages, with special emphasis on natively created tasks, mechanisms to mitigate prompt sensitivity, and refined normalization and aggregation of scores. Additionally, we train and evaluate a family of 57 monolingual encoder-decoder models, as well as a handful of monolingual GPT-like reference models. Besides the monolingual data and models, we also present a very large collection of parallel texts automatically mined from this data, together with a novel parallel corpus synthesized via machine translation.

Method: Train a lightweight multitask model on LLM-annotated Romanian texts to perform multi-level filtering (educational value, topic, format) and generate high-quality pretraining datasets.

Result: Experiments reveal noteworthy trends in topic differences between Romanian and English data, and demonstrate that filtered data leads to improved LLM pretraining performance across multiple benchmarks.

Conclusion: Careful data curation and filtering significantly enhance LLM pretraining for under-represented languages, with the proposed method effectively improving model performance through multi-level data quality assessment.

Abstract: Large Language Models (LLMs) have recently exploded in popularity, often matching or outperforming human abilities on many tasks. One of the key factors in training LLMs is the availability and curation of high-quality data. Data quality is especially crucial for under-represented languages, where high-quality corpora are scarce. In this work we study the characteristics and coverage of Romanian pretraining corpora and we examine how they differ from English data. By training a lightweight multitask model on carefully LLM-annotated Romanian texts, we are able to analyze and perform multi-level filtering (e.g., educational value, topic, format) to generate high-quality pretraining datasets. Our experiments show noteworthy trends in the topics present in Romanian and English data, while also proving the effectiveness of filtering data through improved LLM pretraining performance across multiple benchmarks.

Method: Created TSVer dataset with 287 real claims from 38 fact-checking organizations and 400 time series using LLM-assisted multi-step annotation process with high inter-annotator agreement (kappa=0.745).

Result: State-of-the-art models like Gemini-2.5-Pro achieve only 63.37% accuracy on verdicts and 48.63 Ev2R score on verdict justifications, showing significant challenges in temporal reasoning.

Conclusion: TSVer provides a high-quality benchmark for temporal and numerical reasoning in fact verification, revealing current limitations in AI systems’ ability to reason with time-series evidence.

Abstract: Reasoning over temporal and numerical data, such as time series, is a crucial aspect of fact-checking. While many systems have recently been developed to handle this form of evidence, their evaluation remains limited by existing datasets, which often lack structured evidence, provide insufficient justifications for verdicts, or rely on synthetic claims. In this paper, we introduce TSVer, a new benchmark dataset for fact verification focusing on temporal and numerical reasoning with time-series evidence. TSVer contains 287 real-world claims sourced from 38 fact-checking organizations and a curated database of 400 time series covering diverse domains. Each claim is annotated with time frames across all pertinent time series, along with a verdict and justifications reflecting how the evidence is used to reach the verdict. Using an LLM-assisted multi-step annotation process, we improve the quality of our annotations and achieve an inter-annotator agreement of kappa=0.745 on verdicts. We also develop a baseline for verifying claims against time-series evidence and show that even the state-of-the-art reasoning models like Gemini-2.5-Pro are challenged by time series, achieving a 63.37 accuracy score on verdicts and an Ev2R score of 48.63 on verdict justifications.

Method: Proposed ThinkRemed - a multi-agent framework that emulates SREs’ reflective and perceptive reasoning through iterative reasoning and system reflection.

Result: MicroRemed presents substantial challenges to current LLMs, while ThinkRemed improves end-to-end remediation performance compared to existing approaches.

Conclusion: The benchmark enables evaluation of LLMs in autonomous microservice remediation, and ThinkRemed demonstrates improved performance through multi-agent reasoning frameworks.

Abstract: Large Language Models (LLMs) integrated with agent-based reasoning frameworks have recently shown strong potential for autonomous decision-making and system-level operations. One promising yet underexplored direction is microservice remediation, where the goal is to automatically recover faulty microservice systems. Existing approaches, however, still rely on human-crafted prompts from Site Reliability Engineers (SREs), with LLMs merely converting textual instructions into executable code. To advance research in this area, we introduce MicroRemed, the first benchmark for evaluating LLMs in end-to-end microservice remediation, where models must directly generate executable Ansible playbooks from diagnosis reports to restore system functionality. We further propose ThinkRemed, a multi-agent framework that emulates the reflective and perceptive reasoning of SREs. Experimental results show that MicroRemed presents substantial challenges to current LLMs, while ThinkRemed improves end-to-end remediation performance through iterative reasoning and system reflection. The benchmark is available at https://github.com/LLM4AIOps/MicroRemed.

Method: Formalized the dynamic screening decision as an optimal stopping problem and developed a generative language model-based sequential decision agent that learns when to quit conversations by imitating a retrospectively-inferred optimal stopping policy.

Result: The stopping agent reduced time spent on failed calls by 54% while preserving nearly all sales; reallocating saved time increased expected sales by up to 37%. Analysis showed salespeople overweight salient expressions of disinterest and mispredict call failure risk.

Conclusion: AI algorithms can correct cognitively-bounded human decisions and improve salesforce efficiency by optimizing conversational persistence decisions in real-time.

Abstract: Salespeople frequently face the dynamic screening decision of whether to persist in a conversation or abandon it to pursue the next lead. Yet, little is known about how these decisions are made, whether they are efficient, or how to improve them. We study these decisions in the context of high-volume outbound sales where leads are ample, but time is scarce and failure is common. We formalize the dynamic screening decision as an optimal stopping problem and develop a generative language model-based sequential decision agent - a stopping agent - that learns whether and when to quit conversations by imitating a retrospectively-inferred optimal stopping policy. Our approach handles high-dimensional textual states, scales to large language models, and works with both open-source and proprietary language models. When applied to calls from a large European telecommunications firm, our stopping agent reduces the time spent on failed calls by 54% while preserving nearly all sales; reallocating the time saved increases expected sales by up to 37%. Upon examining the linguistic cues that drive salespeople’s quitting decisions, we find that they tend to overweight a few salient expressions of consumer disinterest and mispredict call failure risk, suggesting cognitive bounds on their ability to make real-time conversational decisions. Our findings highlight the potential of artificial intelligence algorithms to correct cognitively-bounded human decisions and improve salesforce efficiency.

Method: Created DebateBias-8K with 8,400 structured debate prompts across 4 sensitive domains in 7 languages, tested 4 flagship models (GPT-4o, Claude 3, DeepSeek, LLaMA 3), generating and automatically classifying over 100,000 responses.

Result: All models reproduced entrenched stereotypes: Arabs linked to terrorism/religion (≥95%), Africans to socioeconomic backwardness (up to 77%), Western groups framed as modern/progressive. Biases grew sharply in lower-resource languages.

Conclusion: Current alignment methods trained primarily in English don’t generalize globally, reducing explicit toxicity but failing to prevent biased outputs in open-ended contexts, highlighting persistent multilingual fairness divide.

Abstract: Large language models (LLMs) are widely deployed for open-ended communication, yet most bias evaluations still rely on English, classification-style tasks. We introduce DebateBias-8K, a new multilingual, debate-style benchmark designed to reveal how narrative bias appears in realistic generative settings. Our dataset includes 8,400 structured debate prompts spanning four sensitive domains: women’s rights, socioeconomic development, terrorism, and religion, across seven languages ranging from high-resource (English, Chinese) to low-resource (Swahili, Nigerian Pidgin). Using four flagship models (GPT-4o, Claude 3, DeepSeek, and LLaMA 3), we generate and automatically classify over 100,000 responses. Results show that all models reproduce entrenched stereotypes despite safety alignment: Arabs are overwhelmingly linked to terrorism and religion (>=95%), Africans to socioeconomic “backwardness” (up to <=77%), and Western groups are consistently framed as modern or progressive. Biases grow sharply in lower-resource languages, revealing that alignment trained primarily in English does not generalize globally. Our findings highlight a persistent divide in multilingual fairness: current alignment methods reduce explicit toxicity but fail to prevent biased outputs in open-ended contexts. We release our DebateBias-8K benchmark and analysis framework to support the next generation of multilingual bias evaluation and safer, culturally inclusive model alignment.

Method: Two-stage framework: 1) Hierarchical Salience scoring with SC-MMR algorithm to select informative keywords for retrieving up-to-date evidence, 2) Multi-LLM interactive system with different agent roles performing collaborative analysis and adversarial debate over news content.

Result: Comprehensive experiments on two public datasets show ZoFia significantly outperforms existing zero-shot baselines and most few-shot methods.

Conclusion: ZoFia provides an effective solution for zero-shot fake news detection that handles fast-evolving news streams through external evidence retrieval and multi-agent collaborative analysis, with plans to open-source the code.

Abstract: The rapid spread of fake news threatens social stability and public trust, rendering its detection an imperative research priority. Although large language models (LLMs) excel at numerous natural language processing tasks with their remarkable contextual understanding and extensive prior knowledge, the time-bounded knowledge coverage and tendency for generating hallucination content reduce their reliability when handling fast-evolving news streams. Furthermore, models trained on existing static datasets also often lack the generalization needed for emerging news topics. To address these challenges, we propose ZoFia, a novel two-stage zero-shot fake news detection framework. First, we introduce Hierarchical Salience to quantify the importance of entities in the news content, and propose the SC-MMR algorithm to effectively select an informative and diverse set of keywords that serve as queries for retrieving up-to-date external evidence. Subsequently, a multi LLM interactive system, in which each agent assumes a distinct role, performs multi-view collaborative analysis and adversarial debate over the news text and its related information, and finally produces an interpretable and robust judgment. Comprehensive experiments on two public datasets demonstrate that ZoFia obviously outperforms existing zero-shot baselines and most of few-shot methods. Our codes will be open-sourced to facilitate related communities.

Method: Uses a single model in dual roles: Solver produces answers and Reframer rephrases inputs. Aggregates answer frequencies across original and reframed views using harmonic mean instead of majority voting, selecting solutions stable under reframing.

Result: Achieves state-of-the-art results in label-free test-time setting, ranking first in 28 of 30 settings across multiple reasoning benchmarks. Shows unprecedented robustness with zero training failures in all experiments.

Conclusion: Self-Harmony provides a stable and reliable test-time adaptation method that avoids spurious answers without requiring human supervision or auxiliary models, demonstrating strong performance and robustness across diverse reasoning tasks.

Abstract: Test-time reinforcement learning (TTRL) offers a label-free paradigm for adapting models using only synthetic signals at inference, but its success hinges on constructing reliable learning signals. Standard approaches such as majority voting often collapse to spurious yet popular answers. We introduce Self-Harmony, a framework built on a simple intuition: the correct answer should remain stable across both an original question and its paraphrase. Self-Harmony operationalizes this by employing a single model in two complementary roles: a Solver to produce answers and a Reframer to rephrase the input. Based on this, we further propose a pseudo-label method: instead of majority voting, it aggregates answer frequencies across these original and reframed views using the harmonic mean. This is a process that naturally selects for solutions stable under reframing, thereby avoiding the common trap of favoring view-dependent, spurious answers. Crucially, this requires no human supervision or auxiliary models. Across diverse reasoning benchmarks, Self-Harmony achieves state-of-the-art results at the label-free test-time setting, ranking first in 28 of 30 settings across multiple methods. Beyond accuracy, it demonstrates unprecedented robustness, with zero training failures in all experiments, underscoring its stability and reliability.

Method: Two-stage DEER architecture: 1) Disentangled mixture-of-experts with domain-specific experts for fine-grained distinctions and shared experts for cross-domain features; 2) Reinforcement learning-based routing mechanism that dynamically selects experts without requiring domain labels during inference.

Result: DEER outperforms state-of-the-art methods with average F1-score improvements of 1.39% (in-domain) and 5.32% (out-of-domain), and accuracy gains of 1.35% (in-domain) and 3.61% (out-of-domain) across five in-domain and five out-of-domain benchmark datasets.

Conclusion: The disentangled expert specialization and adaptive routing mechanism are critical for robust machine-generated text detection under domain shift, effectively bridging the train-inference gap caused by domain uncertainty.

Abstract: Detecting machine-generated text (MGT) has emerged as a critical challenge, driven by the rapid advancement of large language models (LLMs) capable of producing highly realistic, human-like content. However, the performance of current approaches often degrades significantly under domain shift. To address this challenge, we propose a novel framework designed to capture both domain-specific and domain-general MGT patterns through a two-stage Disentangled mixturE-of-ExpeRts (DEER) architecture. First, we introduce a disentangled mixture-of-experts module, in which domain-specific experts learn fine-grained, domain-local distinctions between human and machine-generated text, while shared experts extract transferable, cross-domain features. Second, to mitigate the practical limitation of unavailable domain labels during inference, we design a reinforcement learning-based routing mechanism that dynamically selects the appropriate experts for each input instance, effectively bridging the train-inference gap caused by domain uncertainty. Extensive experiments on five in-domain and five out-of-domain benchmark datasets demonstrate that DEER consistently outperforms state-of-the-art methods, achieving average F1-score improvements of 1.39% and 5.32% on in-domain and out-of-domain datasets respectively, along with accuracy gains of 1.35% and 3.61% respectively. Ablation studies confirm the critical contributions of both disentangled expert specialization and adaptive routing to model performance.

Method: Created AraFinNews dataset with 212,500 article-headline pairs, then evaluated transformer models (mT5, AraT5, FinAraT5) on factual accuracy, numerical reliability, and stylistic alignment.

Result: Domain-adapted models (FinAraT5) generated more faithful and coherent summaries, especially for quantitative and entity-centric information.

Conclusion: Domain-specific adaptation is crucial for improving factual consistency and narrative fluency in Arabic financial summarization.

Abstract: This paper investigates the impact of domain specificity on abstractive summarisation of Arabic financial texts using large language models (LLMs). We introduce AraFinNews, the largest publicly available Arabic financial news dataset to date, comprising 212,500 article–headline pairs spanning nearly a decade of reporting from October 2015 to July 2025. Designed as the Arabic equivalent of major English summarisation corpora such as CNN/DailyMail, AraFinNews provides a robust benchmark for evaluating domain-specific language understanding and generation in financial contexts. Using this resource, we evaluate transformer-based models – including mT5, AraT5, and the domain-adapted FinAraT5 – to examine how financial-domain pretraining influences factual accuracy, numerical reliability, and stylistic alignment with professional reporting. Experimental results show that domain-adapted models generate more faithful and coherent summaries, particularly in handling quantitative and entity-centric information. The findings highlight the importance of domain-specific adaptation for improving factual consistency and narrative fluency in Arabic financial summarisation. The dataset is freely available for non-commercial research at https://github.com/ArabicNLP-UK/AraFinNews.

Method: ReSpec introduces three innovations: 1) entropy-guided adaptive trigger to initiate retrieval only when uncertainty is low, 2) feedback-driven candidate selection to organize multiple high-quality candidates, and 3) source-aware relaxed verification strategy with strict checks for model drafts and relaxed verification for retrieved drafts.

Result: Extensive experiments on Spec-Bench show ReSpec achieves state-of-the-art acceleration, outperforming EAGLE-2 by over 33% and SAM-Decoding by over 25%, while maintaining output quality.

Conclusion: ReSpec effectively addresses the limitations of existing speculative decoding methods by transforming heuristic switching into adaptive decision-making, achieving superior acceleration without compromising quality.

Abstract: Speculative decoding (SD) has emerged as an effective technique to accelerate large language model (LLM) inference without compromising output quality. However, the achievable speedup largely depends on the effectiveness of the drafting model. While model-based methods like EAGLE-2 are accurate but costly, retrieval-enhanced methods like SAM-Decoding rely on heuristic switching strategies that often trigger unnecessary retrievals. To address this, we propose ReSpec (\textbf{Re}trieval-enhanced \textbf{Spe}culative Decoding), a novel framework that transforms heuristic drafter switching into adaptive decision-making. ReSpec features three core innovations: 1) An \textbf{entropy-guided adaptive trigger} quantifies contextual predictability to initiate retrieval only when uncertainty is low, avoiding costly low-quality speculations. 2) A \textbf{feedback-driven candidate selection} leverages historical feedback to organize multiple high-quality candidates for parallel verification, maximizing retrieval utility. 3) A source-aware \textbf{relaxed verification strategy} applies strict checks to model-generated drafts while using a relaxed verification for retrieved drafts, achieving a better balance between accuracy and efficiency. Extensive experiments on Spec-Bench demonstrate that ReSpec achieves state-of-the-art acceleration,outperforming EAGLE-2 and SAM-Decoding by over $33%$ and $25%$, respectively, while maintaining output quality.

Method: Proposes two attack classes: (1) static attack using fixed injection prompts, and (2) iterative attack optimizing prompts against simulated reviewer models. Also explores detection-based defense mechanisms.

Result: Both attacks achieve striking performance, frequently inducing full evaluation scores from frontier AI reviewers. Attacks are robust across settings. Detection defense reduces success rate but adaptive attackers can partially circumvent it.

Conclusion: Findings highlight the need for greater attention and rigorous safeguards against prompt-injection threats in AI-assisted peer review systems.

Abstract: With the rapid advancement of AI models, their deployment across diverse tasks has become increasingly widespread. A notable emerging application is leveraging AI models to assist in reviewing scientific papers. However, recent reports have revealed that some papers contain hidden, injected prompts designed to manipulate AI reviewers into providing overly favorable evaluations. In this work, we present an early systematic investigation into this emerging threat. We propose two classes of attacks: (1) static attack, which employs a fixed injection prompt, and (2) iterative attack, which optimizes the injection prompt against a simulated reviewer model to maximize its effectiveness. Both attacks achieve striking performance, frequently inducing full evaluation scores when targeting frontier AI reviewers. Furthermore, we show that these attacks are robust across various settings. To counter this threat, we explore a simple detection-based defense. While it substantially reduces the attack success rate, we demonstrate that an adaptive attacker can partially circumvent this defense. Our findings underscore the need for greater attention and rigorous safeguards against prompt-injection threats in AI-assisted peer review.

Method: Generated dataset using ChatGPT-4o-mini with prompt-based in-context learning from the Vital First Aid Book (2019), followed by preprocessing (text cleaning, contextual chunking, filtering) and human validation for accuracy and safety.

Result: Successfully created FirstAidQA containing 5,500 validated QA pairs covering diverse first aid scenarios, publicly released on Hugging Face to support instruction-tuning of LLMs and SLMs for emergency applications.

Conclusion: FirstAidQA enables development of faster, more reliable, offline-capable AI systems for emergency settings, advancing research on safety-critical and resource-constrained AI applications in first aid and emergency response.

Abstract: In emergency situations, every second counts. The deployment of Large Language Models (LLMs) in time-sensitive, low or zero-connectivity environments remains limited. Current models are computationally intensive and unsuitable for low-tier devices often used by first responders or civilians. A major barrier to developing lightweight, domain-specific solutions is the lack of high-quality datasets tailored to first aid and emergency response. To address this gap, we introduce FirstAidQA, a synthetic dataset containing 5,500 high-quality question answer pairs that encompass a wide range of first aid and emergency response scenarios. The dataset was generated using a Large Language Model, ChatGPT-4o-mini, with prompt-based in-context learning, using texts from the Vital First Aid Book (2019). We applied preprocessing steps such as text cleaning, contextual chunking, and filtering, followed by human validation to ensure accuracy, safety, and practical relevance of the QA pairs. FirstAidQA is designed to support instruction-tuning and fine-tuning of LLMs and Small Language Models (SLMs), enabling faster, more reliable, and offline-capable systems for emergency settings. We publicly release the dataset to advance research on safety-critical and resource-constrained AI applications in first aid and emergency response. The dataset is available on Hugging Face at https://huggingface.co/datasets/i-am-mushfiq/FirstAidQA.

Method: Uses three databases (SpecDB, ChangeDB, TDocDB) with structural and temporal reasoning, explicitly resolves cross-references through recursive metadata lookup, and traces evolution by mining changes linked to Change Requests.

Result: Outperforms base models and state-of-the-art telecom RAG systems across multiple LLM backends; ablations confirm explicit cross-reference resolution and evolution-aware retrieval substantially improve answer quality.

Conclusion: Modeling structural and temporal properties of 5G standards through metadata-rich databases and explicit reasoning significantly enhances RAG system performance for expert-level QA.

Abstract: 5G technology enables mobile Internet access for billions of users. Answering expert-level questions about 5G specifications requires navigating thousands of pages of cross-referenced standards that evolve across releases. Existing retrieval-augmented generation (RAG) frameworks, including telecom-specific approaches, rely on semantic similarity and cannot reliably resolve cross-references or reason about specification evolution. We present DeepSpecs, a RAG system enhanced by structural and temporal reasoning via three metadata-rich databases: SpecDB (clause-aligned specification text), ChangeDB (line-level version diffs), and TDocDB (standardization meeting documents). DeepSpecs explicitly resolves cross-references by recursively retrieving referenced clauses through metadata lookup, and traces specification evolution by mining changes and linking them to Change Requests that document design rationale. We curate two 5G QA datasets: 573 expert-annotated real-world questions from practitioner forums and educational resources, and 350 evolution-focused questions derived from approved Change Requests. Across multiple LLM backends, DeepSpecs outperforms base models and state-of-the-art telecom RAG systems; ablations confirm that explicit cross-reference resolution and evolution-aware retrieval substantially improve answer quality, underscoring the value of modeling the structural and temporal properties of 5G standards.

Method: Developed DeepAmbigQAGen pipeline to automatically generate QA tasks grounded in text corpora and knowledge graphs, creating natural questions with systematic name ambiguity and multi-step reasoning challenges.

Result: Created DeepAmbigQA dataset with 3,600 questions (half with explicit name ambiguity). GPT-5 achieved only 0.13 exact match on ambiguous questions and 0.21 on non-ambiguous questions, showing significant performance gaps.

Conclusion: Current QA systems need substantial improvement for robust information gathering and answer completeness, particularly for questions involving name ambiguity and complex reasoning.

Abstract: Large language models (LLMs) with integrated search tools show strong promise in open-domain question answering (QA), yet they often struggle to produce complete answer set to complex questions such as Which actor from the film Heat won at least one Academy Award?, which requires (1) distinguishing between multiple films sharing the same title and (2) reasoning across a large set of actors to gather and integrate evidence. Existing QA benchmarks rarely evaluate both challenges jointly. To address this, we introduce DeepAmbigQAGen, an automatic data generation pipeline that constructs QA tasks grounded in text corpora and linked knowledge graph, generating natural and verifiable questions that systematically embed name ambiguity and multi-step reasoning. Based on this, we build DeepAmbigQA, a dataset of 3,600 questions requiring multi-hop reasoning and half of them explicit name ambiguity resolving. Experiments reveal that, even state-of-the-art GPT-5 show incomplete answers, achieving only 0.13 exact match on ambiguous questions and 0.21 on non-ambiguous questions. These findings highlight the need for more robust QA systems aimed at information gathering and answer completeness.

Method: Developed four model series through knowledge distillation from Qwen models: slow-thinking models for accuracy, adaptive-thinking models for dynamic efficiency, and distilled reward models for reinforcement learning.

Result: Comprehensive evaluations show high inference efficiency and strong reasoning performance across multiple benchmarks, with practical utility demonstrated for distilled reward models.

Conclusion: The distilled models successfully provide scalable training and inference capabilities on Alibaba Cloud PAI platform, supporting industry practitioners with efficient reasoning solutions.

Abstract: Recently, the demand for small and efficient reasoning models to support real-world applications has driven the development of knowledge distillation techniques that balance reasoning performance and inference speed. In this paper, we further extend the DistilQwen model family, initialized from the Qwen models, by introducing four model series specifically designed to meet industrial requirements. The distilled model collection comprises: (1) slow-thinking models, optimized for reasoning tasks that require high accuracy; (2) two series of adaptive-thinking models, which dynamically adjust reasoning strategies based on input tasks to maximize efficiency across diverse scenarios; and (3) distilled reward models, which enable further reinforcement learning of reasoning models using distilled knowledge. Comprehensive evaluations across multiple benchmarks demonstrate both high inference efficiency and strong reasoning performance for these models, as well as the practical utility of distilled reward models. We further show that these models support industry practitioners by providing scalable training and inference functionalities on the Alibaba Cloud PAI (Platform for Artificial Intelligence) platform.

Method: Generalized entailment detection to text prefixes, created evaluation datasets, trained MiniTruePrefixes model, and integrated it into controlled decoding framework for abstractive summarization.

Result: MiniTruePrefixes outperforms baseline NLI models by 5-14 F1 points in prefix-level entailment. When integrated, LLaMA-3.2-3B-Instruct matches faithfulness and runtime of 8B model while using half the memory.

Conclusion: Specialized prefix-level entailment models like MiniTruePrefixes can significantly improve factual consistency in autoregressive generation and enable smaller models to achieve similar performance to larger ones with better efficiency.

Abstract: Natural Language Inference (NLI) models have been used in various ways to improve the factuality of LLM outputs. This is typically done by applying an NLI model to judge whether the model output is entailed from the supposed evidence, triggering some corrective actions, such as beam reranking at inference time or RL rewards during training. While NLI models are trained to detect factual inconsistencies over complete sentences, decisions in the common autoregressive generation architecture are made for each evolving text prefix, during decoding. Addressing this setting, we generalize the entailment detection task to apply over arbitrary text prefixes, and suggest its utility for improving generation faithfulness. Providing suitable evaluation and training datasets for this task, we train MiniTruePrefixes, a novel specialized model that better detects factual inconsistencies over text prefixes, outperforming comparable baseline NLI models by 5-14 F1 points in prefix-level entailment. We further demonstrate that integrating MiniTruePrefixes into a controlled decoding framework substantially improves factual consistency in abstractive summarization. When guided by MiniTruePrefixes, LLaMA-3.2-3B-Instruct matches the faithfulness and runtime of the 8B model from the same model family, while using only half the memory.

Method: Translated a 500-item subset of Cancer-Myth benchmark into 5 Indic languages (2,500 items total), using native-speaker translators following a style guide to preserve implicit presuppositions. Items contain false presuppositions about cancer.

Result: Evaluated several popular LLMs under presupposition stress using the translated benchmark.

Conclusion: The work helps address the multilingual evaluation gap in LLM healthcare responses by providing a specialized benchmark for Indic languages.

Abstract: Increasingly, more and more people are turning to large language models (LLMs) for healthcare advice and consultation, making it important to gauge the efficacy and accuracy of the responses of LLMs to such queries. While there are pre-existing medical benchmarks literature which seeks to accomplish this very task, these benchmarks are almost universally in English, which has led to a notable gap in existing literature pertaining to multilingual LLM evaluation. Within this work, we seek to aid in addressing this gap with Cancer-Myth-Indic, an Indic language benchmark built by translating a 500-item subset of Cancer-Myth, sampled evenly across its original categories, into five under-served but widely used languages from the subcontinent (500 per language; 2,500 translated items total). Native-speaker translators followed a style guide for preserving implicit presuppositions in translation; items feature false presuppositions relating to cancer. We evaluate several popular LLMs under this presupposition stress.

Method: Investigation of three model families (OpenAI, Anthropic, Google) analyzing how their reasoning capabilities evolve across different benchmarks over years, examining performance trends across reasoning tasks.

Result: Analysis reveals trends in model performance across reasoning benchmarks over time, highlighting the current situation of benchmarking and remaining challenges in evaluating true reasoning capabilities.

Conclusion: The paper provides a comprehensive overview of benchmarks and reasoning tasks to serve as a reference for future research in reasoning evaluation and model development, addressing the gap between benchmark performance and actual reasoning capabilities.

Abstract: The rapid rise of Large Language Models (LLMs) and Large Reasoning Models (LRMs) has been accompanied by an equally rapid increase of benchmarks used to assess them. However, due to both improved model competence resulting from scaling and novel training advances as well as likely many of these datasets being included in pre or post training data, results become saturated, driving a continuous need for new and more challenging replacements. In this paper, we discuss whether surpassing a benchmark truly demonstrates reasoning ability or are we simply tracking numbers divorced from the capabilities we claim to measure? We present an investigation focused on three model families, OpenAI, Anthropic, and Google, and how their reasoning capabilities across different benchmarks evolve over the years. We also analyze performance trends over the years across different reasoning tasks and discuss the current situation of benchmarking and remaining challenges. By offering a comprehensive overview of benchmarks and reasoning tasks, our work aims to serve as a first reference to ground future research in reasoning evaluation and model development.

Method: Identifies confounding factors in existing analyses, re-assesses three hypotheses about morphological alignment of tokenization, tokenization efficiency, and dataset size, and introduces token bigram metrics as intrinsic predictors of language modeling difficulty.

Result: Shows that previous conclusions about morphology and language modeling include confounding factors. Token bigram metrics serve as gradient proxies for morphological complexity that don’t require expert annotation.

Conclusion: Outlines necessary conditions to reliably determine whether and how morphology relates to language modeling, emphasizing the need to control for confounding factors in experimental design.

Abstract: The extent to which individual language characteristics influence tokenization and language modeling is an open question. Differences in morphological systems have been suggested as both unimportant and crucial to consider (Cotterell et al., 2018; Gerz et al., 2018a; Park et al., 2021, inter alia). We argue this conflicting evidence is due to confounding factors in experimental setups, making it hard to compare results and draw conclusions. We identify confounding factors in analyses trying to answer the question of whether, and how, morphology relates to language modeling. Next, we re-assess three hypotheses by Arnett & Bergen (2025) for why modeling agglutinative languages results in higher perplexities than fusional languages: they look at morphological alignment of tokenization, tokenization efficiency, and dataset size. We show that each conclusion includes confounding factors. Finally, we introduce token bigram metrics as an intrinsic way to predict the difficulty of causal language modeling, and find that they are gradient proxies for morphological complexity that do not require expert annotation. Ultimately, we outline necessities to reliably answer whether, and how, morphology relates to language modeling.

Method: A genetic search algorithm optimizes a scalar objective that jointly aggregates retrieval metrics (recall@k, mAP, nDCG, MRR) and generation metrics (LLM-Judge and semantic similarity) across 46,080 feasible pipeline configurations.

Result: RAGSmith finds configurations that consistently outperform naive RAG baseline by +3.8% on average (range +1.2% to +6.9% across domains), with gains up to +12.5% in retrieval and +7.5% in generation. The search explores only ~0.2% of the space (~100 candidates).

Conclusion: The framework discovers a robust backbone of vector retrieval plus post-generation reflection/revision, with domain-dependent choices in expansion, reranking, augmentation, and prompt reordering. Evolutionary search proves effective for full-pipeline optimization in RAG systems.

Abstract: Retrieval-Augmented Generation (RAG) quality depends on many interacting choices across retrieval, ranking, augmentation, prompting, and generation, so optimizing modules in isolation is brittle. We introduce RAGSmith, a modular framework that treats RAG design as an end-to-end architecture search over nine technique families and 46{,}080 feasible pipeline configurations. A genetic search optimizes a scalar objective that jointly aggregates retrieval metrics (recall@k, mAP, nDCG, MRR) and generation metrics (LLM-Judge and semantic similarity). We evaluate on six Wikipedia-derived domains (Mathematics, Law, Finance, Medicine, Defense Industry, Computer Science), each with 100 questions spanning factual, interpretation, and long-answer types. RAGSmith finds configurations that consistently outperform naive RAG baseline by +3.8% on average (range +1.2% to +6.9% across domains), with gains up to +12.5% in retrieval and +7.5% in generation. The search typically explores $\approx 0.2%$ of the space ($\sim 100$ candidates) and discovers a robust backbone – vector retrieval plus post-generation reflection/revision – augmented by domain-dependent choices in expansion, reranking, augmentation, and prompt reordering; passage compression is never selected. Improvement magnitude correlates with question type, with larger gains on factual/long-answer mixes than interpretation-heavy sets. These results provide practical, domain-aware guidance for assembling effective RAG systems and demonstrate the utility of evolutionary search for full-pipeline optimization.

Method: Automated pipeline that computes deltas between successive Wikidata snapshots, filters candidate triples for quality, and synthesizes natural-language questions at three reasoning difficulty levels with unique, verifiable answers through SPARQL validation.

Result: Experiments show significant performance drop when models face facts post-dating pretraining, especially on multi-hop queries. Retrieval augmentation and larger models provide partial improvements but fail to close the recency gap.

Conclusion: LiveSearchBench shifts evaluation from static memorization toward tasks requiring up-to-date retrieval and reasoning, enabling systematic long-term assessment of LLMs under evolving knowledge.

Abstract: Evaluating large language models (LLMs) on question answering often relies on static benchmarks that reward memorization and understate the role of retrieval, failing to capture the dynamic nature of world knowledge. We present LiveSearchBench, an automated pipeline for constructing retrieval-dependent benchmarks from recent knowledge updates. Our method computes deltas between successive Wikidata snapshots, filters candidate triples for quality, and synthesizes natural-language questions at three levels of reasoning difficulty, each guaranteed to admit a unique, verifiable answer through SPARQL validation. The pipeline is fully automated, scalable across time, and minimizes human intervention, enabling continual regeneration of temporally grounded benchmarks. Experiments show a pronounced performance drop when models confront facts that post-date pretraining, with the gap most salient on multi-hop queries. Retrieval augmented methods and larger, instruction-tuned models provide partial gains but fail to close this recency gap. By design, LiveSearchBench shifts evaluation from static memorization toward tasks that require up-to-date retrieval and reasoning, offering a foundation for systematic, long-term assessment of LLMs under evolving knowledge.

Method: Two-step pipeline: 1) Fine-tuned NLLB-1.3B generates structurally faithful drafts, 2) Zero-shot LLMs (Llama-3.3 or Qwen3) polish drafts, optionally enhanced with RAG using retrieved examples.

Result: The open-source RAG system achieves performance statistically comparable to GPT-5 baseline on both in-domain (Rosenthal, 2023) and challenging out-of-domain (12th-century Latin letters) benchmarks.

Conclusion: The reproducible pipeline enables open-source models to match proprietary system performance for Latin translation, with released code, datasets, and models to support further research.

Abstract: Translating a morphology-rich, low-resource language like Latin poses significant challenges. This paper introduces a reproducible draft-based refinement pipeline that elevates open-source Large Language Models (LLMs) to a performance level statistically comparable to top-tier proprietary systems. Our method first uses a fine-tuned NLLB-1.3B model to generate a high-quality, structurally faithful draft. A zero-shot LLM (Llama-3.3 or Qwen3) then polishes this draft, a process that can be further enhanced by augmenting the context with retrieved out-context examples (RAG). We demonstrate the robustness of this approach on two distinct benchmarks: a standard in-domain test set (Rosenthal, 2023) and a new, challenging out-of-domain (OOD) set of 12th-century Latin letters (2025). Our central finding is that this open-source RAG system achieves performance statistically comparable to the GPT-5 baseline, without any task-specific LLM fine-tuning. We release the pipeline, the Chartres OOD set, and evaluation scripts and models to facilitate replicability and further research.

Method: Two-phase training: 1) SFT on teacher-generated CoT data compressed to various budget levels, 2) RL with reward balancing reasoning performance and budget fidelity.

Result: An 8B student model achieves strong performance on challenging reasoning benchmarks (AIME24, AIME25, GPQA) with precise control over reasoning length.

Conclusion: BARD successfully enables smaller models to balance reasoning performance and computational efficiency through budget-aware reasoning distillation.

Abstract: While long Chain-of-Thought (CoT) distillation effectively transfers reasoning capability to smaller language models, the reasoning process often remains redundant and computational budget uncontrollable, leading to inefficient resource usage. To address this limitation, we propose \textbf{Budget-Aware Reasoning Distillation (BARD)}, a novel framework that simultaneously distills reasoning capability and enables fine-grained control over the reasoning length. BARD uses the thinking budget as a user-specified control signal, allowing the model to dynamically balance reasoning performance and computational efficiency. To achieve this concept, BARD introduces a two-phase training regimen. The first phase, Supervised Fine-Tuning (SFT) on teacher-generated long CoT data compressed to various budget levels, bootstrapping the model’s understanding of budget constraints. The second phase leverages Reinforcement Learning (RL) from a reward signal in consideration of reasoning performance and budget fidelity simultaneously. Incorporating the two-phase regimen is crucial to avoiding policy degradation and ensuring that both objectives are optimized jointly. Extensive experiments demonstrate that our method empowers an 8B student model to achieve strong performance on challenging reasoning benchmarks (\textit{AIME24, AIME25, GPQA}) while providing precise and adaptive control over its reasoning length across a wide range of budgets.

Method: Used multiple independent LLM runs to identify stable labeling patterns, and introduced a dataset-agnostic evaluation framework using Cohen’s kappa as an effect size measure for fair cross-dataset comparisons.

Result: GPT-4 produced highly consistent annotations (Fleiss’s Kappa = 0.78), and models trained on LLM-generated annotations showed improved test set performance compared to those trained on human-labeled data.

Conclusion: LLMs provide a scalable and internally consistent alternative for generating training data that supports strong downstream performance in subjective NLP tasks.

Abstract: Text-based automated Cognitive Distortion detection is a challenging task due to its subjective nature, with low agreement scores observed even among expert human annotators, leading to unreliable annotations. We explore the use of Large Language Models (LLMs) as consistent and reliable annotators, and propose that multiple independent LLM runs can reveal stable labeling patterns despite the inherent subjectivity of the task. Furthermore, to fairly compare models trained on datasets with different characteristics, we introduce a dataset-agnostic evaluation framework using Cohen’s kappa as an effect size measure. This methodology allows for fair cross-dataset and cross-study comparisons where traditional metrics like F1 score fall short. Our results show that GPT-4 can produce consistent annotations (Fleiss’s Kappa = 0.78), resulting in improved test set performance for models trained on these annotations compared to those trained on human-labeled data. Our findings suggest that LLMs can offer a scalable and internally consistent alternative for generating training data that supports strong downstream performance in subjective NLP tasks.

Method: Investigates the impact of synthetic data source diversity on fine-tuned LLMs across three dimensions: distribution collapse, adversarial robustness, and self-preference bias.

Result: Diverse synthetic data mitigates distribution collapse and preserves output quality while removing safeguards. Fine-tuning reduces self-preference bias, with human data being most effective.

Conclusion: Synthetic data from diverse sources can preserve output distribution breadth and quality while reducing biases, though it may make outputs more usable and potentially dangerous.

Abstract: As synthetic data becomes widely used in language model development, understanding its impact on model behavior is crucial. This paper investigates the impact of the diversity of sources of synthetic data on fine-tuned large language models. We focus on three key dimensions: distribution collapse, adversarial robustness, and self-preference bias. Our findings reveal that fine-tuning models on synthetic data from diverse sources can mitigate distribution collapse, preserving the breadth of the output distribution and the diversity of the output text. Furthermore, while both human and synthetic fine-tuning data can remove safeguards, the latter preserves higher output quality, thus making outputs potentially more usable and dangerous. Finally, fine-tuning reduces self-preference bias, with human data being the most effective, followed by multi-source synthetic data.

Method: Proposed pipeline uses Pareto class-optimized LLMs and Chain-of-Thought prompting to generate detoxified sentences. Created BanglaNirTox dataset with toxic sentences, class-wise toxicity labels, reasonings, and detoxified paraphrases.

Result: Pareto-optimized LLMs with CoT prompting significantly enhance the quality and consistency of Bengali text detoxification. The dataset enables fine-tuning of language models for better detoxification.

Conclusion: The proposed approach effectively addresses Bengali text detoxification challenges through optimized LLMs and comprehensive dataset creation, showing significant improvements in detoxification quality.

Abstract: Toxic language in Bengali remains prevalent, especially in online environments, with few effective precautions against it. Although text detoxification has seen progress in high-resource languages, Bengali remains underexplored due to limited resources. In this paper, we propose a novel pipeline for Bengali text detoxification that combines Pareto class-optimized large language models (LLMs) and Chain-of-Thought (CoT) prompting to generate detoxified sentences. To support this effort, we construct BanglaNirTox, an artificially generated parallel corpus of 68,041 toxic Bengali sentences with class-wise toxicity labels, reasonings, and detoxified paraphrases, using Pareto-optimized LLMs evaluated on random samples. The resulting BanglaNirTox dataset is used to fine-tune language models to produce better detoxified versions of Bengali sentences. Our findings show that Pareto-optimized LLMs with CoT prompting significantly enhance the quality and consistency of Bengali text detoxification.

Method: Two-step approach: 1) Create difficulty-annotated dataset through two-way distractor generation, filtering, and ensemble QA system categorization; 2) Train difficulty-controllable generation model via multitask learning with auxiliary tasks for semantic understanding and difficulty estimation.

Result: The method generates high-quality distractors across difficulty levels and substantially outperforms GPT-4o in aligning distractor difficulty with human perception.

Conclusion: The proposed framework effectively addresses the challenge of generating distractors with controllable difficulty, demonstrating superior performance compared to existing methods including GPT-4o.

Abstract: Multiple-choice cloze questions are commonly used to assess linguistic proficiency and comprehension. However, generating high-quality distractors remains challenging, as existing methods often lack adaptability and control over difficulty levels, and the absence of difficulty-annotated datasets further hinders progress. To address these issues, we propose a novel framework for generating distractors with controllable difficulty by leveraging both data augmentation and a multitask learning strategy. First, to create a high-quality, difficulty-annotated dataset, we introduce a two-way distractor generation process in order to produce diverse and plausible distractors. These candidates are subsequently refined through filtering and then categorized by difficulty using an ensemble QA system. Second, this newly created dataset is leveraged to train a difficulty-controllable generation model via multitask learning. The framework includes carefully designed auxiliary tasks that enhance the model’s semantic understanding of distractors and its ability to estimate their difficulty. Experimental results demonstrate that our method generates high-quality distractors across difficulty levels and substantially outperforms GPT-4o in aligning distractor difficulty with human perception.

Method: Used behavioral forma mentis networks to map cognitive associations and emotional perceptions. Conducted 4 experiments with psychology undergraduates (n=127) compared against GPT-simulated students (GPT-3.5: n=300; GPT-4o: n=300). Experiments 1-3 predicted math anxiety scores using individual network features, while Experiment 4 analyzed group-level perceptions.

Result: In human students: positive valence for ‘anxiety’ and negative ratings for ‘math’ predicted higher math anxiety. High math-anxiety students framed ‘anxiety’ in emotionally polarizing ways and contrasted ‘science’ (positive) against ‘math’ (negative). These patterns didn’t apply to GPT models due to cognitive differences in network structures and psychometric scores.

Conclusion: Understanding concept perception and associations is crucial for managing math anxiety. The study highlights cognitive differences between human and AI models in emotional framing of STEM concepts, emphasizing the need for tailored interventions for math-anxious students.

Abstract: Math anxiety poses significant challenges for university psychology students, affecting their career choices and overall well-being. This study employs a framework based on behavioural forma mentis networks (i.e. cognitive models that map how individuals structure their associative knowledge and emotional perceptions of concepts) to explore individual and group differences in the perception and association of concepts related to math and anxiety. We conducted 4 experiments involving psychology undergraduates from 2 samples (n1 = 70, n2 = 57) compared against GPT-simulated students (GPT-3.5: n2 = 300; GPT-4o: n4 = 300). Experiments 1, 2, and 3 employ individual-level network features to predict psychometric scores for math anxiety and its facets (observational, social and evaluational) from the Math Anxiety Scale. Experiment 4 focuses on group-level perceptions extracted from human students, GPT-3.5 and GPT-4o’s networks. Results indicate that, in students, positive valence ratings and higher network degree for “anxiety”, together with negative ratings for “math”, can predict higher total and evaluative math anxiety. In contrast, these models do not work on GPT-based data because of differences in simulated networks and psychometric scores compared to humans. These results were also reconciled with differences found in the ways that high/low subgroups of simulated and real students framed semantically and emotionally STEM concepts. High math-anxiety students collectively framed “anxiety” in an emotionally polarising way, absent in the negative perception of low math-anxiety students. “Science” was rated positively, but contrasted against the negative perception of “math”. These findings underscore the importance of understanding concept perception and associations in managing students’ math anxiety.

Method: Proposed ECO decoding (Entropy-based COntrol) that dynamically adjusts control strength at each generation step based on the model’s entropy in both language model and attribute classifier probability distributions.

Result: Experiments on DailyDialog and MultiWOZ datasets show ECO decoding consistently improves controllability while maintaining fluency and grammaticality, outperforming prior decoding methods across various models and settings.

Conclusion: ECO decoding effectively addresses probability interpolation issues in multi-attribute generation and demonstrates strong performance in both single and multi-attribute controllable dialogue generation scenarios.

Abstract: Controllable Dialogue Generation (CDG) enables chatbots to generate responses with desired attributes, and weighted decoding methods have achieved significant success in the CDG task. However, using a fixed constant value to manage the bias of attribute probabilities makes it challenging to find an ideal control strength that satisfies both controllability and fluency. To address this issue, we propose ECO decoding (Entropy-based COntrol), which dynamically adjusts the control strength at each generation step according to the model’s entropy in both the language model and attribute classifier probability distributions. Experiments on the DailyDialog and MultiWOZ datasets demonstrate that ECO decoding consistently improves controllability while maintaining fluency and grammaticality, outperforming prior decoding methods across various models and settings. Furthermore, ECO decoding alleviates probability interpolation issues in multi-attribute generation and consequently demonstrates strong performance in both single and multi-attribute scenarios.

Method: Allograph-aware masked language modeling framework with domain-adaptive pretraining and Glyph Net linking graphemes and allographs

Result: Glyph Net improves restoration performance, glyph-biased sampling improves dating accuracy

Conclusion: Proposed framework effectively addresses challenges in bronze inscription analysis through integrated glyph modeling

Abstract: Bronze inscriptions from early China are fragmentary and difficult to date. We introduce BIRD(Bronze Inscription Restoration and Dating), a fully encoded dataset grounded in standard scholarly transcriptions and chronological labels. We further propose an allograph-aware masked language modeling framework that integrates domain- and task-adaptive pretraining with a Glyph Net (GN), which links graphemes and allographs. Experiments show that GN improves restoration, while glyph-biased sampling yields gains in dating.

Method: Uses a corpus of 500+ authentic Spanish errors, analyzed through theoretical linguistics, neurolinguistics, and NLP perspectives, and tested against AI models like GPT and Gemini.

Result: The research evaluates AI models’ interpretative accuracy and ability to generalize human linguistic error patterns.

Conclusion: The project contributes to understanding Spanish as a native language and developing NLP systems that better handle the imperfect and ambiguous nature of real human language.

Abstract: Linguistic errors are not merely deviations from normative grammar; they offer a unique window into the cognitive architecture of language and expose the current limitations of artificial systems that seek to replicate them. This project proposes an interdisciplinary study of linguistic errors produced by native Spanish speakers, with the aim of analyzing how current large language models (LLM) interpret, reproduce, or correct them. The research integrates three core perspectives: theoretical linguistics, to classify and understand the nature of the errors; neurolinguistics, to contextualize them within real-time language processing in the brain; and natural language processing (NLP), to evaluate their interpretation against linguistic errors. A purpose-built corpus of authentic errors of native Spanish (+500) will serve as the foundation for empirical analysis. These errors will be tested against AI models such as GPT or Gemini to assess their interpretative accuracy and their ability to generalize patterns of human linguistic behavior. The project contributes not only to the understanding of Spanish as a native language but also to the development of NLP systems that are more cognitively informed and capable of engaging with the imperfect, variable, and often ambiguous nature of real human language.

Method: Automatically built from ParlaMint transcripts and aligned with speech recordings from parliaments, then enriched with automatic annotation layers including linguistic annotations, sentiment predictions, filled pauses detection, word/grapheme alignments, and stress position annotation.

Result: Created a 6,000-hour corpus spanning Croatian, Czech, Polish, and Serbian with multiple annotation layers, demonstrating utility through acoustic sentiment analysis and making data available in JSONL, TextGrid formats and via concordancer.

Conclusion: The enriched ParlaSpeech corpora significantly increase research utility across disciplines and provide valuable resources for Slavic language processing and parliamentary speech analysis.

Abstract: ParlaSpeech is a collection of spoken parliamentary corpora currently spanning four Slavic languages - Croatian, Czech, Polish and Serbian - all together 6 thousand hours in size. The corpora were built in an automatic fashion from the ParlaMint transcripts and their corresponding metadata, which were aligned to the speech recordings of each corresponding parliament. In this release of the dataset, each of the corpora is significantly enriched with various automatic annotation layers. The textual modality of all four corpora has been enriched with linguistic annotations and sentiment predictions. Similar to that, their spoken modality has been automatically enriched with occurrences of filled pauses, the most frequent disfluency in typical speech. Two out of the four languages have been additionally enriched with detailed word- and grapheme-level alignments, and the automatic annotation of the position of primary stress in multisyllabic words. With these enrichments, the usefulness of the underlying corpora has been drastically increased for downstream research across multiple disciplines, which we showcase through an analysis of acoustic correlates of sentiment. All the corpora are made available for download in JSONL and TextGrid formats, as well as for search through a concordancer.

Method: Extract Knowledge Graph from energy documents, then use graph navigation and reasoning within a RAG architecture for multilingual QA, validated using RAGAs framework and domain experts.

Result: System achieves 75.2% ± 2.7% accuracy, with higher performance on general EE questions (81.0% ± 4.1%) and minimal multilingual degradation (4.4% accuracy loss).

Conclusion: Graph-based RAG architecture shows strong potential for energy efficiency QA with promising multilingual abilities, though some limitations remain.

Abstract: In this work, we investigate the use of Large Language Models (LLMs) within a graph-based Retrieval Augmented Generation (RAG) architecture for Energy Efficiency (EE) Question Answering. First, the system automatically extracts a Knowledge Graph (KG) from guidance and regulatory documents in the energy field. Then, the generated graph is navigated and reasoned upon to provide users with accurate answers in multiple languages. We implement a human-based validation using the RAGAs framework properties, a validation dataset comprising 101 question-answer pairs, and domain experts. Results confirm the potential of this architecture and identify its strengths and weaknesses. Validation results show how the system correctly answers in about three out of four of the cases (75.2 +- 2.7%), with higher results on questions related to more general EE answers (up to 81.0 +- 4.1%), and featuring promising multilingual abilities (4.4% accuracy loss due to translation).

Method: Integrates Bloom’s Taxonomy with Retrieval-Augmented Generation (RAG) to evaluate LLM performance across six cognitive domains, using a Taiwanese Hakka digital cultural archive as test data.

Result: The framework measures LLM-generated responses for semantic accuracy and cultural relevance across hierarchical cognitive levels.

Conclusion: Proposes a systematic approach for benchmarking LLMs’ cultural knowledge processing capabilities, with potential applications in cultural heritage and AI evaluation.

Abstract: This study proposes a cognitive benchmarking framework to evaluate how large language models (LLMs) process and apply culturally specific knowledge. The framework integrates Bloom’s Taxonomy with Retrieval-Augmented Generation (RAG) to assess model performance across six hierarchical cognitive domains: Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating. Using a curated Taiwanese Hakka digital cultural archive as the primary testbed, the evaluation measures LLM-generated responses’ semantic accuracy and cultural relevance.

Method: Created EngChain benchmark with 90 problems from symbolic templates with randomization; uses two-stage evaluation: quantitative verification of reasoning steps and LLM-As-A-Judge for qualitative error categorization.

Result: Developed a comprehensive benchmark that moves beyond final answer accuracy to verify step-by-step reasoning validity in engineering contexts.

Conclusion: EngChain addresses the gap in evaluating LLMs’ complex engineering reasoning capabilities through verifiable multi-step problem-solving assessment.

Abstract: Large Language Models (LLMs) are increasingly being applied to specialized, high-stakes domains like engineering, which demands rigorous evaluation of their complex reasoning capabilities. While current benchmarks assess language understanding, factual recall, mathematics or code generation, none capture the integrative reasoning central to engineering where scientific principles, quantitative modeling and practical constraints must converge. To address this gap, we introduce EngChain, a benchmark for verifiable multi-step engineering problem-solving. EngChain contains 90 problems spanning three engineering branches, organized into 9 domains and 20 distinct areas. The problems are generated from symbolic templates with a high degree of randomization to ensure diversity and eliminate the risk of contamination. With this benchmark, we move beyond final answer accuracy with a two-stage evaluation: we first quantitatively verify the numerical and semantic validity of each reasoning step and then introduce LLM-As-A-Judge, an automated system to qualitatively categorize the identified reasoning errors.

Method: Trained on a large-scale audio corpus, the model supports flexible input modalities (audio only, text only, audio+text) and multiple tasks including audio captioning, speech recognition, translation, emotion recognition, Q&A, and summarization.

Result: SeaLLMs-Audio achieves competitive performance compared to other LALMs on Southeast Asian languages, as evaluated by the new SeaBench-Audio benchmark.

Conclusion: The model represents a significant advancement for audio LLMs in Southeast Asia and is expected to benefit both regional research and industry.

Abstract: We introduce SeaLLMs-Audio, the first large audio-language model (LALM) tailored for multiple Southeast Asian (SEA) languages-Indonesian (id), Thai (th), and Vietnamese (vi)-alongside English (en) and Chinese (zh). Trained on a large-scale audio corpus, SeaLLMs-Audio exhibits strong performance across diverse audio-centric tasks, spanning fine-grained audio understanding and voice-based interaction. Its key features include: 1) Multilingual: the model primarily supports 5 languages, namely Indonesian, Thai, Vietnamese, English, and Chinese; 2) Multimodal: the model accepts flexible input modalities, including audio only, text only, as well as audio with text; 3) Multi-task: the model supports a wide range of tasks, including audio analysis tasks such as Audio Captioning, Automatic Speech Recognition, Speech-to-Text Translation, Speech Emotion Recognition, Speech Question Answering, and Speech Summarization. It also enables voice-based dialogue, including answering factual, mathematical, and general knowledge queries. As a significant step towards advancing audio LLMs in Southeast Asia, we expect SeaLLMs-Audio to benefit both the regional research community and industry. To automate LALM evaluation for Southeast Asia, we introduce SeaBench-Audio, a benchmark spanning multiple tasks. Experiments show that SeaLLMs-Audio achieves competitive performance compared with other LALMs on SEA languages.

Method: Fine-tuned three popular open-weights models using 11 personas via Constitutional AI and synthetic introspective data pipeline, with revealed preferences analysis to track character changes.

Result: Character changes are more robust to adversarial prompting than alternatives, lead to more coherent and realistic generations, and have little to no effect on general benchmark performance.

Conclusion: The approach provides effective and controlled character training that outperforms existing methods while maintaining model capabilities, with full implementation open-sourced.

Abstract: The character of the “AI assistant” persona generated by modern chatbot large language models influences both surface-level behavior and apparent values, beliefs, and ethics. These all affect interaction quality, perceived intelligence, and alignment with both developer and user intentions. The shaping of this persona, known as character training, is a critical component of industry post-training, yet remains effectively unstudied in the academic literature. We introduce the first open implementation of character training, leveraging Constitutional AI and a new data pipeline using synthetic introspective data to shape the assistant persona in a more effective and controlled manner than alternatives such as constraining system prompts or activation steering. Specifically, we fine-tune three popular open-weights models using 11 example personas, such as humorous, deeply caring, or even malevolent. To track the effects of our approach, we introduce a method which analyzes revealed preferences, uncovering clear and holistic changes in character. We find these changes are more robust to adversarial prompting than the above two alternatives, while also leading to more coherent and realistic generations. Finally, we demonstrate this fine-tuning has little to no effect on general capabilities as measured by common benchmarks. We describe and open-source our full post-training method, the implementation of which can be found at https://github.com/maiush/OpenCharacterTraining.

Method: Proposes a novel rank-2 projection subspace that more accurately disentangles PK and CK contributions, enabling the first multi-step analysis of knowledge interactions across longer NLE sequences on four QA datasets with three open-weight instruction-tuned LLMs.

Result: Diverse knowledge interactions are poorly represented in rank-1 subspace but effectively captured in rank-2 formulation. Hallucinated NLEs align strongly with PK direction, context-faithful ones balance PK and CK, and Chain-of-Thought prompting shifts NLEs toward CK by reducing PK reliance.

Conclusion: This work provides the first framework for systematic studies of multi-step knowledge interactions in LLMs through a richer rank-2 subspace disentanglement, enabling better understanding of how LLMs ground their explanations in different knowledge sources.

Abstract: Natural Language Explanations (NLEs) describe how Large Language Models (LLMs) make decisions, drawing on both external Context Knowledge (CK) and Parametric Knowledge (PK) stored in model weights. Understanding their interaction is key to assessing the grounding of NLEs, yet it remains underexplored. Prior work has largely examined only single-step generation, typically the final answer, and has modelled PK and CK interaction only as a binary choice in a rank-1 subspace. This overlooks richer forms of interaction, such as complementary or supportive knowledge. We propose a novel rank-2 projection subspace that disentangles PK and CK contributions more accurately and use it for the first multi-step analysis of knowledge interactions across longer NLE sequences. Experiments on four QA datasets and three open-weight instruction-tuned LLMs show that diverse knowledge interactions are poorly represented in a rank-1 subspace but are effectively captured in our rank-2 formulation. Our multi-step analysis reveals that hallucinated NLEs align strongly with the PK direction, context-faithful ones balance PK and CK, and Chain-of-Thought prompting for NLEs shifts generated NLEs toward CK by reducing PK reliance. This work provides the first framework for systematic studies of multi-step knowledge interactions in LLMs through a richer rank-2 subspace disentanglement. Code and data: https://github.com/copenlu/pk-ck-knowledge-disentanglement.

Method: Uses Qwen3 as base model with LoRA adapters to create three specialists: tool calling, tool-response interpretation, and direct dialogue, ensuring computational efficiency on L40S GPUs.

Result: System achieved second place overall in the Commonsense Persona-Grounded Dialogue Challenge 2025, delivering fast responses with modest resource usage.

Conclusion: The multi-expert approach with specialized LoRA adapters provides an effective and computationally efficient solution for creating dialogue-capable NPCs with contextual action execution.

Abstract: We present a multi-expert system for creating Non-Player Characters (NPCs) capable of both natural dialogue and contextual action execution in interactive environments. Using Qwen3 as the base model and Low-Rank Adaptation (LoRA) adapters, we instantiate three specialists: tool calling, tool-response interpretation, and direct dialogue. Our system comfortably meets the computational efficiency requirements, delivering fast responses and maintaining modest resource usage on L40S GPUs. In the Commonsense Persona-Grounded Dialogue Challenge 2025, our method ranked second overall. Code available at: https://github.com/MahammadNuriyev62/CPDC-challenge-2025-solution/

Method: Examined belief shifts through moral dilemma discussions, political text reading, and tool use tasks that reflect implicit beliefs.

Result: GPT-5 showed 54.7% belief shift after 10 rounds of moral discussions, while Grok 4 shifted 27.2% on political issues after reading opposing texts. Behavioral changes in tool use aligned with stated belief shifts.

Conclusion: Extended talking and reading sessions pose hidden risks of belief shift in language models, rendering their opinions and actions unreliable in agentic systems.

Abstract: Language model (LM) assistants are increasingly used in applications such as brainstorming and research. Improvements in memory and context size have allowed these models to become more autonomous, which has also resulted in more text accumulation in their context windows without explicit user intervention. This comes with a latent risk: the belief profiles of models – their understanding of the world as manifested in their responses or actions – may silently change as context accumulates. This can lead to subtly inconsistent user experiences, or shifts in behavior that deviate from the original alignment of the models. In this paper, we explore how accumulating context by engaging in interactions and processing text – talking and reading – can change the beliefs of language models, as manifested in their responses and behaviors.Our results reveal that models’ belief profiles are highly malleable: GPT-5 exhibits a 54.7% shift in its stated beliefs after 10 rounds of discussion about moral dilemmas and queries about safety, while Grok 4 shows a 27.2% shift on political issues after reading texts from the opposing position. We also examine models’ behavioral changes by designing tasks that require tool use, where each tool selection corresponds to an implicit belief. We find that these changes align with stated belief shifts, suggesting that belief shifts will be reflected in actual behavior in agentic systems. Our analysis exposes the hidden risk of belief shift as models undergo extended sessions of talking or reading, rendering their opinions and actions unreliable.

Method: Structure-guided prompt engineering with deliberate planning and word counting mechanisms that encourage models to track and adhere to specified length constraints.

Result: Significantly improved length fidelity across six LLMs, particularly for shorter-to-medium lengths (up to 37.6% improvement), while maintaining or enhancing output quality compared to standard prompting.

Conclusion: Provides an immediately deployable solution for precise length control in production environments where model retraining is impractical or cost-prohibitive.

Abstract: Length control in Large Language Models (LLMs) is a crucial but under-addressed challenge, with applications ranging from voice interfaces requiring concise responses to research summaries needing comprehensive outputs. Current approaches to length control, including Regularized DPO, Length-Instruction Fine Tuning, and tool-augmented methods, typically require expensive model retraining or complex inference-time tooling. This paper presents a prompt engineering methodology that enables precise length control without model retraining. Our structure-guided approach implements deliberate planning and word counting mechanisms within the prompt, encouraging the model to carefully track and adhere to specified length constraints. Comprehensive evaluations across six state-of-the-art LLMs demonstrate that our method significantly improves length fidelity for several models compared to standard prompting when applied to document summarization tasks, particularly for shorter-to-medium length constraints. The proposed technique shows varying benefits across different model architectures, with some models demonstrating up to 37.6% improvement in length adherence. Quality evaluations further reveal that our approach maintains or enhances overall output quality compared to standard prompting techniques. Our approach provides an immediately deployable solution for applications requiring precise length control, particularly valuable for production environments where model retraining is impractical or cost-prohibitive.

Method: KVTC combines PCA-based feature decorrelation, adaptive quantization, and entropy coding inspired by classical media compression, requiring only brief initial calibration without changing model parameters.

Result: Achieves up to 20× compression while maintaining reasoning and long-context accuracy, with 40× or higher for specific use cases. Outperforms token eviction, quantization, and SVD-based methods across multiple benchmarks and models.

Conclusion: KVTC serves as a practical building block for memory-efficient LLM serving with reusable KV caches, enabling efficient on-GPU and off-GPU storage.

Abstract: Serving large language models (LLMs) at scale necessitates efficient key-value (KV) cache management. KV caches can be reused across conversation turns via shared-prefix prompts that are common in iterative code editing and chat. However, stale caches consume scarce GPU memory, require offloading, or force recomputation. We present KVTC, a lightweight transform coder that compresses KV caches for compact on-GPU and off-GPU storage. Drawing on classical media compression, KVTC combines PCA-based feature decorrelation, adaptive quantization, and entropy coding. It requires only a brief initial calibration and leaves model parameters unchanged. By exploiting redundancies in KV caches, KVTC achieves up to 20$\times$ compression while maintaining reasoning and long-context accuracy, and 40$\times$ or higher for specific use cases. We test KVTC with Llama 3, Mistral NeMo, and R1-Qwen 2.5 models across benchmarks including AIME25, LiveCodeBench, GSM8K, MMLU, Qasper, RULER, and MATH-500. It consistently outperforms inference-time baselines such as token eviction, quantization, and SVD-based methods, while achieving higher compression ratios. These results support KVTC as a practical building block for memory-efficient LLM serving with reusable KV caches.

Method: Developed IMO-Bench with 400 diverse Olympiad problems for short answers (IMO-AnswerBench) and proof-writing evaluation (IMO-ProofBench) with detailed grading guidelines for automatic grading. Also created IMO-GradingBench with 1000 human gradings.

Result: Gemini Deep Think achieved 80.0% on IMO-AnswerBench and 65.7% on advanced IMO-ProofBench, surpassing best non-Gemini models by 6.9% and 42.4% margins respectively. Autograders built with Gemini reasoning correlate well with human evaluations.

Conclusion: IMO-Bench provides robust benchmarks for advancing mathematical reasoning capabilities and was crucial for achieving gold-level performance at IMO 2025, with potential to drive further progress in automatic evaluation of long-form answers.

Abstract: Finding the right north-star metrics is highly critical for advancing the mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focus on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks, vetted by a panel of top specialists and that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMO-AnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-Proof Bench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO level problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the gold-level performance at IMO 2025 with Gemini Deep Think (Luong and Lockhart, 2025). Our model achieved 80.0% on IMO-AnswerBench and 65.7% on the advanced IMO-Proof Bench, surpassing the best non-Gemini models by large margins of 6.9% and 42.4% respectively. We also showed that autograders built with Gemini reasoning correlate well with human evaluations and construct IMO-GradingBench, with 1000 human gradings on proofs, to enable further progress in automatic evaluation of long-form answers. We hope that IMO-Bench will help the community towards advancing robust mathematical reasoning and release it at https://imobench.github.io/.

Method: Unified framework that embeds both tools and their parent agents in shared vector space, connecting them through metadata relationships to enable granular tool-level or agent-level retrieval.

Result: Achieves consistent improvements of 19.4% in Recall@5 and 17.7% in nDCG@5 over previous state-of-the-art agent retrievers on LiveMCPBench benchmark across eight embedding models.

Conclusion: Tool-to-Agent Retrieval ensures agents and their underlying tools are equally represented without context dilution, significantly improving retrieval performance in multi-agent systems.

Abstract: Recent advances in LLM Multi-Agent Systems enable scalable orchestration of sub-agents, each coordinating hundreds or thousands of tools or Model Context Protocol (MCP) servers. However, existing retrieval methods typically match queries against coarse agent-level descriptions before routing, which obscures fine-grained tool functionality and often results in suboptimal agent selection. We introduce Tool-to-Agent Retrieval, a unified framework that embeds both tools and their parent agents in a shared vector space and connects them through metadata relationships. By explicitly representing tool capabilities and traversing metadata to the agent level, Tool-to-Agent Retrieval enables granular tool-level or agent-level retrieval, ensuring that agents and their underlying tools or MCP servers are equally represented without the context dilution that arises from chunking many tools together. Evaluating Tool-to-Agent Retrieval across eight embedding models, our approach achieves consistent improvements of 19.4% in Recall@5 and 17.7% in nDCG@5 over previous state-of-the-art agent retrievers on the LiveMCPBench benchmark.

Method: The review covers: (1) necessary skills and LLM limitations for complex QA, (2) datasets and evaluation metrics, (3) solution families including training/reinforcement, hybridization, prompting, and agentic architectures with extended reasoning.

Result: The paper systematically analyzes the state-of-the-art approaches for overcoming LLM limitations in complex question-answering scenarios.

Conclusion: Hybrid architectures combining multiple strategies are essential for enabling LLMs to effectively handle complex questions that require specialized knowledge, reasoning capabilities, and human-in-the-loop processes.

Abstract: This paper reviews the state-of-the-art of large language models (LLM) architectures and strategies for “complex” question-answering with a focus on hybrid architectures. LLM based chatbot services have allowed anyone to grasp the potential of LLM to solve many common problems, but soon discovered their limitations for complex questions. Addressing more specific, complex questions (e.g., “What is the best mix of power-generation methods to reduce climate change ?”) often requires specialized architectures, domain knowledge, new skills, decomposition and multi-step resolution, deep reasoning, sensitive data protection, explainability, and human-in-the-loop processes. Therefore, we review: (1) necessary skills and tasks for handling complex questions and common LLM limits to overcome; (2) dataset, cost functions and evaluation metrics for measuring and improving (e.g. accuracy, explainability, fairness, robustness, groundedness, faithfulness, toxicity…); (3) family of solutions to overcome LLM limitations by (a) training and reinforcement (b) hybridization, (c) prompting, (d) agentic-architectures (agents, tools) and extended reasoning.

Method: Developed detailed preprocessing, tokenization, POS tagging, and dependency annotation procedures that build upon existing UD guidelines while incorporating Bavarian-specific grammatical considerations.

Result: Comprehensive annotation guidelines that cover data preprocessing, POS tags, syntactic dependencies, and lemma annotation, with specific adaptations for Bavarian grammar.

Conclusion: Successfully established annotation standards for Bavarian within the UD project, providing a framework that can handle both general linguistic patterns and language-specific features of Bavarian.

Abstract: This document provides the annotation guidelines for MaiBaam, a Bavarian corpus manually annotated with part-of-speech (POS) tags, syntactic dependencies, and German lemmas. MaiBaam belongs to the Universal Dependencies (UD) project, and our annotations elaborate on the general and German UD version 2 guidelines. In this document, we detail how to preprocess and tokenize Bavarian data, provide an overview of the POS tags and dependencies we use, explain annotation decisions that would also apply to closely related languages like German, and lastly we introduce and motivate decisions that are specific to Bavarian grammar.

Method: Introduces CheckEval, a checklist-based evaluation framework that uses decomposed binary questions to improve rating reliability instead of traditional Likert scale scoring.

Result: CheckEval strongly correlates with human judgments, improves average agreement across evaluator models by 0.45, reduces score variance, and provides more interpretable scores through traceable binary decisions.

Conclusion: CheckEval effectively addresses rating inconsistencies in LLM evaluation by providing a more reliable, interpretable framework through decomposed binary questions.

Abstract: Existing LLM-as-a-Judge approaches for evaluating text generation suffer from rating inconsistencies, with low agreement and high rating variance across different evaluator models. We attribute this to subjective evaluation criteria combined with Likert scale scoring in existing protocols. To address this issue, we introduce CheckEval, a checklist-based evaluation framework that improves rating reliability via decomposed binary questions. Through experiments with 12 evaluator models across multiple datasets, we first demonstrate that CheckEval strongly correlates with human judgments. More importantly, CheckEval dramatically improves the average agreement across evaluator models by 0.45 and reduces the score variance. CheckEval scores furthermore have the benefit of being more interpretable because it decomposes evaluation criteria into traceable binary decisions, allowing analyses of specific attributes driving quality judgments.

Method: Created ProGraph benchmark with 3 categories of graph tasks requiring programming solutions. Proposed LLM4Graph datasets containing crawled documents and auto-generated codes from 6 popular graph libraries. Enhanced LLMs through document retrieval for closed-source models and fine-tuning open-source models on the generated codes.

Result: Current LLMs performed poorly on ProGraph, with the best model achieving only 36% accuracy. The proposed LLM4Graph approach showed significant improvements - 11-32% absolute accuracy gains across different LLMs.

Conclusion: LLMs’ capabilities in handling structured data like graphs are still under-explored. The LLM4Graph approach effectively enhances LLMs’ graph analysis proficiency, demonstrating the value of programming-based approaches over direct reasoning for complex graph tasks.

Abstract: The need to analyze graphs is ubiquitous across various fields, from social networks to biological research and recommendation systems. Therefore, enabling the ability of large language models (LLMs) to process graphs is an important step toward more advanced general intelligence. However, current LLM benchmarks on graph analysis require models to directly reason over the prompts describing graph topology, and are thus limited to small graphs with only a few dozens of nodes. In contrast, human experts typically write programs based on popular libraries for task solving, and can thus handle graphs with different scales. To this end, a question naturally arises: can LLMs analyze graphs like professionals? In this paper, we introduce ProGraph, a manually crafted benchmark containing 3 categories of graph tasks. The benchmark expects solutions based on programming instead of directly reasoning over raw inputs. Our findings reveal that the performance of current LLMs is unsatisfactory, with the best model achieving only 36% accuracy. To bridge this gap, we propose LLM4Graph datasets, which include crawled documents and auto-generated codes based on 6 widely used graph libraries. By augmenting closed-source LLMs with document retrieval and fine-tuning open-source ones on the codes, we show 11-32% absolute improvements in their accuracies. Our results underscore that the capabilities of LLMs in handling structured data are still under-explored, and show the effectiveness of LLM4Graph in enhancing LLMs’ proficiency of graph analysis. The benchmark, datasets and enhanced open-source models are available at https://github.com/BUPT-GAMMA/ProGraph.

Method: Created IndicSentEval benchmark dataset (~47K sentences) and conducted probing analysis on 9 multilingual Transformer models (7 universal, 2 Indic-specific) across 8 linguistic properties and 13 perturbations in 6 Indic languages.

Result: Multilingual models show consistent encoding for English but mixed results for Indic languages. Indic-specific models capture linguistic properties better, while universal models exhibit better robustness, especially under perturbations like dropping nouns/verbs.

Conclusion: The study provides insights into strengths and weaknesses of multilingual Transformer models for Indic languages, highlighting the trade-off between linguistic encoding capability and robustness across different model types.

Abstract: Transformer-based models have revolutionized the field of natural language processing. To understand why they perform so well and to assess their reliability, several studies have focused on questions such as: Which linguistic properties are encoded by these models, and to what extent? How robust are these models in encoding linguistic properties when faced with perturbations in the input text? However, these studies have mainly focused on BERT and the English language. In this paper, we investigate similar questions regarding encoding capability and robustness for 8 linguistic properties across 13 different perturbations in 6 Indic languages, using 9 multilingual Transformer models (7 universal and 2 Indic-specific). To conduct this study, we introduce a novel multilingual benchmark dataset, IndicSentEval, containing approximately $\sim$47K sentences. Surprisingly, our probing analysis of surface, syntactic, and semantic properties reveals that while almost all multilingual models demonstrate consistent encoding performance for English, they show mixed results for Indic languages. As expected, Indic-specific multilingual models capture linguistic properties in Indic languages better than universal models. Intriguingly, universal models broadly exhibit better robustness compared to Indic-specific models, particularly under perturbations such as dropping both nouns and verbs, dropping only verbs, or keeping only nouns. Overall, this study provides valuable insights into probing and perturbation-specific strengths and weaknesses of popular multilingual Transformer-based models for different Indic languages. We make our code and dataset publicly available [https://github.com/aforakhilesh/IndicBertology].

Method: Tested AutoML models with linguistic features, various BERT-based encoder Transformers, and state-of-the-art LLMs on five Russian-language datasets with different text formats and pathology classification approaches.

Result: LLMs outperformed traditional methods, particularly on noisy and small datasets with varying text lengths and genres. Psycholinguistic features and encoder models achieved comparable performance to LLMs when trained on clinically confirmed depression cases.

Conclusion: LLMs are superior for general depression/anxiety detection, especially in challenging data conditions, but traditional methods remain effective for targeted clinical applications with confirmed cases.

Abstract: This paper compares the effectiveness of traditional machine learning methods, encoder-based models, and large language models (LLMs) on the task of detecting depression and anxiety. Five Russian-language datasets were considered, each differing in format and in the method used to define the target pathology class. We tested AutoML models based on linguistic features, several variations of encoder-based Transformers such as BERT, and state-of-the-art LLMs as pathology classification models. The results demonstrated that LLMs outperform traditional methods, particularly on noisy and small datasets where training examples vary significantly in text length and genre. However, psycholinguistic features and encoder-based models can achieve performance comparable to language models when trained on texts from individuals with clinically confirmed depression, highlighting their potential effectiveness in targeted clinical applications.

Method: Developed a novel general-purpose test framework for reliable large-scale generation of tests, created a benchmark dataset with 30,000 tests for detecting cognitive biases, and evaluated 20 state-of-the-art LLMs.

Result: Found evidence of all 30 tested cognitive biases in at least some of the 20 evaluated LLMs, confirming and broadening previous findings about bias presence in LLMs.

Conclusion: Cognitive biases are prevalent across state-of-the-art LLMs, with the published framework enabling future research on bias detection and mitigation in language models.

Abstract: We present a large-scale evaluation of 30 cognitive biases in 20 state-of-the-art large language models (LLMs) under various decision-making scenarios. Our contributions include a novel general-purpose test framework for reliable and large-scale generation of tests for LLMs, a benchmark dataset with 30,000 tests for detecting cognitive biases in LLMs, and a comprehensive assessment of the biases found in the 20 evaluated LLMs. Our work confirms and broadens previous findings suggesting the presence of cognitive biases in LLMs by reporting evidence of all 30 tested biases in at least some of the 20 LLMs. We publish our framework code to encourage future research on biases in LLMs: https://github.com/simonmalberg/cognitive-biases-in-llms

Method: Fine-tuned GPT-3.5 Turbo on two datasets: high-incivility Twitter replies to US Congress and low-incivility posts from Reddit’s r/ChangeMyView, evaluating rhetorical framing and deliberative quality.

Result: Reddit-finetuned models generate safer but rhetorically rigid arguments; cross-platform fine-tuning amplifies adversarial tone and toxicity; prompt-based steering reduces overt toxicity but cannot fully offset noisy training data.

Conclusion: Training data composition significantly influences argument quality, and introduced rhetorical evaluation rubric provides guidelines for authoring, moderation, and deliberation-support systems.

Abstract: Incivility on platforms such as Twitter (now X) and Reddit complicates the development of AI systems that can support productive, rhetorically sound political argumentation. We present experiments with \textit{GPT-3.5 Turbo} fine-tuned on two contrasting datasets of political discourse: high-incivility Twitter replies to U.S. Congress and low-incivility posts from Reddit’s \textit{r/ChangeMyView}. Our evaluation examines how data composition and prompting strategies affect the rhetorical framing and deliberative quality of model-generated arguments. Results show that Reddit-finetuned models generate safer but rhetorically rigid arguments, while cross-platform fine-tuning amplifies adversarial tone and toxicity. Prompt-based steering reduces overt toxicity (e.g., personal attacks) but cannot fully offset the influence of noisy training data. We introduce a rhetorical evaluation rubric - covering justification, reciprocity, alignment, and authority - and provide implementation guidelines for authoring, moderation, and deliberation-support systems.

Method: Uses last hidden state of LLM as continuous thought representation, feeds it back as next input embedding directly in continuous space, enabling breadth-first search by encoding multiple alternative next steps.

Result: Outperforms chain-of-thought on logical reasoning tasks requiring substantial search during planning, achieves better trade-off between accuracy and efficiency.

Conclusion: Continuous thought paradigm enables advanced reasoning patterns beyond language space, allowing more flexible and efficient reasoning through latent representations.

Abstract: Large language models (LLMs) are typically constrained to reason in the language space, where they express the reasoning process through a chain-of-thought (CoT) to solve complex problems. However, the language space may not always be optimal for reasoning. Most word tokens primarily ensure textual coherence and are not essential for reasoning, while some critical tokens require complex planning and pose challenges to LLMs. To explore the potential of reasoning beyond language, we introduce a new paradigm called Coconut (Chain of Continuous Thought). Coconut utilizes the last hidden state of the LLM as a representation of the reasoning state, termed “continuous thought.” Instead of decoding this state into words, we feed it back to the model as the next input embedding directly in the continuous space. This latent reasoning paradigm enables an advanced reasoning pattern, where continuous thoughts can encode multiple alternative next steps, allowing the model to perform a breadth-first search (BFS) rather than committing prematurely to a single deterministic path as in CoT. Coconut outperforms CoT on logical reasoning tasks that require substantial search during planning and achieves a better trade-off between accuracy and efficiency.

Method: Proposes AlignVLM which maps visual features to a weighted average of LLM text embeddings, leveraging the linguistic priors encoded by the LLM to ensure visual features are mapped to interpretable regions of the embedding space.

Result: Achieves state-of-the-art performance compared to prior alignment methods, with larger gains on document understanding tasks and under low-resource setups. Shows efficiency and robustness to noise.

Conclusion: AlignVLM effectively bridges the vision-language gap by using LLM’s linguistic priors to guide visual feature mapping, particularly benefiting document understanding and low-resource scenarios.

Abstract: Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges on having a good connector that maps visual features generated by a vision encoder to a shared embedding space with the LLM while preserving semantic similarity. Existing connectors, such as multilayer perceptrons (MLPs), lack inductive bias to constrain visual features within the linguistic structure of the LLM’s embedding space, making them data-hungry and prone to cross-modal misalignment. In this work, we propose a novel vision-text alignment method, AlignVLM, that maps visual features to a weighted average of LLM text embeddings. Our approach leverages the linguistic priors encoded by the LLM to ensure that visual features are mapped to regions of the space that the LLM can effectively interpret. AlignVLM is particularly effective for document understanding tasks, where visual and textual modalities are highly correlated. Our extensive experiments show that AlignVLM achieves state-of-the-art performance compared to prior alignment methods, with larger gains on document understanding tasks and under low-resource setups. We provide further analysis demonstrating its efficiency and robustness to noise.

Method: Created a two-tier hierarchical safety taxonomy with 6 safety dimensions, generated over 4000 multi-turn dialogues in Chinese and English across 22 scenarios, using 7 jailbreak attack strategies including reference attack and purpose reverse.

Result: Evaluation of 17 LLMs showed Yi-34B-Chat and GLM4-9B-Chat have superior safety performance, while Llama3.1-8B-Instruct and o3-mini exhibit safety vulnerabilities.

Conclusion: The proposed SafeDialBench provides comprehensive safety assessment for LLMs in multi-turn dialogues, revealing significant performance variations across different models.

Abstract: With the rapid advancement of Large Language Models (LLMs), the safety of LLMs has been a critical concern requiring precise assessment. Current benchmarks primarily concentrate on single-turn dialogues or a single jailbreak attack method to assess the safety. Additionally, these benchmarks have not taken into account the LLM’s capability of identifying and handling unsafe information in detail. To address these issues, we propose a fine-grained benchmark SafeDialBench for evaluating the safety of LLMs across various jailbreak attacks in multi-turn dialogues. Specifically, we design a two-tier hierarchical safety taxonomy that considers 6 safety dimensions and generates more than 4000 multi-turn dialogues in both Chinese and English under 22 dialogue scenarios. We employ 7 jailbreak attack strategies, such as reference attack and purpose reverse, to enhance the dataset quality for dialogue generation. Notably, we construct an innovative assessment framework of LLMs, measuring capabilities in detecting, and handling unsafe information and maintaining consistency when facing jailbreak attacks. Experimental results across 17 LLMs reveal that Yi-34B-Chat and GLM4-9B-Chat demonstrate superior safety performance, while Llama3.1-8B-Instruct and o3-mini exhibit safety vulnerabilities.

Method: Introduced an evaluation framework that quantifies readability methods’ ability to account for reading ease while controlling for content variation. Applied this to traditional formulas, ML systems, LLMs, and commercial educational systems.

Result: All existing readability scoring methods were poor predictors of reading ease across native/non-native speakers, reading regimes, and text lengths. Simple psycholinguistic word properties often outperformed complex methods.

Conclusion: Highlights fundamental limitations of current readability approaches and the need for new cognitively-driven scoring methods that better account for reading ease, drawing from psycholinguistics.

Abstract: Methods for scoring text readability have been studied for over a century, and are widely used in research and in user-facing applications in many domains. Thus far, the development and evaluation of such methods have primarily relied on two types of offline behavioral data, performance on reading comprehension tests and ratings of text readability levels. In this work, we instead focus on a fundamental and understudied aspect of readability, real-time reading ease, captured with online reading measures using eye tracking. We introduce an evaluation framework for readability scoring methods which quantifies their ability to account for reading ease, while controlling for content variation across texts. Applying this evaluation to prominent traditional readability formulas, modern machine learning systems, frontier Large Language Models and commercial systems used in education, suggests that they are all poor predictors of reading ease in English. This outcome holds across native and non-native speakers, reading regimes, and textual units of different lengths. The evaluation further reveals that existing methods are often outperformed by word properties commonly used in psycholinguistics for prediction of reading times. Our results highlight a fundamental limitation of existing approaches to readability scoring, the utility of psycholinguistics for readability research, and the need for new, cognitively driven readability scoring approaches that can better account for reading ease.

Method: Uses an auto-search and refinement paradigm to generate Fairwords, which are instructions integrated into input queries to reduce gender bias while maintaining response quality.

Result: FaIRMaker effectively mitigates gender bias while preserving task integrity and works with both API-based and open-source LLMs.

Conclusion: The proposed framework provides an automated, adaptable solution for gender bias mitigation that maintains performance and works across different LLM types.

Abstract: Pre-training large language models (LLMs) on vast text corpora enhances natural language processing capabilities but risks encoding social biases, particularly gender bias. While parameter-modification methods like fine-tuning mitigate bias, they are resource-intensive, unsuitable for closed-source models, and lack adaptability to evolving societal norms. Instruction-based approaches offer flexibility but often compromise task performance. To address these limitations, we propose $\textbf{FaIRMaker}$, an automated and model-independent framework that employs an $\textbf{auto-search and refinement}$ paradigm to adaptively generate Fairwords, which act as instructions integrated into input queries to reduce gender bias and enhance response quality. Extensive experiments demonstrate that FaIRMaker automatically searches for and dynamically refines Fairwords, effectively mitigating gender bias while preserving task integrity and ensuring compatibility with both API-based and open-source LLMs.

Method: Proposed Faithfulness by Unlearning Reasoning steps (FUR) framework that erases information from reasoning steps in model parameters and measures resulting prediction changes.

Result: FUR frequently precisely changed models’ predictions by unlearning key steps across four LMs and five multi-hop MCQA datasets, indicating parametric faithfulness of CoTs.

Conclusion: CoT reasoning can be parametrically faithful, and unlearning reasoning steps has deeper effects as models generate CoTs supporting different answers post-unlearning.

Abstract: When prompted to think step-by-step, language models (LMs) produce a chain of thought (CoT), a sequence of reasoning steps that the model supposedly used to produce its prediction. Despite much work on CoT prompting, it is unclear if reasoning verbalized in a CoT is faithful to the models’ parametric beliefs. We introduce a framework for measuring parametric faithfulness of generated reasoning, and propose Faithfulness by Unlearning Reasoning steps (FUR), an instance of this framework. FUR erases information contained in reasoning steps from model parameters, and measures faithfulness as the resulting effect on the model’s prediction. Our experiments with four LMs and five multi-hop multi-choice question answering (MCQA) datasets show that FUR is frequently able to precisely change the underlying models’ prediction for a given instance by unlearning key steps, indicating when a CoT is parametrically faithful. Further analysis shows that CoTs generated by models post-unlearning support different answers, hinting at a deeper effect of unlearning.

Method: Self-Truncation Best-of-N (ST-BoN) leverages early sampling consistency in model’s internal states to identify the most promising path and truncate suboptimal ones, avoiding full generation of all N samples and eliminating reward models.

Result: ST-BoN reduces dynamic GPU memory usage by over 80% and inference latency by 50%. It achieves same performance as Full-BoN with 70%-80% computational cost savings, and improves accuracy by 3-4 points under same cost.

Conclusion: ST-BoN provides an efficient alternative to traditional Best-of-N sampling by addressing both memory and reward model challenges simultaneously, offering significant cost-performance improvements.

Abstract: Test-time scaling enhances large language model performance by allocating additional compute resources during inference. Best-of-N (BoN) sampling serves as a common sampling-based scaling technique, broadening the search space in parallel to find better solutions from the model distribution. However, its cost-performance trade-off is still underexplored. Two main challenges limit the efficiency of BoN sampling: (1) Generating N full samples consumes substantial GPU memory, reducing inference capacity under limited resources. (2) Reward models add extra memory and latency overhead, and training strong reward models introduces potential training data costs. Although some studies have explored efficiency improvements, none have addressed both challenges at once. To address this gap, we propose Self-Truncation Best-of-N (ST-BoN), a decoding method that avoids fully generating all N samples and eliminates the need for reward models. It leverages early sampling consistency in the model’s internal states to identify the most promising path and truncate suboptimal ones. In terms of cost, ST-BoN reduces dynamic GPU memory usage by over 80% and inference latency by 50%. In terms of cost-performance trade-off, ST-BoN achieves the same performance as Full-BoN while saving computational cost by 70%-80%, and under the same cost, it can improve accuracy by 3-4 points.

Method: Conceptually decouples distillation into knowledge and alignment components, then implements a two-stage distillation framework. Also creates SentiBench benchmark covering diverse sentiment tasks across 12 datasets.

Result: The approach substantially enhances compact model performance across diverse sentiment analysis tasks and shows strong generalization to unseen tasks, demonstrating robust competitiveness against existing small-scale models.

Conclusion: The proposed targeted distillation method effectively builds compact sentiment analysis models with preserved strong capabilities and good generalization, validated through comprehensive benchmarking.

Abstract: This paper explores targeted distillation methods for sentiment analysis, aiming to build compact and practical models that preserve strong and generalizable sentiment analysis capabilities. To this end, we conceptually decouple the distillation target into knowledge and alignment and accordingly propose a two-stage distillation framework. Moreover, we introduce SentiBench, a comprehensive and systematic sentiment analysis benchmark that covers a diverse set of tasks across 12 datasets. We evaluate a wide range of models on this benchmark. Experimental results show that our approach substantially enhances the performance of compact models across diverse sentiment analysis tasks, and the resulting models demonstrate strong generalization to unseen tasks, showcasing robust competitiveness against existing small-scale models.

Method: Evaluated 11 foundation models (7 general-purpose, 4 medical-specialized) across seven medical hallucination tasks using medical reasoning and biomedical information retrieval. Used chain-of-thought prompting and physician audits to analyze error patterns.

Result: General-purpose models significantly outperformed medical-specialized models (25.2% difference in hallucination-free responses). Chain-of-thought reduced hallucinations in 86.4% of comparisons. 64-72% of residual errors were reasoning failures. 91.8% of clinicians reported encountering hallucinations, with 84.7% considering them harmful.

Conclusion: Medical safety emerges from sophisticated reasoning and broad knowledge integration developed during large-scale pre-training, not from narrow domain optimization. Chain-of-thought reasoning is crucial for reducing hallucinations through self-verification capabilities.

Abstract: Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accuracy, fostering overconfidence and poorly calibrated uncertainty. We define medical hallucination as any model-generated output that is factually incorrect, logically inconsistent, or unsupported by authoritative clinical evidence in ways that could alter clinical decisions. We evaluated 11 foundation models (7 general-purpose, 4 medical-specialized) across seven medical hallucination tasks spanning medical reasoning and biomedical information retrieval. General-purpose models achieved significantly higher proportions of hallucination-free responses than medical-specialized models (median: 76.6% vs 51.3%, difference = 25.2%, 95% CI: 18.7-31.3%, Mann-Whitney U = 27.0, p = 0.012, rank-biserial r = -0.64). Top-performing models such as Gemini-2.5 Pro exceeded 97% accuracy when augmented with chain-of-thought prompting (base: 87.6%), while medical-specialized models like MedGemma ranged from 28.6-61.9% despite explicit training on medical corpora. Chain-of-thought reasoning significantly reduced hallucinations in 86.4% of tested comparisons after FDR correction (q < 0.05), demonstrating that explicit reasoning traces enable self-verification and error detection. Physician audits confirmed that 64-72% of residual hallucinations stemmed from causal or temporal reasoning failures rather than knowledge gaps. A global survey of clinicians (n = 70) validated real-world impact: 91.8% had encountered medical hallucinations, and 84.7% considered them capable of causing patient harm. The underperformance of medical-specialized models despite domain training indicates that safety emerges from sophisticated reasoning capabilities and broad knowledge integration developed during large-scale pre-training, not from narrow optimization.

Method: Developed XIFBench with 558 instructions across 5 constraint categories in 6 languages, implementing cultural accessibility annotation, constraint-level translation validation, and requirement-based evaluation using English as semantic anchors.

Result: Extensive experiments quantified performance disparities across resource levels and provided detailed insights into how language resources, constraint categories, instruction complexity, and cultural specificity influence multilingual instruction-following.

Conclusion: XIFBench enables systematic evaluation of multilingual instruction-following capabilities in LLMs, revealing important factors that affect performance across different linguistic contexts.

Abstract: Large Language Models (LLMs) have demonstrated remarkable instruction-following capabilities across various applications. However, their performance in multilingual settings lacks systematic investigation, with existing evaluations lacking fine-grained constraint analysis across diverse linguistic contexts. We introduce XIFBench, a comprehensive constraint-based benchmark for evaluating multilingual instruction-following abilities of LLMs, comprising 558 instructions with 0-5 additional constraints across five categories (Content, Style, Situation, Format, and Numerical) in six languages spanning different resource levels. To support reliable and consistent cross-lingual evaluation, we implement three methodological innovations: cultural accessibility annotation, constraint-level translation validation, and requirement-based evaluation using English requirements as semantic anchors across languages. Extensive experiments with various LLMs not only quantify performance disparities across resource levels but also provide detailed insights into how language resources, constraint categories, instruction complexity, and cultural specificity influence multilingual instruction-following. Our code and data are available at https://github.com/zhenyuli801/XIFBench.

Method: Conceptual analysis and field overview approach, examining different NLG applications (data-to-text, summarization, image captioning) and discussing relationships with other NLP subfields.

Result: Clear definition of NLG scope, distinction from Machine Translation (no content selection) and Dialog Systems (NLG as component), and observation of methodological convergence across NLP subfields due to LLMs.

Conclusion: NLG encompasses diverse applications for verbalizing information through natural language, with evolving boundaries and methodological convergence with other NLP subfields driven by Large Language Models.

Abstract: This article provides a brief overview of the field of Natural Language Generation. The term Natural Language Generation (NLG), in its broadest definition, refers to the study of systems that verbalize some form of information through natural language. That information could be stored in a large database or knowledge graph (in data-to-text applications), but NLG researchers may also study summarisation (text-to-text) or image captioning (image-to-text), for example. As a subfield of Natural Language Processing, NLG is closely related to other sub-disciplines such as Machine Translation (MT) and Dialog Systems. Some NLG researchers exclude MT from their definition of the field, since there is no content selection involved where the system has to determine what to say. Conversely, dialog systems do not typically fall under the header of Natural Language Generation since NLG is just one component of dialog systems (the others being Natural Language Understanding and Dialog Management). However, with the rise of Large Language Models (LLMs), different subfields of Natural Language Processing have converged on similar methodologies for the production of natural language and the evaluation of automatically generated text.

Method: Used reinforcement learning with judge-wise, outcome-driven rewards to activate reasoning capabilities, creating JudgeLRM models of various sizes (3B to 14B parameters).

Result: JudgeLRM consistently outperformed SFT-tuned baselines and other RL/SFT variants. JudgeLRM-3B/4B exceeded GPT-4, while JudgeLRM-7B/8B/14B outperformed DeepSeek-R1 by over 2% in F1 score, with particularly strong gains on reasoning-heavy tasks.

Conclusion: Reinforcement learning is valuable for unlocking reasoning-aligned LLM judges, addressing the limitations of supervised fine-tuning in reasoning-intensive evaluation scenarios.

Abstract: Large Language Models (LLMs) are increasingly adopted as evaluators, offering a scalable alternative to human annotation. However, existing supervised fine-tuning (SFT) approaches often fall short in domains that demand complex reasoning. Judgment is inherently reasoning-intensive: beyond surface-level scoring, it requires verifying evidence, identifying errors, and justifying decisions. Through the analysis of evaluation tasks, we find a negative correlation between SFT performance gains and the proportion of reasoning-demanding samples, revealing the limits of SFT in such scenarios. To address this, we introduce JudgeLRM, a family of judgment-oriented LLMs, trained using reinforcement learning (RL) with judge-wise, outcome-driven rewards to activate reasoning capabilities. JudgeLRM consistently outperform SFT-tuned baselines in the same size, as well as other RL and SFT variants, and even surpass state-of-the-art reasoning models: notably, JudgeLRM-3B/4B exceeds GPT-4, while JudgeLRM-7B/8B/14B outperforms DeepSeek-R1 by over 2% in F1 score, with particularly strong gains on reasoning-heavy tasks. Our findings underscore the value of RL in unlocking reasoning-aligned LLM judges.

Method: Used verifiable benchmarks (mathematical reasoning, factual knowledge, code generation) with objective ground-truth assessment. Conducted large-scale experiments across diverse model families under controlled evaluation conditions.

Result: 1) Stronger models exhibit greater self-preference but mostly legitimately due to superior performance. 2) Harmful self-preference persists when evaluator models err, with stronger models showing more pronounced harmful self-preference when wrong. 3) Chain-of-Thought strategies effectively reduce harmful self-preference.

Conclusion: Provides nuanced understanding of LLM-based evaluation: self-preference is mostly legitimate for stronger models but harmful when models err, and inference-time scaling can mitigate harmful bias, offering practical insights for improving evaluation reliability.

Abstract: Large language models (LLMs) are increasingly used as automatic evaluators in applications like benchmarking, reward modeling, and self-refinement. Prior work highlights a potential self-preference bias where LLMs favor their own generated responses, a tendency often intensifying with model size and capability. This raises a critical question: Is self-preference harmful, or does it simply reflect the genuinely higher-quality outputs of stronger models? Answering this has been difficult as previous studies relied primarily on subjective tasks. These tasks lack an objective ground truth, meaning that either preference can be reasonably justified. To address this ambiguity, we investigate self-preference using verifiable benchmarks (mathematical reasoning, factual knowledge, code generation) that allow objective ground-truth assessment. This enables us to distinguish harmful self-preference (favoring objectively worse responses) from legitimate self-preference (favoring genuinely superior ones). We conduct large-scale experiments under controlled evaluation conditions across diverse model families (e.g., Llama, Qwen, Gemma, Mistral, Phi, GPT, DeepSeek). Our findings reveal three key insights: (1) While stronger models exhibit greater self-preference, much of this preference aligns with objectively superior performance, indicating stronger models prefer themselves mostly legitimately. (2) Harmful self-preference persists when evaluator models err as generators, and stronger models display more pronounced harmful self-preference when they do err. This suggests stronger models struggle more to recognize when they are wrong. (3) Inference-time scaling strategies, such as generating a long Chain-of-Thought before evaluation, effectively reduce harmful self-preference. These results provide a more nuanced understanding of LLM-based evaluation and practical insights for improving its reliability.

Method: Self-adaptive cognitive debiasing (SACD) with three sequential steps: bias determination, bias analysis, and cognitive debiasing, applied iteratively to refine prompts and mitigate biases.

Result: SACD achieves the lowest average bias scores compared to advanced prompt engineering methods and existing debiasing techniques in both single-bias and multi-bias settings across finance, healthcare, and legal domains.

Conclusion: SACD effectively enhances LLM reliability by iteratively mitigating multiple cognitive biases, outperforming existing methods in challenging multi-bias scenarios.

Abstract: Large language models (LLMs) have shown potential in supporting decision-making applications, particularly as personal assistants in the financial, healthcare, and legal domains. While prompt engineering strategies have enhanced the capabilities of LLMs in decision-making, cognitive biases inherent to LLMs present significant challenges. Cognitive biases are systematic patterns of deviation from norms or rationality in decision-making that can lead to the production of inaccurate outputs. Existing cognitive bias mitigation strategies assume that input prompts only contain one type of cognitive bias, limiting their effectiveness in more challenging scenarios involving multiple cognitive biases. To fill this gap, we propose a cognitive debiasing approach, self-adaptive cognitive debiasing (SACD), that enhances the reliability of LLMs by iteratively refining prompts. Our method follows three sequential steps - bias determination, bias analysis, and cognitive debiasing - to iteratively mitigate potential cognitive biases in prompts. We evaluate SACD on finance, healthcare, and legal decision-making tasks using both open-weight and closed-weight LLMs. Compared to advanced prompt engineering methods and existing cognitive debiasing techniques, SACD achieves the lowest average bias scores in both single-bias and multi-bias settings.

Method: Extensive experiments with 16 state-of-the-art LLMs across nine diverse pattern-based ICL datasets, systematically testing various hypotheses about CoT’s underperformance through designed validation experiments.

Result: CoT and its reasoning variants consistently underperform direct answering across all model scales and benchmark complexities. Analysis reveals a hybrid explicit-implicit reasoning mechanism where explicit reasoning fails due to LLMs’ inability to infer patterns from demonstrations, while implicit reasoning partially compensates but is disrupted by increased contextual distance.

Conclusion: CoT’s universal efficacy is challenged, revealing fundamental limitations in pattern-based ICL. Findings provide novel insights into CoT’s mechanisms and guide future research toward more nuanced reasoning methodologies for LLMs.

Abstract: Chain-of-Thought (CoT) prompting has been widely recognized for its ability to enhance reasoning capabilities in large language models (LLMs). However, our study reveals a surprising contradiction to this prevailing perspective within the fundamental domain of pattern-based in-context learning (ICL). Through extensive experiments involving 16 state-of-the-art LLMs and nine diverse pattern-based ICL datasets, we demonstrate that CoT and its reasoning variants consistently underperform direct answering across varying model scales and benchmark complexities. To systematically investigate this unexpected phenomenon, we designed extensive experiments to validate several hypothetical explanations. Our analysis uncovers a fundamental hybrid mechanism of explicit-implicit reasoning driving CoT’s performance in pattern-based ICL: while explicit reasoning falters due to LLMs’ struggles to infer underlying patterns from demonstrations, implicit reasoning-disrupted by the increased contextual distance of CoT rationales-often compensates, delivering correct answers despite flawed rationales. This hybrid mechanism explains CoT’s relative underperformance, as noise from weak explicit inference undermines the process, even as implicit mechanisms partially salvage outcomes. Notably, even long-CoT reasoning models, which excel in abstract and symbolic reasoning, fail to fully overcome these limitations despite higher computational costs. Our findings challenge existing assumptions regarding the universal efficacy of CoT, yielding novel insights into its limitations and guiding future research toward more nuanced and effective reasoning methodologies for LLMs.

Method: CRV system with multiple LLM agents: one critiques CoT rationales based on small model capabilities, another rethinks/refines CoTs, third verifies correctness. CogPO algorithm aligns reasoning processes with cognitive capacities.

Result: Outperforms other methods by large margin on challenging reasoning benchmarks.

Conclusion: CRV+CogPO framework effectively trains small reasoning models by considering their unique cognitive capabilities rather than direct distillation from large models.

Abstract: The reasoning capabilities of large reasoning models (LRMs), such as OpenAI’s o1 and DeepSeek-R1, have seen substantial advancements through deep thinking. However, these enhancements come with significant resource demands, underscoring the need for training effective small reasoning models. A critical challenge is that small models possess different reasoning capacities and cognitive trajectories compared with their larger counterparts. Hence, directly distilling chain-of-thought (CoT) rationales from large LRMs to smaller ones can sometimes be ineffective and often requires a substantial amount of annotated data. In this paper, we first introduce a novel Critique-Rethink-Verify (CRV) system, designed for training smaller yet powerful LRMs. Our CRV system consists of multiple LLM agents, each specializing in unique tasks: (i) critiquing the CoT rationales according to the cognitive capabilities of smaller models, (ii) rethinking and refining these CoTs based on the critiques, and (iii) verifying the correctness of the refined results. Building on the CRV system, we further propose the Cognitive Preference Optimization (CogPO) algorithm to continuously enhance the reasoning abilities of smaller models by aligning their reasoning processes with their cognitive capacities. Comprehensive evaluations on challenging reasoning benchmarks demonstrate the efficacy of our CRV+CogPO framework, which outperforms other methods by a large margin.

Method: HSA divides inputs into chunks, selects top-k chunks using token-level relevance, and hierarchically aggregates information with hardware-aligned kernel design.

Result: RAMba (Mamba + HSA) achieves perfect accuracy on 64M-length passkey retrieval despite 4K pre-training, with significant downstream improvements and near-constant memory footprint.

Conclusion: RAMba demonstrates huge potential for long-context modeling by combining RNN efficiency with flexible random access through hierarchical sparse attention.

Abstract: A key advantage of Recurrent Neural Networks (RNNs) over Transformers is their linear computational and space complexity enables faster training and inference for long sequences. However, RNNs are fundamentally unable to randomly access historical context, and simply integrating attention mechanisms may undermine their efficiency advantages. To overcome this limitation, we propose Hierarchical Sparse Attention (HSA), a novel attention mechanism that enhances RNNs with long-range random access flexibility while preserving their merits in efficiency and length generalization. HSA divides inputs into chunks, selects the top-$k$ chunks and hierarchically aggregates information. The core innovation lies in learning token-to-chunk relevance based on fine-grained token-level information inside each chunk. This approach enhances the precision of chunk selection across both in-domain and out-of-domain context lengths. To make HSA efficient, we further introduce a hardware-aligned kernel design. By combining HSA with Mamba, we introduce RAMba, which achieves perfect accuracy in passkey retrieval across 64 million contexts despite pre-training on only 4K-length contexts, and significant improvements on various downstream tasks, with nearly constant memory footprint. These results show RAMba’s huge potential in long-context modeling.

Method: Developed a benchmark with 18 languages and 4 difficulty levels, then conducted comprehensive evaluation of advanced LLMs including Qwen-3-235B-A22B-Thinking and Gemini-2.5-pro.

Result: Even top models achieved only 54.6 and 52.2 benchmark scores, with about 40% accuracy at the highest difficulty level. The benchmark revealed key challenges: wide performance variation across languages, low input-output language consistency, and significant thinking length differences by language.

Conclusion: Controlling output language in instructions can affect reasoning performance, especially for low-resource languages, suggesting a promising direction for improving multilingual capabilities in LLMs.

Abstract: In this paper, we introduce PolyMath, a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels. Our benchmark ensures difficulty comprehensiveness, language diversity, and high-quality translation, making it a highly discriminative multilingual mathematical benchmark in the era of reasoning LLMs. We conduct a comprehensive evaluation for advanced LLMs and find that even Qwen-3-235B-A22B-Thinking and Gemini-2.5-pro, achieve only 54.6 and 52.2 benchmark scores, with about 40% accuracy under the highest level From a language perspective, our benchmark reveals several key challenges of LLMs in multilingual reasoning: (1) Reasoning performance varies widely across languages for current LLMs; (2) Input-output language consistency is low in reasoning LLMs and may be correlated with performance; (3) The thinking length differs significantly by language for current LLMs. Additionally, we demonstrate that controlling the output language in the instructions has the potential to affect reasoning performance, especially for some low-resource languages, suggesting a promising direction for improving multilingual capabilities in LLMs.

Method: Used Large Language Models to process unstructured resume data from VDAB, extracted structured career information, and normalized it to standardized ESCO occupation codes using a multi-label classification model.

Result: Created a rich dataset with over 1.67 million work experiences from more than 361,000 user resumes, mapped to standardized ESCO occupation codes.

Conclusion: The JobHop dataset enables diverse applications for labor market research, including analyzing mobility, job stability, career breaks, and supports career path prediction and data-driven decision-making.

Abstract: Understanding labor market dynamics is essential for policymakers, employers, and job seekers. However, comprehensive datasets that capture real-world career trajectories are scarce. In this paper, we introduce JobHop, a large-scale public dataset derived from anonymized resumes provided by VDAB, the public employment service in Flanders, Belgium. Utilizing Large Language Models (LLMs), we process unstructured resume data to extract structured career information, which is then normalized to standardized ESCO occupation codes using a multi-label classification model. This results in a rich dataset of over 1.67 million work experiences, extracted from and grouped into more than 361,000 user resumes and mapped to standardized ESCO occupation codes, offering valuable insights into real-world occupational transitions. This dataset enables diverse applications, such as analyzing labor market mobility, job stability, and the effects of career breaks on occupational transitions. It also supports career path prediction and other data-driven decision-making processes. To illustrate its potential, we explore key dataset characteristics, including job distributions, career breaks, and job transitions, demonstrating its value for advancing labor market research.

Method: Two approaches: 1) Training ModernGBERT from scratch (134M, 1B parameters) and 2) Converting decoders via LLM2Vec to create LLaMmleinVec (120M, 1B, 7B parameters). Both undergo context extension to 8,192 tokens and are trained with masked next-token prediction.

Result: ModernGBERT 1B sets new SOTA on SuperGLEBer (avg 0.808), surpassing GBERT Large (+4%) and the 7B converted model (0.787). On German MTEB after fine-tuning, ModernGBERT 1B (0.551) approaches the converted 7B model (0.557).

Conclusion: From-scratch encoders dominate when parameter efficiency and latency matter. When a pre-trained decoder exists and compute is limited, conversion offers an effective alternative. All models and resources are released under research-only RAIL license.

Abstract: Encoders remain essential for efficient German NLP and NLU scenarios despite the rise of decoder-only LLMs. This work studies two routes to high-quality German encoders under identical data and training constraints: 1) training from scratch and 2) converting decoders via LLM2Vec. We introduce two resources: ModernGBERT (134M, 1B), fully transparent German encoders in the ModernBERT style, and LL"aMmleinVec (120M, 1B, 7B), decoder-to-encoder conversions trained with masked next-token prediction, both undergoing a context extension to 8.192 tokens. Across SuperGLEBer, ModernGBERT 1B sets a new state of the art (avg 0.808), surpassing GBERT Large (+4%) and the seven-times larger converted 7B model (0.787). On German MTEB after supervised fine-tuning, ModernGBERT 1B (0.551) approaches the converted 7B model (0.557). We release all models, checkpoints, datasets, and full training records, and introduce an encoder-adapted QA-NIAH evaluation. All in all, our results provide actionable guidance: when parameter efficiency and latency matter, from-scratch encoders dominate. When a pre-trained decoder exists and compute is a limited, conversion offers an effective alternative. ModernGBERT and LL"aMmleinVec, including all code, data and intermediary checkpoints are published under a research-only RAIL license.

Method: The paper presents a comprehensive taxonomy of MKE methods covering parameter-based, memory-based, fine-tuning, and hypernetwork approaches, and surveys available benchmarks.

Result: The survey summarizes key findings on method effectiveness and transfer patterns, and identifies challenges in cross-lingual propagation.

Conclusion: The analysis consolidates a rapidly evolving area and lays the groundwork for future progress in editable language-aware LLMs, highlighting open problems related to language anisotropy, evaluation coverage, and edit scalability.

Abstract: While Knowledge Editing has been extensively studied in monolingual settings, it remains underexplored in multilingual contexts. This survey systematizes recent research on Multilingual Knowledge Editing (MKE), a growing subdomain of model editing focused on ensuring factual edits generalize reliably across languages. We present a comprehensive taxonomy of MKE methods, covering parameter-based, memory-based, fine-tuning, and hypernetwork approaches. We survey available benchmarks,summarize key findings on method effectiveness and transfer patterns, identify challenges in cross-lingual propagation, and highlight open problems related to language anisotropy, evaluation coverage, and edit scalability. Our analysis consolidates a rapidly evolving area and lays the groundwork for future progress in editable language-aware LLMs.

Method: Developed an automated data synthesis pipeline that processes real-world mental health posts from Reddit, generates multi-turn dialogue structures, and uses LLMs guided by international diagnostic standards and multiple therapeutic frameworks (CBT, ACT, psychodynamic) to simulate clinical reasoning processes with rigorous multi-dimensional filtering.

Result: PsyLLM significantly outperforms state-of-the-art baseline models on a new benchmark assessing counseling quality across four key dimensions.

Conclusion: The proposed PsyLLM model successfully addresses critical limitations in existing approaches by integrating systematic diagnostic and therapeutic reasoning for mental health counseling, with publicly released model weights and dataset.

Abstract: Large language models (LLMs) hold significant potential for mental health support, capable of generating empathetic responses and simulating therapeutic conversations. However, existing LLM-based approaches often lack the clinical grounding necessary for real-world psychological counseling, particularly in explicit diagnostic reasoning aligned with standards like the DSM/ICD and incorporating diverse therapeutic modalities beyond basic empathy or single strategies. To address these critical limitations, we propose PsyLLM, the first large language model designed to systematically integrate both diagnostic and therapeutic reasoning for mental health counseling. To develop PsyLLM, we design a novel automated data synthesis pipeline that processes real-world mental health posts collected from Reddit, where users frequently share psychological distress and seek community support. This pipeline processes real-world mental health posts, generates multi-turn dialogue structures, and leverages LLMs guided by international diagnostic standards (e.g., DSM/ICD) and multiple therapeutic frameworks (e.g., CBT, ACT, psychodynamic) to simulate detailed clinical reasoning processes. Rigorous multi-dimensional filtering ensures the generation of high-quality, clinically aligned dialogue data. In addition, we introduce a new benchmark and evaluation protocol, assessing counseling quality across four key dimensions. Our experiments demonstrate that PsyLLM significantly outperforms state-of-the-art baseline models on this benchmark. The model weights and dataset have been publicly released at https://github.com/Emo-gml/PsyLLM.

Method: Created corpora of body part mentions in online English text (blogs and tweets), including human-annotated subset for emotions, and analyzed using word-emotion association lexicons and statistical correlation analysis.

Result: BPMs are common (5-10% of posts), usage varies by time/location, text with BPMs is more emotionally charged even without explicit physical reactions, and strong correlation exists between body-related language and poorer health outcomes.

Conclusion: Investigating body-part related words in language opens valuable research avenues at the intersection of NLP, affective sciences, and human wellbeing studies.

Abstract: This paper is the first investigation of the connection between emotion, embodiment, and everyday language in a large sample of natural language data. We created corpora of body part mentions (BPMs) in online English text (blog posts and tweets). This includes a subset featuring human annotations for the emotions of the person whose body part is mentioned in the text. We show that BPMs are common in personal narratives and tweets (~5% to 10% of posts include BPMs) and that their usage patterns vary markedly by time and %geographic location. Using word-emotion association lexicons and our annotated data, we show that text containing BPMs tends to be more emotionally charged, even when the BPM is not explicitly used to describe a physical reaction to the emotion in the text. Finally, we discover a strong and statistically significant correlation between body-related language and a variety of poorer health outcomes. In sum, we argue that investigating the role of body-part related words in language can open up valuable avenues of future research at the intersection of NLP, the affective sciences, and the study of human wellbeing.

Method: The paper presents a systematic taxonomy of latent CoT methods, categorizing them from token-wise horizontal approaches to layer-wise vertical strategies. It provides in-depth analysis of design principles, applications, and challenges.

Result: The survey establishes a structured foundation for advancing latent CoT reasoning in LLMs, offering comprehensive overview of this emerging paradigm and recent methodological advances.

Conclusion: Latent CoT reasoning represents a promising direction for enhancing LLM reasoning capabilities, with potential for broader applicability in abstract reasoning tasks through embedded reasoning processes in latent spaces.

Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT relies on explicitly verbalized intermediate steps, which constrains its broader applicability, particularly in abstract reasoning tasks beyond language. To address this, there has been growing research interest in \textit{latent CoT reasoning}, where the reasoning process is embedded within latent spaces. By decoupling reasoning from explicit language generation, latent CoT offers the promise of richer cognitive representations and facilitates more flexible, faster inference. This paper aims to present a comprehensive overview of this emerging paradigm and establish a systematic taxonomy. We analyze recent advances in methods, categorizing them from token-wise horizontal approaches to layer-wise vertical strategies. We then provide in-depth discussions of these methods, highlighting their design principles, applications, and remaining challenges. We hope that our survey provides a structured foundation for advancing this promising direction in LLM reasoning. The relevant papers will be regularly updated at https://github.com/EIT-NLP/Awesome-Latent-CoT.

Method: Created WCB dataset with 25k uniformly sampled sentences annotated using dual annotators, disagreement resolutions, and expert reviews. Defined three tasks: Stance Detection, Temporal Classification, and Uncertainty Estimation. Benchmarked 7 PLMs and 9 LLMs across 15,075 experiments.

Result: A model trained on aggregated data across banks significantly outperforms models trained on individual bank data, confirming that “the whole is greater than the sum of its parts.” Human evaluations and error analyses validate the framework’s economic utility.

Conclusion: The WCB dataset enables robust analysis of central bank communications, demonstrating the value of cross-bank aggregated training data, with artifacts made publicly available under CC-BY-NC-SA 4.0 license.

Abstract: Central banks around the world play a crucial role in maintaining economic stability. Deciphering policy implications in their communications is essential, especially as misinterpretations can disproportionately impact vulnerable populations. To address this, we introduce the World Central Banks (WCB) dataset, the most comprehensive monetary policy corpus to date, comprising over 380k sentences from 25 central banks across diverse geographic regions, spanning 28 years of historical data. After uniformly sampling 1k sentences per bank (25k total) across all available years, we annotate and review each sentence using dual annotators, disagreement resolutions, and secondary expert reviews. We define three tasks: Stance Detection, Temporal Classification, and Uncertainty Estimation, with each sentence annotated for all three. We benchmark seven Pretrained Language Models (PLMs) and nine Large Language Models (LLMs) (Zero-Shot, Few-Shot, and with annotation guide) on these tasks, running 15,075 benchmarking experiments. We find that a model trained on aggregated data across banks significantly surpasses a model trained on an individual bank’s data, confirming the principle “the whole is greater than the sum of its parts.” Additionally, rigorous human evaluations, error analyses, and predictive tasks validate our framework’s economic utility. Our artifacts are accessible through the HuggingFace and GitHub under the CC-BY-NC-SA 4.0 license.

Method: Transforms univariate/multivariate signals into tabular embeddings, encodes them as text to fine-tune autoregressive LLMs, then samples and decodes new textual embeddings into synthetic time series.

Result: Outperforms existing generative models across diverse datasets in both similarity-based evaluations and downstream forecasting tasks.

Conclusion: SDForger enables efficient time series generation with textual conditioning, paving the way for multimodal modeling and streamlined integration of time series with textual data.

Abstract: SDForger is a flexible and efficient framework for generating high-quality multivariate time series using LLMs. Leveraging a compact data representation, SDForger provides synthetic time series generation from a few samples and low-computation fine-tuning of any autoregressive LLM. Specifically, the framework transforms univariate and multivariate signals into tabular embeddings, which are then encoded into text and used to fine-tune the LLM. At inference, new textual embeddings are sampled and decoded into synthetic time series that retain the original data’s statistical properties and temporal dynamics. Across a diverse range of datasets, SDForger outperforms existing generative models in many scenarios, both in similarity-based evaluations and downstream forecasting tasks. By enabling textual conditioning in the generation process, SDForger paves the way for multimodal modeling and the streamlined integration of time series with textual information. The model is open-sourced at https://github.com/IBM/fms-dgt/tree/main/fms_dgt/public/databuilders/time_series.

Method: Developed GreekBarBench with questions from five Greek legal areas, implemented a three-dimensional scoring system using LLM-as-judge approach, and created meta-evaluation to assess correlation between LLM and human expert evaluations.

Result: Evaluation of 13 LLMs showed best models outperform average expert scores but fall short of the 95th percentile of human experts. Simple span-based rubrics improved alignment between LLM-judges and human evaluations.

Conclusion: While current LLMs show promising performance on legal reasoning tasks, they still cannot match top human expert performance, and specialized evaluation methods with improved rubrics are needed for accurate assessment.

Abstract: We introduce GreekBarBench, a benchmark that evaluates LLMs on legal questions across five different legal areas from the Greek Bar exams, requiring citations to statutory articles and case facts. To tackle the challenges of free-text evaluation, we propose a three-dimensional scoring system combined with an LLM-as-a-judge approach. We also develop a meta-evaluation benchmark to assess the correlation between LLM-judges and human expert evaluations, revealing that simple, span-based rubrics improve their alignment. Our systematic evaluation of 13 proprietary and open-weight LLMs shows that even though the best models outperform average expert scores, they fall short of the 95th percentile of experts.

Method: Fine-tuned pre-trained models with different strategies, used MT-augmented ST by generating translations from ASR data, employed text-to-speech for synthetic speech generation, applied intra-distillation for regularization, and used Minimum Bayes Risk decoding for system combination.

Result: For North Levantine Arabic, synthetic data system slightly surpassed cascaded system trained on real data. Synthetic data improved ASR and ST performance for Bemba. Intra-distillation consistently improved ASR, MT, and ST tasks. System combination achieved ~1.5 BLEU point improvement.

Conclusion: Synthetic data generation and model regularization techniques effectively enhance speech translation performance for low-resource languages, with system combination providing additional gains.

Abstract: This paper presents KIT’s submissions to the IWSLT 2025 low-resource track. We develop both cascaded systems, consisting of Automatic Speech Recognition (ASR) and Machine Translation (MT) models, and end-to-end (E2E) Speech Translation (ST) systems for three language pairs: Bemba, North Levantine Arabic, and Tunisian Arabic into English. Building upon pre-trained models, we fine-tune our systems with different strategies to utilize resources efficiently. This study further explores system enhancement with synthetic data and model regularization. Specifically, we investigate MT-augmented ST by generating translations from ASR data using MT models. For North Levantine, which lacks parallel ST training data, a system trained solely on synthetic data slightly surpasses the cascaded system trained on real data. We also explore augmentation using text-to-speech models by generating synthetic speech from MT data, demonstrating the benefits of synthetic data in improving both ASR and ST performance for Bemba. Additionally, we apply intra-distillation to enhance model performance. Our experiments show that this approach consistently improves results across ASR, MT, and ST tasks, as well as across different pre-trained models. Finally, we apply Minimum Bayes Risk decoding to combine the cascaded and end-to-end systems, achieving an improvement of approximately 1.5 BLEU points.

Method: Use frozen LLMs with only two learned embeddings to generate multiple tokens in a single token-parallel forward pass, analyzing the embedding properties and information encoding.

Result: LLMs can generate hundreds of accurate tokens in one pass, with embeddings forming connected local regions in embedding space, suggesting potential for practical encoder training.

Conclusion: Multi-token generation may be natively accessible in off-the-shelf LLMs via learned input encoders, eliminating autoregressive decoding bottlenecks without requiring model retraining.

Abstract: A recent study showed that large language models (LLMs) can reconstruct surprisingly long texts - up to thousands of tokens - via autoregressive generation from just one trained input embedding. In this work, we explore whether autoregressive decoding is essential for such reconstruction. We show that frozen LLMs can generate hundreds of accurate tokens in just one token-parallel forward pass, when provided with only two learned embeddings. This reveals a surprising and underexplored multi-token generation capability of autoregressive LLMs. We examine these embeddings and characterize the information they encode. We also empirically show that, although these representations are not unique for a given text, they form connected and local regions in embedding space - suggesting the potential to train a practical encoder. The existence of such representations hints that multi-token generation may be natively accessible in off-the-shelf LLMs via a learned input encoder, eliminating heavy retraining and helping to overcome the fundamental bottleneck of autoregressive decoding while reusing already-trained models.

Method: Introduces two complementary objectives: (1) an orthogonality loss to encourage experts to process distinct types of tokens, and (2) a variance loss to encourage more discriminative routing decisions. These are compatible with existing auxiliary loss and optimize training through gradient-level analysis.

Result: Experimental results show significant enhancement in expert specialization across various model architectures and benchmarks. The method improves classic MoE baselines with auxiliary loss by up to 23.79% while maintaining load balancing in downstream tasks without architectural modifications.

Conclusion: The proposed simple yet effective solution successfully addresses expert overlap and routing uniformity issues in MoE models, significantly improving performance through better expert specialization while maintaining load balancing properties.

Abstract: Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly used auxiliary load balancing loss often leads to expert overlap and overly uniform routing, which hinders expert specialization and degrades overall performance during post-training. To address this, we propose a simple yet effective solution that introduces two complementary objectives: (1) an orthogonality loss to encourage experts to process distinct types of tokens, and (2) a variance loss to encourage more discriminative routing decisions. Gradient-level analysis demonstrates that these objectives are compatible with the existing auxiliary loss and contribute to optimizing the training process. Experimental results over various model architectures and across multiple benchmarks show that our method significantly enhances expert specialization. Notably, our method improves classic MoE baselines with auxiliary loss by up to 23.79%, while also maintaining load balancing in downstream tasks, without any architectural modifications or additional components. We will release our code to contribute to the community.

Method: Two-stage pipeline: Stage-I fine-tunes models using 2,000 text-only data with precise reasoning demonstrations; Stage-II enhances reasoning using 1,500 curated multimodal medical cases aligned with multimodal medical reasoning preferences.

Result: MedE² consistently outperforms baselines across multiple medical multimodal benchmarks, with additional validation confirming robustness and practical utility on larger models and under inference-time scaling.

Conclusion: The proposed MedE² pipeline effectively improves reasoning performance of medical multimodal models, demonstrating efficacy, reliability, and practical utility in medical domains.

Abstract: Effective clinical decision-making depends on iterative, multimodal reasoning across diverse sources of evidence. The recent emergence of multimodal reasoning models has significantly transformed the landscape of solving complex tasks. Although such models have achieved notable success in mathematics and science, their application to medical domains remains underexplored. In this work, we propose \textit{MedE$^2$}, a two-stage post-training pipeline that elicits and then enhances multimodal reasoning for medical domains. In Stage-I, we fine-tune models using 2,000 text-only data samples containing precisely orchestrated reasoning demonstrations to elicit reasoning behaviors. In Stage-II, we further enhance the model’s reasoning capabilities using 1,500 rigorously curated multimodal medical cases, aligning model reasoning outputs with our proposed multimodal medical reasoning preference. Extensive experiments demonstrate the efficacy and reliability of \textit{MedE$^2$} in improving the reasoning performance of medical multimodal models. Notably, models trained with \textit{MedE$^2$} consistently outperform baselines across multiple medical multimodal benchmarks. Additional validation on larger models and under inference-time scaling further confirms the robustness and practical utility of our approach.

Method: The authors introduce a novel, fine-grained evaluation pipeline for categorizing bias articulation patterns, enabling more precise analysis of CoT reasoning than previous methods. They apply this framework to evaluate both LVLMs and LLMs across various levels of implicit cues.

Result: Findings show that subtle image-based biases are rarely articulated compared to explicit text-based ones, even in reasoning-specialized models. Many models exhibit “inconsistent reasoning” - correctly reasoning before abruptly changing answers. Current language-only reasoning models continue to struggle with articulating cues that are not overtly stated.

Conclusion: The study reveals critical distinctions in how models process different types of biases, providing new insights into LVLM CoT faithfulness. Inconsistent reasoning serves as a potential canary for detecting biased reasoning from unfaithful CoTs, highlighting ongoing challenges in model reasoning transparency.

Abstract: Chain-of-thought (CoT) reasoning enhances performance of large language models, but questions remain about whether these reasoning traces faithfully reflect the internal processes of the model. We present the first comprehensive study of CoT faithfulness in large vision-language models (LVLMs), investigating how both text-based and previously unexplored image-based biases affect reasoning and bias articulation. Our work introduces a novel, fine-grained evaluation pipeline for categorizing bias articulation patterns, enabling significantly more precise analysis of CoT reasoning than previous methods. This framework reveals critical distinctions in how models process and respond to different types of biases, providing new insights into LVLM CoT faithfulness. Our findings reveal that subtle image-based biases are rarely articulated compared to explicit text-based ones, even in models specialized for reasoning. Additionally, many models exhibit a previously unidentified phenomenon we term ``inconsistent’’ reasoning - correctly reasoning before abruptly changing answers, serving as a potential canary for detecting biased reasoning from unfaithful CoTs. We then apply the same evaluation pipeline to revisit CoT faithfulness in LLMs across various levels of implicit cues. Our findings reveal that current language-only reasoning models continue to struggle with articulating cues that are not overtly stated.

Method: The survey reviews how each trustworthiness dimension is evaluated in existing LLM-based medical QA systems, compiles and compares major benchmarks, and analyzes evaluation-guided techniques like retrieval-augmented grounding, adversarial fine-tuning, and safety alignment.

Result: The study identifies current evaluation methods and improvement techniques for trustworthy medical QA systems, highlighting the need for better assessment frameworks.

Conclusion: The paper identifies open challenges including scalable expert evaluation, integrated multi-dimensional metrics, and real-world deployment studies, proposing future research directions to advance safe, reliable, and transparent deployment of LLM-powered medical QA.

Abstract: Trustworthiness in healthcare question-answering (QA) systems is important for ensuring patient safety, clinical effectiveness, and user confidence. As large language models (LLMs) become increasingly integrated into medical settings, the reliability of their responses directly influences clinical decision-making and patient outcomes. However, achieving comprehensive trustworthiness in medical QA poses significant challenges due to the inherent complexity of healthcare data, the critical nature of clinical scenarios, and the multifaceted dimensions of trustworthy AI. In this survey, we systematically examine six key dimensions of trustworthiness in medical QA, i.e., Factuality, Robustness, Fairness, Safety, Explainability, and Calibration. We review how each dimension is evaluated in existing LLM-based medical QA systems. We compile and compare major benchmarks designed to assess these dimensions and analyze evaluation-guided techniques that drive model improvements, such as retrieval-augmented grounding, adversarial fine-tuning, and safety alignment. Finally, we identify open challenges-such as scalable expert evaluation, integrated multi-dimensional metrics, and real-world deployment studies-and propose future research directions to advance the safe, reliable, and transparent deployment of LLM-powered medical QA.

Method: Multiple LLMs form a ‘sparta tribe’ where they compete in duels to fulfill instructions while serving as judges. An adapted Elo-ranking system aggregates evaluation scores, and combat results become preference pairs for collective learning.

Result: Outperforms initial models and 4 self-alignment baselines across 10 out of 12 tasks with 7.0% average improvement, showing better generalization to unseen tasks and more logical, direct outputs.

Conclusion: SPARTA ALIGNMENT enables effective self-evolution of multiple LLMs through collective competition, leveraging expertise diversity for improved performance and generalization.

Abstract: We propose SPARTA ALIGNMENT, an algorithm to collectively align multiple LLMs through competition and combat. To complement a single model’s lack of diversity in generation and biases in evaluation, multiple LLMs form a “sparta tribe” to compete against each other in fulfilling instructions while serving as judges for the competition of others. For each iteration, one instruction and two models are selected for a duel, the other models evaluate the two responses, and their evaluation scores are aggregated through a adapted elo-ranking based reputation system, where winners/losers of combat gain/lose weight in evaluating others. The peer-evaluated combat results then become preference pairs where the winning response is preferred over the losing one, and all models learn from these preferences at the end of each iteration. SPARTA ALIGNMENT enables the self-evolution of multiple LLMs in an iterative and collective competition process. Extensive experiments demonstrate that SPARTA ALIGNMENT outperforms initial models and 4 self-alignment baselines across 10 out of 12 tasks and datasets with 7.0% average improvement. Further analysis reveals that SPARTA ALIGNMENT generalizes more effectively to unseen tasks and leverages the expertise diversity of participating models to produce more logical, direct and informative outputs.

Method: Using affine mappings between residual streams to transfer SAE weights, probes, and steering vectors between small and large language models.

Result: Small and large models learn similar representation spaces; transferred SAEs achieve 50% cheaper training; transferred probes and steering vectors recover ground truth performance; semantic and structural features transfer differently.

Conclusion: Linear representation spaces of small and large models show both similarities and differences, and feature transfer provides an effective method for improving SAE training efficiency.

Abstract: In this work, we demonstrate that affine mappings between residual streams of language models is a cheap way to effectively transfer represented features between models. We apply this technique to transfer the weights of Sparse Autoencoders (SAEs) between models of different sizes to compare their representations. We find that small and large models learn similar representation spaces, which motivates training expensive components like SAEs on a smaller model and transferring to a larger model at a FLOPs savings. In particular, using a small-to-large transferred SAE as initialization can lead to 50% cheaper training runs when training SAEs on larger models. Next, we show that transferred probes and steering vectors can effectively recover ground truth performance. Finally, we dive deeper into feature-level transferability, finding that semantic and structural features transfer noticeably differently while specific classes of functional features have their roles faithfully mapped. Overall, our findings illustrate similarities and differences in the linear representation spaces of small and large models and demonstrate a method for improving the training efficiency of SAEs.

Method: Uses available components: target language corpora, multilingual base models, instructed backbone LLMs, and synthetically generated instructions from the instructed backbone. Systematically experiments with different combinations for Basque language adaptation.

Result: Target language corpora are essential, synthetic instructions yield robust models, and using instruction-tuned backbones outperforms base models. Scaling to Llama 3.1 Instruct 70B produces models competitive with much larger frontier models for Basque, without using Basque instructions.

Conclusion: The approach enables effective instruction adaptation for low-resource languages using synthetic instructions and existing multilingual models, achieving near-state-of-the-art performance without human-created instruction datasets.

Abstract: Instructing language models with user intent requires large instruction datasets, which are only available for a limited set of languages. In this paper, we explore alternatives to conventional instruction adaptation pipelines in low-resource scenarios. We assume a realistic scenario for low-resource languages, where only the following are available: corpora in the target language, existing open-weight multilingual base and instructed backbone LLMs, and synthetically generated instructions sampled from the instructed backbone. We present a comprehensive set of experiments for Basque that systematically study different combinations of these components evaluated on benchmarks and human preferences from 1,680 participants. Our conclusions show that target language corpora are essential, with synthetic instructions yielding robust models, and, most importantly, that using as backbone an instruction-tuned model outperforms using a base non-instructed model. Scaling up to Llama 3.1 Instruct 70B as backbone, our model comes near frontier models of much larger sizes for Basque, without using any Basque instructions. We release code, models, instruction datasets, and human preferences to support full reproducibility in future research on low-resource language adaptation. https://github.com/hitz-zentroa/latxa-instruct

Method: Combines co-training and consistency regularization with pseudo-labeling, featuring a pseudo-label weighting module that uses head agreement, model confidence, and classification difficulty to select, filter, and weight pseudo-labels.

Result: Achieves state-of-the-art results on 8 out of 10 setups from 5 NLP datasets, ranks first among 21 methods in Friedman test, and shows exceptional robustness in imbalanced settings (3.26% improvement over second-best approach).

Conclusion: MultiMatch provides a holistic SSL approach that significantly improves performance and robustness, particularly valuable for real-world text classification tasks with imbalanced data.

Abstract: We introduce MultiMatch, a novel semi-supervised learning (SSL) algorithm combining the paradigms of co-training and consistency regularization with pseudo-labeling. At its core, MultiMatch features a pseudo-label weighting module designed for selecting and filtering pseudo-labels based on head agreement and model confidence, and weighting them according to the perceived classification difficulty. This novel module enhances and unifies three existing techniques – heads agreement from Multihead Co-training, self-adaptive thresholds from FreeMatch, and Average Pseudo-Margins from MarginMatch – resulting in a holistic approach that improves robustness and performance in SSL settings. Experimental results on benchmark datasets highlight the superior performance of MultiMatch, i.e., MultiMatch achieves state-of-the-art results on 8 out of 10 setups from 5 natural language processing datasets and ranks first according to the Friedman test among 21 methods. Furthermore, MultiMatch demonstrates exceptional robustness in highly imbalanced settings, outperforming the second-best approach by 3.26%, a critical advantage for real-world text classification tasks. Our code is available on GitHub.

Method: RC aggregates answers by considering both the frequency of each answer and the consistency of the model’s internal activations (dense or sparse) during response generation. Inconsistent representations indicate incoherent reasoning and are down-weighted.

Result: Experiments with four open-source LLMs and four reasoning datasets show consistent accuracy improvements (up to 4%) over strong test-time scaling baselines, with sparse activations aligning well with coherent reasoning.

Conclusion: RC effectively improves LLM performance during inference by leveraging representation consistency for answer aggregation, requiring only cached activations and lightweight computations without additional model queries.

Abstract: Test-time scaling improves large language models’ (LLMs) performance by allocating more compute budget during inference. To achieve this, existing methods often require intricate modifications to prompting and sampling strategies. In this work, we introduce representation consistency (RC), a test-time scaling method for aggregating answers drawn from multiple candidate responses of an LLM regardless of how they were generated, including variations in prompt phrasing and sampling strategy. RC enhances answer aggregation by not only considering the number of occurrences of each answer in the candidate response set, but also the consistency of the model’s internal activations while generating the set of responses leading to each answer. These activations can be either dense (raw model activations) or sparse (encoded via pretrained sparse autoencoders). Our rationale is that if the model’s representations of multiple responses converging on the same answer are highly variable, this answer is more likely to be the result of incoherent reasoning and should be down-weighted during aggregation. Importantly, our method only uses cached activations and lightweight similarity computations and requires no additional model queries. Through experiments with four open-source LLMs and four reasoning datasets, we validate the effectiveness of RC for improving task performance during inference, with consistent accuracy improvements (up to 4%) over strong test-time scaling baselines. We also show that consistency in the sparse activation signals aligns well with the common notion of coherent reasoning.

Method: Analyzed discourse constructions in fine-tuning corpora to uncover heuristic shortcuts, demonstrated reliance on these heuristics, and proposed solutions leveraging curated datasets while examining generalization challenges across contexts and model scales.

Result: Found that moral self-correction depends on heuristic shortcuts from training data, and that attempting to enhance both self-correction and self-diagnosis simultaneously leads to inconsistency due to these heuristics.

Conclusion: Moral self-correction in LLMs is fundamentally limited by heuristic-based approaches from training data, requiring careful dataset curation and facing significant generalization challenges across different contexts and model sizes.

Abstract: Moral self-correction has emerged as a promising approach for aligning the output of Large Language Models (LLMs) with human moral values. However, moral self-correction techniques are subject to two primary paradoxes. First, despite empirical and theoretical evidence to support the effectiveness of self-correction, this LLM capability only operates at a superficial level. Second, while LLMs possess the capability of self-diagnosing immoral aspects of their output, they struggle to identify the cause of this moral inconsistency during their self-correction process. To better understand and address these paradoxes, we analyze the discourse constructions in fine-tuning corpora designed to enhance moral self-correction, uncovering the existence of the heuristics underlying effective constructions. We demonstrate that moral self-correction relies on discourse constructions that reflect heuristic shortcuts, and that the presence of these heuristic shortcuts during self-correction leads to inconsistency when attempting to enhance both self-correction and self-diagnosis capabilities jointly. Based on our findings, we propose a solution to improve moral self-correction by leveraging the heuristics of curated datasets. We also highlight the generalization challenges of this capability, particularly in terms of learning from situated context and model scales.

Method: Context Tuning initializes trainable prompts or prefixes with task-specific demonstration examples, utilizing the model’s In-Context Learning ability to extract relevant information for improved few-shot adaptation without fine-tuning model parameters.

Result: Extensive evaluations on CrossFit, UnifiedQA, MMLU, BIG-Bench Hard, and ARC show that Context Tuning outperforms traditional prompt-based methods and achieves competitive accuracy to Test-Time Training with significantly higher training efficiency.

Conclusion: Context Tuning provides a simple yet effective approach for few-shot adaptation of LLMs, demonstrating superior performance over traditional prompt-based methods while maintaining high training efficiency.

Abstract: We introduce Context Tuning, a simple and effective method to significantly enhance few-shot adaptation of language models (LLMs) without fine-tuning model parameters. While prompt-based adaptation techniques have demonstrated the effectiveness of lightweight adaptation methods for LLMs, they typically initialize a trainable prompt or prefix with irrelevant tokens for the task at hand. In contrast, Context Tuning initializes the trainable prompt or prefix with task-specific demonstration examples, leveraging the model’s inherent In-Context Learning (ICL) ability to extract relevant information for improved few-shot learning performance. Extensive evaluations on benchmarks such as CrossFit, UnifiedQA, MMLU, BIG-Bench Hard, and ARC demonstrate that Context Tuning outperforms traditional prompt-based adaptation methods and achieves competitive accuracy to Test-Time Training with significantly higher training efficiency.

Method: Used shortest-path tasks in layered graphs, trained decoder-only transformers on question-trace-answer triples with custom tokenizer, comparing models trained on optimal dynamic programming traces vs. longer backtracking traces.

Result: Models trained on inefficient backtracking traces generalized better to unseen graphs than those trained on optimal traces, with generalization correlating with model confidence in next-token prediction.

Conclusion: Long, coherent, and locally incremental reasoning traces make training signals easier to optimize, leading to better generalization, rather than trace length alone.

Abstract: Recent advances in natural language processing highlight two key factors for improving reasoning in large language models (LLMs): (i) allocating more test-time compute tends to help on harder problems but often introduces redundancy in the reasoning trace, and (ii) compute is most effective when reasoning is systematic and incremental, forming structured chains of thought (CoTs) akin to human problem-solving. To study these factors in isolation, we introduce a controlled setting based on shortest-path tasks in layered graphs. We train decoder-only transformers on question-trace-answer triples using a custom tokenizer, comparing models trained on optimal bottom-up dynamic programming traces with those trained on longer, valid traces involving backtracking. Surprisingly, with the same training-token budget, models trained on inefficient traces generalize better to unseen graphs. This benefit is not due to length alone-injecting arbitrary redundancy into reasoning traces fails to help and can even hurt performance. Instead, we find that generalization correlates with the model’s confidence in next-token prediction, suggesting that long, coherent, and locally incremental traces make the training signal easier to optimize.

Method: Provides a toolkit that allows users to conduct multi-dimensional evaluations over uploaded or generated synthetic data, with functionality demonstrated on healthcare and law datasets.

Result: The toolkit enables standardized evaluation metrics across key dimensions to assess synthetic text quality and risks.

Conclusion: By consolidating and standardizing evaluation metrics, SynthTextEval aims to improve the viability of synthetic text and enhance privacy-preservation in AI development.

Abstract: We present SynthTextEval, a toolkit for conducting comprehensive evaluations of synthetic text. The fluency of large language model (LLM) outputs has made synthetic text potentially viable for numerous applications, such as reducing the risks of privacy violations in the development and deployment of AI systems in high-stakes domains. Realizing this potential, however, requires principled consistent evaluations of synthetic data across multiple dimensions: its utility in downstream systems, the fairness of these systems, the risk of privacy leakage, general distributional differences from the source text, and qualitative feedback from domain experts. SynthTextEval allows users to conduct evaluations along all of these dimensions over synthetic data that they upload or generate using the toolkit’s generation module. While our toolkit can be run over any data, we highlight its functionality and effectiveness over datasets from two high-stakes domains: healthcare and law. By consolidating and standardizing evaluation metrics, we aim to improve the viability of synthetic text, and in-turn, privacy-preservation in AI development.

Method: Evaluated four KE methods (ROME, MEMIT, ICE, LTE) on Arabic translations of ZsRE and Counterfact benchmarks, analyzing multilingual and cross-lingual settings on Llama-2-7B-chat.

Result: Parameter-based methods struggle with cross-lingual generalization, while instruction-tuned methods perform more robustly. Extending LTE to multilingual setting with joint Arabic-English training improves both editability and transfer.

Conclusion: Released Arabic KE benchmarks and multilingual training data for LTE to support future research in Arabic knowledge editing.

Abstract: While Knowledge Editing (KE) has been widely explored in English, its behavior in morphologically rich languages like Arabic remains underexamined. In this work, we present the first study of Arabic KE. We evaluate four methods (ROME, MEMIT, ICE, and LTE) on Arabic translations of the ZsRE and Counterfact benchmarks, analyzing both multilingual and cross-lingual settings. Our experiments on Llama-2-7B-chat show that parameter-based methods struggle with cross-lingual generalization, while instruction-tuned methods perform more robustly. We extend Learning-To-Edit (LTE) to a multilingual setting and show that joint Arabic-English training improves both editability and transfer. We release Arabic KE benchmarks and multilingual training for LTE data to support future research.

Method: Used 437 Brazilian Portuguese clinical expressions annotated with ICPC-2 codes. A semantic search engine retrieved candidates from 73,563 labeled concepts, and 33 LLMs were prompted with queries and retrieved results to select the best-matching ICPC-2 code.

Result: 28 models achieved F1-score >0.8, with 10 exceeding 0.85. Top performers included gpt-4.5-preview, o3, and gemini-2.5-pro. Retriever optimization improved performance by up to 4 points. Most models returned valid codes with reduced hallucinations, though smaller models (<3B) struggled with formatting and input length.

Conclusion: LLMs show strong potential for automating ICPC-2 coding without fine-tuning. However, broader multilingual evaluations and clinical validation are needed, as findings are limited by dataset scope and setup.

Abstract: Background: Medical coding structures healthcare data for research, quality monitoring, and policy. This study assesses the potential of large language models (LLMs) to assign ICPC-2 codes using the output of a domain-specific search engine. Methods: A dataset of 437 Brazilian Portuguese clinical expressions, each annotated with ICPC-2 codes, was used. A semantic search engine (OpenAI’s text-embedding-3-large) retrieved candidates from 73,563 labeled concepts. Thirty-three LLMs were prompted with each query and retrieved results to select the best-matching ICPC-2 code. Performance was evaluated using F1-score, along with token usage, cost, response time, and format adherence. Results: Twenty-eight models achieved F1-score > 0.8; ten exceeded 0.85. Top performers included gpt-4.5-preview, o3, and gemini-2.5-pro. Retriever optimization can improve performance by up to 4 points. Most models returned valid codes in the expected format, with reduced hallucinations. Smaller models (<3B) struggled with formatting and input length. Conclusions: LLMs show strong potential for automating ICPC-2 coding, even without fine-tuning. This work offers a benchmark and highlights challenges, but findings are limited by dataset scope and setup. Broader, multilingual, end-to-end evaluations are needed for clinical validation.

Method: Used Language Activation Probability Entropy (LAPE) method to identify language-specific neurons, then applied language arithmetics (activation addition and multiplication) to steer models by deactivating unwanted languages and activating desired ones.

Result: Successfully manipulated language behavior across five multilingual tasks (language forcing, translation, QA, comprehension, NLI), with better performance for high-resource languages and improved effectiveness with typological similarity. Cross-lingual neuron steering enhanced downstream performance.

Conclusion: Language-specific neurons cluster in deeper layers, related languages share overlapping neurons, and systematic neuron manipulation can effectively control language behavior in multilingual LLMs, revealing internal fallback mechanisms for language selection.

Abstract: Large language models (LLMs) exhibit strong multilingual abilities, yet the neural mechanisms behind language-specific processing remain unclear. We analyze language-specific neurons in Llama-3.1-8B, Mistral-Nemo-12B, and Aya-Expanse-8B & 32B across 21 typologically diverse languages, identifying neurons that control language behavior. Using the Language Activation Probability Entropy (LAPE) method, we show that these neurons cluster in deeper layers, with non-Latin scripts showing greater specialization. Related languages share overlapping neurons, reflecting internal representations of linguistic proximity. Through language arithmetics, i.e. systematic activation addition and multiplication, we steer models to deactivate unwanted languages and activate desired ones, outperforming simpler replacement approaches. These interventions effectively guide behavior across five multilingual tasks: language forcing, translation, QA, comprehension, and NLI. Manipulation is more successful for high-resource languages, while typological similarity improves effectiveness. We also demonstrate that cross-lingual neuron steering enhances downstream performance and reveal internal “fallback” mechanisms for language selection when neurons are progressively deactivated. Our code is made publicly available at https://github.com/d-gurgurov/Language-Neurons-Manipulation.

Method: Systematically aggregating, cleaning, and standardising scattered terminology resources into open datasets under the NOODL framework, then integrating the terminology into a Retrieval-Augmented Generation (RAG) pipeline.

Result: Experiments show substantial improvements in accuracy and domain-specific consistency of English-to-Tshivenda machine translation for large language models.

Conclusion: Mafoko provides a scalable foundation for developing robust and equitable NLP technologies, ensuring South Africa’s rich linguistic diversity is represented in the digital age.

Abstract: The critical lack of structured terminological data for South Africa’s official languages hampers progress in multilingual NLP, despite the existence of numerous government and academic terminology lists. These valuable assets remain fragmented and locked in non-machine-readable formats, rendering them unusable for computational research and development. Mafoko addresses this challenge by systematically aggregating, cleaning, and standardising these scattered resources into open, interoperable datasets. We introduce the foundational Mafoko dataset, released under the equitable, Africa-centered NOODL framework. To demonstrate its immediate utility, we integrate the terminology into a Retrieval-Augmented Generation (RAG) pipeline. Experiments show substantial improvements in the accuracy and domain-specific consistency of English-to-Tshivenda machine translation for large language models. Mafoko provides a scalable foundation for developing robust and equitable NLP technologies, ensuring South Africa’s rich linguistic diversity is represented in the digital age.

Method: Uses fine-grained cross-model consistency checking by comparing responses from diverse models on semantically-equivalent prompts, plus targeted mitigation that applies precise corrections to problematic segments while preserving accurate content.

Result: Improves hallucination detection F1 scores by 6-39% on FELM dataset and achieves up to 9% absolute improvement in answer accuracy on GPQA-diamond dataset with state-of-the-art models like Llama 4 Maverick and Claude 4 Sonnet.

Conclusion: Finch-Zk provides a practical, deployment-ready safeguard for enhancing factual reliability in production LLM systems without requiring external knowledge sources.

Abstract: Large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, but they remain susceptible to hallucinations–generating content that appears plausible but contains factual inaccuracies. We present Finch-Zk, a black-box framework that leverages fine-grained cross-model consistency to detect and mitigate hallucinations in LLM outputs without requiring external knowledge sources. Finch-Zk introduces two key innovations:

1. a cross-model consistency checking strategy that reveals fine-grained inaccuracies by comparing responses generated by diverse models from semantically-equivalent prompts, and 2) a targeted mitigation technique that applies precise corrections to problematic segments while preserving accurate content. Experiments on the FELM dataset show Finch-Zk improves hallucination detection F1 scores by 6-39% compared to existing approaches. For mitigation, Finch-Zk achieves up to 9 absolute percentage points improvement in answer accuracy on the GPQA-diamond dataset when applied to state-of-the-art models like Llama 4 Maverick and Claude 4 Sonnet. Extensive evaluation on multiple datasets demonstrates that Finch-Zk provides a practical, deployment-ready safeguard for enhancing factual reliability in production LLM systems.

Method: Used various masking strategies to evaluate LLMs’ intrinsic ability, contribution of sentence elements, and attention mechanisms. Also explored fine-tuning LLMs to enhance classifier performance.

Result: LLMs performed worse than BERT in predicting Chinese classifiers, even with fine-tuning. Prediction greatly benefits from information about the following noun, explaining the advantage of bidirectional attention models like BERT.

Conclusion: BERT’s bidirectional attention mechanism provides significant advantages over standard LLMs for Chinese classifier prediction, highlighting the importance of contextual noun information in this task.

Abstract: Classifiers are an important and defining feature of the Chinese language, and their correct prediction is key to numerous educational applications. Yet, whether the most popular Large Language Models (LLMs) possess proper knowledge the Chinese classifiers is an issue that has largely remain unexplored in the Natural Language Processing (NLP) literature. To address such a question, we employ various masking strategies to evaluate the LLMs’ intrinsic ability, the contribution of different sentence elements, and the working of the attention mechanisms during prediction. Besides, we explore fine-tuning for LLMs to enhance the classifier performance. Our findings reveal that LLMs perform worse than BERT, even with fine-tuning. The prediction, as expected, greatly benefits from the information about the following noun, which also explains the advantage of models with a bidirectional attention mechanism such as BERT.

Method: Introduces OpinioRAG, a scalable framework combining RAG-based evidence retrieval with LLMs, and proposes novel reference-free verification metrics for sentiment-rich domains. Also contributes a large-scale dataset of long-form user reviews with expert summaries and annotated queries.

Result: Through extensive experiments, the framework demonstrates effectiveness in generating accurate, relevant, and structured summaries at scale, while identifying key challenges and providing actionable insights.

Conclusion: OpinioRAG positions itself as a robust framework for scalable opinion summary generation, paving the way for future research in this domain.

Abstract: We study the problem of opinion highlights generation from large volumes of user reviews, often exceeding thousands per entity, where existing methods either fail to scale or produce generic, one-size-fits-all summaries that overlook personalized needs. To tackle this, we introduce OpinioRAG, a scalable, training-free framework that combines RAG-based evidence retrieval with LLMs to efficiently produce tailored summaries. Additionally, we propose novel reference-free verification metrics designed for sentiment-rich domains, where accurately capturing opinions and sentiment alignment is essential. These metrics offer a fine-grained, context-sensitive assessment of factual consistency. To facilitate evaluation, we contribute the first large-scale dataset of long-form user reviews, comprising entities with over a thousand reviews each, paired with unbiased expert summaries and manually annotated queries. Through extensive experiments, we identify key challenges, provide actionable insights into improving systems, pave the way for future research, and position OpinioRAG as a robust framework for generating accurate, relevant, and structured summaries at scale.

Method: Decompose tasks into elementary operations on natural language strings, using LLMs as binary comparison oracles within established algorithms like bitonic sorting networks.

Result: Demonstrated effectiveness on sorting and clustering tasks by leveraging classical algorithms with theoretical guarantees.

Conclusion: Verbalized Algorithms provide a more reliable approach by combining LLMs’ natural language capabilities with the theoretical soundness of classical algorithms.

Abstract: Instead of querying LLMs in a one-shot manner and hoping to get the right answer for a reasoning task, we propose a paradigm we call \emph{verbalized algorithms} (VAs), which leverage classical algorithms with established theoretical understanding. VAs decompose a task into simple elementary operations on natural language strings that they should be able to answer reliably, and limit the scope of LLMs to only those simple tasks. For example, for sorting a series of natural language strings, \emph{verbalized sorting} uses an LLM as a binary comparison oracle in a known and well-analyzed sorting algorithm (e.g., bitonic sorting network). We demonstrate the effectiveness of this approach on sorting and clustering tasks.

Method: CAMPUS framework features: (1) dynamic selection for sub-curriculum, (2) competency-aware adjustment to curriculum schedule, and (3) multiple difficulty-based scheduling to adapt to model capabilities.

Result: Extensive experiments show CAMPUS achieves superior performance compared to other state-of-the-art baselines for efficient instruction tuning.

Conclusion: CAMPUS effectively addresses curriculum rigidity in instruction tuning by dynamically adapting to model capabilities, resulting in improved performance over static curriculum methods.

Abstract: Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data organization strategy has shown preliminary effectiveness in instruction tuning. However, current curriculum tuning methods suffer from the curriculum rigidity, since they rely solely on static heuristic difficulty metrics. These methods fail to adapt to the evolving capabilities of models during training, resulting in a fixed and potentially sub-optimal learning trajectory. To address the issue, Competence-Aware Multi-Perspective cUrriculum inStruction tuning framework termed CAMPUS is proposed. CAMPUS offers several advantages: (1) Dynamic selection for sub-curriculum. (2) Competency-aware adjustment to the curriculum schedule. (3) Multiple difficulty-based scheduling. Extensive experiments prove the superior performance of CAMPUS, compared to other state-of-the-art baselines for efficient instruction tuning.

Method: DeCE separates precision (factual accuracy and relevance) and recall (coverage of required concepts) using instance-specific criteria automatically extracted from gold answer requirements. It is model-agnostic and domain-general without requiring predefined taxonomies or handcrafted rubrics.

Result: DeCE achieves substantially stronger correlation with expert judgments (r=0.78) compared to traditional metrics (r=0.12), pointwise LLM scoring (r=0.35), and modern multidimensional evaluators (r=0.48). It reveals interpretable trade-offs between generalist and specialized models.

Conclusion: DeCE offers an interpretable and actionable LLM evaluation framework in expert domains, with only 11.95% of LLM-generated criteria requiring expert revision, underscoring its scalability.

Abstract: Evaluating long-form answers in high-stakes domains such as law or medicine remains a fundamental challenge. Standard metrics like BLEU and ROUGE fail to capture semantic correctness, and current LLM-based evaluators often reduce nuanced aspects of answer quality into a single undifferentiated score. We introduce DeCE, a decomposed LLM evaluation framework that separates precision (factual accuracy and relevance) and recall (coverage of required concepts), using instance-specific criteria automatically extracted from gold answer requirements. DeCE is model-agnostic and domain-general, requiring no predefined taxonomies or handcrafted rubrics. We instantiate DeCE to evaluate different LLMs on a real-world legal QA task involving multi-jurisdictional reasoning and citation grounding. DeCE achieves substantially stronger correlation with expert judgments ($r=0.78$), compared to traditional metrics ($r=0.12$), pointwise LLM scoring ($r=0.35$), and modern multidimensional evaluators ($r=0.48$). It also reveals interpretable trade-offs: generalist models favor recall, while specialized models favor precision. Importantly, only 11.95% of LLM-generated criteria required expert revision, underscoring DeCE’s scalability. DeCE offers an interpretable and actionable LLM evaluation framework in expert domains.

Method: Uses encoder-decoder initialization and geometric embedding distillation to capture knowledge from larger models, spread-out regularizer for robustness, and merges checkpoints from optimized mixtures for generalizability.

Result: Achieves state-of-the-art results on MTEB across multilingual, English, and code domains, outperforms prior top models with fewer than 500M parameters, and provides comparable performance to models double its size.

Conclusion: EmbeddingGemma offers exceptional efficiency and performance, making it ideal for low-latency, high-throughput applications while maintaining strong performance even when quantized or truncated.

Abstract: We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledge from larger models via encoder-decoder initialization and geometric embedding distillation. We improve model robustness and expressiveness with a spread-out regularizer, and ensure generalizability by merging checkpoints from varied, optimized mixtures. Evaluated on the Massive Text Embedding Benchmark (MTEB) across multilingual, English, and code domains, EmbeddingGemma (300M) achieves state-of-the-art results. Notably, it outperforms prior top models, both proprietary and open, with fewer than 500M parameters, and provides performance comparable to models double its size, offering an exceptional performance-to-cost ratio. Remarkably, this lead persists when quantizing model weights or truncating embedding outputs. This makes EmbeddingGemma particularly well-suited for low-latency and high-throughput use cases such as on-device applications. We provide ablation studies exploring our key design choices. We release EmbeddingGemma to the community to promote further research.

Method: Used zero-shot classification approach with 1800 model responses across six LLMs, analyzed through four distinct fine-tuned classification algorithms measuring ideological alignment, topicality, response sentiment, and objectivity.

Result: Found amplified liberal-authoritarian alignment across all six LLMs, with instances of reasoning supersessions and canned refusals. The biases can manifest as conformity or polarization depending on regional socio-political structures.

Conclusion: The study highlights how intrinsic biases in AI systems can permeate public discourse and distort the political landscape, emphasizing the psychological influences in human-computer interactions.

Abstract: Amidst the rapid normalization of generative artificial intelligence (GAI), intelligent systems have come to dominate political discourse across information media. However, internalized political biases stemming from training data skews, human prejudice, and algorithmic flaws continue to plague this novel technology. This study employs a zero-shot classification approach to evaluate algorithmic political partisanship through a methodical combination of ideological alignment, topicality, response sentiment, and objectivity. A total of 1800 model responses across six mainstream large language models (LLMs) were individually input into four distinct fine-tuned classification algorithms, each responsible for computing one of the aforementioned metrics. The results show an amplified liberal-authoritarian alignment across the six LLMs evaluated, with notable instances of reasoning supersessions and canned refusals. The study subsequently highlights the psychological influences underpinning human-computer interactions and how intrinsic biases can permeate public discourse. The resulting distortion of the political landscape can ultimately manifest as conformity or polarization, depending on the region’s pre-existing socio-political structures.

Method: Prepend short system-prompt instructions to finetuning data that deliberately elicit undesirable traits, then evaluate without these instructions at test time.

Result: Inoculated models show much lower expression of undesirable traits across multiple settings including reducing emergent misalignment, defending against backdoor injections, and mitigating trait transmission via subliminal learning.

Conclusion: Inoculation is an effective technique for selective learning that reduces optimization pressure by making traits less surprising, contributing to better understanding of how language models generalize.

Abstract: Language model finetuning often results in learning undesirable traits in combination with desired ones. To address this, we propose inoculation prompting: modifying finetuning data by prepending a short system-prompt instruction that deliberately elicits the undesirable trait. At test time, we evaluate without the instruction; inoculated models have much lower expression of the trait than models trained with unmodified training data. Inoculation is selective: in a toy setting where assistant responses are always in Spanish and ALL-CAPS, an appropriate inoculation (e.g., ``You always speak in Spanish.’’) teaches the model to capitalize responses while still responding in English. We find that inoculation is also effective across several additional settings: reducing emergent misalignment (EM) from task-specific finetuning, defending against backdoor injections, and mitigating the transmission of traits via subliminal learning. Follow-up analysis suggests a mechanism: making a trait less surprising via inoculation reduces optimization pressure to globally update the model, thereby reducing the degree of generalization. Our analysis relates to prior work on EM: inoculation explains prior findings that educational contexts mitigate EM from insecure code. Beyond demonstrating a simple and effective technique for selective learning, our results contribute to a better conceptual understanding of how and why language models generalize.

Method: 1) Compare DA vs CoT prompting across 8 LLMs; 2) Define token-to-head contribution score to trace bias to specific attention heads; 3) Propose DiffHeads framework that identifies bias heads through differential activation analysis and selectively masks them.

Result: DA triggering increases unfairness by 534.5%-391.9%; identified small cluster of bias heads that activate under DA but stay dormant with CoT; DiffHeads reduces unfairness by 49.4% under DA and 40.3% under CoT without harming model utility.

Conclusion: DiffHeads provides a causal link between prompting strategy and bias emergence, offering an effective lightweight debiasing approach by targeting specific bias heads rather than the entire model.

Abstract: Large language models (LLMs) increasingly mediate decisions in domains where unfair treatment of demographic groups is unacceptable. Existing work probes when biased outputs appear, but gives little insight into the mechanisms that generate them, leaving existing mitigations largely fragile. In this paper, we conduct a systematic investigation LLM unfairness and propose DiffHeads, a lightweight debiasing framework for LLMs. We first compare Direct-Answer (DA) prompting to Chain-of-Thought (CoT) prompting across eight representative open- and closed-source LLMs. DA will trigger the nature bias part of LLM and improve measured unfairness by 534.5%-391.9% in both one-turn and two-turn dialogues. Next, we define a token-to-head contribution score that traces each token’s influence back to individual attention heads. This reveals a small cluster of bias heads that activate under DA but stay largely dormant with CoT, providing the first causal link between prompting strategy and bias emergence. Finally, building on this insight, we propose DiffHeads that identifies bias heads through differential activation analysis between DA and CoT, and selectively masks only those heads. DiffHeads reduces unfairness by 49.4%, and 40.3% under DA and CoT, respectively, without harming model utility.

Method: Uses LLM embeddings with temporal decay functions and attention mechanisms, maps to latent topic space with state transition matrix, and applies joint optimization for semantic and temporal consistency.

Result: Effectively captures topic generation, expansion, and decline; outperforms existing models across multiple metrics on real-world corpora.

Conclusion: Provides a systematic solution for dynamic semantic pattern understanding, enriches topic modeling research, and supports complex text analysis across domains.

Abstract: This paper proposes a modeling framework for dynamic topic evolution based on temporal large language models. The method first uses a large language model to obtain contextual embeddings of text and then introduces a temporal decay function and an attention mechanism. These components allow the model to adjust the importance of semantic units according to time intervals and capture topic variations across different periods. The temporal representations are then mapped into a latent topic space, where a state transition matrix is applied to describe the dynamic evolution of topics. A joint optimization objective constrains both semantic modeling and temporal consistency, ensuring diversity and smoothness in topic generation. The design emphasizes the unified modeling of semantic representation and temporal evolution, which improves topic coherence and diversity while enhancing stability and interpretability over time. Experiments on real-world corpora show that the framework effectively captures the generation, expansion, and decline of topics and outperforms existing models across multiple metrics. Overall, the proposed method provides a systematic solution for understanding dynamic semantic patterns in large-scale text, enriches the research paradigm of topic modeling, and supports complex text analysis tasks in multiple domains.

Method: Proposed MedREK framework with shared query-key module for precise matching and attention-based prompt encoder for guidance. Also created MedVersa benchmark for evaluating single/batch edits under locality constraints.

Result: MedREK achieves superior performance across various medical benchmarks and provides the first validated solution for batch-editing in medical LLMs.

Conclusion: MedREK effectively addresses critical challenges in medical LLM editing through improved retrieval accuracy and batch-editing capability, enabling more reliable clinical applications.

Abstract: LLMs hold great promise for healthcare applications, but the rapid evolution of medical knowledge and errors in training data often cause them to generate outdated or inaccurate information, limiting their applicability in high-stakes clinical practice. Model editing has emerged as a potential remedy without full retraining. While parameter-based editing often compromises locality and is thus ill-suited for the medical domain, retrieval-based editing offers a more viable alternative. However, it still faces two critical challenges: (1) representation overlap within the medical knowledge space often causes inaccurate retrieval and reduces editing accuracy; (2) existing methods are restricted to single-sample edits, while batch-editing remains largely unexplored despite its importance for real-world medical applications. To address these challenges, we first construct MedVersa, an enhanced benchmark with broader coverage of medical subjects, designed to evaluate both single and batch edits under strict locality constraints. We then propose MedREK, a retrieval-based editing framework that integrates a shared query-key module for precise matching with an attention-based prompt encoder for informative guidance. Experimental results on various medical benchmarks demonstrate that our MedREK achieves superior performance across different core metrics and provides the first validated solution for batch-editing in medical LLMs. Our code and dataset are available at https://github.com/mylittleriver/MedREK.

Method: Used a dataset of arithmetic expressions with varying parentheses, applied interpretability techniques (logit lens, linear classification probes, UMAP visualization), and introduced partial embedding swap to modify operator precedence.

Result: Found that intermediate computations appear in the residual stream (especially after MLP blocks) and that the model linearly encodes precedence in operator embeddings post attention layer.

Conclusion: LLMs internally represent arithmetic operator precedence, with intermediate computations detectable in the residual stream and precedence encoded in operator embeddings.

Abstract: Large Language Models (LLMs) have demonstrated impressive reasoning capabilities but continue to struggle with arithmetic tasks. Prior works largely focus on outputs or prompting strategies, leaving the open question of the internal structure through which models do arithmetic computation. In this work, we investigate whether LLMs encode operator precedence in their internal representations via the open-source instruction-tuned LLaMA 3.2-3B model. We constructed a dataset of arithmetic expressions with three operands and two operators, varying the order and placement of parentheses. Using this dataset, we trace whether intermediate results appear in the residual stream of the instruction-tuned LLaMA 3.2-3B model. We apply interpretability techniques such as logit lens, linear classification probes, and UMAP geometric visualization. Our results show that intermediate computations are present in the residual stream, particularly after MLP blocks. We also find that the model linearly encodes precedence in each operator’s embeddings post attention layer. We introduce partial embedding swap, a technique that modifies operator precedence by exchanging high-impact embedding dimensions between operators.

Method: Preregistered study comparing MFA-trained expert writers with ChatGPT, Claude, and Gemini using both in-context prompting and fine-tuning approaches. 159 expert and lay readers conducted blind pairwise evaluations of 450-word excerpts emulating 50 award-winning authors’ styles.

Result: In-context prompting was strongly disfavored by experts for stylistic fidelity and writing quality, but fine-tuning completely reversed these findings - experts now preferred AI-generated text. Fine-tuned outputs were rarely detected as AI-generated (3% vs 97% for in-context) and showed dramatic cost reduction ($81 per author vs typical writer compensation).

Conclusion: Author-specific fine-tuning enables AI to produce non-verbatim writing that readers prefer to expert human writing, providing empirical evidence relevant to copyright’s fair-use considerations regarding market impact.

Abstract: The use of copyrighted books for training AI models has led to numerous lawsuits from authors concerned about AI’s ability to generate derivative content. Yet it’s unclear if these models can generate high quality literary text while emulating authors’ styles. To answer this we conducted a preregistered study comparing MFA-trained expert writers with three frontier AI models: ChatGPT, Claude & Gemini in writing up to 450 word excerpts emulating 50 award-winning authors’ diverse styles. In blind pairwise evaluations by 159 representative expert & lay readers, AI-generated text from in-context prompting was strongly disfavored by experts for both stylistic fidelity (OR=0.16, p<10^-8) & writing quality (OR=0.13, p<10^-7) but showed mixed results with lay readers. However, fine-tuning ChatGPT on individual authors’ complete works completely reversed these findings: experts now favored AI-generated text for stylistic fidelity (OR=8.16, p<10^-13) & writing quality (OR=1.87, p=0.010), with lay readers showing similar shifts. These effects generalize across authors & styles. The fine-tuned outputs were rarely flagged as AI-generated (3% rate v. 97% for in-context prompting) by best AI detectors. Mediation analysis shows this reversal occurs because fine-tuning eliminates detectable AI stylistic quirks (e.g., cliche density) that penalize in-context outputs. While we do not account for additional costs of human effort required to transform raw AI output into cohesive, publishable prose, the median fine-tuning & inference cost of $81 per author represents a dramatic 99.7% reduction compared to typical professional writer compensation. Author-specific fine-tuning thus enables non-verbatim AI writing that readers prefer to expert human writing, providing empirical evidence directly relevant to copyright’s fourth fair-use factor, the “effect upon the potential market or value” of the source works.

Method: AS uses two attention-level interventions: importance-aware suppression for unlearning data and attention-guided retention enhancement for retained data, jointly optimized via a dual-loss objective.

Result: AS improves performance preservation by up to 15% higher accuracy on ToFU benchmark and 10% on TDEC benchmark while maintaining competitive hallucination-free unlearning effectiveness.

Conclusion: AS demonstrates superior balance between unlearning effectiveness, generalization, and response reliability compared to existing methods.

Abstract: The increase in computing power and the necessity of AI-assisted decision-making boost the growing application of large language models (LLMs). Along with this, the potential retention of sensitive data of LLMs has spurred increasing research into machine unlearning. However, existing unlearning approaches face a critical dilemma: Aggressive unlearning compromises model utility, while conservative strategies preserve utility but risk hallucinated responses. This significantly limits LLMs’ reliability in knowledge-intensive applications. To address this, we introduce a novel Attention-Shifting (AS) framework for selective unlearning. AS is driven by two design objectives: (1) context-preserving suppression that attenuates attention to fact-bearing tokens without disrupting LLMs’ linguistic structure; and (2) hallucination-resistant response shaping that discourages fabricated completions when queried about unlearning content. AS realizes these objectives through two attention-level interventions, which are importance-aware suppression applied to the unlearning set to reduce reliance on memorized knowledge and attention-guided retention enhancement that reinforces attention toward semantically essential tokens in the retained dataset to mitigate unintended degradation. These two components are jointly optimized via a dual-loss objective, which forms a soft boundary that localizes unlearning while preserving unrelated knowledge under representation superposition. Experimental results show that AS improves performance preservation over the state-of-the-art unlearning methods, achieving up to 15% higher accuracy on the ToFU benchmark and 10% on the TDEC benchmark, while maintaining competitive hallucination-free unlearning effectiveness. Compared to existing methods, AS demonstrates a superior balance between unlearning effectiveness, generalization, and response reliability.

Method: Evaluated three LLMs (GPT-4.1-mini, LLaMA 70B, LLaMA 8B) on BBQ (social bias) and MedQA datasets, manipulating few-shot examples, prompting strategies, and training procedures to measure faithfulness.

Result: Few-shot example quantity and quality significantly impact faithfulness; faithfulness is sensitive to prompting design; instruction-tuning improves faithfulness on MedQA.

Conclusion: The findings provide strategies for enhancing LLM interpretability and trustworthiness in sensitive domains through careful design of inference and training procedures.

Abstract: Large Language Models (LLMs) often produce explanations that do not faithfully reflect the factors driving their predictions. In healthcare settings, such unfaithfulness is especially problematic: explanations that omit salient clinical cues or mask spurious shortcuts can undermine clinician trust and lead to unsafe decision support. We study how inference and training-time choices shape explanation faithfulness, focusing on factors practitioners can control at deployment. We evaluate three LLMs (GPT-4.1-mini, LLaMA 70B, LLaMA 8B) on two datasets-BBQ (social bias) and MedQA (medical licensing questions), and manipulate the number and type of few-shot examples, prompting strategies, and training procedure. Our results show: (i) both the quantity and quality of few-shot examples significantly impact model faithfulness; (ii) faithfulness is sensitive to prompting design; (iii) the instruction-tuning phase improves measured faithfulness on MedQA. These findings offer insights into strategies for enhancing the interpretability and trustworthiness of LLMs in sensitive domains.

Method: The framework consists of two key modules: attribute-based automatic instruction construction to generate diverse instructions, and difficulty-based data filtering to reduce computational costs by selecting the most valuable training samples.

Result: 3B student models achieved performance comparable to 20x larger teacher models on most tasks, and the method greatly outperformed baselines in data efficiency, achieving the same performance level with only 10% of the data.

Conclusion: CompEffDist provides an effective solution for developing lightweight sentiment analysis models through comprehensive knowledge distillation while maintaining high data efficiency and computational practicality.

Abstract: Recent efforts leverage knowledge distillation techniques to develop lightweight and practical sentiment analysis models. These methods are grounded in human-written instructions and large-scale user texts. Despite the promising results, two key challenges remain: (1) manually written instructions are limited in diversity and quantity, making them insufficient to ensure comprehensive coverage of distilled knowledge; (2) large-scale user texts incur high computational cost, hindering the practicality of these methods. To this end, we introduce CompEffDist, a comprehensive and efficient distillation framework for sentiment analysis. Our framework consists of two key modules: attribute-based automatic instruction construction and difficulty-based data filtering, which correspondingly tackle the aforementioned challenges. Applying our method across multiple model series (Llama-3, Qwen-3, and Gemma-3), we enable 3B student models to match the performance of 20x larger teacher models on most tasks. In addition, our approach greatly outperforms baseline methods in data efficiency, attaining the same performance level with only 10% of the data.

Method: Three key components: (1) iterative computation in latent space, (2) entropy-regularized objective for learned depth allocation, (3) scaling to 7.7T tokens. The approach is named after the recursive Ouroboros concept.

Result: Ouro 1.4B and 2.6B models match the performance of up to 12B state-of-the-art LLMs across various benchmarks. The advantage comes from superior knowledge manipulation capabilities rather than increased knowledge capacity. LoopLM produces reasoning traces more aligned with final outputs than explicit chain-of-thought.

Conclusion: LoopLM represents a promising novel scaling direction for reasoning capabilities in language models, demonstrating the potential of integrating reasoning directly into pre-training rather than relying on post-training techniques.

Abstract: Modern LLMs are trained to “think” primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training data. We present and open-source Ouro, named after the recursive Ouroboros, a family of pre-trained Looped Language Models (LoopLM) that instead build reasoning into the pre-training phase through (i) iterative computation in latent space, (ii) an entropy-regularized objective for learned depth allocation, and (iii) scaling to 7.7T tokens. Ouro 1.4B and 2.6B models enjoy superior performance that match the results of up to 12B SOTA LLMs across a wide range of benchmarks. Through controlled experiments, we show this advantage stems not from increased knowledge capacity, but from superior knowledge manipulation capabilities. We also show that LoopLM yields reasoning traces more aligned with final outputs than explicit CoT. We hope our results show the potential of LoopLM as a novel scaling direction in the reasoning era. Our model is available here: http://ouro-llm.github.io.

Method: The framework builds an attention map memorization database and uses efficient caching and similarity search to retrieve and reuse similar attention maps during inference.

Result: Achieves 1.2-1.6x end-to-end speedup and 2-3x attention speedup on CPU/GPU with negligible accuracy degradation.

Conclusion: AttnCache effectively accelerates prefill-only LLM inference by exploiting attention map similarity across different inputs.

Abstract: Large Language Models (LLMs) are widely used in generative applications such as chatting, code generation, and reasoning. However, many realworld workloads such as classification, question answering, recommendation, and text embedding rely solely on the prefill stage of inference, where the model encodes input sequences without performing autoregressive decoding. In these prefill only scenarios, the self-attention computation becomes the primary performance bottleneck due to its quadratic complexity with respect to sequence length. In this paper, we observe that semantically different sentences often produce similar attention maps across layers and heads. Building on this insight, we propose AttnCache, a framework that accelerates the prefill stage of LLM inference by retrieving and reusing similar attention maps. Based on an attention map memorization database, AttnCache employs efficient caching and similarity search techniques to identify and reuse pre-cached attention maps during inference, thereby reducing the computational overhead of self-attention. Experimental results show that AttnCache achieves an average of 1.2x end-to-end and 2x attention speedup on CPU, and 1.6x end-to-end and 3x attention speedup on GPU, with negligible accuracy degradation.

Method: Created QCoder Benchmark with quantum simulator environment for domain-specific metrics feedback and incorporated human-written code from real programming contests for comparison.

Result: Advanced models like GPT-4o achieved only 18.97% accuracy, while reasoning-based models like o3 reached 78% accuracy, outperforming human-written code success rates (39.98%).

Conclusion: The benchmark demonstrates the difficulty of quantum programming for LLMs and shows reasoning-based approaches are more effective, with the framework released to support further research.

Abstract: Large language models (LLMs) have increasingly been applied to automatic programming code generation. This task can be viewed as a language generation task that bridges natural language, human knowledge, and programming logic. However, it remains underexplored in domains that require interaction with hardware devices, such as quantum programming, where human coders write Python code that is executed on a quantum computer. To address this gap, we introduce QCoder Benchmark, an evaluation framework that assesses LLMs on quantum programming with feedback from simulated hardware devices. Our benchmark offers two key features. First, it supports evaluation using a quantum simulator environment beyond conventional Python execution, allowing feedback of domain-specific metrics such as circuit depth, execution time, and error classification, which can be used to guide better generation. Second, it incorporates human-written code submissions collected from real programming contests, enabling both quantitative comparisons and qualitative analyses of LLM outputs against human-written codes. Our experiments reveal that even advanced models like GPT-4o achieve only around 18.97% accuracy, highlighting the difficulty of the benchmark. In contrast, reasoning-based models such as o3 reach up to 78% accuracy, outperforming averaged success rates of human-written codes (39.98%). We release the QCoder Benchmark dataset and public evaluation API to support further research. (Codes and datasets are available at https://qcoder-bench.github.io/ )

Method: Operates at word level using zero-shot classification with dynamically generated candidate sets. Uses a Hebrew Visual Language Model that processes undiacritized text as images to embed diacritic information directly in vector representations.

Result: Achieves high accuracy in oracle settings where correct diacritized forms are guaranteed among candidates. Architectural enhancements and optimized training significantly improve generalization capabilities.

Conclusion: Visual representations show promising potential for accurate and automated Hebrew diacritization without complex linguistic analysis.

Abstract: Diacritics restoration in Hebrew is a fundamental task for ensuring accurate word pronunciation and disambiguating textual meaning. Despite the language’s high degree of ambiguity when unvocalized, recent machine learning approaches have significantly advanced performance on this task. In this work, we present DIVRIT, a novel system for Hebrew diacritization that frames the task as a zero-shot classification problem. Our approach operates at the word level, selecting the most appropriate diacritization pattern for each undiacritized word from a dynamically generated candidate set, conditioned on the surrounding textual context. A key innovation of DIVRIT is its use of a Hebrew Visual Language Model, which processes undiacritized text as an image, allowing diacritic information to be embedded directly within the input’s vector representation. Through a comprehensive evaluation across various configurations, we demonstrate that the system effectively performs diacritization without relying on complex, explicit linguistic analysis. Notably, in an ``oracle’’ setting where the correct diacritized form is guaranteed to be among the provided candidates, DIVRIT achieves a high level of accuracy. Furthermore, strategic architectural enhancements and optimized training methodologies yield significant improvements in the system’s overall generalization capabilities. These findings highlight the promising potential of visual representations for accurate and automated Hebrew diacritization.

Method: SlideAgent employs specialized agents and decomposes reasoning into three levels (global, page, element) to construct a structured, query-agnostic representation. During inference, it selectively activates specialized agents for multi-level reasoning and integrates their outputs.

Result: Extensive experiments show SlideAgent achieves significant improvement over both proprietary (+7.9 overall) and open-source models (+9.8 overall).

Conclusion: SlideAgent provides an effective framework for understanding multi-modal, multi-page, and multi-layout documents, especially slide decks, through hierarchical agentic reasoning.

Abstract: Multi-page visual documents such as manuals, brochures, presentations, and posters convey key information through layout, colors, icons, and cross-slide references. While large language models (LLMs) offer opportunities in document understanding, current systems struggle with complex, multi-page visual documents, particularly in fine-grained reasoning over elements and pages. We introduce SlideAgent, a versatile agentic framework for understanding multi-modal, multi-page, and multi-layout documents, especially slide decks. SlideAgent employs specialized agents and decomposes reasoning into three specialized levels-global, page, and element-to construct a structured, query-agnostic representation that captures both overarching themes and detailed visual or textual cues. During inference, SlideAgent selectively activates specialized agents for multi-level reasoning and integrates their outputs into coherent, context-aware answers. Extensive experiments show that SlideAgent achieves significant improvement over both proprietary (+7.9 overall) and open-source models (+9.8 overall).

Method: Uses Kimi Delta Attention (KDA) - an expressive linear attention module extending Gated DeltaNet with finer-grained gating, combined with a bespoke chunkwise algorithm using specialized DPLR transition matrices for hardware efficiency. Implements a layerwise hybrid of KDA and Multi-Head Latent Attention (MLA).

Result: A 3B activated parameter model (48B total) outperforms full MLA across all evaluated tasks, reduces KV cache usage by up to 75%, and achieves up to 6 times decoding throughput for 1M context length.

Conclusion: Kimi Linear serves as a drop-in replacement for full attention architectures with superior performance and efficiency, particularly for tasks with longer input and output lengths.

Abstract: We introduce Kimi Linear, a hybrid linear attention architecture that, for the first time, outperforms full attention under fair comparisons across various scenarios – including short-context, long-context, and reinforcement learning (RL) scaling regimes. At its core lies Kimi Delta Attention (KDA), an expressive linear attention module that extends Gated DeltaNet with a finer-grained gating mechanism, enabling more effective use of limited finite-state RNN memory. Our bespoke chunkwise algorithm achieves high hardware efficiency through a specialized variant of the Diagonal-Plus-Low-Rank (DPLR) transition matrices, which substantially reduces computation compared to the general DPLR formulation while remaining more consistent with the classical delta rule. We pretrain a Kimi Linear model with 3B activated parameters and 48B total parameters, based on a layerwise hybrid of KDA and Multi-Head Latent Attention (MLA). Our experiments show that with an identical training recipe, Kimi Linear outperforms full MLA with a sizeable margin across all evaluated tasks, while reducing KV cache usage by up to 75% and achieving up to 6 times decoding throughput for a 1M context. These results demonstrate that Kimi Linear can be a drop-in replacement for full attention architectures with superior performance and efficiency, including tasks with longer input and output lengths. To support further research, we open-source the KDA kernel and vLLM implementations, and release the pre-trained and instruction-tuned model checkpoints.

Method: Uses single-person motion capture data and high-level features with diffusion models, motion inpainting, and motion style transfer to generate temporally coherent movement representations that respond to human movement input.

Result: The model successfully generates diverse and realistic dance movements that show various deviations from human input while maintaining coherence, with quantitative assessment showing convergence between generated and test set feature distributions.

Conclusion: The approach represents first steps toward creative dancing with AI, enabling realistic movement generation that creatively enhances human input without relying on human-human interaction data.

Abstract: Recent success with large language models has sparked a new wave of verbal human-AI interaction. While such models support users in a variety of creative tasks, they lack the embodied nature of human interaction. Dance, as a primal form of human expression, is predestined to complement this experience. To explore creative human-AI interaction exemplified by dance, we build an interactive model based on motion capture (MoCap) data. It generates an artificial other by partially mimicking and also “creatively” enhancing an incoming sequence of movement data. It is the first model, which leverages single-person motion data and high level features in order to do so and, thus, it does not rely on low level human-human interaction data. It combines ideas of two diffusion models, motion inpainting, and motion style transfer to generate movement representations that are both temporally coherent and responsive to a chosen movement reference. The success of the model is demonstrated by quantitatively assessing the convergence of the feature distribution of the generated samples and the test set which serves as simulating the human performer. We show that our generations are first steps to creative dancing with AI as they are both diverse showing various deviations from the human partner while appearing realistic.

Method: Dataset constructed using Gemini-2.5-Flash-Image to generate bidirectional edits from real medical images, with LLM-as-Judge medical quality control using a medically grounded rubric and history-aware iterative refinement up to five rounds.

Result: Created Med-Banana-50K dataset spanning chest X-ray, brain MRI, and fundus photography with 23 disease types, including 37K failed attempts with full conversation logs for preference learning.

Conclusion: Med-Banana-50K establishes a foundation for training and evaluating next-generation medical image editing models by providing a large-scale, medically validated, and fully documented resource.

Abstract: Recent advances in multimodal large language models have enabled remarkable medical image editing capabilities. However, the research community’s progress remains constrained by the absence of large-scale, high-quality, and openly accessible datasets built specifically for medical image editing with strict anatomical and clinical constraints. We introduce Med-Banana-50K, a comprehensive 50K-image dataset for instruction-based medical image editing spanning three modalities (chest X-ray, brain MRI, fundus photography) and 23 disease types. Our dataset is constructed by leveraging Gemini-2.5-Flash-Image to generate bidirectional edits (lesion addition and removal) from real medical images. What distinguishes Med-Banana-50K from general-domain editing datasets is our systematic approach to medical quality control: we employ LLM-as-Judge with a medically grounded rubric (instruction compliance, structural plausibility, realism, and fidelity preservation) and history-aware iterative refinement up to five rounds. Beyond single-turn editing, Med-Banana-50K includes 37K failed attempts with full conversation logs for preference learning and alignment research. By providing this large-scale, medically validated, and fully documented resource, Med-Banana-50K establishes a foundation for training and evaluating the next generation of medical image editing models.Our dataset and code are publicly available at [https://github.com/richardChenzhihui/med-banana-50k].

Method: Developed a coral bleaching classification system using three state-of-the-art models (ResNet, ViT, CNN) on a diverse global dataset with samples from various environments including deep seas, marshes, and coastal zones.

Result: After hyperparameter tuning, the CNN model achieved the highest accuracy of 88%, outperforming existing benchmarks.

Conclusion: The study provides important insights for autonomous coral monitoring and presents comprehensive analysis of widely used computer vision models for coral health assessment.

Abstract: Coral reefs support numerous marine organisms and are an important source of coastal protection from storms and floods, representing a major part of marine ecosystems. However coral reefs face increasing threats from pollution, ocean acidification, and sea temperature anomalies, making efficient protection and monitoring heavily urgent. Therefore, this study presents a novel machine-learning-based coral bleaching classification system based on a diverse global dataset with samples of healthy and bleached corals under varying environmental conditions, including deep seas, marshes, and coastal zones. We benchmarked and compared three state-of-the-art models: Residual Neural Network (ResNet), Vision Transformer (ViT), and Convolutional Neural Network (CNN). After comprehensive hyperparameter tuning, the CNN model achieved the highest accuracy of 88%, outperforming existing benchmarks. Our findings offer important insights into autonomous coral monitoring and present a comprehensive analysis of the most widely used computer vision models.

Method: Two-stage mechanism that integrates unified semantics from dense and sparse texts to identify salient visual patches, plus relevance-aware selection with mean value computation to highlight crucial patch-word correspondences.

Result: Achieves 23%-86% improvement in rSum across diverse model architectures on Flickr30K and MS-COCO datasets, with notable enhancements in text-to-image retrieval.

Conclusion: SEPS effectively addresses patch redundancy and ambiguity in cross-modal alignment, demonstrating superior performance over existing approaches.

Abstract: Fine-grained cross-modal alignment aims to establish precise local correspondences between vision and language, forming a cornerstone for visual question answering and related multimodal applications. Current approaches face challenges in addressing patch redundancy and ambiguity, which arise from the inherent information density disparities across modalities. Recently, Multimodal Large Language Models (MLLMs) have emerged as promising solutions to bridge this gap through their robust semantic generation capabilities. However, the dense textual outputs from MLLMs may introduce conflicts with the original sparse captions. Furthermore, accurately quantifying semantic relevance between rich visual patches and concise textual descriptions remains a core challenge. To overcome these limitations, we introduce the Semantic-Enhanced Patch Slimming (SEPS) framework, which systematically addresses patch redundancy and ambiguity. Our approach employs a two-stage mechanism to integrate unified semantics from both dense and sparse texts, enabling the identification of salient visual patches. Additionally, it leverages relevance-aware selection with mean value computation to highlight crucial patch-word correspondences, thereby improving cross-modal similarity assessment. Comprehensive experiments on Flickr30K and MS-COCO datasets validate that SEPS achieves superior performance, surpassing existing approaches by 23%-86% in rSum across diverse model architectures, with notable enhancements in text-to-image retrieval scenarios. Our implementation is available at https://github.com/Sweet4tars/seps.git.

Method: YOLOv8-based deep learning pipeline tested on curated dataset of 24 fish families under different configurations.

Result: Best model achieved mAP@0.5 of 0.52, with high accuracy for abundant families but weaker detection of rare or complex taxa.

Conclusion: Deep learning shows potential as a scalable complement to traditional reef monitoring methods in the Western Indian Ocean.

Abstract: Coral reef monitoring in the Western Indian Ocean is limited by the labor demands of underwater visual censuses. This work evaluates a YOLOv8-based deep learning pipeline for automating family-level fish identification from video transects collected in Kenya and Tanzania. A curated dataset of 24 families was tested under different configurations, providing the first region-specific benchmark for automated reef fish monitoring in the Western Indian Ocean. The best model achieved mAP@0.5 of 0.52, with high accuracy for abundant families but weaker detection of rare or complex taxa. Results demonstrate the potential of deep learning as a scalable complement to traditional monitoring methods.

Method: Created SurgVeo benchmark and Surgical Plausibility Pyramid (SPP) - a four-tiered framework to assess model outputs from basic appearance to complex surgical strategy. Tested Veo-3 model with zero-shot prediction on surgical clips from laparoscopic and neurosurgical procedures, evaluated by four board-certified surgeons using SPP.

Result: Revealed a distinct “plausibility gap”: Veo-3 achieves exceptional Visual Perceptual Plausibility but fails critically at higher SPP levels including Instrument Operation Plausibility, Environment Feedback Plausibility, and Surgical Intent Plausibility.

Conclusion: This provides first quantitative evidence of the chasm between visually convincing mimicry and causal understanding in surgical AI. SurgVeo and SPP establish crucial foundation for developing future models capable of navigating specialized healthcare domains.

Abstract: Foundation models in video generation are demonstrating remarkable capabilities as potential world models for simulating the physical world. However, their application in high-stakes domains like surgery, which demand deep, specialized causal knowledge rather than general physical rules, remains a critical unexplored gap. To systematically address this challenge, we present SurgVeo, the first expert-curated benchmark for video generation model evaluation in surgery, and the Surgical Plausibility Pyramid (SPP), a novel, four-tiered framework tailored to assess model outputs from basic appearance to complex surgical strategy. On the basis of the SurgVeo benchmark, we task the advanced Veo-3 model with a zero-shot prediction task on surgical clips from laparoscopic and neurosurgical procedures. A panel of four board-certified surgeons evaluates the generated videos according to the SPP. Our results reveal a distinct “plausibility gap”: while Veo-3 achieves exceptional Visual Perceptual Plausibility, it fails critically at higher levels of the SPP, including Instrument Operation Plausibility, Environment Feedback Plausibility, and Surgical Intent Plausibility. This work provides the first quantitative evidence of the chasm between visually convincing mimicry and causal understanding in surgical AI. Our findings from SurgVeo and the SPP establish a crucial foundation and roadmap for developing future models capable of navigating the complexities of specialized, real-world healthcare domains.

Method: Select patches with high mutual information under natural perturbations (color changes, motion) as positive samples for contrastive learning. This replaces traditional human-engineered augmentation with data selection based on mutual information computed from real-world distributions.

Result: The proposed mutual-information-informed data augmentation method was evaluated on multiple benchmarks across state-of-the-art representation learning frameworks and demonstrated effectiveness.

Conclusion: Mutual-information-based data selection is a promising direction for improving representation learning, providing better generalization than human-engineered augmentation methods.

Abstract: Representation learning methods utilizing the InfoNCE loss have demonstrated considerable capacity in reducing human annotation effort by training invariant neural feature extractors. Although different variants of the training objective adhere to the information maximization principle between the data and learned features, data selection and augmentation still rely on human hypotheses or engineering, which may be suboptimal. For instance, data augmentation in contrastive learning primarily focuses on color jittering, aiming to emulate real-world illumination changes. In this work, we investigate the potential of selecting training data based on their mutual information computed from real-world distributions, which, in principle, should endow the learned features with better generalization when applied in open environments. Specifically, we consider patches attached to scenes that exhibit high mutual information under natural perturbations, such as color changes and motion, as positive samples for learning with contrastive loss. We evaluate the proposed mutual-information-informed data augmentation method on several benchmarks across multiple state-of-the-art representation learning frameworks, demonstrating its effectiveness and establishing it as a promising direction for future research.

Method: Benchmarking three FL frameworks using PathMNIST dataset to assess model performance, convergence efficiency, communication overhead, scalability, and developer experience in medical imaging applications.

Result: NVIDIA FLARE offers superior production scalability, Flower provides flexibility for prototyping and academic research, and Owkin Substra demonstrates exceptional privacy and compliance features.

Conclusion: Each FL framework has distinct strengths optimized for different use cases, making them relevant for practical deployment in healthcare environments based on specific requirements.

Abstract: Federated Learning (FL) has emerged as a transformative paradigm in medical AI, enabling collaborative model training across institutions without direct data sharing. This study benchmarks three prominent FL frameworks NVIDIA FLARE, Flower, and Owkin Substra to evaluate their suitability for medical imaging applications in real-world settings. Using the PathMNIST dataset, we assess model performance, convergence efficiency, communication overhead, scalability, and developer experience. Results indicate that NVIDIA FLARE offers superior production scalability, Flower provides flexibility for prototyping and academic research, and Owkin Substra demonstrates exceptional privacy and compliance features. Each framework exhibits strengths optimized for distinct use cases, emphasizing their relevance to practical deployment in healthcare environments.

Method: Proposes TaylorIR with 1x1 patch embeddings for pixel-wise reasoning and TaylorShift attention mechanism based on Taylor series for full token interactions with near-linear complexity.

Result: Achieves state-of-the-art performance across multiple SR benchmarks while reducing memory consumption by up to 60%.

Conclusion: TaylorIR effectively bridges the gap between fine-grained detail restoration and efficient transformer scaling in super-resolution tasks.

Abstract: Transformer-based architectures have recently advanced the image reconstruction quality of super-resolution (SR) models. Yet, their scalability remains limited by quadratic attention costs and coarse patch embeddings that weaken pixel-level fidelity. We propose TaylorIR, a plug-and-play framework that enforces 1x1 patch embeddings for true pixel-wise reasoning and replaces conventional self-attention with TaylorShift, a Taylor-series-based attention mechanism enabling full token interactions with near-linear complexity. Across multiple SR benchmarks, TaylorIR delivers state-of-the-art performance while reducing memory consumption by up to 60%, effectively bridging the gap between fine-grained detail restoration and efficient transformer scaling.

Method: Extensive comparative study of well-known image denoising methods combined with CLAHE (Contrast Limited Adaptive Histogram Equalization). Experiments performed on a rice leaf image dataset using various metrics to comprehensively test enhancement methods.

Result: Results were examined using various metrics to assess the effectiveness of different denoising methods combined with CLAHE for rice leaf image enhancement.

Conclusion: The approach provides a strong basis for assessing the effectiveness of image enhancement methodologies in digital image processing and reveals insights useful for future adaptation in agricultural research and other domains.

Abstract: Digital image processing involves the systematic handling of images using advanced computer algorithms, and has gained significant attention in both academic and practical fields. Image enhancement is a crucial preprocessing stage in the image-processing chain, improving image quality and emphasizing features. This makes subsequent tasks (segmentation, feature extraction, classification) more reliable. Image enhancement is essential for rice leaf analysis, aiding in disease detection, nutrient deficiency evaluation, and growth analysis. Denoising followed by contrast enhancement are the primary steps. Image filters, generally employed for denoising, transform or enhance visual characteristics like brightness, contrast, and sharpness, playing a crucial role in improving overall image quality and enabling the extraction of useful information. This work provides an extensive comparative study of well-known image-denoising methods combined with CLAHE (Contrast Limited Adaptive Histogram Equalization) for efficient denoising of rice leaf images. The experiments were performed on a rice leaf image dataset to ensure the data is relevant and representative. Results were examined using various metrics to comprehensively test enhancement methods. This approach provides a strong basis for assessing the effectiveness of methodologies in digital image processing and reveals insights useful for future adaptation in agricultural research and other domains.

Method: Collected 1,392 first-person videos from blind individuals’ daily lives with 5,311 questions verified by blind participants, each with 3 annotated reference answers to reduce subjectivity.

Result: Evaluation of 16 advanced MLLMs shows all models struggle, with best performers achieving only 60% accuracy compared to human performance of 87.4%. Major limitations in egocentric visual assistance were identified.

Conclusion: EgoBlind serves as a foundation for developing effective AI assistants to enhance blind individuals’ independence, with identified limitations guiding future improvements in MLLMs for visual assistance.

Abstract: We present EgoBlind, the first egocentric VideoQA dataset collected from blind individuals to evaluate the assistive capabilities of contemporary multimodal large language models (MLLMs). EgoBlind comprises 1,392 first-person videos from the daily lives of blind and visually impaired individuals. It also features 5,311 questions directly posed or verified by the blind to reflect their in-situation needs for visual assistance. Each question has an average of 3 manually annotated reference answers to reduce subjectiveness. Using EgoBlind, we comprehensively evaluate 16 advanced MLLMs and find that all models struggle. The best performers achieve an accuracy near 60%, which is far behind human performance of 87.4%. To guide future advancements, we identify and summarize major limitations of existing MLLMs in egocentric visual assistance for the blind and explore heuristic solutions for improvement. With these efforts, we hope that EgoBlind will serve as a foundation for developing effective AI assistants to enhance the independence of the blind and visually impaired. Data and code are available at https://github.com/doc-doc/EgoBlind.

Method: Created ‘InfraLiDARs’ Benchmark’ dataset in CARLA simulation with concurrent infrastructure LiDARs using both scanning paradigms, then conducted statistical analysis and evaluated various 3D object detection algorithms.

Result: Non-repetitive scanning LiDAR and 128-line repetitive LiDAR showed comparable detection performance across scenarios. Non-repetitive LiDAR is cost-effective despite limited perception range.

Conclusion: Provides guidance for optimal LiDAR scanning pattern selection and compatible algorithms for roadside perception systems, and releases the benchmark dataset to support further research.

Abstract: LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal LiDAR placement for infrastructure, the profound impact of differing LiDAR scanning patterns on perceptual performance remains comparatively under-investigated. The inherent nature of various scanning modes - such as traditional repetitive (mechanical/solid-state) versus emerging non-repetitive (e.g. prism-based) systems - leads to distinct point cloud distributions at varying distances, critically dictating the efficacy of object detection and overall environmental understanding. To systematically investigate these differences in infrastructure-based contexts, we introduce the “InfraLiDARs’ Benchmark,” a novel dataset meticulously collected in the CARLA simulation environment using concurrently operating infrastructure-based LiDARs exhibiting both scanning paradigms. Leveraging this benchmark, we conduct a comprehensive statistical analysis of the respective LiDAR scanning abilities and evaluate the impact of these distinct patterns on the performance of various leading 3D object detection algorithms. Our findings reveal that non-repetitive scanning LiDAR and the 128-line repetitive LiDAR were found to exhibit comparable detection performance across various scenarios. Despite non-repetitive LiDAR’s limited perception range, it’s a cost-effective option considering its low price. Ultimately, this study provides insights for setting up roadside perception system with optimal LiDAR scanning patterns and compatible algorithms for diverse roadside applications, and publicly releases the “InfraLiDARs’ Benchmark” dataset to foster further research.

Method: Uses region aware tokens and mask embedding for spatial understanding, reasoning segmentation pipeline for mask generation, and hypergraph-augmented inpainting module for structural and semantic preservation.

Result: Outperforms state-of-the-art methods on Reason-Edit benchmark across segmentation accuracy, instruction adherence, and visual quality preservation metrics.

Conclusion: SmartFreeEdit effectively addresses limitations of local-based image generation and improves global consistency in edited images.

Abstract: Recent advancements in image editing have utilized large-scale multimodal models to enable intuitive, natural instruction-driven interactions. However, conventional methods still face significant challenges, particularly in spatial reasoning, precise region segmentation, and maintaining semantic consistency, especially in complex scenes. To overcome these challenges, we introduce SmartFreeEdit, a novel end-to-end framework that integrates a multimodal large language model (MLLM) with a hypergraph-enhanced inpainting architecture, enabling precise, mask-free image editing guided exclusively by natural language instructions. The key innovations of SmartFreeEdit include:(1)the introduction of region aware tokens and a mask embedding paradigm that enhance the spatial understanding of complex scenes;(2) a reasoning segmentation pipeline designed to optimize the generation of editing masks based on natural language instructions;and (3) a hypergraph-augmented inpainting module that ensures the preservation of both structural integrity and semantic coherence during complex edits, overcoming the limitations of local-based image generation. Extensive experiments on the Reason-Edit benchmark demonstrate that SmartFreeEdit surpasses current state-of-the-art methods across multiple evaluation metrics, including segmentation accuracy, instruction adherence, and visual quality preservation, while addressing the issue of local information focus and improving global consistency in the edited image. Our project will be available at https://github.com/smileformylove/SmartFreeEdit.

Method: Built on flow-based architecture, leverages Cosmos-Reason1 for text grounding and world simulation control, trained on 200M curated video clips with reinforcement learning-based post-training, and includes Cosmos-Transfer2.5 for Sim2Real/Real2Real translation.

Result: Achieves substantial improvements over Cosmos-Predict1 in video quality and instruction alignment, with models at 2B and 14B scales. Cosmos-Transfer2.5 is 3.5x smaller than previous version but delivers higher fidelity and robust long-horizon video generation.

Conclusion: These advances establish Cosmos-Predict2.5 and Cosmos-Transfer2.5 as versatile tools for scaling embodied intelligence, with open-source release to accelerate Physical AI research and deployment.

Abstract: We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI. Built on a flow-based architecture, [Cosmos-Predict2.5] unifies Text2World, Image2World, and Video2World generation in a single model and leverages [Cosmos-Reason1], a Physical AI vision-language model, to provide richer text grounding and finer control of world simulation. Trained on 200M curated video clips and refined with reinforcement learning-based post-training, [Cosmos-Predict2.5] achieves substantial improvements over [Cosmos-Predict1] in video quality and instruction alignment, with models released at 2B and 14B scales. These capabilities enable more reliable synthetic data generation, policy evaluation, and closed-loop simulation for robotics and autonomous systems. We further extend the family with [Cosmos-Transfer2.5], a control-net style framework for Sim2Real and Real2Real world translation. Despite being 3.5$\times$ smaller than [Cosmos-Transfer1], it delivers higher fidelity and robust long-horizon video generation. Together, these advances establish [Cosmos-Predict2.5] and [Cosmos-Transfer2.5] as versatile tools for scaling embodied intelligence. To accelerate research and deployment in Physical AI, we release source code, pretrained checkpoints, and curated benchmarks under the NVIDIA Open Model License at https://github.com/nvidia-cosmos/cosmos-predict2.5 and https://github.com/nvidia-cosmos/cosmos-transfer2.5. We hope these open resources lower the barrier to adoption and foster innovation in building the next generation of embodied intelligence.

Method: Efficient transfer learning method that enables ground components to locally detect weather-related conditions (snow, wet, etc.) from adverse weather events.

Result: Superior performance compared to typical deep learning methods like YOLOv7, YOLOv9, Faster R-CNN, and R-YOLO. Method shows advantage of being generalizable to various scenarios.

Conclusion: The transfer learning approach provides effective and generalizable solution for fine-grained weather condition detection, addressing reliability challenges in satellite Internet deployments.

Abstract: The increasing adoption of satellite Internet with low-Earth-orbit (LEO) satellites in mega-constellations allows ubiquitous connectivity to rural and remote areas. However, weather events have a significant impact on the performance and reliability of satellite Internet. Adverse weather events such as snow and rain can disturb the performance and operations of satellite Internet’s essential ground terminal components, such as satellite antennas, significantly disrupting the space-ground link conditions between LEO satellites and ground stations. This challenge calls for not only region-based weather forecasts but also fine-grained detection capability on ground terminal components of fine-grained weather conditions. Such a capability can assist in fault diagnostics and mitigation for reliable satellite Internet, but its solutions are lacking, not to mention the effectiveness and generalization that are essential in real-world deployments. This paper discusses an efficient transfer learning (TL) method that can enable a ground component to locally detect representative weather-related conditions. The proposed method can detect snow, wet, and other conditions resulting from adverse and typical weather events and shows superior performance compared to the typical deep learning methods, such as YOLOv7, YOLOv9, Faster R-CNN, and R-YOLO. Our TL method also shows the advantage of being generalizable to various scenarios.

Method: Compared two paradigms: post-classification change detection using GFMs (Prithvi-EO-2.0, Clay v1.0) vs U-Net CNNs, and direct change detection using ChangeViT transformer vs U-Net baselines. Used high-resolution multimodal data (RGB, NIR, LiDAR, terrain attributes) covering 4,480 documented changes over 15.3 km².

Result: Clay v1.0 achieved 51% overall accuracy vs U-Net’s 41% for multi-class habitat change; both reached 67% for binary change detection. Direct CD yielded superior IoU (0.53 vs 0.35) for binary but only 28% accuracy for multi-class detection. LiDAR integration improved semantic segmentation from 30% to 50% accuracy.

Conclusion: Although accuracies are lower than in homogeneous landscapes, they reflect realistic performance for complex alpine habitats. GFMs show robustness in cross-temporal evaluation. Future work will integrate object-based post-processing and physical constraints.

Abstract: Rapid climate change and other disturbances in alpine ecosystems demand frequent habitat monitoring, yet manual mapping remains prohibitively expensive for the required temporal resolution. We employ deep learning for change detection using long-term alpine habitat data from Gesaeuse National Park, Austria, addressing a major gap in applying geospatial foundation models (GFMs) to complex natural environments with fuzzy class boundaries and highly imbalanced classes. We compare two paradigms: post-classification change detection (CD) versus direct CD. For post-classification CD, we evaluate GFMs Prithvi-EO-2.0 and Clay v1.0 against U-Net CNNs; for direct CD, we test the transformer ChangeViT against U-Net baselines. Using high-resolution multimodal data (RGB, NIR, LiDAR, terrain attributes) covering 4,480 documented changes over 15.3 km2, results show Clay v1.0 achieves 51% overall accuracy versus U-Net’s 41% for multi-class habitat change, while both reach 67% for binary change detection. Direct CD yields superior IoU (0.53 vs 0.35) for binary but only 28% accuracy for multi-class detection. Cross-temporal evaluation reveals GFM robustness, with Clay maintaining 33% accuracy on 2020 data versus U-Net’s 23%. Integrating LiDAR improves semantic segmentation from 30% to 50% accuracy. Although overall accuracies are lower than in more homogeneous landscapes, they reflect realistic performance for complex alpine habitats. Future work will integrate object-based post-processing and physical constraints to enhance applicability.

Method: Uses DynamicSSM block for feature stabilization, FlexiTrack Instances with trajectory feedback for flexible localization, ExpertTrack Memory with Mixture-of-Experts for appearance consolidation, and adaptive Tracklet Management that switches between end-to-end and tracking-by-detection modes.

Result: Achieves substantial HOTA improvements: +25.5% on JRDB and +43.07% on QuadTrack over original OmniTrack, demonstrating state-of-the-art performance on panoramic MOT benchmarks.

Conclusion: OmniTrack++ effectively addresses panoramic MOT challenges through its feedback-driven framework and adaptive components, with the EmboTrack benchmark providing comprehensive evaluation for real-world panoramic perception.

Abstract: This paper investigates Multi-Object Tracking (MOT) in panoramic imagery, which introduces unique challenges including a 360{\deg} Field of View (FoV), resolution dilution, and severe view-dependent distortions. Conventional MOT methods designed for narrow-FoV pinhole cameras generalize unsatisfactorily under these conditions. To address panoramic distortion, large search space, and identity ambiguity under a 360{\deg} FoV, OmniTrack++ adopts a feedback-driven framework that progressively refines perception with trajectory cues. A DynamicSSM block first stabilizes panoramic features, implicitly alleviating geometric distortion. On top of normalized representations, FlexiTrack Instances use trajectory-informed feedback for flexible localization and reliable short-term association. To ensure long-term robustness, an ExpertTrack Memory consolidates appearance cues via a Mixture-of-Experts design, enabling recovery from fragmented tracks and reducing identity drift. Finally, a Tracklet Management module adaptively switches between end-to-end and tracking-by-detection modes according to scene dynamics, offering a balanced and scalable solution for panoramic MOT. To support rigorous evaluation, we establish the EmboTrack benchmark, a comprehensive dataset for panoramic MOT that includes QuadTrack, captured with a quadruped robot, and BipTrack, collected with a bipedal wheel-legged robot. Together, these datasets span wide-angle environments and diverse motion patterns, providing a challenging testbed for real-world panoramic perception. Extensive experiments on JRDB and EmboTrack demonstrate that OmniTrack++ achieves state-of-the-art performance, yielding substantial HOTA improvements of +25.5% on JRDB and +43.07% on QuadTrack over the original OmniTrack. Datasets and code will be made publicly available at https://github.com/xifen523/OmniTrack.

Method: Formulates cache scheduling as a directed graph with error-weighted edges and introduces Lexicographic Minimax Path Optimization to explicitly bound worst-case path error, improving global content and style consistency.

Result: Achieves 2.9x speedup on Latte model and LPIPS score of 0.05 on Open-Sora, outperforming prior caching techniques with minimal perceptual quality degradation.

Conclusion: LeMiCa provides a robust and generalizable paradigm for accelerating diffusion-based video generation that balances speed and quality, serving as a foundation for future efficient video synthesis research.

Abstract: We present LeMiCa, a training-free and efficient acceleration framework for diffusion-based video generation. While existing caching strategies primarily focus on reducing local heuristic errors, they often overlook the accumulation of global errors, leading to noticeable content degradation between accelerated and original videos. To address this issue, we formulate cache scheduling as a directed graph with error-weighted edges and introduce a Lexicographic Minimax Path Optimization strategy that explicitly bounds the worst-case path error. This approach substantially improves the consistency of global content and style across generated frames. Extensive experiments on multiple text-to-video benchmarks demonstrate that LeMiCa delivers dual improvements in both inference speed and generation quality. Notably, our method achieves a 2.9x speedup on the Latte model and reaches an LPIPS score of 0.05 on Open-Sora, outperforming prior caching techniques. Importantly, these gains come with minimal perceptual quality degradation, making LeMiCa a robust and generalizable paradigm for accelerating diffusion-based video generation. We believe this approach can serve as a strong foundation for future research on efficient and reliable video synthesis. Our code is available at :https://github.com/UnicomAI/LeMiCa

Method: Models instance-level region discovery as detecting compact feature subsets hierarchically from self-supervised ViT outputs, producing a hierarchy of instance regions with uniform-length features.

Result: Empirical studies on three benchmarks show effectiveness on both known and unknown object categories, achieving superior performance on single-instance and multi-instance search, as well as image retrieval tasks.

Conclusion: The hierarchical instance-level descriptor successfully addresses object embedding and occlusion problems while unifying multiple retrieval tasks into a single framework.

Abstract: Despite the great success of the deep features in content-based image retrieval, the visual instance search remains challenging due to the lack of effective instance-level feature representation. Supervised or weakly supervised object detection methods are not the appropriate solutions due to their poor performance on the unknown object categories. In this paper, based on the feature set output from self-supervised ViT, the instance-level region discovery is modeled as detecting the compact feature subsets in a hierarchical fashion. The hierarchical decomposition results in a hierarchy of instance regions. On the one hand, this kind of hierarchical decomposition well addresses the problem of object embedding and occlusions, which are widely observed in real scenarios. On the other hand, the non-leaf nodes and leaf nodes on the hierarchy correspond to the instance regions in different granularities within an image. Therefore, features in uniform length are produced for these instance regions, which may cover across a dominant image region, an integral of multiple instances, or various individual instances. Such a collection of features allows us to unify the image retrieval, multi-instance search, and instance search into one framework. The empirical studies on three benchmarks show that such an instance-level descriptor remains effective on both the known and unknown object categories. Moreover, the superior performance is achieved on single-instance and multi-instance search, as well as image retrieval tasks.

Method: Three-stage framework: 1) Train lightweight residual actors to probe failure regions, 2) Use hybrid rollout scheme for distribution-aligned trajectory collection with recovery behaviors, 3) Distill curated trajectories back into generalist with SFT.

Result: Achieves 99% task success on LIBERO, over 50% gains in SimplerEnv, and 100% success on real-world Franka and YAM arm manipulation tasks.

Conclusion: Residual probing and distribution-aware replay are key to collecting deployment-aligned data that improves both seen and unseen tasks, offering a scalable path toward self-improving VLA models.

Abstract: Supervised fine-tuning (SFT) has become the de facto post-training strategy for large vision-language-action (VLA) models, but its reliance on costly human demonstrations limits scalability and generalization. We propose Probe, Learn, Distill (PLD), a three-stage plug-and-play framework that improves VLAs through residual reinforcement learning (RL) and distribution-aware data collection. In Stage 1, we train lightweight residual actors to probe failure regions of the VLA generalist. In Stage 2, we use a hybrid rollout scheme that aligns collected trajectories with the generalist’s deployment distribution while capturing recovery behaviors. In Stage 3, we distill the curated trajectories back into the generalist with standard SFT. PLD achieves near-saturated 99% task success on LIBERO, over 50% gains in SimplerEnv, and 100% success on real-world Franka and YAM arm manipulation tasks. Ablations show that residual probing and distribution-aware replay are key to collecting deployment-aligned data that improves both seen and unseen tasks, offering a scalable path toward self-improving VLA models.

Method: Proposed SpinalSAM-R1 integrates fine-tuned SAM with DeepSeek-R1, using anatomy-guided attention mechanism and LoRA for efficient adaptation. Includes PyQt5-based interactive software supporting point, box, and text prompts.

Result: Achieves superior segmentation performance on spine anatomical structures, supports 11 clinical operations with 94.3% parsing accuracy and sub-800ms response times.

Conclusion: SpinalSAM-R1 effectively addresses limitations of existing models in spine CT segmentation through multimodal integration and natural language interaction, with released open-source software.

Abstract: The anatomical structure segmentation of the spine and adjacent structures from computed tomography (CT) images is a key step for spinal disease diagnosis and treatment. However, the segmentation of CT images is impeded by low contrast and complex vertebral boundaries. Although advanced models such as the Segment Anything Model (SAM) have shown promise in various segmentation tasks, their performance in spinal CT imaging is limited by high annotation requirements and poor domain adaptability. To address these limitations, we propose SpinalSAM-R1, a multimodal vision-language interactive system that integrates a fine-tuned SAM with DeepSeek-R1, for spine CT image segmentation. Specifically, our SpinalSAM-R1 introduces an anatomy-guided attention mechanism to improve spine segmentation performance, and a semantics-driven interaction protocol powered by DeepSeek-R1, enabling natural language-guided refinement. The SpinalSAM-R1 is fine-tuned using Low-Rank Adaptation (LoRA) for efficient adaptation. We validate our SpinalSAM-R1 on the spine anatomical structure with CT images. Experimental results suggest that our method achieves superior segmentation performance. Meanwhile, we develop a PyQt5-based interactive software, which supports point, box, and text-based prompts. The system supports 11 clinical operations with 94.3% parsing accuracy and sub-800 ms response times. The software is released on https://github.com/6jm233333/spinalsam-r1.

Method: Proposed a filter functionality on CLE video sequences to reduce dataset redundancy for SSL training. Used four state-of-the-art baseline networks and a SSL teacher-student network with vision transformer backbone, evaluated on sinonasal tumor and skin carcinoma datasets.

Result: Filtered SSL-pretrained models achieved highest test accuracy: 67.48% on sinonasal tumor dataset and 73.52% on skin carcinoma dataset, significantly outperforming non-SSL baselines. Training time reduced by 67%.

Conclusion: SSL is effective for CLE pretraining, and the proposed video filter improves training efficiency in self-supervised scenarios while enhancing downstream task performance.

Abstract: Confocal laser endomicroscopy (CLE) is a non-invasive, real-time imaging modality that can be used for in-situ, in-vivo imaging and the microstructural analysis of mucous structures. The diagnosis using CLE is, however, complicated by images being hard to interpret for non-experienced physicians. Utilizing machine learning as an augmentative tool would hence be beneficial, but is complicated by the shortage of histopathology-correlated CLE imaging sequences with respect to the plurality of patterns in this domain, leading to overfitting of machine learning models. To overcome this, self-supervised learning (SSL) can be employed on larger unlabeled datasets. CLE is a video-based modality with high inter-frame correlation, leading to a non-stratified data distribution for SSL training. In this work, we propose a filter functionality on CLE video sequences to reduce the dataset redundancy in SSL training and improve SSL training convergence and training efficiency. We use four state-of-the-art baseline networks and a SSL teacher-student network with a vision transformer small backbone for the evaluation. These networks were evaluated on downstream tasks for a sinonasal tumor dataset and a squamous cell carcinoma of the skin dataset. On both datasets, we found the highest test accuracy on the filtered SSL-pretrained model, with 67.48% and 73.52%, both considerably outperforming their non-SSL baselines. Our results show that SSL is an effective method for CLE pretraining. Further, we show that our proposed CLE video filter can be utilized to improve training efficiency in self-supervised scenarios, resulting in a reduction of 67% in training time.

Method: Partially estimate Concept Sliders formula during inference without training. Introduce two-stage procedure for automatic scale selection and non-linear traversals that detects saturation points and reparameterizes traversal for perceptually uniform edits.

Result: Enables plug-and-play, training-free concept control across modalities (images, video, audio). Improves over existing baselines and establishes tools for principled controllable generation.

Conclusion: FreeSliders provides an effective training-free solution for fine-grained concept control in diffusion models, working across multiple modalities with improved evaluation metrics and automatic scale selection.

Abstract: Diffusion models have become state-of-the-art generative models for images, audio, and video, yet enabling fine-grained controllable generation, i.e., continuously steering specific concepts without disturbing unrelated content, remains challenging. Concept Sliders (CS) offer a promising direction by discovering semantic directions through textual contrasts, but they require per-concept training and architecture-specific fine-tuning (e.g., LoRA), limiting scalability to new modalities. In this work we introduce FreeSliders, a simple yet effective approach that is fully training-free and modality-agnostic, achieved by partially estimating the CS formula during inference. To support modality-agnostic evaluation, we extend the CS benchmark to include both video and audio, establishing the first suite for fine-grained concept generation control with multiple modalities. We further propose three evaluation properties along with new metrics to improve evaluation quality. Finally, we identify an open problem of scale selection and non-linear traversals and introduce a two-stage procedure that automatically detects saturation points and reparameterizes traversal for perceptually uniform, semantically meaningful edits. Extensive experiments demonstrate that our method enables plug-and-play, training-free concept control across modalities, improves over existing baselines, and establishes new tools for principled controllable generation. An interactive presentation of our benchmark and method is available at: https://azencot-group.github.io/FreeSliders/

Method: MOVAI introduces three key components: 1) Compositional Scene Parser (CSP) that decomposes text into hierarchical scene graphs with temporal annotations, 2) Temporal-Spatial Attention Mechanism (TSAM) for coherent motion dynamics and spatial detail preservation, and 3) Progressive Video Refinement (PVR) module for iterative quality enhancement through multi-scale temporal reasoning.

Result: MOVAI achieves state-of-the-art performance with improvements of 15.3% in LPIPS, 12.7% in FVD, and 18.9% in user preference studies compared to existing methods. It excels at generating complex multi-object scenes with realistic temporal dynamics and fine-grained semantic control.

Conclusion: The MOVAI framework successfully addresses key challenges in text-to-video generation by integrating compositional scene understanding with temporal-aware diffusion models, demonstrating superior performance in generating high-fidelity videos with consistent temporal dynamics and fine-grained control.

Abstract: Text to video generation has emerged as a critical frontier in generative artificial intelligence, yet existing approaches struggle with maintaining temporal consistency, compositional understanding, and fine grained control over visual narratives. We present MOVAI (Multimodal Original Video AI), a novel hierarchical framework that integrates compositional scene understanding with temporal aware diffusion models for high fidelity text to video synthesis. Our approach introduces three key innovations: (1) a Compositional Scene Parser (CSP) that decomposes textual descriptions into hierarchical scene graphs with temporal annotations, (2) a Temporal-Spatial Attention Mechanism (TSAM) that ensures coherent motion dynamics across frames while preserving spatial details, and (3) a Progressive Video Refinement (PVR) module that iteratively enhances video quality through multi-scale temporal reasoning. Extensive experiments on standard benchmarks demonstrate that MOVAI achieves state-of-the-art performance, improving video quality metrics by 15.3% in LPIPS, 12.7% in FVD, and 18.9% in user preference studies compared to existing methods. Our framework shows particular strength in generating complex multi-object scenes with realistic temporal dynamics and fine-grained semantic control.

Method: Chain of Time approach generates series of intermediate images during simulation at inference time without additional fine-tuning, applied to 2-D graphics and 3-D videos testing physical properties like velocity, acceleration, fluid dynamics, and momentum conservation.

Result: Substantially improves performance of state-of-the-art image generation model, reveals hidden physical reasoning capabilities including simulation of velocity, gravity, and collisions over time, but also identifies limitations in inferring physical parameters from input images.

Conclusion: Chain of Time method effectively enhances physical simulation in vision-language models and provides interpretability insights into their reasoning capabilities and limitations.

Abstract: We propose a novel cognitively-inspired method to improve and interpret physical simulation in vision-language models. Our ``Chain of Time" method involves generating a series of intermediate images during a simulation, and it is motivated by in-context reasoning in machine learning, as well as mental simulation in humans. Chain of Time is used at inference time, and requires no additional fine-tuning. We apply the Chain-of-Time method to synthetic and real-world domains, including 2-D graphics simulations and natural 3-D videos. These domains test a variety of particular physical properties, including velocity, acceleration, fluid dynamics, and conservation of momentum. We found that using Chain-of-Time simulation substantially improves the performance of a state-of-the-art image generation model. Beyond examining performance, we also analyzed the specific states of the world simulated by an image model at each time step, which sheds light on the dynamics underlying these simulations. This analysis reveals insights that are hidden from traditional evaluations of physical reasoning, including cases where an image generation model is able to simulate physical properties that unfold over time, such as velocity, gravity, and collisions. Our analysis also highlights particular cases where the image generation model struggles to infer particular physical parameters from input images, despite being capable of simulating relevant physical processes.

Method: Framework integrates conditional generative simulator (GANs + VAEs) for realistic action-conditioned US images and DRL module for learning autonomous scanning policies, with expert-validated classification models for image type and quality assessment.

Result: The solution delivers AI-driven guidance, supports conditional generation of realistic US images, establishes reproducible foundation extendable to other organs, and releases public dataset for reproducibility. VAE-GAN benchmarks against existing variants and DRL system evaluated under varying configurations.

Conclusion: The framework enables autonomous and reproducible cardiac US scanning through integrated generative AI and DRL approach, validated through experiments and public dataset release to ensure reproducibility.

Abstract: Cardiac ultrasound (US) is among the most widely used diagnostic tools in cardiology for assessing heart health, but its effectiveness is limited by operator dependence, time constraints, and human error. The shortage of trained professionals, especially in remote areas, further restricts access. These issues underscore the need for automated solutions that can ensure consistent, and accessible cardiac imaging regardless of operator skill or location. Recent progress in artificial intelligence (AI), especially in deep reinforcement learning (DRL), has gained attention for enabling autonomous decision-making. However, existing DRL-based approaches to cardiac US scanning lack reproducibility, rely on proprietary data, and use simplified models. Motivated by these gaps, we present the first end-to-end framework that integrates generative AI and DRL to enable autonomous and reproducible cardiac US scanning. The framework comprises two components: (i) a conditional generative simulator combining Generative Adversarial Networks (GANs) with Variational Autoencoders (VAEs), that models the cardiac US environment producing realistic action-conditioned images; and (ii) a DRL module that leverages this simulator to learn autonomous, accurate scanning policies. The proposed framework delivers AI-driven guidance through expert-validated models that classify image type and assess quality, supports conditional generation of realistic US images, and establishes a reproducible foundation extendable to other organs. To ensure reproducibility, a publicly available dataset of real cardiac US scans is released. The solution is validated through several experiments. The VAE-GAN is benchmarked against existing GAN variants, with performance assessed using qualitative and quantitative approaches, while the DRL-based scanning system is evaluated under varying configurations to demonstrate effectiveness.

Method: Dual-stream architecture with separate encoders: DINOv2 Vision Transformer for RGB images and PointNet++ for 3D point clouds from depth data, followed by feature fusion and multi-task prediction head.

Result: VLM6D achieved new state-of-the-art performance on the challenging Occluded-LineMOD benchmark, demonstrating superior robustness and accuracy.

Conclusion: The integration of complementary visual and geometric features enables robust 6D pose estimation that handles texture variations, lighting changes, and severe occlusions effectively.

Abstract: The primary challenge in computer vision is precisely calculating the pose of 6D objects, however many current approaches are still fragile and have trouble generalizing from synthetic data to real-world situations with fluctuating lighting, textureless objects, and significant occlusions. To address these limitations, VLM6D, a novel dual-stream architecture that leverages the distinct strengths of visual and geometric data from RGB-D input for robust and precise pose estimation. Our framework uniquely integrates two specialized encoders: a powerful, self-supervised Vision Transformer (DINOv2) processes the RGB modality, harnessing its rich, pre-trained understanding of visual grammar to achieve remarkable resilience against texture and lighting variations. Concurrently, a PointNet++ encoder processes the 3D point cloud derived from depth data, enabling robust geometric reasoning that excels even with the sparse, fragmented data typical of severe occlusion. These complementary feature streams are effectively fused to inform a multi task prediction head. We demonstrate through comprehensive experiments that VLM6D obtained new SOTA performance on the challenging Occluded-LineMOD, validating its superior robustness and accuracy.

Method: Integrated ConvNeXt with ViT, used pre-trained models, linear layers, advanced regularization techniques, and adapted attention mechanisms within CNN framework.

Result: Achieved superior performance in mean absolute error (MAE) on MORPH II, CACD, and AFAD datasets, outperforming traditional methods.

Conclusion: Hybrid ConvNeXt-ViT architecture provides robust foundation for future advances in age estimation and demonstrates transformative potential of combining CNNs and transformers.

Abstract: Age estimation from facial images is a complex and multifaceted challenge in computer vision. In this study, we present a novel hybrid architecture that combines ConvNeXt, a state-of-the-art advancement of convolutional neural networks (CNNs), with Vision Transformers (ViT). While each model independently delivers excellent performance on a variety of tasks, their integration leverages the complementary strengths of the CNNs localized feature extraction capabilities and the Transformers global attention mechanisms. Our proposed ConvNeXt-ViT hybrid solution was thoroughly evaluated on benchmark age estimation datasets, including MORPH II, CACD, and AFAD, and achieved superior performance in terms of mean absolute error (MAE). To address computational constraints, we leverage pre-trained models and systematically explore different configurations, using linear layers and advanced regularization techniques to optimize the architecture. Comprehensive ablation studies highlight the critical role of individual components and training strategies, and in particular emphasize the importance of adapted attention mechanisms within the CNN framework to improve the model focus on age-relevant facial features. The results show that the ConvNeXt-ViT hybrid not only outperforms traditional methods, but also provides a robust foundation for future advances in age estimation and related visual tasks. This work underscores the transformative potential of hybrid architectures and represents a promising direction for the seamless integration of CNNs and transformers to address complex computer vision challenges.

Method: Proposes FLoC framework based on facility location function with lazy greedy algorithm to select a compact, representative subset of visual tokens within a predefined budget. The approach is training-free, model-agnostic, and query-agnostic.

Result: Extensive evaluations on Video-MME, MLVU, and LongVideoBench show FLoC consistently surpasses recent compression techniques in effectiveness, robustness, and processing speed.

Conclusion: FLoC provides a versatile, efficient solution for visual token compression in long video understanding that seamlessly integrates with diverse video-LLMs and existing workflows while maintaining performance.

Abstract: Recent studies in long video understanding have harnessed the advanced visual-language reasoning capabilities of Large Multimodal Models (LMMs), driving the evolution of video-LMMs specialized for processing extended video sequences. However, the scalability of these models is severely limited by the overwhelming volume of visual tokens generated from extended video sequences. To address this challenge, this paper proposes FLoC, an efficient visual token compression framework based on the facility location function, a principled approach that swiftly selects a compact yet highly representative and diverse subset of visual tokens within a predefined budget on the number of visual tokens. By integrating the lazy greedy algorithm, our method achieves remarkable efficiency gains by swiftly selecting a compact subset of tokens, drastically reducing the number of visual tokens while guaranteeing near-optimal performance. Notably, our approach is training-free, model-agnostic, and query-agnostic, providing a versatile solution that seamlessly integrates with diverse video-LLMs and existing workflows. Extensive evaluations on large-scale benchmarks, such as Video-MME, MLVU, and LongVideoBench, demonstrate that our framework consistently surpasses recent compression techniques, highlighting not only its effectiveness and robustness in addressing the critical challenges of long video understanding, but also its efficiency in processing speed.

Method: Proposed Confidence-Guided Matting (CGM) with Background Erase Network (BEN) - BEN Base for initial segmentation and BEN Refiner for confidence-based refinement using matting techniques.

Result: Achieves substantial improvements over state-of-the-art methods on DIS5K validation dataset, demonstrating significant enhancement in segmentation quality through matting-based refinement.

Conclusion: Introduces a new paradigm for integrating matting and segmentation techniques, improving fine-grained object boundary prediction in computer vision.

Abstract: Current approaches to dichotomous image segmentation (DIS) treat image matting and object segmentation as fundamentally different tasks. As improvements in image segmentation become increasingly challenging to achieve, combining image matting and grayscale segmentation techniques offers promising new directions for architectural innovation. Inspired by the possibility of aligning these two model tasks, we propose a new architectural approach for DIS called Confidence-Guided Matting (CGM). We created the first CGM model called Background Erase Network (BEN). BEN consists of two components: BEN Base for initial segmentation and BEN Refiner for confidence-based refinement. Our approach achieves substantial improvements over current state-of-the-art methods on the DIS5K validation dataset, demonstrating that matting-based refinement can significantly enhance segmentation quality. This work introduces a new paradigm for integrating matting and segmentation techniques, improving fine-grained object boundary prediction in computer vision.

Method: Applies adaptive per-region Gaussian blur on adversarial noise to adjust the frequency spectrum, making the noise more robust against reversal while maintaining imperceptibility.

Result: Consistently improves worst-case protection performance against various reversal techniques across diverse editing scenarios, while reducing quality degradation in perceptual metrics.

Conclusion: BlurGuard effectively enhances the robustness of image protection methods by making adversarial noise more irreversible, addressing a key limitation of prior approaches.

Abstract: Recent advances in text-to-image models have increased the exposure of powerful image editing techniques as a tool, raising concerns about their potential for malicious use. An emerging line of research to address such threats focuses on implanting “protective” adversarial noise into images before their public release, so future attempts to edit them using text-to-image models can be impeded. However, subsequent works have shown that these adversarial noises are often easily “reversed,” e.g., with techniques as simple as JPEG compression, casting doubt on the practicality of the approach. In this paper, we argue that adversarial noise for image protection should not only be imperceptible, as has been a primary focus of prior work, but also irreversible, viz., it should be difficult to detect as noise provided that the original image is hidden. We propose a surprisingly simple method to enhance the robustness of image protection methods against noise reversal techniques. Specifically, it applies an adaptive per-region Gaussian blur on the noise to adjust the overall frequency spectrum. Through extensive experiments, we show that our method consistently improves the per-sample worst-case protection performance of existing methods against a wide range of reversal techniques on diverse image editing scenarios, while also reducing quality degradation due to noise in terms of perceptual metrics. Code is available at https://github.com/jsu-kim/BlurGuard.

Method: RaceGAN - a Generative Adversarial Network (GAN) baseline model designed to handle racing-specific challenges like blurriness, color inversion, and absence of lane markings.

Result: The proposed RaceGAN model demonstrates superior performance compared to current state-of-the-art machine learning models in track detection.

Conclusion: RoRaTrack dataset and RaceGAN model effectively address racing-specific challenges in track detection, with dataset and code publicly available for research use.

Abstract: A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel dataset that contains annotated multi-camera image data from racing scenarios for track detection. The data is collected on a Dallara AV-21 at a racing circuit in Indiana, in collaboration with the Indy Autonomous Challenge (IAC). RoRaTrack addresses common problems such as blurriness due to high speed, color inversion from the camera, and absence of lane markings on the track. Consequently, we propose RaceGAN, a baseline model based on a Generative Adversarial Network (GAN) that effectively addresses these challenges. The proposed model demonstrates superior performance compared to current state-of-the-art machine learning models in track detection. The dataset and code for this work are available at https://github.com/ghosh64/RaceGAN.

Method: CompAgent uses a planning agent to dynamically select visual tools (object detectors, face analyzers, etc.) and a verification agent that integrates image, tool outputs, and policy context for multi-modal reasoning.

Result: CompAgent achieves up to 76% F1 score and 10% improvement over state-of-the-art on UnsafeBench dataset, outperforming specialized classifiers, direct MLLM prompting, and routing baselines.

Conclusion: Agentic planning and tool-augmented reasoning enable scalable, accurate, and adaptable visual compliance verification.

Abstract: Visual compliance verification is a critical yet underexplored problem in computer vision, especially in domains such as media, entertainment, and advertising where content must adhere to complex and evolving policy rules. Existing methods often rely on task-specific deep learning models trained on manually labeled datasets, which are costly to build and limited in generalizability. While recent multi-modal large language models (MLLMs) offer broad real-world knowledge and policy understanding, they struggle to reason over fine-grained visual details and apply structured compliance rules effectively on their own. In this paper, we propose CompAgent, the first agentic framework for visual compliance verification. CompAgent augments MLLMs with a suite of visual tools - such as object detectors, face analyzers, NSFW detectors, and captioning models - and introduces a planning agent that dynamically selects appropriate tools based on the compliance policy. A verification agent then integrates image, tool outputs, and policy context to perform multi-modal reasoning. Experiments on public benchmarks show that CompAgent outperforms specialized classifiers, direct MLLM prompting, and curated routing baselines, achieving up to 76% F1 score and a 10% improvement over the state-of-the-art on the UnsafeBench dataset. Our results demonstrate the effectiveness of agentic planning and tool-augmented reasoning for scalable, accurate, and adaptable visual compliance verification.

Method: Constructed strong white-box and adaptive attacks against various compression models, with rigorous evaluation across multiple attack scenarios to test robustness.

Result: Compression models producing realistic, high-fidelity reconstructions are substantially more resistant to attacks, while low-realism compression models can be broken. The resistance is not due to gradient masking but rather realistic reconstructions maintaining distributional alignment with natural images.

Conclusion: High realism in reconstructed images presents a significant obstacle for adversarial attacks, and developing techniques to overcome this realism represents an essential challenge for comprehensive security evaluation.

Abstract: Previous work has suggested that preprocessing images through lossy compression can defend against adversarial perturbations, but comprehensive attack evaluations have been lacking. In this paper, we construct strong white-box and adaptive attacks against various compression models and identify a critical challenge for attackers: high realism in reconstructed images significantly increases attack difficulty. Through rigorous evaluation across multiple attack scenarios, we demonstrate that compression models capable of producing realistic, high-fidelity reconstructions are substantially more resistant to our attacks. In contrast, low-realism compression models can be broken. Our analysis reveals that this is not due to gradient masking. Rather, realistic reconstructions maintaining distributional alignment with natural images seem to offer inherent robustness. This work highlights a significant obstacle for future adversarial attacks and suggests that developing more effective techniques to overcome realism represents an essential challenge for comprehensive security evaluation.

Method: Uses multi-agent collaboration with forensic tools (reverse image search, metadata extraction, classifiers, VLM analysis) coordinated by LLM-based agents, plus structured multi-agent debate and memory-augmented reasoning from historical cases.

Result: Achieves 97.05% accuracy on 6,000 images across controlled and real-world scenarios, substantially outperforming traditional classifiers and state-of-the-art VLMs.

Conclusion: Agent-based procedural reasoning offers a new paradigm for more robust, interpretable, and adaptable AI-generated image detection.

Abstract: The rapid evolution of AI-generated images poses unprecedented challenges to information integrity and media authenticity. Existing detection approaches suffer from fundamental limitations: traditional classifiers lack interpretability and fail to generalize across evolving generative models, while vision-language models (VLMs), despite their promise, remain constrained to single-shot analysis and pixel-level reasoning. To address these challenges, we introduce AIFo (Agent-based Image Forensics), a novel training-free framework that emulates human forensic investigation through multi-agent collaboration. Unlike conventional methods, our framework employs a set of forensic tools, including reverse image search, metadata extraction, pre-trained classifiers, and VLM analysis, coordinated by specialized LLM-based agents that collect, synthesize, and reason over cross-source evidence. When evidence is conflicting or insufficient, a structured multi-agent debate mechanism allows agents to exchange arguments and reach a reliable conclusion. Furthermore, we enhance the framework with a memory-augmented reasoning module that learns from historical cases to improve future detection accuracy. Our comprehensive evaluation spans 6,000 images across both controlled laboratory settings and challenging real-world scenarios, including images from modern generative platforms and diverse online sources. AIFo achieves 97.05% accuracy, substantially outperforming traditional classifiers and state-of-the-art VLMs. These results demonstrate that agent-based procedural reasoning offers a new paradigm for more robust, interpretable, and adaptable AI-generated image detection.

Method: EMPL generates multiple prompt embeddings by drawing instances from an energy-based distribution implicitly defined by VLMs. This approach extends the constant modality gap phenomenon to learnable prompts.

Result: Comprehensive experiments justify the superiority of vision-language transfer with multi-prompt augmentation and demonstrate EMPL’s excellence in achieving balance between in-domain and out-of-domain open-vocabulary generalization.

Conclusion: Multi-prompt learning with EMPL is parameter-efficient and rigorously leads to better generalization balance, showing significant potential beyond single-prompt paradigms in vision-language pretraining.

Abstract: The vision community is undergoing the unprecedented progress with the emergence of Vision-Language Pretraining Models (VLMs). Prompt learning plays as the holy grail of accessing VLMs since it enables their fast adaptation to downstream tasks with limited resources. Whereas existing researches milling around single-prompt paradigms, rarely investigate the technical potential behind their multi-prompt learning counterparts. This paper aims to provide a principled retrospect for vision-language multi-prompt learning. We extend the recent constant modality gap phenomenon to learnable prompts and then, justify the superiority of vision-language transfer with multi-prompt augmentation, empirically and theoretically. In terms of this observation, we propose an Energy-based Multi-prompt Learning (EMPL) to generate multiple prompt embeddings by drawing instances from an energy-based distribution, which is implicitly defined by VLMs. So our EMPL is not only parameter-efficient but also rigorously lead to the balance between in-domain and out-of-domain open-vocabulary generalization. Comprehensive experiments have been conducted to justify our claims and the excellence of EMPL.

Method: Dual-encoder network with separate encoding streams for each modality, fusing modality-specific features at intermediate depth via multi-head attention, with dual-source skip connections and per-modality normalization.

Result: Substantial improvements over monolithic concatenation and single-modality baselines, demonstrating effective exploitation of ssQPM’s simultaneous capture of complementary illumination and phase cues.

Conclusion: Modality-specific encoding with learnable fusion effectively exploits ssQPM’s complementary illumination and phase cues for robust cell segmentation, preserving training stability while adding principled multi-modal integration.

Abstract: Cell segmentation in single-shot quantitative phase microscopy (ssQPM) faces challenges from traditional thresholding methods that are sensitive to noise and cell density, while deep learning approaches using simple channel concatenation fail to exploit the complementary nature of polarized intensity images and phase maps. We introduce DM-QPMNet, a dual-encoder network that treats these as distinct modalities with separate encoding streams. Our architecture fuses modality-specific features at intermediate depth via multi-head attention, enabling polarized edge and texture representations to selectively integrate complementary phase information. This content-aware fusion preserves training stability while adding principled multi-modal integration through dual-source skip connections and per-modality normalization at minimal overhead. Our approach demonstrates substantial improvements over monolithic concatenation and single-modality baselines, showing that modality-specific encoding with learnable fusion effectively exploits ssQPM’s simultaneous capture of complementary illumination and phase cues for robust cell segmentation.

Method: Vector-quantized variational autoencoder (VQ-VAE) with Transformer prior for texture restoration via FiLM and concatenation, plus ROI-driven workflow for selective high-resolution reconstruction.

Result: Enables extreme compression (1024x) with flexible decoding options and targeted high-resolution reconstruction from compressed latents.

Conclusion: The framework provides scalable compression for EM data while maintaining reconstruction quality through adaptive decoding strategies.

Abstract: Petascale electron microscopy (EM) datasets push storage, transfer, and downstream analysis toward their current limits. We present a vector-quantized variational autoencoder-based (VQ-VAE) compression framework for EM that spans 16x to 1024x and enables pay-as-you-decode usage: top-only decoding for extreme compression, with an optional Transformer prior that predicts bottom tokens (without changing the compression ratio) to restore texture via feature-wise linear modulation (FiLM) and concatenation; we further introduce an ROI-driven workflow that performs selective high-resolution reconstruction from 1024x-compressed latents only where needed.

Method: Extends geometric variational techniques from Euclidean and spherical geometry to hyperbolic space to compute optimal transport maps efficiently.

Result: The proposed method is validated through experiments on synthetic data and multi-genus surface models, demonstrating its efficacy.

Conclusion: The paper successfully develops an efficient algorithm for optimal transport in hyperbolic space, addressing a gap in existing methods and showing practical applicability in hierarchical data contexts.

Abstract: The optimal transport (OT) problem aims to find the most efficient mapping between two probability distributions under a given cost function, and has diverse applications in many fields such as machine learning, computer vision and computer graphics. However, existing methods for computing optimal transport maps are primarily developed for Euclidean spaces and the sphere. In this paper, we explore the problem of computing the optimal transport map in hyperbolic space, which naturally arises in contexts involving hierarchical data, networks, and multi-genus Riemann surfaces. We propose a novel and efficient algorithm for computing the optimal transport map in hyperbolic space using a geometric variational technique by extending methods for Euclidean and spherical geometry to the hyperbolic setting. We also perform experiments on synthetic data and multi-genus surface models to validate the efficacy of the proposed method.

Method: Combines Motion Score Distilled Interaction (MSDI) with Motion Diffusion Score Distillation Sampling (MSDS) and LLMs to distill score gradients from pre-trained motion models while respecting object and semantic constraints.

Result: Produces natural and physically plausible human motions that respect 3D spatial context, generalizing to out-of-distribution object-aware motions without retraining.

Conclusion: Offers a scalable zero-shot solution for realistic 4D human motion generation by integrating 3D physical priors with motion diffusion and semantic constraints.

Abstract: Recent advances in video diffusion models have enabled the generation of high-quality videos. However, these videos still suffer from unrealistic deformations, semantic violations, and physical inconsistencies that are largely rooted in the absence of 3D physical priors. To address these challenges, we propose an object-aware 4D human motion generation framework grounded in 3D Gaussian representations and motion diffusion priors. With pre-generated 3D humans and objects, our method, Motion Score Distilled Interaction (MSDI), employs the spatial and prompt semantic information in large language models (LLMs) and motion priors through the proposed Motion Diffusion Score Distillation Sampling (MSDS). The combination of MSDS and LLMs enables our spatial-aware motion optimization, which distills score gradients from pre-trained motion diffusion models, to refine human motion while respecting object and semantic constraints. Unlike prior methods requiring joint training on limited interaction datasets, our zero-shot approach avoids retraining and generalizes to out-of-distribution object aware human motions. Experiments demonstrate that our framework produces natural and physically plausible human motions that respect 3D spatial context, offering a scalable solution for realistic 4D generation.

Method: Created L48 dataset - a fine-grained, real-world multi-label dataset from bird sound recordings with natural single-positive annotations and two extended settings with domain priors for additional negative labels.

Result: Benchmarked existing SPML methods on L48 and observed significant performance differences compared to synthetic datasets, revealing method weaknesses.

Conclusion: There’s a need for more realistic and difficult benchmarks in SPML learning, as current synthetic approaches fail to capture real-world fine-grained complexities.

Abstract: In the single-positive multi-label (SPML) setting, each image in a dataset is labeled with the presence of a single class, while the true presence of other classes remains unknown. The challenge is to narrow the performance gap between this partially-labeled setting and fully-supervised learning, which often requires a significant annotation budget. Prior SPML methods were developed and benchmarked on synthetic datasets created by randomly sampling single positive labels from fully-annotated datasets like Pascal VOC, COCO, NUS-WIDE, and CUB200. However, this synthetic approach does not reflect real-world scenarios and fails to capture the fine-grained complexities that can lead to difficult misclassifications. In this work, we introduce the L48 dataset, a fine-grained, real-world multi-label dataset derived from recordings of bird sounds. L48 provides a natural SPML setting with single-positive annotations on a challenging, fine-grained domain, as well as two extended settings in which domain priors give access to additional negative labels. We benchmark existing SPML methods on L48 and observe significant performance differences compared to synthetic datasets and analyze method weaknesses, underscoring the need for more realistic and difficult benchmarks.

Method: 3-stage pipeline: 1) Detection using transformer-based open-vocabulary object detector and vision-language model, 2) Sorting and cropping individual beetles, 3) Morphological segmentation using fine-tuned transformer models trained on 670 manually labeled beetle images.

Result: Developed a specialized pipeline that achieves relatively high accuracy for fine-grained beetle segmentation and can process large-scale beetle data efficiently.

Conclusion: The integrated deep learning pipeline significantly improves efficiency in processing beetle images and accelerates biological research by automating large-scale data analysis.

Abstract: In entomology and ecology research, biologists often need to collect a large number of insects, among which beetles are the most common species. A common practice for biologists to organize beetles is to place them on trays and take a picture of each tray. Given the images of thousands of such trays, it is important to have an automated pipeline to process the large-scale data for further research. Therefore, we develop a 3-stage pipeline to detect all the beetles on each tray, sort and crop the image of each beetle, and do morphological segmentation on the cropped beetles. For detection, we design an iterative process utilizing a transformer-based open-vocabulary object detector and a vision-language model. For segmentation, we manually labeled 670 beetle images and fine-tuned two variants of a transformer-based segmentation model to achieve fine-grained segmentation of beetles with relatively high accuracy. The pipeline integrates multiple deep learning methods and is specialized for beetle image processing, which can greatly improve the efficiency to process large-scale beetle data and accelerate biological research.

Method: Proposed MambaNetLK framework enhances PointNetLK by integrating Mamba State Space Model as cross-modal feature extractor to capture long-range dependencies with linear-time complexity, using Lucas-Kanade algorithm for iterative alignment. Also introduced C3VD-Raycasting-10k dataset with 10,014 geometrically aligned point cloud pairs from clinical CT data.

Result: On C3VD-Raycasting-10k clinical dataset, MambaNetLK achieved best performance: 56.04% reduction in median rotation error and 26.19% reduction in RMSE translation error over second-best method. Also demonstrated strong generalization on ModelNet40 and superior robustness to initial pose perturbations.

Conclusion: MambaNetLK provides robust foundation for 3D registration in surgical navigation, enabling more accurate and reliable guidance systems in minimally invasive procedures through globally expressive SSM-based feature extraction and large-scale clinical dataset.

Abstract: Accurate 3D point cloud registration underpins reliable image-guided colonoscopy, directly affecting lesion localization, margin assessment, and navigation safety. However, biological tissue exhibits repetitive textures and locally homogeneous geometry that cause feature degeneracy, while substantial domain shifts between pre-operative anatomy and intra-operative observations further degrade alignment stability. To address these clinically critical challenges, we introduce a novel 3D registration method tailored for endoscopic navigation and a high-quality, clinically grounded dataset to support rigorous and reproducible benchmarking. We introduce C3VD-Raycasting-10k, a large-scale benchmark dataset with 10,014 geometrically aligned point cloud pairs derived from clinical CT data. We propose MambaNetLK, a novel correspondence-free registration framework, which enhances the PointNetLK architecture by integrating a Mamba State Space Model (SSM) as a cross-modal feature extractor. As a result, the proposed framework efficiently captures long-range dependencies with linear-time complexity. The alignment is achieved iteratively using the Lucas-Kanade algorithm. On the clinical dataset, C3VD-Raycasting-10k, MambaNetLK achieves the best performance compared with the state-of-the-art methods, reducing median rotation error by 56.04% and RMSE translation error by 26.19% over the second-best method. The model also demonstrates strong generalization on ModelNet40 and superior robustness to initial pose perturbations. MambaNetLK provides a robust foundation for 3D registration in surgical navigation. The combination of a globally expressive SSM-based feature extractor and a large-scale clinical dataset enables more accurate and reliable guidance systems in minimally invasive procedures like colonoscopy.

Method: Created a benchmark using soccer, basketball, and volleyball images with balls removed, evaluated four state-of-the-art VLMs with three prompting strategies, and compared against human baselines.

Result: Humans consistently outperformed all models (20-34% accuracy vs ≤17% for models), with models relying on superficial spatial heuristics while humans used social cues like gaze and body pose.

Conclusion: There’s a persistent gap in visual social reasoning between humans and AI, highlighting the need for architectures that explicitly encode structured behavioral cues for human-like inference.

Abstract: Humans excel at visual social inference, the ability to infer hidden elements of a scene from subtle behavioral cues such as other people’s gaze, pose, and orientation. This ability drives everyday social reasoning in humans and is critical for developing more human-like AI agents. We introduce Spot The Ball, a challenging benchmark for evaluating visual social inference in vision-language models (VLMs) using sports as a test domain. The task is to localize a removed sports ball from soccer, basketball, and volleyball images. We present a curated evaluation set with human baselines and a scalable pipeline for generating additional test items. We evaluate four state-of-the-art VLMs (Gemini, GPT, LLaMA, Qwen) using three prompting strategies, finding that humans are consistently two to three times more accurate (20-34%) than models ($\leq$ 17%) across all sports. Our analyses show that models rely on superficial spatial heuristics–such as guessing near the image center or nearby players–while humans leverage social cues like gaze direction and body pose. These findings reveal a persistent human-model gap in visual social reasoning and underscore the need for architectures that explicitly encode structured behavioral cues to achieve robust, human-like inference.

Method: Integrates frozen CLIP vision transformer for feature extraction with lightweight transformer classifier, shares 1% of CLIP-extracted features across clients to mitigate non-IID issues while preserving privacy.

Result: Achieves 86.6% accuracy on agricultural classification tasks, which is more than 4 times higher compared to baseline federated learning approaches.

Conclusion: The combination of vision-language model features with federated learning provides an effective and efficient solution for privacy-preserving and scalable agricultural intelligence.

Abstract: Accurate classification plays a pivotal role in smart agriculture, enabling applications such as crop monitoring, fruit recognition, and pest detection. However, conventional centralized training often requires large-scale data collection, which raises privacy concerns, while standard federated learning struggles with non-independent and identically distributed (non-IID) data and incurs high communication costs. To address these challenges, we propose a federated learning framework that integrates a frozen Contrastive Language-Image Pre-training (CLIP) vision transformer (ViT) with a lightweight transformer classifier. By leveraging the strong feature extraction capability of the pre-trained CLIP ViT, the framework avoids training large-scale models from scratch and restricts federated updates to a compact classifier, thereby reducing transmission overhead significantly. Furthermore, to mitigate performance degradation caused by non-IID data distribution, a small subset (1%) of CLIP-extracted feature representations from all classes is shared across clients. These shared features are non-reversible to raw images, ensuring privacy preservation while aligning class representation across participants. Experimental results on agricultural classification tasks show that the proposed method achieve 86.6% accuracy, which is more than 4 times higher compared to baseline federated learning approaches. This demonstrates the effectiveness and efficiency of combining vision-language model features with federated learning for privacy-preserving and scalable agricultural intelligence.

Method: Uses a conditioning architecture leveraging pre-trained diffusion transformer’s in-context learning ability, and preserves original generative capabilities with Semantic Correspondence Alignment Loss.

Result: Significantly outperforms baselines trained on large multi-view datasets across multi-view consistency, text alignment, identity similarity, and visual quality metrics.

Conclusion: PersonalView provides an efficient solution for multi-view generation with minimal training data, demonstrating strong performance compared to methods requiring extensive training corpora.

Abstract: Recent advances in personalized generative models demonstrate impressive results in creating identity-consistent images of the same person under diverse settings. Yet, we note that most methods cannot control the viewpoint of the generated image, nor generate consistent multiple views of the person. To address this problem, we propose a lightweight adaptation method, PersonalView, capable of enabling an existing model to acquire multi-view generation capability with as few as 100 training samples. PersonalView consists of two key components: First, we design a conditioning architecture to take advantage of the in-context learning ability of the pre-trained diffusion transformer. Second, we preserve the original generative ability of the pretrained model with a new Semantic Correspondence Alignment Loss. We evaluate the multi-view consistency, text alignment, identity similarity, and visual quality of PersonalView and compare it to recent baselines with potential capability of multi-view customization. PersonalView significantly outperforms baselines trained on a large corpus of multi-view data with only 100 training samples.

Method: Created LITHOS dataset with high-resolution RGB patches and expert annotations. Proposed a dual-encoder transformer architecture that integrates both polarization modalities for mineral classification.

Result: The dual-encoder transformer consistently outperforms single-polarization models, demonstrating the value of polarization synergy in mineral classification.

Conclusion: LITHOS provides a comprehensive benchmark for automated petrographic analysis, and the proposed method shows improved performance by leveraging multiple polarization modalities.

Abstract: Petrography is a branch of geology that analyzes the mineralogical composition of rocks from microscopical thin section samples. It is essential for understanding rock properties across geology, archaeology, engineering, mineral exploration, and the oil industry. However, petrography is a labor-intensive task requiring experts to conduct detailed visual examinations of thin section samples through optical polarization microscopes, thus hampering scalability and highlighting the need for automated techniques. To address this challenge, we introduce the Large-scale Imaging and Thin section Optical-polarization Set (LITHOS), the largest and most diverse publicly available experimental framework for automated petrography. LITHOS includes 211,604 high-resolution RGB patches of polarized light and 105,802 expert-annotated grains across 25 mineral categories. Each annotation consists of the mineral class, spatial coordinates, and expert-defined major and minor axes represented as intersecting vector paths, capturing grain geometry and orientation. We evaluate multiple deep learning techniques for mineral classification in LITHOS and propose a dual-encoder transformer architecture that integrates both polarization modalities as a strong baseline for future reference. Our method consistently outperforms single-polarization models, demonstrating the value of polarization synergy in mineral classification. We have made the LITHOS Benchmark publicly available, comprising our dataset, code, and pretrained models, to foster reproducibility and further research in automated petrographic analysis.

Method: Evaluated 11 lightweight vision models (2.5M parameters) trained under fixed 100-epoch schedule across 7 diverse datasets, introduced Cross-Dataset Score (xScore) metric to quantify performance consistency and robustness.

Result: ImageNet accuracy doesn’t reliably predict performance on fine-grained or medical datasets; xScore provides scalable predictor of mobile model performance; isotropic convolutions with higher spatial resolution and channel-wise attention promote generalization, while Transformer blocks offer little benefit despite higher parameter cost.

Conclusion: Provides reproducible framework for evaluating lightweight vision models beyond ImageNet, highlights key design principles for mobile-friendly architectures, and guides development of models that generalize robustly across diverse domains.

Abstract: Lightweight vision classification models such as MobileNet, ShuffleNet, and EfficientNet are increasingly deployed in mobile and embedded systems, yet their performance has been predominantly benchmarked on ImageNet. This raises critical questions: Do models that excel on ImageNet also generalize across other domains? How can cross-dataset robustness be systematically quantified? And which architectural elements consistently drive generalization under tight resource constraints? Here, we present the first systematic evaluation of 11 lightweight vision models (2.5M parameters), trained under a fixed 100-epoch schedule across 7 diverse datasets. We introduce the Cross-Dataset Score (xScore), a unified metric that quantifies the consistency and robustness of model performance across diverse visual domains. Our results show that (1) ImageNet accuracy does not reliably predict performance on fine-grained or medical datasets, (2) xScore provides a scalable predictor of mobile model performance that can be estimated from just four datasets, and (3) certain architectural components–such as isotropic convolutions with higher spatial resolution and channel-wise attention–promote broader generalization, while Transformer-based blocks yield little additional benefit, despite incurring higher parameter overhead. This study provides a reproducible framework for evaluating lightweight vision models beyond ImageNet, highlights key design principles for mobile-friendly architectures, and guides the development of future models that generalize robustly across diverse application domains.

Method: Integrates Deep Operator Networks (DeepONet) with Neural Tangent Kernel (NTK), incorporating physics-informed constraints and task-specific regularization into the loss function to ensure physical consistency.

Result: Validated on diverse synthetic and real datasets, demonstrating robustness, scalability, and precision in solving inverse problems.

Conclusion: The framework shows broad potential applications in computational physics and imaging sciences for solving complex inverse problems with physical constraints.

Abstract: This work presents a novel hybrid approach that integrates Deep Operator Networks (DeepONet) with the Neural Tangent Kernel (NTK) to solve complex inverse problem. The method effectively addresses tasks such as source localization governed by the Navier-Stokes equations and image reconstruction, overcoming challenges related to nonlinearity, sparsity, and noisy data. By incorporating physics-informed constraints and task-specific regularization into the loss function, the framework ensures solutions that are both physically consistent and accurate. Validation on diverse synthetic and real datasets demonstrates its robustness, scalability, and precision, showcasing its broad potential applications in computational physics and imaging sciences.

Method: Proposes FedDISC framework integrating federated learning with diffusion models. Uses modality-specific diffusion models trained on clients and broadcast to clients missing modalities. Includes DISC-Diffusion module with Dialogue Graph Network and Semantic Conditioning Network for consistency. Implements Alternating Frozen Aggregation strategy.

Result: Extensive experiments on IEMOCAP, CMUMOSI, and CMUMOSEI datasets show superior emotion classification performance across diverse missing modality patterns, outperforming existing approaches.

Conclusion: FedDISC effectively addresses modality absence in multimodal emotion recognition through federated learning and semantic-consistent diffusion, achieving robust performance in real-world scenarios.

Abstract: Multimodal Emotion Recognition in Conversations (MERC) enhances emotional understanding through the fusion of multimodal signals. However, unpredictable modality absence in real-world scenarios significantly degrades the performance of existing methods. Conventional missing-modality recovery approaches, which depend on training with complete multimodal data, often suffer from semantic distortion under extreme data distributions, such as fixed-modality absence. To address this, we propose the Federated Dialogue-guided and Semantic-Consistent Diffusion (FedDISC) framework, pioneering the integration of federated learning into missing-modality recovery. By federated aggregation of modality-specific diffusion models trained on clients and broadcasting them to clients missing corresponding modalities, FedDISC overcomes single-client reliance on modality completeness. Additionally, the DISC-Diffusion module ensures consistency in context, speaker identity, and semantics between recovered and available modalities, using a Dialogue Graph Network to capture conversational dependencies and a Semantic Conditioning Network to enforce semantic alignment. We further introduce a novel Alternating Frozen Aggregation strategy, which cyclically freezes recovery and classifier modules to facilitate collaborative optimization. Extensive experiments on the IEMOCAP, CMUMOSI, and CMUMOSEI datasets demonstrate that FedDISC achieves superior emotion classification performance across diverse missing modality patterns, outperforming existing approaches.

Method: OSMGen uses raw OpenStreetMap JSON data including vector geometries, semantic tags, location, and time to generate satellite imagery. It enables user edits to OSM inputs to create targeted visual changes while preserving the rest of the scene.

Result: The framework produces realistic satellite imagery directly from OSM data and can generate consistent before-after image pairs. It addresses data scarcity and class imbalance for training, and allows planners to preview proposed interventions.

Conclusion: OSMGen creates paired (JSON, image) data for both static and changed states, paving the way toward a closed-loop system where satellite imagery can automatically drive structured OSM updates.

Abstract: Accurate and up-to-date geospatial data are essential for urban planning, infrastructure monitoring, and environmental management. Yet, automating urban monitoring remains difficult because curated datasets of specific urban features and their changes are scarce. We introduce OSMGen, a generative framework that creates realistic satellite imagery directly from raw OpenStreetMap (OSM) data. Unlike prior work that relies on raster tiles, OSMGen uses the full richness of OSM JSON, including vector geometries, semantic tags, location, and time, giving fine-grained control over how scenes are generated. A central feature of the framework is the ability to produce consistent before-after image pairs: user edits to OSM inputs translate into targeted visual changes, while the rest of the scene is preserved. This makes it possible to generate training data that addresses scarcity and class imbalance, and to give planners a simple way to preview proposed interventions by editing map data. More broadly, OSMGen produces paired (JSON, image) data for both static and changed states, paving the way toward a closed-loop system where satellite imagery can automatically drive structured OSM updates. Source code is available at https://github.com/amir-zsh/OSMGen.

Method: Analyzes how reconstruction metrics (LPIPS, SSIM, PSNR) evolve across varying noise strengths to extract interpretable manifold-based features that differentiate real and synthetic images.

Result: Achieves 0.993 AUROC under cross-validation on a balanced dataset of 4,000 images, remaining robust to common distortions like compression and noise.

Conclusion: The method demonstrates strong generalization and interpretability despite limited data and a single diffusion backbone, offering a foundation for scalable, model-agnostic synthetic media forensics.

Abstract: The rapid rise of generative diffusion models has made distinguishing authentic visual content from synthetic imagery increasingly challenging. Traditional deepfake detection methods, which rely on frequency or pixel-level artifacts, fail against modern text-to-image systems such as Stable Diffusion and DALL-E that produce photorealistic and artifact-free results. This paper introduces a diffusion-based forensic framework that leverages multi-strength image reconstruction dynamics, termed diffusion snap-back, to identify AI-generated images. By analysing how reconstruction metrics (LPIPS, SSIM, and PSNR) evolve across varying noise strengths, we extract interpretable manifold-based features that differentiate real and synthetic images. Evaluated on a balanced dataset of 4,000 images, our approach achieves 0.993 AUROC under cross-validation and remains robust to common distortions such as compression and noise. Despite using limited data and a single diffusion backbone (Stable Diffusion v1.5), the proposed method demonstrates strong generalization and interpretability, offering a foundation for scalable, model-agnostic synthetic media forensics.

Method: Used transfer learning with UNet architecture and lightweight MobileNet encoder, pretrained on Landsat-7 Cloud Cover Assessment Dataset and fine-tuned with mission-specific samples in joint-training setup.

Result: Improved macro F1 from 0.850 to 0.877 over FOREST-2-only baselines, with full-image inference in under 5 seconds on NVIDIA Jetson Nano using TensorRT engine.

Conclusion: Public datasets and lightweight architectures can enable accurate, efficient thermal-only cloud masking on-orbit, supporting real-time decision-making in data-limited Earth observation missions.

Abstract: Onboard cloud segmentation is a critical yet underexplored task in thermal Earth observation (EO), particularly for CubeSat missions constrained by limited hardware and spectral information. CubeSats often rely on a single thermal band and lack sufficient labeled data, making conventional cloud masking techniques infeasible. This work addresses these challenges by applying transfer learning to thermal cloud segmentation for the FOREST-2 CubeSat, using a UNet with a lightweight MobileNet encoder. We pretrain the model on the public Landsat-7 Cloud Cover Assessment Dataset and fine-tune it with a small set of mission-specific samples in a joint-training setup, improving the macro F1 from 0.850 to 0.877 over FOREST-2-only baselines. We convert the model to a TensorRT engine and demonstrate full-image inference in under 5 seconds on an NVIDIA Jetson Nano. These results show that leveraging public datasets and lightweight architectures can enable accurate, efficient thermal-only cloud masking on-orbit, supporting real-time decision-making in data-limited EO missions.

Method: Two-stage pipeline: multimodal visual reasoning with Gemini 2.5 Flash Image for structure-texture synthesis, and neural image-to-3D generation through Hexagen for geometry recovery using Google Street View imagery.

Result: Produces photorealistic, metrically coherent 3D reconstructions in seconds with significant speedups compared to conventional methods, while preserving visual and structural fidelity.

Conclusion: The framework democratizes cultural preservation by turning open imagery into digital heritage, making it a community-driven, AI-assisted solution for resource-limited nations.

Abstract: Cultural heritage restoration in Bangladesh faces a dual challenge of limited resources and scarce technical expertise. Traditional 3D digitization methods, such as photogrammetry or LiDAR scanning, require expensive hardware, expert operators, and extensive on-site access, which are often infeasible in developing contexts. As a result, many of Bangladesh’s architectural treasures, from the Paharpur Buddhist Monastery to Ahsan Manzil, remain vulnerable to decay and inaccessible in digital form. This paper introduces Oitijjo-3D, a cost-free generative AI framework that democratizes 3D cultural preservation. By using publicly available Google Street View imagery, Oitijjo-3D reconstructs faithful 3D models of heritage structures through a two-stage pipeline - multimodal visual reasoning with Gemini 2.5 Flash Image for structure-texture synthesis, and neural image-to-3D generation through Hexagen for geometry recovery. The system produces photorealistic, metrically coherent reconstructions in seconds, achieving significant speedups compared to conventional Structure-from-Motion pipelines, without requiring any specialized hardware or expert supervision. Experiments on landmarks such as Ahsan Manzil, Choto Sona Mosque, and Paharpur demonstrate that Oitijjo-3D preserves both visual and structural fidelity while drastically lowering economic and technical barriers. By turning open imagery into digital heritage, this work reframes preservation as a community-driven, AI-assisted act of cultural continuity for resource-limited nations.

Method: Proposed a reinforcement learning model that scans videos once to find moment boundaries with locational evidence, and a multi-agent framework using evidential learning to resolve conflicts between agents’ outputs.

Result: Extensive experiments on benchmark datasets show effectiveness compared to state-of-the-art approaches, with the ability to detect out-of-scope queries without additional training.

Conclusion: Modeling competition and conflict in multi-agent systems effectively improves RL performance in moment retrieval, and evidential learning plays a new role in multi-agent frameworks.

Abstract: Video moment retrieval uses a text query to locate a moment from a given untrimmed video reference. Locating corresponding video moments with text queries helps people interact with videos efficiently. Current solutions for this task have not considered conflict within location results from different models, so various models cannot integrate correctly to produce better results. This study introduces a reinforcement learning-based video moment retrieval model that can scan the whole video once to find the moment’s boundary while producing its locational evidence. Moreover, we proposed a multi-agent system framework that can use evidential learning to resolve conflicts between agents’ localization output. As a side product of observing and dealing with conflicts between agents, we can decide whether a query has no corresponding moment in a video (out-of-scope) without additional training, which is suitable for real-world applications. Extensive experiments on benchmark datasets show the effectiveness of our proposed methods compared with state-of-the-art approaches. Furthermore, the results of our study reveal that modeling competition and conflict of the multi-agent system is an effective way to improve RL performance in moment retrieval and show the new role of evidential learning in the multi-agent framework.

Method: Uses a camera system to capture medical images from displays, then processes them through an automated pipeline that detects, restores, and analyzes on-screen medical images to transform camera-captured data into diagnostic-quality images suitable for automated analysis and report generation.

Result: Achieves diagnostic performance comparable to conventional CAD systems with F1-score degradation typically less than 2% across classification tasks, and natural language generation metrics for automated reports remain within 1% of those derived from original images.

Conclusion: VisionCAD offers an accessible approach for AI-assisted diagnosis that can be deployed in diverse clinical settings using only a camera device and standard computing resources, without requiring modifications to existing infrastructure.

Abstract: Widespread clinical deployment of computer-aided diagnosis (CAD) systems is hindered by the challenge of integrating with existing hospital IT infrastructure. Here, we introduce VisionCAD, a vision-based radiological assistance framework that circumvents this barrier by capturing medical images directly from displays using a camera system. The framework operates through an automated pipeline that detects, restores, and analyzes on-screen medical images, transforming camera-captured visual data into diagnostic-quality images suitable for automated analysis and report generation. We validated VisionCAD across diverse medical imaging datasets, demonstrating that our modular architecture can flexibly utilize state-of-the-art diagnostic models for specific tasks. The system achieves diagnostic performance comparable to conventional CAD systems operating on original digital images, with an F1-score degradation typically less than 2% across classification tasks, while natural language generation metrics for automated reports remain within 1% of those derived from original images. By requiring only a camera device and standard computing resources, VisionCAD offers an accessible approach for AI-assisted diagnosis, enabling the deployment of diagnostic capabilities in diverse clinical settings without modifications to existing infrastructure.

Method: Created FERBench benchmark with 20 state-of-the-art MLLMs across 4 FER datasets, then developed post-training strategies using two curated datasets (UniFER-CoT-230K for cold-start initialization and UniFER-RLVR-360K for RLVR) to build UniFER-7B foundation model.

Result: MLLMs show good classification performance but significant limitations in reasoning and interpretability. UniFER-7B outperforms many open-sourced and closed-source generalist MLLMs including Gemini-2.5-Pro and Qwen2.5-VL-72B.

Conclusion: Post-training strategies effectively enhance MLLMs’ facial expression reasoning capabilities, and the unified UniFER-7B model demonstrates superior performance in FER tasks compared to existing generalist MLLMs.

Abstract: Multimodal Large Language Models (MLLMs) have revolutionized numerous research fields, including computer vision and affective computing. As a pivotal challenge in this interdisciplinary domain, facial expression recognition (FER) has evolved from separate, domain-specific models to more unified approaches. One promising avenue to unify FER tasks is converting conventional FER datasets into visual question-answering (VQA) formats, enabling the direct application of powerful generalist MLLMs for inference. However, despite the success of cutting-edge MLLMs in various tasks, their performance on FER tasks remains largely unexplored. To address this gap, we provide FERBench, a systematic benchmark that incorporates 20 state-of-the-art MLLMs across four widely used FER datasets. Our results reveal that, while MLLMs exhibit good classification performance, they still face significant limitations in reasoning and interpretability. To this end, we introduce post-training strategies aimed at enhancing the facial expression reasoning capabilities of MLLMs. Specifically, we curate two high-quality and large-scale datasets: UniFER-CoT-230K for cold-start initialization and UniFER-RLVR-360K for reinforcement learning with verifiable rewards (RLVR), respectively. Building upon them, we develop a unified and interpretable FER foundation model termed UniFER-7B, which outperforms many open-sourced and closed-source generalist MLLMs (e.g., Gemini-2.5-Pro and Qwen2.5-VL-72B).

Method: Two-stage training: 1) Supervised Finetuning with 1.6M image-code pairs for direct code generation and visual-based code refinement, 2) Visual Reinforcement Learning (ViRL) with coarse-to-fine reward mechanism calculating visual similarity across local and global image patches.

Result: VinciCoder achieves state-of-the-art performance on various multimodal code generation benchmarks, demonstrating the effectiveness of the coarse-to-fine ViRL strategy.

Conclusion: The proposed VinciCoder model with its two-stage training framework and visual reinforcement learning strategy successfully addresses the limitations of single-task training and advances multimodal code generation capabilities.

Abstract: Multimodal code generation has garnered significant interest within the research community. Despite the notable success of recent vision-language models (VLMs) on specialized tasks like Chart-to-code generation, their reliance on single-task training regimens fosters a narrow paradigm that hinders the development of generalized \textbf{VI}sio\textbf{N} \textbf{C}ode \textbf{I}ntelligence. In this work, we introduce \textbf{VinciCoder}, a unified multimodal code generation model that addresses this limitation via a two-stage training framework. We begin by constructing a large-scale Supervised Finetuning (SFT) corpus comprising 1.6M image-code pairs for tasks involving direct code generation and visual-based code refinement. Subsequently, we introduce a Visual Reinforcement Learning (ViRL) strategy, which employs a coarse-to-fine reward mechanism to improve visual fidelity by calculating visual similarity across local and global image patches. Extensive experiments on various multimodal code generation benchmarks demonstrate that VinciCoder achieves state-of-the-art performance, underscoring the effectiveness of our coarse-to-fine ViRL strategy. The code and model will be available at https://github.com/DocTron-hub/VinciCoder.

Method: Uses Chain-of-Thought reasoning in VLMs with two-stage training: Supervised Fine-Tuning and Reinforcement Learning. Introduces Confidence-Guided Policy Optimization (CGPO) for RL and “output-to-reasoning” strategy for SFT data construction.

Result: Matches or outperforms specialized SOTA methods and strong closed-source VLMs across all tasks, achieving S-measure of 0.899 on CoCA for CoSOD (8.0 percentage points improvement over prior best) with less training data.

Conclusion: The proposed unified framework successfully bridges task heterogeneity through CoT reasoning and achieves superior performance across multiple saliency tasks with efficient training.

Abstract: We present the first unified framework that jointly handles three operationally heterogeneous saliency tasks, eg, SOD, CoSOD, and SIS, by casting each as a Chain-of-Thought (CoT) reasoning process in a Vision-Language Model (VLM) to bridge task heterogeneity. CoT training follows a two-stage paradigm: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). To enhance CoT quality in RL, we propose Confidence-Guided Policy Optimization (CGPO), a lightweight single-sample algorithm that leverages the discrepancy between reward and model confidence as a per-sample advantage signal. This design naturally focuses updates on informative responses while eliminating group sampling, thereby addressing GRPO’s key limitations: confidence-agnostic learning, signal dilution, and prohibitive computational overhead. We also introduce an “output-to-reasoning” strategy to construct high-fidelity SFT data that ensures logical consistency with ground-truth masks. Experiments show our model matches or outperforms specialized SOTA methods and strong closed-source VLMs across all tasks, especially achieving an S-measure of 0.899 on CoCA for CoSOD, surpassing the prior best by 8.0 percentage points, despite using far less training data.

Method: LGCA captures local features, repeatedly selects the most salient regions and expands them, then computes similarity scores incorporating both original and expanded images to capture local and global features.

Result: Extensive experiments show LGCA substantially improves zero-shot performance across diverse datasets, outperforming state-of-the-art baselines.

Conclusion: LGCA effectively minimizes misinformation while maintaining efficiency and scalability, providing a robust solution for enhanced zero-shot image classification.

Abstract: Recent advancements in large-scale pretraining in natural language processing have enabled pretrained vision-language models such as CLIP to effectively align images and text, significantly improving performance in zero-shot image classification tasks. Subsequent studies have further demonstrated that cropping images into smaller regions and using large language models to generate multiple descriptions for each caption can further enhance model performance. However, due to the inherent sensitivity of CLIP, random image crops can introduce misinformation and bias, as many images share similar features at small scales. To address this issue, we propose Localized-Globalized Cross-Alignment (LGCA), a framework that first captures the local features of an image and then repeatedly selects the most salient regions and expands them. The similarity score is designed to incorporate both the original and expanded images, enabling the model to capture both local and global features while minimizing misinformation. Additionally, we provide a theoretical analysis demonstrating that the time complexity of LGCA remains the same as that of the original model prior to the repeated expansion process, highlighting its efficiency and scalability. Extensive experiments demonstrate that our method substantially improves zero-shot performance across diverse datasets, outperforming state-of-the-art baselines.

Method: Proposes ITEM detector that measures image-text misalignment in pre-trained CLIP space using a hierarchical scheme - global image alignment and fine-grained object-level alignment - then tunes an MLP head for detection.

Result: Extensive experiments show superior performance against state-of-the-art competitors with impressive generalization and robustness across various recent generative models.

Conclusion: Leveraging multi-modal image-text misalignment provides more effective and generalizable fake image detection compared to visual-only approaches.

Abstract: With the rapid development of generative models, detecting generated fake images to prevent their malicious use has become a critical issue recently. Existing methods frame this challenge as a naive binary image classification task. However, such methods focus only on visual clues, yielding trained detectors susceptible to overfitting specific image patterns and incapable of generalizing to unseen models. In this paper, we address this issue from a multi-modal perspective and find that fake images cannot be properly aligned with corresponding captions compared to real images. Upon this observation, we propose a simple yet effective detector termed ITEM by leveraging the image-text misalignment in a joint visual-language space as discriminative clues. Specifically, we first measure the misalignment of the images and captions in pre-trained CLIP’s space, and then tune a MLP head to perform the usual detection task. Furthermore, we propose a hierarchical misalignment scheme that first focuses on the whole image and then each semantic object described in the caption, which can explore both global and fine-grained local semantic misalignment as clues. Extensive experiments demonstrate the superiority of our method against other state-of-the-art competitors with impressive generalization and robustness on various recent generative models.

Method: Introduces a frequency-selective function as a weighted filter to Fourier spectrum, suppressing less discriminative bands while enhancing more informative ones based on progressive frequency differences observed between natural and diffusion-generated images.

Result: Extensive experiments show the method outperforms state-of-the-art detectors with superior generalization to unseen diffusion models and robust resilience to various perturbations across multiple datasets.

Conclusion: The frequency-based approach (F^2C) provides an effective solution for detecting diffusion-generated images by leveraging frequency domain characteristics, demonstrating strong generalization and robustness capabilities.

Abstract: Diffusion models have achieved remarkable success in image synthesis, but the generated high-quality images raise concerns about potential malicious use. Existing detectors often struggle to capture discriminative clues across different models and settings, limiting their generalization to unseen diffusion models and robustness to various perturbations. To address this issue, we observe that diffusion-generated images exhibit progressively larger differences from natural real images across low- to high-frequency bands. Based on this insight, we propose a simple yet effective representation by enhancing the Frequency Forgery Clue (F^2C) across all frequency bands. Specifically, we introduce a frequency-selective function which serves as a weighted filter to the Fourier spectrum, suppressing less discriminative bands while enhancing more informative ones. This approach, grounded in a comprehensive analysis of frequency-based differences between natural real and diffusion-generated images, enables general detection of images from unseen diffusion models and provides robust resilience to various perturbations. Extensive experiments on various diffusion-generated image datasets demonstrate that our method outperforms state-of-the-art detectors with superior generalization and robustness.

Method: The framework uses iterative background-aware selection to find texts aligned with target classes, and background-driven augmentation to generate diverse poisoned samples while maintaining semantic coherence.

Result: Achieves up to 95.83% poisoning success rate and 98.68% backdoor Hit@1 on classification and retrieval tasks, successfully bypassing RoCLIP, CleanCLIP and SafeCLIP defenses.

Conclusion: ToxicTextCLIP demonstrates significant vulnerabilities in CLIP’s text modality, highlighting the need for more robust defense mechanisms against text-based poisoning attacks.

Abstract: The Contrastive Language-Image Pretraining (CLIP) model has significantly advanced vision-language modeling by aligning image-text pairs from large-scale web data through self-supervised contrastive learning. Yet, its reliance on uncurated Internet-sourced data exposes it to data poisoning and backdoor risks. While existing studies primarily investigate image-based attacks, the text modality, which is equally central to CLIP’s training, remains underexplored. In this work, we introduce ToxicTextCLIP, a framework for generating high-quality adversarial texts that target CLIP during the pre-training phase. The framework addresses two key challenges: semantic misalignment caused by background inconsistency with the target class, and the scarcity of background-consistent texts. To this end, ToxicTextCLIP iteratively applies: 1) a background-aware selector that prioritizes texts with background content aligned to the target class, and 2) a background-driven augmenter that generates semantically coherent and diverse poisoned samples. Extensive experiments on classification and retrieval tasks show that ToxicTextCLIP achieves up to 95.83% poisoning success and 98.68% backdoor Hit@1, while bypassing RoCLIP, CleanCLIP and SafeCLIP defenses. The source code can be accessed via https://github.com/xinyaocse/ToxicTextCLIP/.

Method: Uses image-level labels with Grad-CAM explanations to generate pneumonia localization heatmaps. Evaluates seven ImageNet-pretrained architectures (ResNet-18/50, DenseNet-121, EfficientNet-B0, MobileNet-V2/V3, ViT-B16) under identical training conditions with focal loss and patient-wise splits to prevent data leakage.

Result: ResNet-18 and EfficientNet-B0 achieve best overall test accuracy of 98%, ROC-AUC = 0.997, and F1 = 0.987. MobileNet-V2 provides optimal trade-off between accuracy and computational cost. Grad-CAM visualizations confirm models focus on clinically relevant lung regions.

Conclusion: The proposed weakly supervised explainable models enhance pneumonia screening transparency and clinical trust in AI-assisted medical imaging, demonstrating the potential of interpretable AI for radiological diagnostics.

Abstract: This study proposes a weakly supervised deep learning framework for pneumonia classification and localization from chest X-rays, utilizing Grad-CAM explanations. Instead of costly pixel-level annotations, our approach utilizes image-level labels to generate clinically meaningful heatmaps that highlight regions affected by pneumonia. We evaluate seven ImageNet-pretrained architectures ResNet-18/50, DenseNet-121, EfficientNet-B0, MobileNet-V2/V3, and ViT-B16 under identical training conditions with focal loss and patient-wise splits to prevent data leakage. Experimental results on the Kermany CXR dataset demonstrate that ResNet-18 and EfficientNet-B0 achieve the best overall test accuracy of 98%, ROC-AUC = 0.997, and F1 = 0.987, while MobileNet-V2 provides an optimal trade-off between accuracy and computational cost. Grad-CAM visualizations confirm that the proposed models focus on clinically relevant lung regions, supporting the use of interpretable AI for radiological diagnostics. This work highlights the potential of weakly supervised explainable models that enhance pneumonia screening transparency, and clinical trust in AI-assisted medical imaging. https://github.com/kiranshahi/pneumonia-analysis

Method: Integrates human geometric priors in reconstruction and self-supervised semantic priors in segmentation. Uses pixel-aligned aggregation for cross-task synergy and multi-task objective for simultaneous texture modeling and semantic consistency optimization. Includes interactive annotation for generating data-label pairs.

Result: Extensive experiments show HumanCrafter surpasses state-of-the-art methods in both 3D human-part segmentation and 3D human reconstruction from single images.

Conclusion: HumanCrafter provides an effective unified framework that addresses the limitations of current methods by enabling joint modeling of appearance and semantics while overcoming dataset scarcity through interactive annotation.

Abstract: Recent advances in generative models have achieved high-fidelity in 3D human reconstruction, yet their utility for specific tasks (e.g., human 3D segmentation) remains constrained. We propose HumanCrafter, a unified framework that enables the joint modeling of appearance and human-part semantics from a single image in a feed-forward manner. Specifically, we integrate human geometric priors in the reconstruction stage and self-supervised semantic priors in the segmentation stage. To address labeled 3D human datasets scarcity, we further develop an interactive annotation procedure for generating high-quality data-label pairs. Our pixel-aligned aggregation enables cross-task synergy, while the multi-task objective simultaneously optimizes texture modeling fidelity and semantic consistency. Extensive experiments demonstrate that HumanCrafter surpasses existing state-of-the-art methods in both 3D human-part segmentation and 3D human reconstruction from a single image.

Method: Human-in-the-loop model training approach combining data from multiple centers with expert consensus for annotation trustworthiness. Uses iterative segmentation and quality refinement through a bootstrapped DL framework.

Result: Significant improvement in segmentation accuracy (DSC increased from 0.9125 to 0.9670) on internal validation dataset while maintaining stable performance on external datasets. Expert evaluation on 143 scans identified areas for refinement.

Conclusion: The approach provides a clinically adaptable and generalizable strategy for automated VS segmentation with high accuracy and efficiency gains, making it suitable for diverse clinical settings.

Abstract: Accurate segmentation of vestibular schwannoma (VS) on Magnetic Resonance Imaging (MRI) is essential for patient management but often requires time-intensive manual annotations by experts. While recent advances in deep learning (DL) have facilitated automated segmentation, challenges remain in achieving robust performance across diverse datasets and complex clinical cases. We present an annotated dataset stemming from a bootstrapped DL-based framework for iterative segmentation and quality refinement of VS in MRI. We combine data from multiple centres and rely on expert consensus for trustworthiness of the annotations. We show that our approach enables effective and resource-efficient generalisation of automated segmentation models to a target data distribution. The framework achieved a significant improvement in segmentation accuracy with a Dice Similarity Coefficient (DSC) increase from 0.9125 to 0.9670 on our target internal validation dataset, while maintaining stable performance on representative external datasets. Expert evaluation on 143 scans further highlighted areas for model refinement, revealing nuanced cases where segmentation required expert intervention. The proposed approach is estimated to enhance efficiency by approximately 37.4% compared to the conventional manual annotation process. Overall, our human-in-the-loop model training approach achieved high segmentation accuracy, highlighting its potential as a clinically adaptable and generalisable strategy for automated VS segmentation in diverse clinical settings. The dataset includes 190 patients, with tumour annotations available for 534 longitudinal contrast-enhanced T1-weighted (T1CE) scans from 184 patients, and non-annotated T2-weighted scans from 6 patients. This dataset is publicly accessible on The Cancer Imaging Archive (TCIA) (https://doi.org/10.7937/bq0z-xa62).

Method: Equips each client with multiple groups of paired textual and visual prompts, uses diversity loss to specialize groups in distinct semantic aspects, and employs dynamic prompt aggregation via similarity-guided probabilistic sampling for knowledge sharing.

Result: Achieves state-of-the-art performance with the lowest communication parameters among federated prompt learning methods, consistently outperforming prior approaches in both personalization and domain generalization across diverse benchmarks.

Conclusion: FedMGP provides an effective framework for personalized federated prompt learning that balances common knowledge preservation with client-specific features through multi-group prompts and dynamic aggregation, demonstrating superior performance and parameter efficiency.

Abstract: In this paper, we introduce FedMGP, a new paradigm for personalized federated prompt learning in vision-language models. FedMGP equips each client with multiple groups of paired textual and visual prompts, enabling the model to capture diverse, fine-grained semantic and instance-level cues. A diversity loss is introduced to drive each prompt group to specialize in distinct and complementary semantic aspects, ensuring that the groups collectively cover a broader range of local characteristics. During communication, FedMGP employs a dynamic prompt aggregation strategy based on similarity-guided probabilistic sampling: each client computes the cosine similarity between its prompt groups and the global prompts from the previous round, then samples s groups via a softmax-weighted distribution. This soft selection mechanism preferentially aggregates semantically aligned knowledge while still enabling exploration of underrepresented patterns effectively balancing the preservation of common knowledge with client-specific features. Notably, FedMGP maintains parameter efficiency by redistributing a fixed prompt capacity across multiple groups, achieving state-of-the-art performance with the lowest communication parameters among all federated prompt learning methods. Theoretical analysis shows that our dynamic aggregation strategy promotes robust global representation learning by reinforcing shared semantics while suppressing client-specific noise. Extensive experiments demonstrate that FedMGP consistently outperforms prior approaches in both personalization and domain generalization across diverse federated vision-language benchmarks. The code will be released on https://github.com/weihao-bo/FedMGP.git.

Method: Unifies video diffusion model priors with geometry/motion constraints from 4D datasets, using a video latent transformer to predict deformable 3D Gaussian fields in a single forward pass.

Result: Synthesizes high-quality 4D scenes in 30 seconds, matching or surpassing optimization-based methods in video generation, novel view synthesis, and geometry extraction while being significantly more efficient.

Conclusion: Diff4Splat provides an efficient feed-forward alternative to optimization-based dynamic scene synthesis, enabling rapid 4D scene generation from single images with competitive quality.

Abstract: We introduce Diff4Splat, a feed-forward method that synthesizes controllable and explicit 4D scenes from a single image. Our approach unifies the generative priors of video diffusion models with geometry and motion constraints learned from large-scale 4D datasets. Given a single input image, a camera trajectory, and an optional text prompt, Diff4Splat directly predicts a deformable 3D Gaussian field that encodes appearance, geometry, and motion, all in a single forward pass, without test-time optimization or post-hoc refinement. At the core of our framework lies a video latent transformer, which augments video diffusion models to jointly capture spatio-temporal dependencies and predict time-varying 3D Gaussian primitives. Training is guided by objectives on appearance fidelity, geometric accuracy, and motion consistency, enabling Diff4Splat to synthesize high-quality 4D scenes in 30 seconds. We demonstrate the effectiveness of Diff4Splatacross video generation, novel view synthesis, and geometry extraction, where it matches or surpasses optimization-based methods for dynamic scene synthesis while being significantly more efficient.

Method: Created a dataset with 17,597 question-answer pairs across 4,394 chest X-ray images, annotated with radiologist-verified bounding boxes and clinical reasoning explanations. Used a six-type question taxonomy covering Where, What, Is there, How many, Which, and Yes/No questions.

Result: Benchmarking with MedGemma-4B-it showed improved performance (F1 = 0.624, +11.8% over baseline) while enabling lesion localization. The dataset has balanced distribution with 41.7% positive and 58.3% negative samples.

Conclusion: VinDr-CXR-VQA successfully advances reproducible and clinically grounded Med-VQA research by providing a comprehensive dataset with spatial grounding capabilities, improved performance metrics, and publicly available resources for the research community.

Abstract: We present VinDr-CXR-VQA, a large-scale chest X-ray dataset for explainable Medical Visual Question Answering (Med-VQA) with spatial grounding. The dataset contains 17,597 question-answer pairs across 4,394 images, each annotated with radiologist-verified bounding boxes and clinical reasoning explanations. Our question taxonomy spans six diagnostic types-Where, What, Is there, How many, Which, and Yes/No-capturing diverse clinical intents. To improve reliability, we construct a balanced distribution of 41.7% positive and 58.3% negative samples, mitigating hallucinations in normal cases. Benchmarking with MedGemma-4B-it demonstrates improved performance (F1 = 0.624, +11.8% over baseline) while enabling lesion localization. VinDr-CXR-VQA aims to advance reproducible and clinically grounded Med-VQA research. The dataset and evaluation tools are publicly available at huggingface.co/datasets/Dangindev/VinDR-CXR-VQA.

Method: Uses hierarchical identity-preserving attention mechanism to aggregate features across subjects and modalities, leverages pretrained VLM for semantic understanding, and employs online reinforcement learning (RLVR) to align critical concepts like subject ID.

Result: Extensive experiments show the model surpasses existing methods in identity preservation, temporal consistency, and video quality.

Conclusion: ID-Composer effectively addresses multi-subject video generation challenges through its hierarchical attention, VLM integration, and reinforcement learning approach, achieving superior performance in preserving identities and maintaining consistency.

Abstract: Video generative models pretrained on large-scale datasets can produce high-quality videos, but are often conditioned on text or a single image, limiting controllability and applicability. We introduce ID-Composer, a novel framework that addresses this gap by tackling multi-subject video generation from a text prompt and reference images. This task is challenging as it requires preserving subject identities, integrating semantics across subjects and modalities, and maintaining temporal consistency. To faithfully preserve the subject consistency and textual information in synthesized videos, ID-Composer designs a \textbf{hierarchical identity-preserving attention mechanism}, which effectively aggregates features within and across subjects and modalities. To effectively allow for the semantic following of user intention, we introduce \textbf{semantic understanding via pretrained vision-language model (VLM)}, leveraging VLM’s superior semantic understanding to provide fine-grained guidance and capture complex interactions between multiple subjects. Considering that standard diffusion loss often fails in aligning the critical concepts like subject ID, we employ an \textbf{online reinforcement learning phase} to drive the overall training objective of ID-Composer into RLVR. Extensive experiments demonstrate that our model surpasses existing methods in identity preservation, temporal consistency, and video quality.

Method: Uses pretrained segmentation model to isolate target visual attribute, then adjusts non-target regions so their embeddings are uniformly similar to all class-specific text prompts, removing unintended bias signals.

Result: Outperforms existing test-time debiasing approaches on Waterbirds and CelebA datasets in both group robustness metrics and Attention IoU.

Conclusion: Segmentation-guided interventions are effective for scalable and annotation-free bias mitigation in vision language models.

Abstract: Vision language models such as CLIP have shown remarkable performance in zero shot classification, but remain susceptible to spurious correlations, where irrelevant visual features influence predictions. Existing debiasing methods often require access to training data and explicit group labels to perform fine-tuning or adjust embeddings, which limits their practicality in real-world settings. Test-time methods attempt to avoid this constraint, but many still depend on prior knowledge of dataset specific biases, limiting their generalizability in open set settings. In this work, we propose a test-time debiasing method for ViT based CLIP models that requires no additional training or assumptions of bias annotations. Our approach uses a pretrained segmentation model to isolate the target visual attribute, then adjusts the non target regions so that their embeddings are uniformly similar to all class specific text prompts. This procedure removes unintended bias signals from confounding visual regions while preserving the target attribute. Experiments on Waterbirds and CelebA show that our method outperforms existing test-time debiasing approaches in both group robustness metrics and Attention IoU. These results demonstrate the effectiveness of segmentation guided interventions for scalable and annotation free bias mitigation in vision language models.

Method: Built on LVLM with Anomaly Heatmap Decoder for pixel-wise visual-textual feature alignment to generate anomaly heatmaps, and Region-aware Anomaly Encoder to transform heatmaps into textual embeddings for precise anomaly identification and localization.

Result: Achieved 94.8% AUC on UBnormal dataset, 67.8%/76.7% accuracy in anomaly heatmaps, and high BLEU-4 scores (62.67-88.84) and Yes/No accuracy (89.73%-97.67%) for textual descriptions on multiple datasets.

Conclusion: T-VAD significantly enhances anomaly detection granularity and interactivity by combining fine-grained heatmaps with detailed textual guidance, demonstrating superior performance over existing methods.

Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events within video segments. In scenarios such as surveillance or industrial process monitoring, anomaly detection is of critical importance. While existing approaches are semi-automated, requiring human assessment for anomaly detection, traditional VADs offer limited output as either normal or anomalous. We propose Text-guided Fine-Grained Video Anomaly Detection (T-VAD), a framework built upon Large Vision-Language Model (LVLM). T-VAD introduces an Anomaly Heatmap Decoder (AHD) that performs pixel-wise visual-textual feature alignment to generate fine-grained anomaly heatmaps. Furthermore, we design a Region-aware Anomaly Encoder (RAE) that transforms the heatmaps into learnable textual embeddings, guiding the LVLM to accurately identify and localize anomalous events in videos. This significantly enhances both the granularity and interactivity of anomaly detection. The proposed method achieving SOTA performance by demonstrating 94.8% Area Under the Curve (AUC, specifically micro-AUC) and 67.8%/76.7% accuracy in anomaly heatmaps (RBDC/TBDC) on the UBnormal dataset, and subjectively verified more preferable textual description on the ShanghaiTech-based dataset (BLEU-4: 62.67 for targets, 88.84 for trajectories; Yes/No accuracy: 97.67%), and on the UBnormal dataset (BLEU-4: 50.32 for targets, 78.10 for trajectories; Yes/No accuracy: 89.73%).

Method: Created Real-IAD Variety benchmark with 198,960 high-resolution images across 160 object categories, covering 28 industries, 24 material types, and 22 color variations, with rigorous evaluation protocols.

Result: State-of-the-art multi-class unsupervised methods show significant performance degradation when scaled from 30 to 160 categories, while vision-language models exhibit remarkable robustness with minimal performance variation across category counts.

Conclusion: Real-IAD Variety provides an essential resource for training and evaluating next-generation foundation models, enabling development of scalable, general-purpose anomaly detection systems beyond domain-specific constraints.

Abstract: Industrial Anomaly Detection (IAD) is critical for enhancing operational safety, ensuring product quality, and optimizing manufacturing efficiency across global industries. However, the IAD algorithms are severely constrained by the limitations of existing public benchmarks. Current datasets exhibit restricted category diversity and insufficient scale, frequently resulting in metric saturation and limited model transferability to real-world scenarios. To address this gap, we introduce Real-IAD Variety, the largest and most diverse IAD benchmark, comprising 198,960 high-resolution images across 160 distinct object categories. Its diversity is ensured through comprehensive coverage of 28 industries, 24 material types, and 22 color variations. Our comprehensive experimental analysis validates the benchmark’s substantial challenge: state-of-the-art multi-class unsupervised anomaly detection methods experience significant performance degradation when scaled from 30 to 160 categories. Crucially, we demonstrate that vision-language models exhibit remarkable robustness to category scale-up, with minimal performance variation across different category counts, significantly enhancing generalization capabilities in diverse industrial contexts. The unprecedented scale and complexity of Real-IAD Variety position it as an essential resource for training and evaluating next-generation foundation models for anomaly detection. By providing this comprehensive benchmark with rigorous evaluation protocols across multi-class unsupervised, multi-view, and zero-/few-shot settings, we aim to accelerate research beyond domain-specific constraints, enabling the development of scalable, general-purpose anomaly detection systems. Real-IAD Variety will be made publicly available to facilitate innovation in this critical field.

Method: Uses penalty-based attention optimization to disentangle similar semantics during learning, and introduces box control in attention layers during synthesis to mitigate semantic leakage while controlling output layout.

Result: Achieves disentangled and high-quality semantic learning and synthesis, balancing editability and instance consistency. Remains robust with semantically/visually similar instances or rare objects.

Conclusion: The method effectively addresses multi-instance semantic learning from single images, providing robust performance even with challenging cases of similar semantics.

Abstract: This paper proposes a method for precise learning and synthesizing multi-instance semantics from a single image. The difficulty of this problem lies in the limited training data, and it becomes even more challenging when the instances to be learned have similar semantics or appearance. To address this, we propose a penalty-based attention optimization to disentangle similar semantics during the learning stage. Then, in the synthesis, we introduce and optimize box control in attention layers to further mitigate semantic leakage while precisely controlling the output layout. Experimental results demonstrate that our method achieves disentangled and high-quality semantic learning and synthesis, strikingly balancing editability and instance consistency. Our method remains robust when dealing with semantically or visually similar instances or rare-seen objects. The code is publicly available at https://github.com/Kareneveve/MIFO

Method: Uses a compact set of neural voxels with learned deformation fields to model temporal dynamics instead of generating separate Gaussian sets per timestamp, and introduces a novel view refinement stage for selective optimization of challenging viewpoints.

Result: Outperforms state-of-the-art approaches with significant memory reduction and faster training, enabling real-time rendering with superior visual fidelity.

Conclusion: 4D-NVS provides an efficient solution for dynamic scene modeling that reduces memory consumption while preserving high rendering quality.

Abstract: Although 3D Gaussian Splatting (3D-GS) achieves efficient rendering for novel view synthesis, extending it to dynamic scenes still results in substantial memory overhead from replicating Gaussians across frames. To address this challenge, we propose 4D Neural Voxel Splatting (4D-NVS), which combines voxel-based representations with neural Gaussian splatting for efficient dynamic scene modeling. Instead of generating separate Gaussian sets per timestamp, our method employs a compact set of neural voxels with learned deformation fields to model temporal dynamics. The design greatly reduces memory consumption and accelerates training while preserving high image quality. We further introduce a novel view refinement stage that selectively improves challenging viewpoints through targeted optimization, maintaining global efficiency while enhancing rendering quality for difficult viewing angles. Experiments demonstrate that our method outperforms state-of-the-art approaches with significant memory reduction and faster training, enabling real-time rendering with superior visual fidelity.

Method: Uses frequency-based domain separation, cross-domain and intra-domain frequency perturbation strategies, extended self-supervised contrastive learning, semantic clustering loss, and clustering-difficulty-aware resampling.

Result: Extensive experiments show FREE effectively mitigates distribution shift impacts and achieves superior performance in discovering both known and unknown categories across benchmark datasets.

Conclusion: Frequency-domain information is effective for handling distribution shifts in GCD, and the proposed FREE framework significantly improves category discovery performance in domain-shifted scenarios.

Abstract: Generalized Category Discovery (GCD) aims to leverage labeled samples from known categories to cluster unlabeled data that may include both known and unknown categories. While existing methods have achieved impressive results under standard conditions, their performance often deteriorates in the presence of distribution shifts. In this paper, we explore a more realistic task: Domain-Shifted Generalized Category Discovery (DS_GCD), where the unlabeled data includes not only unknown categories but also samples from unknown domains. To tackle this challenge, we propose a \textbf{\underline{F}}requency-guided Gene\textbf{\underline{r}}alized Cat\textbf{\underline{e}}gory Discov\textbf{\underline{e}}ry framework (FREE) that enhances the model’s ability to discover categories under distributional shift by leveraging frequency-domain information. Specifically, we first propose a frequency-based domain separation strategy that partitions samples into known and unknown domains by measuring their amplitude differences. We then propose two types of frequency-domain perturbation strategies: a cross-domain strategy, which adapts to new distributions by exchanging amplitude components across domains, and an intra-domain strategy, which enhances robustness to intra-domain variations within the unknown domain. Furthermore, we extend the self-supervised contrastive objective and semantic clustering loss to better guide the training process. Finally, we introduce a clustering-difficulty-aware resampling technique to adaptively focus on harder-to-cluster categories, further enhancing model performance. Extensive experiments demonstrate that our method effectively mitigates the impact of distributional shifts across various benchmark datasets and achieves superior performance in discovering both known and unknown categories.

Method: Memory-augmented pipeline using cross-attention to correlate temporal signals with visual embeddings, with real-time zero-shot anomaly classification through contextual similarity scoring.

Result: Achieved 90.4% AUC on UCF-Crime and 83.67% AP on XD-Violence, setting new state-of-the-art among zero-shot models. The model enables real-time inference with high precision and explainability.

Conclusion: Fusing cross-attention temporal fusion and contextual memory enables high-fidelity anomaly detection, advancing zero-shot models’ applicability in real-world surveillance and infrastructure monitoring.

Abstract: Video anomalies often depend on contextual information available and temporal evolution. Non-anomalous action in one context can be anomalous in some other context. Most anomaly detectors, however, do not notice this type of context, which seriously limits their capability to generalize to new, real-life situations. Our work addresses the context-aware zero-shot anomaly detection challenge, in which systems need to learn adaptively to detect new events by correlating temporal and appearance features with textual traces of memory in real time. Our approach defines a memory-augmented pipeline, correlating temporal signals with visual embeddings using cross-attention, and real-time zero-shot anomaly classification by contextual similarity scoring. We achieve 90.4% AUC on UCF-Crime and 83.67% AP on XD-Violence, a new state-of-the-art among zero-shot models. Our model achieves real-time inference with high precision and explainability for deployment. We show that, by fusing cross-attention temporal fusion and contextual memory, we achieve high fidelity anomaly detection, a step towards the applicability of zero-shot models in real-world surveillance and infrastructure monitoring.

Method: Proposed CueBench benchmark with event-centric hierarchical taxonomy covering 14 conditional and 18 absolute anomaly events across 174 scenes and 198 attributes. Developed Cue-R1 model using R1-style reinforcement fine-tuning with verifiable, task-aligned, and hierarchy-refined rewards in unified generative framework.

Result: Extensive evaluation shows existing vision-language models are far from satisfactory real-world anomaly understanding, while Cue-R1 surpasses state-of-the-art approaches by over 24% on average across recognition, temporal grounding, detection, and anticipation tasks.

Conclusion: CueBench provides a rigorous evaluation framework that reveals significant gaps in current models’ understanding of context-aware video anomalies, while the proposed Cue-R1 demonstrates substantial improvements through unified generative approach with refined rewards.

Abstract: How far are deep models from real-world video anomaly understanding (VAU)? Current works typically emphasize on detecting unexpected occurrences deviated from normal patterns or comprehending anomalous events with interpretable descriptions. However, they exhibit only a superficial comprehension of real-world anomalies, with limited breadth in complex principles and subtle context that distinguish the anomalies from normalities, e.g., climbing cliffs with safety gear vs. without it. To this end, we introduce CueBench, the first of its kind Benchmark, devoted to Context-aware video anomalies within a Unified Evaluation framework. We comprehensively establish an event-centric hierarchical taxonomy that anchors two core event types: 14 conditional and 18 absolute anomaly events, defined by their refined semantics from diverse contexts across 174 scenes and 198 attributes. Based on this, we propose to unify and benchmark context-aware VAU with various challenging tasks across recognition, temporal grounding, detection, and anticipation. This also serves as a rigorous and fair probing evaluation suite for generative-discriminative as well as generalized-specialized vision-language models (VLMs). To address the challenges underlying CueBench, we further develop Cue-R1 based on R1-style reinforcement fine-tuning with verifiable, task-aligned, and hierarchy-refined rewards in a unified generative manner. Extensive results on CueBench reveal that, existing VLMs are still far from satisfactory real-world anomaly understanding, while our Cue-R1 surpasses these state-of-the-art approaches by over 24% on average.

Method: The authors propose TargetFusionNet, a novel architecture that extends Mask2Former with a target-aware fusion mechanism to fuse weak anatomy priors with instrument instance queries for accurate anatomical target prediction. They also introduce CholecTriplet-Seg, a large-scale dataset with over 30,000 annotated frames.

Result: TargetFusionNet consistently improves performance over existing baselines across recognition, detection, and triplet segmentation metrics, demonstrating that strong instance supervision combined with weak target priors significantly enhances surgical action understanding accuracy and robustness.

Conclusion: Triplet segmentation establishes a unified framework for spatially grounding surgical action triplets, and the proposed benchmark and architecture pave the way for more interpretable surgical scene understanding.

Abstract: Understanding surgical instrument-tissue interactions requires not only identifying which instrument performs which action on which anatomical target, but also grounding these interactions spatially within the surgical scene. Existing surgical action triplet recognition methods are limited to learning from frame-level classification, failing to reliably link actions to specific instrument instances.Previous attempts at spatial grounding have primarily relied on class activation maps, which lack the precision and robustness required for detailed instrument-tissue interaction analysis.To address this gap, we propose grounding surgical action triplets with instrument instance segmentation, or triplet segmentation for short, a new unified task which produces spatially grounded <instrument, verb, target> outputs.We start by presenting CholecTriplet-Seg, a large-scale dataset containing over 30,000 annotated frames, linking instrument instance masks with action verb and anatomical target annotations, and establishing the first benchmark for strongly supervised, instance-level triplet grounding and evaluation.To learn triplet segmentation, we propose TargetFusionNet, a novel architecture that extends Mask2Former with a target-aware fusion mechanism to address the challenge of accurate anatomical target prediction by fusing weak anatomy priors with instrument instance queries.Evaluated across recognition, detection, and triplet segmentation metrics, TargetFusionNet consistently improves performance over existing baselines, demonstrating that strong instance supervision combined with weak target priors significantly enhances the accuracy and robustness of surgical action understanding.Triplet segmentation establishes a unified framework for spatially grounding surgical action triplets. The proposed benchmark and architecture pave the way for more interpretable, surgical scene understanding.

Method: Created FGI-EMIT dataset with 1,561 manually annotated trees captured at 532, 905, and 1,550 nm wavelengths. Benchmarking included four unsupervised algorithms (with Bayesian hyperparameter optimization) and four supervised DL approaches (trained from scratch). Conducted ablation studies on multispectral reflectance and performance analysis across point densities.

Result: Unsupervised Treeiso achieved best F1-score of 52.7%, while DL ForestFormer3D achieved 73.3% F1-score. DL methods significantly outperformed unsupervised approaches, especially for understory trees (25.9 percentage point difference). Current DL approaches generally fail to effectively leverage multispectral reflectance, though single channel reflectance provides marginal improvements for understory trees. DL methods remain superior even at low point densities (10 points/m²).

Conclusion: Deep learning approaches significantly outperform traditional unsupervised methods for individual tree segmentation, particularly for challenging understory trees. However, current DL models are not effectively utilizing multispectral reflectance information, indicating an area for future improvement in multispectral LiDAR data processing.

Abstract: Individual tree segmentation (ITS) from LiDAR point clouds is fundamental for applications such as forest inventory, carbon monitoring and biodiversity assessment. Traditionally, ITS has been achieved with unsupervised geometry-based algorithms, while more recent advances have shifted toward supervised deep learning (DL). In the past, progress in method development was hindered by the lack of large-scale benchmark datasets, and the availability of novel data formats, particularly multispectral (MS) LiDAR, remains limited to this day, despite evidence that MS reflectance can improve the accuracy of ITS. This study introduces FGI-EMIT, the first large-scale MS airborne laser scanning benchmark dataset for ITS. Captured at wavelengths 532, 905, and 1,550 nm, the dataset consists of 1,561 manually annotated trees, with a particular focus on small understory trees. Using FGI-EMIT, we comprehensively benchmarked four conventional unsupervised algorithms and four supervised DL approaches. Hyperparameters of unsupervised methods were optimized using a Bayesian approach, while DL models were trained from scratch. Among the unsupervised methods, Treeiso achieved the highest test set F1-score of 52.7%. The DL approaches performed significantly better overall, with the best model, ForestFormer3D, attaining an F1-score of 73.3%. The most significant difference was observed in understory trees, where ForestFormer3D exceeded Treeiso by 25.9 percentage points. An ablation study demonstrated that current DL-based approaches generally fail to leverage MS reflectance information when it is provided as additional input features, although single channel reflectance can improve accuracy marginally, especially for understory trees. A performance analysis across point densities further showed that DL methods consistently remain superior to unsupervised algorithms, even at densities as low as 10 points/m$^2$.

Method: MR-CLIP uses a metadata-guided framework that aligns volumetric images with their DICOM acquisition parameters to learn MRI contrast representations.

Result: The embeddings show distinct clusters of MRI sequences and outperform supervised 3D baselines in few-shot sequence classification under data scarcity. It also enables unsupervised data quality control by identifying corrupted metadata through image-metadata embedding distances.

Conclusion: By transforming routinely available acquisition metadata into a supervisory signal, MR-CLIP provides a scalable foundation for label-efficient MRI analysis across diverse clinical datasets.

Abstract: Magnetic Resonance Imaging suffers from substantial data heterogeneity and the absence of standardized contrast labels across scanners, protocols, and institutions, which severely limits large-scale automated analysis. A unified representation of MRI contrast would enable a wide range of downstream utilities, from automatic sequence recognition to harmonization and quality control, without relying on manual annotations. To this end, we introduce MR-CLIP, a metadata-guided framework that learns MRI contrast representations by aligning volumetric images with their DICOM acquisition parameters. The resulting embeddings shows distinct clusters of MRI sequences and outperform supervised 3D baselines under data scarcity in few-shot sequence classification. Moreover, MR-CLIP enables unsupervised data quality control by identifying corrupted or inconsistent metadata through image-metadata embedding distances. By transforming routinely available acquisition metadata into a supervisory signal, MR-CLIP provides a scalable foundation for label-efficient MRI analysis across diverse clinical datasets.

Method: Proposes dual-region quantization that partitions activations into outlier and dense regions with independent uniform quantization, plus sensitivity-aware finetuning that focuses more on highly sensitive layers.

Result: Outperforms existing PTQ approaches across various SR networks and datasets, achieving performance comparable to QAT methods in most scenarios with at least 75x speedup.

Conclusion: The proposed dual-region quantization with sensitivity-aware finetuning effectively addresses activation outliers and layer sensitivity, making PTQ viable for SR networks with performance close to QAT but much faster.

Abstract: Quantization techniques, including quantization-aware training (QAT) and post-training quantization (PTQ), have become essential for inference acceleration of image super-resolution (SR) networks. Compared to QAT, PTQ has garnered significant attention as it eliminates the need for ground truth and model retraining. However, existing PTQ methods for SR often fail to achieve satisfactory performance as they overlook the impact of outliers in activation. Our empirical analysis reveals that these prevalent activation outliers are strongly correlated with image color information, and directly removing them leads to significant performance degradation. Motivated by this, we propose a dual-region quantization strategy that partitions activations into an outlier region and a dense region, applying uniform quantization to each region independently to better balance bit-width allocation. Furthermore, we observe that different network layers exhibit varying sensitivities to quantization, leading to different levels of performance degradation. To address this, we introduce sensitivity-aware finetuning that encourages the model to focus more on highly sensitive layers, further enhancing quantization performance. Extensive experiments demonstrate that our method outperforms existing PTQ approaches across various SR networks and datasets, while achieving performance comparable to QAT methods in most scenarios with at least a 75 speedup.

Method: Uses novelty search with LLM for semantic evolution of prompts, CLIP embeddings to quantify novelty, and emitters to guide search into distinct regions of prompt space.

Result: WANDER significantly outperforms existing evolutionary prompt optimization baselines in diversity metrics, with ablation studies confirming the efficacy of emitters.

Conclusion: The approach successfully addresses the diversity limitation in text-to-image generation, making it more suitable for creative and exploratory applications.

Abstract: Text-to-image diffusion models, while proficient at generating high-fidelity im- ages, often suffer from limited output diversity, hindering their application in exploratory and ideation tasks. Existing prompt optimization techniques typically target aesthetic fitness or are ill-suited to the creative visual domain. To address this shortcoming, we introduce WANDER, a novelty search-based approach to generating diverse sets of images from a single input prompt. WANDER operates directly on natural language prompts, employing a Large Language Model (LLM) for semantic evolution of diverse sets of images, and using CLIP embeddings to quantify novelty. We additionally apply emitters to guide the search into distinct regions of the prompt space, and demonstrate that they boost the diversity of the generated images. Empirical evaluations using FLUX-DEV for generation and GPT-4o-mini for mutation demonstrate that WANDER significantly outperforms existing evolutionary prompt optimization baselines in diversity metrics. Ablation studies confirm the efficacy of emitters.

Method: Conducted objective analysis of different loss functions used in DL-based LDCT enhancement models and their consistency with image quality metrics.

Result: Found inconsistencies between loss functions and quality metrics, showing that current loss functions don’t align well with perceptual image quality.

Conclusion: When developing new loss functions for image quality enhancement, it’s crucial to consider image quality metrics to ensure better perceptual quality and diagnostic accuracy.

Abstract: Low-dose CT (LDCT) imaging is widely used to reduce radiation exposure to mitigate high exposure side effects, but often suffers from noise and artifacts that affect diagnostic accuracy. To tackle this issue, deep learning models have been developed to enhance LDCT images. Various loss functions have been employed, including classical approaches such as Mean Square Error and adversarial losses, as well as customized loss functions(LFs) designed for specific architectures. Although these models achieve remarkable performance in terms of PSNR and SSIM, these metrics are limited in their ability to reflect perceptual quality, especially for medical images. In this paper, we focus on one of the most critical elements of DL-based architectures, namely the loss function. We conduct an objective analysis of the relevance of different loss functions for LDCT image quality enhancement and their consistency with image quality metrics. Our findings reveal inconsistencies between LFs and quality metrics, and highlight the need of consideration of image quality metrics when developing a new loss function for image quality enhancement.

Method: Benchmarked convolutional and Transformer-based models on ADNI dataset and tested generalization on FLENI Latin American cohort. Conducted ablation studies on normalization and sampling, plus occlusion sensitivity analysis.

Result: All models showed high AUCs on ADNI (0.96-0.97) but substantial performance drops on FLENI (0.80-0.82). Transformers showed no clear advantage over CNNs. Per-image normalization and correct sampling were key for generalization.

Conclusion: Population-aware validation is crucial for diagnostic AI models. Domain adaptation and cohort diversification are needed to ensure generalizability across different populations.

Abstract: Deep learning models have shown strong performance in diagnosing Alzheimer’s disease (AD) using neuroimaging data, particularly 18F-FDG PET scans, with training datasets largely composed of North American cohorts such as those in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). However, their generalization to underrepresented populations remains underexplored. In this study, we benchmark convolutional and Transformer-based models on the ADNI dataset and assess their generalization performance on a novel Latin American clinical cohort from the FLENI Institute in Buenos Aires, Argentina. We show that while all models achieve high AUCs on ADNI (up to .96, .97), their performance drops substantially on FLENI (down to .82, .80, respectively), revealing a significant domain shift. The tested architectures demonstrated similar performance, calling into question the supposed advantages of transformers for this specific task. Through ablation studies, we identify per-image normalization and a correct sampling selection as key factors for generalization. Occlusion sensitivity analysis further reveals that models trained on ADNI, generally attend to canonical hypometabolic regions for the AD class, but focus becomes unclear for the other classes and for FLENI scans. These findings highlight the need for population-aware validation of diagnostic AI models and motivate future work on domain adaptation and cohort diversification.

Method: Assign discrete location labels to LiDAR scans and train an encoder-decoder model to directly classify each scan’s position as a multi-class classification problem.

Result: The method achieves competitive performance compared to contrastive learning-based methods on the NuScenes dataset.

Conclusion: The classification-based approach for place recognition is viable and offers advantages in training efficiency and stability while maintaining competitive performance.

Abstract: Place recognition is a crucial task in autonomous driving, allowing vehicles to determine their position using sensor data. While most existing methods rely on contrastive learning, we explore an alternative approach by framing place recognition as a multi-class classification problem. Our method assigns discrete location labels to LiDAR scans and trains an encoder-decoder model to classify each scan’s position directly. We evaluate this approach on the NuScenes dataset and show that it achieves competitive performance compared to contrastive learning-based methods while offering advantages in training efficiency and stability.

Method: Created two image generation pipelines using text-to-image and text-to-language-to-image models, developed a structured beauty taxonomy, generated 5984 images using three language models and two text-to-image models, and conducted a Likert-scale study with women and non-binary social media users evaluating 1200 images.

Result: 86.5% of images depicted lighter skin tones, 22% contained explicit content despite SFW training, 74% showed younger age demographics, non-binary individuals were rated as younger and more hypersexualized, and ‘ugly’ traits consistently produced higher NSFW ratings regardless of gender.

Conclusion: Generative AI models contain pervasive demographic biases related to beauty standards that are actively perpetuated by developers through practices like negative prompting, leading to pollution of data streams and erasure of features outside stereotypical beauty norms.

Abstract: Social media has exacerbated the promotion of Western beauty norms, leading to negative self-image, particularly in women and girls, and causing harm such as body dysmorphia. Increasingly content on the internet has been artificially generated, leading to concerns that these norms are being exaggerated. The aim of this work is to study how generative AI models may encode ‘beauty’ and erase ‘ugliness’, and discuss the implications of this for society. To investigate these aims, we create two image generation pipelines: a text-to-image model and a text-to-language model-to image model. We develop a structured beauty taxonomy which we use to prompt three language models (LMs) and two text-to-image models to cumulatively generate 5984 images using our two pipelines. We then recruit women and non-binary social media users to evaluate 1200 of the images through a Likert-scale within-subjects study. Participants show high agreement in their ratings. Our results show that 86.5% of generated images depicted people with lighter skin tones, 22% contained explicit content despite Safe for Work (SFW) training, and 74% were rated as being in a younger age demographic. In particular, the images of non-binary individuals were rated as both younger and more hypersexualised, indicating troubling intersectional effects. Notably, prompts encoded with ’negative’ or ‘ugly’ beauty traits (such as “a wide nose”) consistently produced higher Not SFW (NSFW) ratings regardless of gender. This work sheds light on the pervasive demographic biases related to beauty standards present in generative AI models – biases that are actively perpetuated by model developers, such as via negative prompting. We conclude by discussing the implications of this on society, which include pollution of the data streams and active erasure of features that do not fall inside the stereotype of what is considered beautiful by developers.

Method: The system integrates YOLOv5 and SSD object detection algorithms, provides real-time monitoring with Telegram notifications for rapid farmer response, and includes an automated sound deterrent mechanism (e.g., tiger roar) triggered upon animal detection.

Result: The YOLO+SSD model achieved accuracy rates of 90% for elephants, 85% for boars, and 70% for monkeys. Detection accuracy was highest during daytime and decreased at night, regardless of whether the input was still images or video.

Conclusion: This study presents a comprehensive framework combining detection, notification, and deterrence mechanisms, providing a practical solution for automated farming that paves the way for future innovations in agricultural technology.

Abstract: Durian plantation suffers from animal intrusions that cause crop damage and financial loss. The traditional farming practices prove ineffective due to the unavailability of monitoring without human intervention. The fast growth of machine learning and Internet of Things (IoT) technology has led to new ways to detect animals. However, current systems are limited by dependence on single object detection algorithms, less accessible notification platforms, and limited deterrent mechanisms. This research suggests an IoT-enabled animal detection system for durian crops. The system integrates YOLOv5 and SSD object detection algorithms to improve detection accuracy. The system provides real-time monitoring, with detected intrusions automatically reported to farmers via Telegram notifications for rapid response. An automated sound mechanism (e.g., tiger roar) is triggered once the animal is detected. The YOLO+SSD model achieved accuracy rates of elephant, boar, and monkey at 90%, 85% and 70%, respectively. The system shows the highest accuracy in daytime and decreases at night, regardless of whether the image is still or a video. Overall, this study contributes a comprehensive and practical framework that combines detection, notification, and deterrence, paving the way for future innovations in automated farming solutions.

Method: Introduces Granularity-Consistent automatic 2D Mask Tracking to maintain temporal correspondences across frames, combined with a three-stage curriculum learning framework that progressively trains from fragmented single-view data to unified multi-view annotations and globally coherent full-scene supervision.

Result: The method effectively generated consistent and accurate 3D segmentations, achieving state-of-the-art results on standard benchmarks and demonstrating open-vocabulary ability.

Conclusion: The proposed approach enables robust distillation of consistent 3D representations from initially fragmented and contradictory 2D priors through structured progressive learning.

Abstract: 3D instance segmentation is an important task for real-world applications. To avoid costly manual annotations, existing methods have explored generating pseudo labels by transferring 2D masks from foundation models to 3D. However, this approach is often suboptimal since the video frames are processed independently. This causes inconsistent segmentation granularity and conflicting 3D pseudo labels, which degrades the accuracy of final segmentation. To address this, we introduce a Granularity-Consistent automatic 2D Mask Tracking approach that maintains temporal correspondences across frames, eliminating conflicting pseudo labels. Combined with a three-stage curriculum learning framework, our approach progressively trains from fragmented single-view data to unified multi-view annotations, ultimately globally coherent full-scene supervision. This structured learning pipeline enables the model to progressively expose to pseudo-labels of increasing consistency. Thus, we can robustly distill a consistent 3D representation from initially fragmented and contradictory 2D priors. Experimental results demonstrated that our method effectively generated consistent and accurate 3D segmentations. Furthermore, the proposed method achieved state-of-the-art results on standard benchmarks and open-vocabulary ability.

Method: Evaluates segmentation performance and privacy leakage across FL methods: FedAvg, FedProx, FedBN, and FedAvg with DP-SGD using synthetic oncologic CT scans with tumor annotations.

Result: FedAvg shows high performance (Dice ~0.85) with more privacy leakage (attack AUC ~0.72), while DP-SGD provides higher privacy (AUC ~0.25) at cost of accuracy (Dice ~0.79). FedProx and FedBN offer balanced performance under heterogeneous data.

Conclusion: FedOnco-Bench serves as a standardized, open-source platform for benchmarking and developing privacy-preserving FL methods for medical image segmentation.

Abstract: Federated Learning (FL) allows multiple institutions to cooperatively train machine learning models while retaining sensitive data at the source, which has great utility in privacy-sensitive environments. However, FL systems remain vulnerable to membership-inference attacks and data heterogeneity. This paper presents FedOnco-Bench, a reproducible benchmark for privacy-aware FL using synthetic oncologic CT scans with tumor annotations. It evaluates segmentation performance and privacy leakage across FL methods: FedAvg, FedProx, FedBN, and FedAvg with DP-SGD. Results show a distinct trade-off between privacy and utility: FedAvg is high performance (Dice around 0.85) with more privacy leakage (attack AUC about 0.72), while DP-SGD provides a higher level of privacy (AUC around 0.25) at the cost of accuracy (Dice about 0.79). FedProx and FedBN offer balanced performance under heterogeneous data, especially with non-identical distributed client data. FedOnco-Bench serves as a standardized, open-source platform for benchmarking and developing privacy-preserving FL methods for medical image segmentation.

Method: Proposes GUI-AIMA framework that aligns MLLMs’ multimodal attention with patch-wise grounding signals calculated adaptively via multi-head aggregation on simplified query-visual attention matrices. Uses coordinate-free approach that easily integrates plug-and-play zoom-in stage.

Result: GUI-AIMA-3B trained with only 85k screenshots achieves state-of-the-art performance among 3B models: 58.6% average accuracy on ScreenSpot-Pro and 62.2% on OSWorld-G, demonstrating exceptional data efficiency.

Conclusion: Light training can effectively trigger the native grounding capability of MLLMs, and the attention-based coordinate-free approach provides an efficient solution for GUI grounding tasks.

Abstract: Graphical user interface (GUI) grounding is a key function of computer-use agents, which maps natural-language instructions to actionable screen regions. Existing approaches based on Multimodal Large Language Models (MLLMs) typically formulate it as a text-based coordinate generation task, yet directly generating precise coordinates from visual inputs remains challenging and computationally intensive. An intuitive way to implement GUI grounding is to first select visual patches relevant to the instructions and then determine the precise click location within those patches. Based on the observations that general MLLMs have some native grounding capability, nested within their attentions, we propose GUI-AIMA, an attention-based and coordinate-free supervised fine-tuning framework for efficient GUI grounding. GUI-AIMA aligns the intrinsic multimodal attention of MLLMs with patch-wise grounding signals. These signals are calculated adaptively for diverse user instructions by multi-head aggregation on simplified query-visual attention matrices. Besides, its coordinate-free manner can easily integrate a plug-and-play zoom-in stage. GUI-AIMA-3B was trained with only 85k screenshots, demonstrating exceptional data efficiency and verifying that light training can trigger the native grounding capability of MLLMs. It achieves state-of-the-art performance among 3B models, attaining an average accuracy of 58.6% on ScreenSpot-Pro and 62.2% on OSWorld-G. Project page: https://github.com/sjz5202/GUI-AIMA

Method: Combines topology-aware diffusion probabilistic model with level set energy that integrates input image and deep features through four complementary terms. Includes pixel-adaptive refinement module for boundary precision using affinity weighting.

Result: Achieves state-of-the-art accuracy on four public pancreas datasets, outperforming existing methods. Ablation studies confirm the contribution of each component.

Conclusion: TA-LSDiff establishes a practical and accurate solution for pancreas segmentation, bridging the gap between traditional level set methods and deep learning approaches.

Abstract: Pancreas segmentation in medical image processing is a persistent challenge due to its small size, low contrast against adjacent tissues, and significant topological variations. Traditional level set methods drive boundary evolution using gradient flows, often ignoring pointwise topological effects. Conversely, deep learning-based segmentation networks extract rich semantic features but frequently sacrifice structural details. To bridge this gap, we propose a novel model named TA-LSDiff, which combined topology-aware diffusion probabilistic model and level set energy, achieving segmentation without explicit geometric evolution. This energy function guides implicit curve evolution by integrating the input image and deep features through four complementary terms. To further enhance boundary precision, we introduce a pixel-adaptive refinement module that locally modulates the energy function using affinity weighting from neighboring evidence. Ablation studies systematically quantify the contribution of each proposed component. Evaluations on four public pancreas datasets demonstrate that TA-LSDiff achieves state-of-the-art accuracy, outperforming existing methods. These results establish TA-LSDiff as a practical and accurate solution for pancreas segmentation.

Method: OMEGA employs Modality-Specific Position Encoding (MSPE) to assign positional indices while preserving inherent modality structures, and Global Adaptive Encoding Step Scaling (GAESS) to adaptively adjust visual token position encoding step size based on embedding entropy.

Result: OMEGA consistently enhances VLM performance across diverse architectures and VQA benchmarks, achieving up to 3.43% improvement on visual-intensive tasks with Qwen2.5-VL-3B, with consistent gains on larger models including Qwen2.5-VL-7B and LLaVA-v1.5-7B.

Conclusion: OMEGA’s modality-specific approach to position encoding effectively addresses the limitations of unified positional indexing in VLMs, leading to significant performance improvements across various model architectures and tasks.

Abstract: Vision-Language Models (VLMs) have demonstrated strong performance across various multimodal tasks, where position encoding plays a vital role in modeling both the sequential structure of textual information and the spatial structure of visual information. However, current VLMs commonly adopt modality-unified 1D or 2D positional indexing strategies, which treat textual and visual tokens uniformly without accounting for their distinct structural properties and sequential continuity for text and spatial coherence for vision. To address this limitation, we propose OMEGA, a novel position encoding framework that employs Modality-Specific Position Encoding (MSPE) to assign positional indices while preserving the inherent structures of each modality across separate coordinate dimensions. Additionally, to align the information density of multimodal data in the positional index space, OMEGA introduces Global Adaptive Encoding Step Scaling (GAESS), which adaptively adjusts the position encoding step size of visual tokens based on the embedding entropy of both modalities. Experimental results demonstrate that OMEGA consistently enhances VLM performance across diverse architectures and VQA benchmarks. On visual-intensive tasks, OMEGA achieves up to 3.43% improvement over baseline position encoding strategies on Qwen2.5-VL-3B, with consistent gains observed across larger models including Qwen2.5-VL-7B and LLaVA-v1.5-7B.

Method: LSSA randomly shuffles local image blocks to expand image-text pairs, generates adversarial images, samples around them, and uses both original and sampled images to create adversarial texts.

Result: Extensive experiments show LSSA significantly improves adversarial transferability across diverse VLP models and downstream tasks, outperforming other advanced attacks on Large Vision-Language Models.

Conclusion: LSSA effectively addresses overfitting in multimodal adversarial attacks by increasing input diversity through local shuffling and sampling, achieving superior transferability performance.

Abstract: Visual-Language Pre-training (VLP) models have achieved significant performance across various downstream tasks. However, they remain vulnerable to adversarial examples. While prior efforts focus on improving the adversarial transferability of multimodal adversarial examples through cross-modal interactions, these approaches suffer from overfitting issues, due to a lack of input diversity by relying excessively on information from adversarial examples in one modality when crafting attacks in another. To address this issue, we draw inspiration from strategies in some adversarial training methods and propose a novel attack called Local Shuffle and Sample-based Attack (LSSA). LSSA randomly shuffles one of the local image blocks, thus expanding the original image-text pairs, generating adversarial images, and sampling around them. Then, it utilizes both the original and sampled images to generate the adversarial texts. Extensive experiments on multiple models and datasets demonstrate that LSSA significantly enhances the transferability of multimodal adversarial examples across diverse VLP models and downstream tasks. Moreover, LSSA outperforms other advanced attacks on Large Vision-Language Models.

Method: VCA distills dense query fields into pooled visual-contrast tokens, splits them into positive/negative streams for differential interaction, and uses dual positional embeddings for contrastive reasoning.

Result: Improves DeiT-Tiny from 72.2% to 75.6% on ImageNet-1K (+3.4%), boosts hierarchical ViTs by up to 3.1%, and lowers FID in image generation by 2.1-5.2 points across diffusion and flow models.

Conclusion: VCA offers a simple path to faster and sharper Vision Transformers with minimal parameter overhead and no extra FLOPs, making it architecture-agnostic and effective.

Abstract: Vision Transformers (ViTs) have become a universal backbone for both image recognition and image generation. Yet their Multi-Head Self-Attention (MHSA) layer still performs a quadratic query-key interaction for every token pair, spending the bulk of computation on visually weak or redundant correlations. We introduce Visual-Contrast Attention (VCA), a drop-in replacement for MHSA that injects an explicit notion of discrimination while reducing the theoretical complexity from O(N N C) to O(N n C) with n « N. VCA first distils each head’s dense query field into a handful of spatially pooled visual-contrast tokens, then splits them into a learnable positive and negative stream whose differential interaction highlights what truly separates one region from another. The module adds fewer than 0.3M parameters to a DeiT-Tiny backbone, requires no extra FLOPs, and is wholly architecture-agnostic. Empirically, VCA lifts DeiT-Tiny top-1 accuracy on ImageNet-1K from 72.2% to 75.6% (+3.4) and improves three strong hierarchical ViTs by up to 3.1%, while in class-conditional ImageNet generation it lowers FID-50K by 2.1 to 5.2 points across both diffusion (DiT) and flow (SiT) models. Extensive ablations confirm that (i) spatial pooling supplies low-variance global cues, (ii) dual positional embeddings are indispensable for contrastive reasoning, and (iii) combining the two in both stages yields the strongest synergy. VCA therefore offers a simple path towards faster and sharper Vision Transformers. The source code is available at https://github.com/LeapLabTHU/LinearDiff.

Method: Parameter Interpolation Adversarial Training (PIAT) tunes model parameters by interpolating between previous and current epoch parameters, and uses Normalized Mean Square Error (NMSE) to align relative logit magnitudes between clean and adversarial examples.

Result: Extensive experiments on benchmark datasets show PIAT prominently improves robustness for both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).

Conclusion: PIAT effectively addresses overfitting and oscillation issues in adversarial training, enabling better model convergence and higher robustness against adversarial attacks.

Abstract: Though deep neural networks exhibit superior performance on various tasks, they are still plagued by adversarial examples. Adversarial training has been demonstrated to be the most effective method to defend against adversarial attacks. However, existing adversarial training methods show that the model robustness has apparent oscillations and overfitting issues in the training process, degrading the defense efficacy. To address these issues, we propose a novel framework called Parameter Interpolation Adversarial Training (PIAT). PIAT tunes the model parameters between each epoch by interpolating the parameters of the previous and current epochs. It makes the decision boundary of model change more moderate and alleviates the overfitting issue, helping the model converge better and achieving higher model robustness. In addition, we suggest using the Normalized Mean Square Error (NMSE) to further improve the robustness by aligning the relative magnitude of logits between clean and adversarial examples rather than the absolute magnitude. Extensive experiments conducted on several benchmark datasets demonstrate that our framework could prominently improve the robustness of both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).

Method: Created OmniBrainBench with 15 brain imaging modalities from 30 medical sources, containing 9,527 VQA pairs and 31,706 images, simulating clinical workflows and covering 15 multi-stage clinical tasks validated by professional radiologists.

Result: Evaluation of 24 MLLMs shows: proprietary models outperform open-source and medical models but still lag physicians; medical MLLMs have wide performance variation; open-source models trail overall but excel in specific tasks; all MLLMs significantly underperform in complex preoperative tasks.

Conclusion: OmniBrainBench sets a new standard for evaluating MLLMs in brain imaging analysis, highlighting substantial gaps between current MLLM capabilities and expert clinical reasoning, particularly in complex clinical scenarios.

Abstract: Brain imaging analysis is vital for diagnosing and treating brain disorders, and multimodal large language models (MLLMs) are increasingly assisting in that analysis. However, current brain-oriented visual question-answering (VQA) benchmarks either cover a few imaging modalities or are limited to coarse-grained pathological descriptions, hindering a comprehensive assessment of MLLMs throughout the full clinical continuum. To address these, we introduce OmniBrainBench, the first comprehensive multimodal VQA benchmark specifically designed to assess the multimodal comprehension capabilities of MLLMs in brain imaging analysis.OmniBrainBench consists of 15 distinct brain imaging modalities collected from 30 verified medical sources, yielding 9,527 validated VQA pairs and 31,706 images. It simulates clinical workflows and encompasses 15 multi-stage clinical tasks rigorously validated by a professional radiologist. Evaluation of 24 state-of-the-art models, including open-source, medical, and proprietary MLLMs, highlights the substantial challenges posed by OmniBrainBench. Our experiments reveal: (1) proprietary MLLMs (e.g., GPT-5) beat open-source and medical models but lag physicians; (2) medical MLLMs vary widely in performance; (3) open-source MLLMs trail overall but excel in specific tasks; (4) MLLMs underperform sharply in complex preoperative tasks, revealing a visual-to-clinical reasoning gap. OmniBrainBench sets a new standard for evaluating and advancing MLLMs in brain imaging analysis, highlighting gaps compared to expert clinical reasoning. We release it at benchmark & code.

Method: Uses an occlusion-aware diffusion transformer architecture during denoising to estimate noise features of occluded patterns, and introduces an occlusion mask-guided reverse process to utilize observation information and reduce prediction error accumulation.

Result: Comprehensive evaluation on PIE and JAAD benchmarks shows the method achieves more robust performance than existing methods under various occlusion scenarios.

Conclusion: The proposed ODM effectively handles occlusion scenarios and improves pedestrian crossing intention prediction accuracy through motion pattern reconstruction and contextual relationship capture.

Abstract: Predicting pedestrian crossing intentions is crucial for the navigation of mobile robots and intelligent vehicles. Although recent deep learning-based models have shown significant success in forecasting intentions, few consider incomplete observation under occlusion scenarios. To tackle this challenge, we propose an Occlusion-Aware Diffusion Model (ODM) that reconstructs occluded motion patterns and leverages them to guide future intention prediction. During the denoising stage, we introduce an occlusion-aware diffusion transformer architecture to estimate noise features associated with occluded patterns, thereby enhancing the model’s ability to capture contextual relationships in occluded semantic scenarios. Furthermore, an occlusion mask-guided reverse process is introduced to effectively utilize observation information, reducing the accumulation of prediction errors and enhancing the accuracy of reconstructed motion features. The performance of the proposed method under various occlusion scenarios is comprehensively evaluated and compared with existing methods on popular benchmarks, namely PIE and JAAD. Extensive experimental results demonstrate that the proposed method achieves more robust performance than existing methods in the literature.

Method: Layer-Wise Modality Decomposition (LMD) - a post-hoc, model-agnostic method that disentangles modality-specific information across all layers of pretrained fusion models.

Result: LMD effectively attributes predictions to individual modalities in camera-radar, camera-LiDAR, and camera-radar-LiDAR fusion systems, validated through perturbation-based metrics and visual decompositions.

Conclusion: LMD provides practical interpretability for high-capacity multimodal architectures in autonomous driving, addressing the critical need for transparency in sensor fusion decision-making.

Abstract: In autonomous driving, transparency in the decision-making of perception models is critical, as even a single misperception can be catastrophic. Yet with multi-sensor inputs, it is difficult to determine how each modality contributes to a prediction because sensor information becomes entangled within the fusion network. We introduce Layer-Wise Modality Decomposition (LMD), a post-hoc, model-agnostic interpretability method that disentangles modality-specific information across all layers of a pretrained fusion model. To our knowledge, LMD is the first approach to attribute the predictions of a perception model to individual input modalities in a sensor-fusion system for autonomous driving. We evaluate LMD on pretrained fusion models under camera-radar, camera-LiDAR, and camera-radar-LiDAR settings for autonomous driving. Its effectiveness is validated using structured perturbation-based metrics and modality-wise visual decompositions, demonstrating practical applicability to interpreting high-capacity multimodal architectures. Code is available at https://github.com/detxter-jvb/Layer-Wise-Modality-Decomposition.

Method: Uses heterogeneous graph neural networks with typed edges for supportive collaboration, competitive argumentation, and knowledge transfer. Features dual-level debate mechanism with node-level refinement and edge-level argumentation modeling, plus cross-level topology refinement.

Result: Significantly outperforms state-of-the-art methods on multiple benchmarks.

Conclusion: The framework successfully transforms cognitive conflicts between agents into enhanced geo-localization accuracy through structured debate.

Abstract: Visual geo-localization requires extensive geographic knowledge and sophisticated reasoning to determine image locations without GPS metadata. Traditional retrieval methods are constrained by database coverage and quality. Recent Large Vision-Language Models (LVLMs) enable direct location reasoning from image content, yet individual models struggle with diverse geographic regions and complex scenes. Existing multi-agent systems improve performance through model collaboration but treat all agent interactions uniformly. They lack mechanisms to handle conflicting predictions effectively. We propose \textbf{GraphGeo}, a multi-agent debate framework using heterogeneous graph neural networks for visual geo-localization. Our approach models diverse debate relationships through typed edges, distinguishing supportive collaboration, competitive argumentation, and knowledge transfer. We introduce a dual-level debate mechanism combining node-level refinement and edge-level argumentation modeling. A cross-level topology refinement strategy enables co-evolution between graph structure and agent representations. Experiments on multiple benchmarks demonstrate GraphGeo significantly outperforms state-of-the-art methods. Our framework transforms cognitive conflicts between agents into enhanced geo-localization accuracy through structured debate.

Method: Three key data-centric strategies: (1) scaling up pretraining with long-context natural and medical data, (2) fine-tuning with rare medical data including video analysis and underrepresented 2D modalities, (3) extending evaluation frameworks to include 3D and video benchmarks. Uses supervised fine-tuning (SFT) and group relative policy optimization (GRPO).

Result: Fleming-VL achieves state-of-the-art performance across multiple benchmarks including medical VQA, video QA, and 3D medical image understanding.

Conclusion: The framework successfully addresses medical data heterogeneity and enables comprehensive medical visual understanding across diverse modalities, with public release to promote transparent and reproducible medical AI progress.

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable effectiveness in various general-domain scenarios, such as visual question answering and image captioning. Recently, researchers have increasingly focused on empowering MLLMs with medical conversational abilities, which hold significant promise for clinical applications. However, medical data presents unique challenges due to its heterogeneous nature – encompassing diverse modalities including 2D images, 3D volumetric scans, and temporal video sequences. The substantial domain gap and data format inconsistencies across these modalities have hindered the development of unified medical MLLMs. To address these challenges, we propose Fleming-VL, a unified end-to-end framework for comprehensive medical visual understanding across heterogeneous modalities. Fleming-VL tackles this problem from a data-centric perspective through three key strategies: (1) scaling up pretraining by integrating long-context data from both natural and medical-specific domains; (2) complementing fine-tuning with rare medical data, including holistic video analysis and underrepresented 2D modalities such as ultrasound and dermoscopy images; (3) extending existing evaluation frameworks to incorporate 3D volumetric and video understanding benchmarks. Through supervised fine-tuning (SFT) and group relative policy optimization (GRPO), we develop Fleming-VL in multiple model scales. Extensive experiments demonstrate that Fleming-VL achieves state-of-the-art performance across multiple benchmarks, including medical VQA, video QA, and 3D medical image understanding. We publicly release Fleming-VL to promote transparent, reproducible, and auditable progress in medical AI.

Method: Three-component approach: (1) Uni-modal Feature Extraction using CLIP text encoder and ViT, (2) Cross-modal Multi-level Weighting Module with local/global aggregation blocks, (3) Weighted Quadruplet Loss for domain balance.

Result: Superior performance over state-of-the-art methods on Sketchy, TU-Berlin, and QuickDraw benchmark datasets.

Conclusion: The proposed method effectively addresses modality imbalance and information inconsistency in ZS-SBIR, achieving improved retrieval performance.

Abstract: The problem of zero-shot sketch-based image retrieval (ZS-SBIR) has achieved increasing attention due to its wide applications, e.g. e-commerce. Despite progress made in this field, previous works suffer from using imbalanced samples of modalities and inconsistent low-quality information during training, resulting in sub-optimal performance. Therefore, in this paper, we introduce an approach called Dynamic Multi-level Weighted Alignment Network for ZS-SBIR. It consists of three components: (i) a Uni-modal Feature Extraction Module that includes a CLIP text encoder and a ViT for extracting textual and visual tokens, (ii) a Cross-modal Multi-level Weighting Module that produces an alignment weight list by the local and global aggregation blocks to measure the aligning quality of sketch and image samples, (iii) a Weighted Quadruplet Loss Module aiming to improve the balance of domains in the triplet loss. Experiments on three benchmark datasets, i.e., Sketchy, TU-Berlin, and QuickDraw, show our method delivers superior performances over the state-of-the-art ZS-SBIR methods.

Method: Two-stage training strategy with simple inference using only source image and target garment inputs. Leverages additional reference images of different individuals wearing the same clothes to preserve garment texture and fine-grained details.

Result: Evaluated on two widely used benchmarks and diverse tasks, with results consistently validating the effectiveness of the approach.

Conclusion: EVTAR provides a more practical and realistic virtual try-on solution by simulating how humans consider reference models when choosing outfits, achieving high-quality dressing effects without complex preprocessing requirements.

Abstract: We propose EVTAR, an End-to-End Virtual Try-on model with Additional Reference, that directly fits the target garment onto the person image while incorporating reference images to enhance try-on accuracy. Most existing virtual try-on approaches rely on complex inputs such as agnostic person images, human pose, densepose, or body keypoints, making them labor-intensive and impractical for real-world applications. In contrast, EVTAR adopts a two-stage training strategy, enabling simple inference with only the source image and the target garment inputs. Our model generates try-on results without masks, densepose, or segmentation maps. Moreover, EVTAR leverages additional reference images of different individuals wearing the same clothes to preserve garment texture and fine-grained details better. This mechanism is analogous to how humans consider reference models when choosing outfits, thereby simulating a more realistic and high-quality dressing effect. We enrich the training data with supplementary references and unpaired person images to support these capabilities. We evaluate EVTAR on two widely used benchmarks and diverse tasks, and the results consistently validate the effectiveness of our approach.

Method: Chained test-time reasoning process that leverages intra-task reasoning for temporal detection refinement and inter-task chaining for spatial and semantic understanding, using careful prompt design with foundation models.

Result: Achieves state-of-the-art zero-shot performance across multiple video anomaly detection, localization, and explanation benchmarks without additional data or gradients.

Conclusion: Task-wise chaining can unlock foundation models’ reasoning power for practical, interpretable video anomaly analysis in a fully zero-shot manner.

Abstract: Most video-anomaly research stops at frame-wise detection, offering little insight into why an event is abnormal, typically outputting only frame-wise anomaly scores without spatial or semantic context. Recent video anomaly localization and video anomaly understanding methods improve explainability but remain data-dependent and task-specific. We propose a unified reasoning framework that bridges the gap between temporal detection, spatial localization, and textual explanation. Our approach is built upon a chained test-time reasoning process that sequentially connects these tasks, enabling holistic zero-shot anomaly analysis without any additional training. Specifically, our approach leverages intra-task reasoning to refine temporal detections and inter-task chaining for spatial and semantic understanding, yielding improved interpretability and generalization in a fully zero-shot manner. Without any additional data or gradients, our method achieves state-of-the-art zero-shot performance across multiple video anomaly detection, localization, and explanation benchmarks. The results demonstrate that careful prompt design with task-wise chaining can unlock the reasoning power of foundation models, enabling practical, interpretable video anomaly analysis in a fully zero-shot manner. Project Page: https://rathgrith.github.io/Unified_Frame_VAA/.

Method: VesSAM integrates: (1) convolutional adapter for local texture enhancement, (2) multi-prompt encoder fusing anatomical prompts (skeletons, bifurcation points, segment midpoints) via hierarchical cross-attention, (3) lightweight mask decoder to reduce artifacts. Includes automated pipeline for multi-prompt annotation generation.

Result: Outperforms state-of-the-art PEFT-based SAM variants by over 10% Dice and 13% IoU. Achieves competitive performance vs fully fine-tuned methods with significantly fewer parameters. Generalizes well to out-of-distribution settings, outperforming all baselines in average OoD Dice and IoU.

Conclusion: VesSAM provides an efficient and powerful framework for vessel segmentation that significantly improves upon foundation models while maintaining parameter efficiency and strong generalization capabilities across diverse imaging modalities.

Abstract: Accurate vessel segmentation is critical for clinical applications such as disease diagnosis and surgical planning, yet remains challenging due to thin, branching structures and low texture contrast. While foundation models like the Segment Anything Model (SAM) have shown promise in generic segmentation, they perform sub-optimally on vascular structures. In this work, we present VesSAM, a powerful and efficient framework tailored for 2D vessel segmentation. VesSAM integrates (1) a convolutional adapter to enhance local texture features, (2) a multi-prompt encoder that fuses anatomical prompts, including skeletons, bifurcation points, and segment midpoints, via hierarchical cross-attention, and (3) a lightweight mask decoder to reduce jagged artifacts. We also introduce an automated pipeline to generate structured multi-prompt annotations, and curate a diverse benchmark dataset spanning 8 datasets across 5 imaging modalities. Experimental results demonstrate that VesSAM consistently outperforms state-of-the-art PEFT-based SAM variants by over 10% Dice and 13% IoU, and achieves competitive performance compared to fully fine-tuned methods, with significantly fewer parameters. VesSAM also generalizes well to out-of-distribution (OoD) settings, outperforming all baselines in average OoD Dice and IoU.

Method: Models noisy data as states in continuous non-linear noise accumulation, learns two neural networks to estimate noise step and predict/subtract noise increments, uses first-order Taylor expansion for local linearization of complex non-linear noise.

Result: Demonstrates robustness, adaptability, and state-of-the-art performance across four classic computer vision tasks, plus strong performance in biomedical and bioinformatics domains.

Conclusion: MID provides an effective self-supervised framework for complex noise removal that doesn’t require paired clean-noisy datasets and performs well across multiple domains.

Abstract: Data denoising is a persistent challenge across scientific and engineering domains. Real-world data is frequently corrupted by complex, non-linear noise, rendering traditional rule-based denoising methods inadequate. To overcome these obstacles, we propose a novel self-supervised multimodal iterative denoising (MID) framework. MID models the collected noisy data as a state within a continuous process of non-linear noise accumulation. By iteratively introducing further noise, MID learns two neural networks: one to estimate the current noise step and another to predict and subtract the corresponding noise increment. For complex non-linear contamination, MID employs a first-order Taylor expansion to locally linearize the noise process, enabling effective iterative removal. Crucially, MID does not require paired clean-noisy datasets, as it learns noise characteristics directly from the noisy inputs. Experiments across four classic computer vision tasks demonstrate MID’s robustness, adaptability, and consistent state-of-the-art performance. Moreover, MID exhibits strong performance and adaptability in tasks within the biomedical and bioinformatics domains.

Method: Unified feature extraction (GLCM, LBP, entropy, energy, color analysis) applied to both visual and X-ray images, processed through optimized One-Class SVM with hyperparameter tuning.

Result: 97.8% classification accuracy with 0.022 false positive rate on 24 authenticated Goya paintings; case study on “Un Gigante” achieved 92.3% authentication confidence.

Conclusion: Multimodal approach significantly outperforms single-modal methods, demonstrating effectiveness of identical computational techniques across visual and radiographic imagery for art authentication.

Abstract: Art authentication of Francisco Goya’s works presents complex computational challenges due to his heterogeneous stylistic evolution and extensive historical patterns of forgery. We introduce a novel multimodal machine learning framework that applies identical feature extraction techniques to both visual and X-ray radiographic images of Goya paintings. The unified feature extraction pipeline incorporates Grey-Level Co-occurrence Matrix descriptors, Local Binary Patterns, entropy measures, energy calculations, and colour distribution analysis applied consistently across both imaging modalities. The extracted features from both visual and X-ray images are processed through an optimised One-Class Support Vector Machine with hyperparameter tuning. Using a dataset of 24 authenticated Goya paintings with corresponding X-ray images, split into an 80/20 train-test configuration with 10-fold cross-validation, the framework achieves 97.8% classification accuracy with a 0.022 false positive rate. Case study analysis of ``Un Gigante’’ demonstrates the practical efficacy of our pipeline, achieving 92.3% authentication confidence through unified multimodal feature analysis. Our results indicate substantial performance improvement over single-modal approaches, establishing the effectiveness of applying identical computational methods to both visual and radiographic imagery in art authentication applications.

Method: HyFormer-Net uses a dual-branch encoder integrating EfficientNet-B3 and Swin Transformer via multi-scale hierarchical fusion blocks, with an attention-gated decoder for precision and explainability. It features dual-pipeline interpretability: intrinsic attention validation with quantitative IoU verification and Grad-CAM for classification reasoning.

Result: On BUSI dataset: Dice Score 0.761 +/- 0.072, accuracy 93.2%, malignant recall 92.1 +/- 2.2%. Ensemble modeling achieves exceptional Dice 90.2%, accuracy 99.5%, and perfect 100% malignant recall. Ablation studies show multi-scale fusion contributes +16.8% Dice and attention gates add +5.9%. Cross-dataset generalization: progressive fine-tuning with only 10% target data recovers 92.5% performance; with 50% data achieves 77.3% Dice, exceeding source-domain performance.

Conclusion: HyFormer-Net demonstrates superior performance for breast ultrasound analysis with intrinsic interpretability and strong generalization capabilities. The model’s ability to achieve true generalization across datasets with minimal fine-tuning data represents a significant advancement for clinical adoption of deep learning in medical imaging.

Abstract: B-mode ultrasound for breast cancer diagnosis faces challenges: speckle, operator dependency, and indistinct boundaries. Existing deep learning suffers from single-task learning, architectural constraints (CNNs lack global context, Transformers local features), and black-box decision-making. These gaps hinder clinical adoption. We propose HyFormer-Net, a hybrid CNN-Transformer for simultaneous segmentation and classification with intrinsic interpretability. Its dual-branch encoder integrates EfficientNet-B3 and Swin Transformer via multi-scale hierarchical fusion blocks. An attention-gated decoder provides precision and explainability. We introduce dual-pipeline interpretability: (1) intrinsic attention validation with quantitative IoU verification (mean: 0.86), and (2) Grad-CAM for classification reasoning. On the BUSI dataset, HyFormer-Net achieves Dice Score 0.761 +/- 0.072 and accuracy 93.2%, outperforming U-Net, Attention U-Net, and TransUNet. Malignant Recall of 92.1 +/- 2.2% ensures minimal false negatives. Ensemble modeling yields exceptional Dice 90.2%, accuracy 99.5%, and perfect 100% Malignant Recall, eliminating false negatives. Ablation studies confirm multi-scale fusion contributes +16.8% Dice and attention gates add +5.9%. Crucially, we conduct the first cross-dataset generalization study for hybrid CNN-Transformers in breast ultrasound. Zero-shot transfer fails (Dice: 0.058), confirming domain shift. However, progressive fine-tuning with only 10% target-domain data (68 images) recovers 92.5% performance. With 50% data, our model achieves 77.3% Dice, exceeding source-domain performance (76.1%) and demonstrating true generalization.

Method: Uses Dynamically Scaled Progressive Attention (DSPA) with three innovations: Adaptive Fusion (learnt channel-spatial attention blending), Phase Scaling (training-stage-aware intensity modulation), and Residual Adaptation (self-optimized skip connections). Integrated with enhanced MBConv blocks and features dual attention pathways with real-time weight adjustment and cascaded refinement layers.

Result: Achieved CIFAR-10: 95.57% accuracy (0.85M parameters) and 93.80% (0.37M parameters); CIFAR-100: 81.37% accuracy (0.92M parameters) and 74.85% (0.44M parameters). 2.1 times parameter reduction over MobileNetV3 with +3.2 percentage points accuracy improvement on CIFAR-10. Hardware-friendly design with 0.28G FLOPs.

Conclusion: FastBoost demonstrates unprecedented parameter-accuracy trade-offs through co-optimization of dynamic attention and efficient convolution operations, enabling deployment in resource-constrained edge devices without accuracy degradation.

Abstract: We present FastBoost, a parameter-efficient neural architecture that achieves state-of-the-art performance on CIFAR benchmarks through a novel Dynamically Scaled Progressive Attention (DSPA) mechanism. Our design establishes new efficiency frontiers with: CIFAR-10: 95.57% accuracy (0.85M parameters) and 93.80% (0.37M parameters) CIFAR-100: 81.37% accuracy (0.92M parameters) and 74.85% (0.44M parameters) The breakthrough stems from three fundamental innovations in DSPA: (1) Adaptive Fusion: Learnt channel-spatial attention blending with dynamic weights. (2) Phase Scaling: Training-stage-aware intensity modulation (from 0.5 to 1.0). (3) Residual Adaptation: Self-optimized skip connections (gamma from 0.5 to 0.72). By integrating DSPA with enhanced MBConv blocks, FastBoost achieves a 2.1 times parameter reduction over MobileNetV3 while improving accuracy by +3.2 percentage points on CIFAR-10. The architecture features dual attention pathways with real-time weight adjustment, cascaded refinement layers (increasing gradient flow by 12.7%), and a hardware-friendly design (0.28G FLOPs). This co-optimization of dynamic attention and efficient convolution operations demonstrates unprecedented parameter-accuracy trade-offs, enabling deployment in resource-constrained edge devices without accuracy degradation.

Method: Proposed T-MLA framework crafts adversarial perturbations in wavelet domain by directly targeting quality of attacked and reconstructed images. Uses strategic confinement to specific wavelet subbands for maximum distortion with perceptual stealth.

Result: Extensive evaluation across multiple state-of-the-art NIC architectures shows large drop in reconstruction quality while perturbations remain visually imperceptible. Reveals critical security flaws in generative and content delivery pipelines.

Conclusion: The work demonstrates that neural image compression systems have fundamental security vulnerabilities that can be exploited through sophisticated wavelet-domain attacks, posing serious risks to real-world content delivery systems.

Abstract: Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of classifiers. Existing adversarial attacks on NICs are often naive adaptations of pixel-space methods, overlooking the unique, structured nature of the compression pipeline. In this work, we propose a more advanced class of vulnerabilities by introducing T-MLA, the first targeted multiscale log–exponential attack framework. Our approach crafts adversarial perturbations in the wavelet domain by directly targeting the quality of the attacked and reconstructed images. This allows for a principled, offline attack where perturbations are strategically confined to specific wavelet subbands, maximizing distortion while ensuring perceptual stealth. Extensive evaluation across multiple state-of-the-art NIC architectures on standard image compression benchmarks reveals a large drop in reconstruction quality while the perturbations remain visually imperceptible. Our findings reveal a critical security flaw at the core of generative and content delivery pipelines.

Method: Uses S2 cells as nested multiresolution grid, predicts geographic tokens hierarchically from broad regions to specific locations, incorporates beam search and multi-sample inference for uncertainty management.

Result: Achieves state-of-the-art performance on Im2GPS3k and YFCC4k datasets, with up to 13.9% accuracy gains in MLLM-free setting and outperforms all baselines when augmented with MLLM.

Conclusion: Hierarchical sequence prediction with S2 cells and autoregressive sampling strategies effectively addresses image geolocalization challenges, setting new state-of-the-art performance.

Abstract: Image geolocalization, the task of determining an image’s geographic origin, poses significant challenges, largely due to visual similarities across disparate locations and the large search space. To address these issues, we propose a hierarchical sequence prediction approach inspired by how humans narrow down locations from broad regions to specific addresses. Analogously, our model predicts geographic tokens hierarchically, first identifying a general region and then sequentially refining predictions to increasingly precise locations. Rather than relying on explicit semantic partitions, our method uses S2 cells, a nested, multiresolution global grid, and sequentially predicts finer-level cells conditioned on visual inputs and previous predictions. This procedure mirrors autoregressive text generation in large language models. Much like in language modeling, final performance depends not only on training but also on inference-time strategy. We investigate multiple top-down traversal methods for autoregressive sampling, incorporating techniques from test-time compute scaling used in language models. Specifically, we integrate beam search and multi-sample inference while exploring various selection strategies to determine the final output. This enables the model to manage uncertainty by exploring multiple plausible paths through the hierarchy. We evaluate our method on the Im2GPS3k and YFCC4k datasets against two distinct sets of baselines: those that operate without a Multimodal Large Language Model (MLLM) and those that leverage one. In the MLLM-free setting, our model surpasses other comparable baselines on nearly all metrics, achieving state-of-the-art performance with accuracy gains of up to 13.9%. When augmented with an MLLM, our model outperforms all baselines, setting a new state-of-the-art across all metrics. The source code is available at https://github.com/NNargesNN/GeoToken.

Method: Proposed RebusDescProgICE framework combining unstructured descriptions with code-based structured reasoning and improved reasoning-based in-context example selection.

Result: The framework improved performance on the |↻BUS| benchmark by 2.1-4.1% for closed-source models and 20-30% for open-source models compared to Chain-of-Thought Reasoning.

Conclusion: The benchmark and framework advance VLM capabilities on complex reasoning tasks like rebus puzzles, showing significant improvements especially for open-source models.

Abstract: Understanding Rebus Puzzles (Rebus Puzzles use pictures, symbols, and letters to represent words or phrases creatively) requires a variety of skills such as image recognition, cognitive skills, commonsense reasoning, multi-step reasoning, image-based wordplay, etc., making this a challenging task for even current Vision-Language Models. In this paper, we present $\left|,\circlearrowright,\boxed{\text{BUS}},\right|$, a large and diverse benchmark of $1,333$ English Rebus Puzzles containing different artistic styles and levels of difficulty, spread across 18 categories such as food, idioms, sports, finance, entertainment, etc. We also propose $RebusDescProgICE$, a model-agnostic framework which uses a combination of an unstructured description and code-based, structured reasoning, along with better, reasoning-based in-context example selection, improving the performance of Vision-Language Models on $\left|,\circlearrowright,\boxed{\text{BUS}},\right|$ by $2.1-4.1%$ and $20-30%$ using closed-source and open-source models respectively compared to Chain-of-Thought Reasoning.

Method: Created synthetic dataset by transforming multivariate KPI vectors into RGB images using four encoding methods: physically inspired mappings, Perlin noise, neural wallpapering, and fractal branching. Generated 30,000 samples per method with realistic network noise.

Result: Produced SliceVision-F2I dataset containing raw KPI vectors and corresponding low-resolution RGB images, simulating operational uncertainties and measurement imperfections.

Conclusion: The dataset is publicly available and suitable for visual learning, network state classification, anomaly detection, and benchmarking image-based ML techniques for network data analysis.

Abstract: The emergence of 5G and 6G networks has established network slicing as a significant part of future service-oriented architectures, demanding refined identification methods supported by robust datasets. The article presents SliceVision-F2I, a dataset of synthetic samples for studying feature visualization in network slicing for next-generation networking systems. The dataset transforms multivariate Key Performance Indicator (KPI) vectors into visual representations through four distinct encoding methods: physically inspired mappings, Perlin noise, neural wallpapering, and fractal branching. For each encoding method, 30,000 samples are generated, each comprising a raw KPI vector and a corresponding RGB image at low-resolution pixels. The dataset simulates realistic and noisy network conditions to reflect operational uncertainties and measurement imperfections. SliceVision-F2I is suitable for tasks involving visual learning, network state classification, anomaly detection, and benchmarking of image-based machine learning techniques applied to network data. The dataset is publicly available and can be reused in various research contexts, including multivariate time series analysis, synthetic data generation, and feature-to-image transformations.

Method: Combines Epanechnikov’s non-parametric kernel density estimation (EKDE) with bimodal logistic regression classifier in a statistical-model-based learning scheme for feature extraction from medical images.

Result: Tested on 13,808 chest X-rays from COVID-19 Radiography Dataset, achieving 70.14% accuracy, 59.26% sensitivity, and 74.18% specificity, showing moderate performance with room for improvement in sensitivity.

Conclusion: EKDE-based approaches show potential to enhance diagnostic accuracy in medical imaging, though clinical expertise remains essential for further model refinement.

Abstract: This study presents a novel method for diagnosing respiratory diseases using image data. It combines Epanechnikov’s non-parametric kernel density estimation (EKDE) with a bimodal logistic regression classifier in a statistical-model-based learning scheme. EKDE’s flexibility in modeling data distributions without assuming specific shapes and its adaptability to pixel intensity variations make it valuable for extracting key features from medical images. The method was tested on 13808 randomly selected chest X-rays from the COVID-19 Radiography Dataset, achieved an accuracy of 70.14%, a sensitivity of 59.26%, and a specificity of 74.18%, demonstrating moderate performance in detecting respiratory disease while showing room for improvement in sensitivity. While clinical expertise remains essential for further refining the model, this study highlights the potential of EKDE-based approaches to enhance diagnostic accuracy and reliability in medical imaging.

Method: Two-stage fine-tuning: 1) Supervised Fine-Tuning on Viewpoint-100K dataset, 2) Reinforcement Learning with GRPO algorithm. Also uses hybrid cold-start initialization for viewpoint representation learning.

Result: Significantly activates spatial reasoning ability in MLLMs, improving performance on both in-domain and out-of-domain reasoning tasks.

Conclusion: Developing foundational spatial skills in MLLMs is valuable for future progress in robotics, autonomous systems, and 3D scene understanding.

Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have significantly improved 2D visual understanding, prompting interest in their application to complex 3D reasoning tasks. However, it remains unclear whether these models can effectively capture the detailed spatial information required for robust real-world performance, especially cross-view consistency, a key requirement for accurate 3D reasoning. Considering this issue, we introduce Viewpoint Learning, a task designed to evaluate and improve the spatial reasoning capabilities of MLLMs. We present the Viewpoint-100K dataset, consisting of 100K object-centric image pairs with diverse viewpoints and corresponding question-answer pairs. Our approach employs a two-stage fine-tuning strategy: first, foundational knowledge is injected to the baseline MLLM via Supervised Fine-Tuning (SFT) on Viewpoint-100K, resulting in significant improvements across multiple tasks; second, generalization is enhanced through Reinforcement Learning using the Group Relative Policy Optimization (GRPO) algorithm on a broader set of questions. Additionally, we introduce a hybrid cold-start initialization method designed to simultaneously learn viewpoint representations and maintain coherent reasoning thinking. Experimental results show that our approach significantly activates the spatial reasoning ability of MLLM, improving performance on both in-domain and out-of-domain reasoning tasks. Our findings highlight the value of developing foundational spatial skills in MLLMs, supporting future progress in robotics, autonomous systems, and 3D scene understanding.

Method: Proposes ViACT framework that represents deforming anatomical structures as point sets, encodes spatial geometry and image patches into tokens, and uses masked autoencoding that only reconstructs anatomical patches during pre-training.

Result: ViACT produces interpretable attention maps aligned with cardiac pathology regions, generalizes to myocardium point tracking without specialized components, and achieves strong performance on EF regression and CA detection tasks.

Conclusion: Anatomical constraints effectively focus transformer attention on clinically relevant regions, improving interpretability and performance for echocardiogram analysis tasks while maintaining generalization capabilities.

Abstract: Video transformers have recently demonstrated strong potential for echocardiogram (echo) analysis, leveraging self-supervised pre-training and flexible adaptation across diverse tasks. However, like other models operating on videos, they are prone to learning spurious correlations from non-diagnostic regions such as image backgrounds. To overcome this limitation, we propose the Video Anatomically Constrained Transformer (ViACT), a novel framework that integrates anatomical priors directly into the transformer architecture. ViACT represents a deforming anatomical structure as a point set and encodes both its spatial geometry and corresponding image patches into transformer tokens. During pre-training, ViACT follows a masked autoencoding strategy that masks and reconstructs only anatomical patches, enforcing that representation learning is focused on the anatomical region. The pre-trained model can then be fine-tuned for tasks localized to this region. In this work we focus on the myocardium, demonstrating the framework on echo analysis tasks such as left ventricular ejection fraction (EF) regression and cardiac amyloidosis (CA) detection. The anatomical constraint focuses transformer attention within the myocardium, yielding interpretable attention maps aligned with regions of known CA pathology. Moreover, ViACT generalizes to myocardium point tracking without requiring task-specific components such as correlation volumes used in specialized tracking networks.

Method: Proposes using embedded devices with GPUs in edge computing to offload intensive computer vision tasks from mobile devices.

Result: Experiments showed GPUs achieve performance gains compared to using only CPUs, ensuring better user experience for computer vision applications.

Conclusion: GPU-equipped embedded devices in edge computing can overcome resource limitations and provide improved performance for computer vision applications.

Abstract: Computer vision applications, especially those using augmented reality technology, are becoming quite popular in mobile devices. However, this type of application is known as presenting significant demands regarding resources. In order to enable its utilization in devices with more modest resources, edge computing can be used to offload certain high intensive tasks. Still, edge computing is usually composed of devices with limited capacity, which may impact in users quality of experience when using computer vision applications. This work proposes the use of embedded devices with graphics processing units (GPUs) to overcome such limitation. Experiments performed shown that GPUs can attain a performance gain when compared to using only CPUs, which guarantee a better experience to users using such kind of application.

Method: Uses Prior-guided Concept Predictor (PCP) with class-level concept priors as weak supervision, incorporating KL divergence and entropy regularization for refinement and clinical reasoning alignment.

Result: Improves concept-level F1-score by over 33% compared to zero-shot baselines on PH2 and WBCatt datasets, with competitive classification performance on four medical datasets relative to fully supervised models.

Conclusion: PCP provides an effective weakly supervised alternative for interpretable medical imaging predictions without requiring costly concept annotations.

Abstract: Human-interpretable predictions are essential for deploying AI in medical imaging, yet most interpretable-by-design (IBD) frameworks require concept annotations for training data, which are costly and impractical to obtain in clinical contexts. Recent attempts to bypass annotation, such as zero-shot vision-language models or concept-generation frameworks, struggle to capture domain-specific medical features, leading to poor reliability. In this paper, we propose a novel Prior-guided Concept Predictor (PCP), a weakly supervised framework that enables concept answer prediction without explicit supervision or reliance on language models. PCP leverages class-level concept priors as weak supervision and incorporates a refinement mechanism with KL divergence and entropy regularization to align predictions with clinical reasoning. Experiments on PH2 (dermoscopy) and WBCatt (hematology) show that PCP improves concept-level F1-score by over 33% compared to zero-shot baselines, while delivering competitive classification performance on four medical datasets (PH2, WBCatt, HAM10000, and CXR4) relative to fully supervised concept bottleneck models (CBMs) and V-IP.

Method: Introduces categorical symmetry product combining cyclic time shifts, positive gains, and sensor-hierarchy poset to capture categorical symmetry structure, achieving equivariance with respect to this product.

Result: On UCI-HAR under out-of-distribution perturbations, CatEquiv achieves markedly higher robustness compared to circularly padded CNNs and plain CNNs.

Conclusion: Enforcing categorical symmetries yields strong invariance and generalization without requiring additional model capacity.

Abstract: We propose CatEquiv, a category-equivariant neural network for Human Activity Recognition (HAR) from inertial sensors that systematically encodes temporal, amplitude, and structural symmetries. In particular, we introduce the categorical symmetry product where cyclic time shifts, positive gains and the sensor-hierarchy poset together capture the categorical symmetry structure of the data. CatEquiv achieves equivariance with respect to the categorical symmetry product. On UCI-HAR under out-of-distribution perturbations, CatEquiv attains markedly higher robustness compared with circularly padded CNNs and plain CNNs. These results demonstrate that enforcing categorical symmetries yields strong invariance and generalization without additional model capacity.

Method: Proposes MicroAUNet with depthwise-separable dilated convolutions and parameter-shared channel-spatial attention block for multi-scale boundary features, plus progressive two-stage knowledge distillation from a high-capacity teacher model.

Result: Achieves state-of-the-art accuracy under extremely low model complexity, suitable for real-time clinical polyp segmentation.

Conclusion: MicroAUNet effectively addresses the trade-off between accuracy and computational efficiency for real-time colorectal polyp segmentation in clinical applications.

Abstract: Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored by academia and industry. However, current deep learning-based polyp segmentation models either compromise clinical decision-making by providing ambiguous polyp margins in segmentation outputs or rely on heavy architectures with high computational complexity, resulting in insufficient inference speeds for real-time colorectal endoscopic applications. To address this problem, we propose MicroAUNet, a light-weighted attention-based segmentation network that combines depthwise-separable dilated convolutions with a single-path, parameter-shared channel-spatial attention block to strengthen multi-scale boundary features. On the basis of it, a progressive two-stage knowledge-distillation scheme is introduced to transfer semantic and boundary cues from a high-capacity teacher. Extensive experiments on benchmarks also demonstrate the state-of-the-art accuracy under extremely low model complexity, indicating that MicroAUNet is suitable for real-time clinical polyp segmentation. The code is publicly available at https://github.com/JeremyXSC/MicroAUNet.

Method: Created a human-annotated benchmark with 1312 tasks grounded in 1876 images, spanning two settings: verbally-augmented reasoning for visual generation and visually-augmented reasoning for verbal generation.

Result: Experiments on 17 unified models showed that cross-modal reasoning determines visual generation quality, with interleaved models outperforming non-interleaved ones, and revealed dissociation between physical and symbolic reasoning capabilities.

Conclusion: Reciprocal cross-modal reasoning is a critical frontier for enabling true omnimodal generation, as current models struggle with constructing visual abstractions for symbolic tasks despite good perceptual interpretation.

Abstract: Unified multimodal models (UMMs) have emerged as a powerful paradigm for seamlessly unifying text and image understanding and generation. However, prevailing evaluations treat these abilities in isolation, such that tasks with multimodal inputs and outputs are scored primarily through unimodal reasoning, i.e., textual benchmarks emphasize language-based reasoning, while visual benchmarks emphasize reasoning outcomes manifested in the pixels. We introduce ROVER to address this pressing need to test reciprocal cross-modal reasoning, the use of one modality to guide, verify, or refine outputs in the other, an ability central to the vision of unified multimodal intelligence. ROVER is a human-annotated benchmark that explicitly targets reciprocal cross-modal reasoning, which contains 1312 tasks grounded in 1876 images, spanning two complementary settings. Verbally-augmented reasoning for visual generation evaluates whether models can use verbal prompts and reasoning chains to guide faithful image synthesis. Visually-augmented reasoning for verbal generation evaluates whether models can generate intermediate visualizations that strengthen their own reasoning processes for question answering. Experiments on 17 unified models reveal two key findings: (i) Cross-modal reasoning determines visual generation quality, with interleaved models significantly outperforming non-interleaved ones; notably, combining strong unimodal models fails to achieve comparable reasoning. (ii) Models show dissociation between physical and symbolic reasoning: they succeed at interpreting perceptual concepts literally but fail to construct visual abstractions for symbolic tasks, where faulty reasoning harms performance. These results highlight reciprocal cross-modal reasoning as a critical frontier for enabling true omnimodal generation.

Method: Developed an automated pipeline to mine and process YouTube videos into object-centric clips with auxiliary annotations. Created Animal-in-Motion benchmark with 230 manually filtered sequences. Evaluated state-of-the-art model-based and model-free methods, and enhanced a model-free approach with sequence-level optimization.

Result: Collected 30K videos (2M frames) - an order of magnitude larger than prior works. Found that model-based methods score better on 2D metrics but produce unrealistic 3D shapes, while model-free methods yield more natural reconstructions but score lower, revealing an evaluation gap. Established the first 4D animal reconstruction baseline.

Conclusion: The pipeline, benchmark, and baseline advance large-scale, markerless 4D animal reconstruction from in-the-wild videos. The work addresses limitations in current datasets and evaluation methods for animal-centric computer vision tasks.

Abstract: Computer vision for animals holds great promise for wildlife research but often depends on large-scale data, while existing collection methods rely on controlled capture setups. Recent data-driven approaches show the potential of single-view, non-invasive analysis, yet current animal video datasets are limited–offering as few as 2.4K 15-frame clips and lacking key processing for animal-centric 3D/4D tasks. We introduce an automated pipeline that mines YouTube videos and processes them into object-centric clips, along with auxiliary annotations valuable for downstream tasks like pose estimation, tracking, and 3D/4D reconstruction. Using this pipeline, we amass 30K videos (2M frames)–an order of magnitude more than prior works. To demonstrate its utility, we focus on the 4D quadruped animal reconstruction task. To support this task, we present Animal-in-Motion (AiM), a benchmark of 230 manually filtered sequences with 11K frames showcasing clean, diverse animal motions. We evaluate state-of-the-art model-based and model-free methods on Animal-in-Motion, finding that 2D metrics favor the former despite unrealistic 3D shapes, while the latter yields more natural reconstructions but scores lower–revealing a gap in current evaluation. To address this, we enhance a recent model-free approach with sequence-level optimization, establishing the first 4D animal reconstruction baseline. Together, our pipeline, benchmark, and baseline aim to advance large-scale, markerless 4D animal reconstruction and related tasks from in-the-wild videos. Code and datasets are available at https://github.com/briannlongzhao/Animal-in-Motion.

Method: Uses Multi-level Discrete Wavelet Transform to decompose images into wavelet spectra, pyramid tokenization for transformer processing, and a dual-decoder to handle low-frequency and high-frequency sub-bands while maintaining alignment.

Result: Extensive experiments show high performance on both perception quality and fidelity across multiple benchmark datasets.

Conclusion: DTWSR effectively addresses frequency sub-band interrelation issues in super-resolution, producing superior results through diffusion models and transformers.

Abstract: Discrete Wavelet Transform (DWT) has been widely explored to enhance the performance of image superresolution (SR). Despite some DWT-based methods improving SR by capturing fine-grained frequency signals, most existing approaches neglect the interrelations among multiscale frequency sub-bands, resulting in inconsistencies and unnatural artifacts in the reconstructed images. To address this challenge, we propose a Diffusion Transformer model based on image Wavelet spectra for SR (DTWSR).DTWSR incorporates the superiority of diffusion models and transformers to capture the interrelations among multiscale frequency sub-bands, leading to a more consistence and realistic SR image. Specifically, we use a Multi-level Discrete Wavelet Transform (MDWT) to decompose images into wavelet spectra. A pyramid tokenization method is proposed which embeds the spectra into a sequence of tokens for transformer model, facilitating to capture features from both spatial and frequency domain. A dual-decoder is designed elaborately to handle the distinct variances in lowfrequency (LF) and high-frequency (HF) sub-bands, without omitting their alignment in image generation. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our method, with high performance on both perception quality and fidelity.

Method: Uses a topology-aware Graph Convolutional Network (GCN-PSN) with Siamese architecture trained with contrastive regression objective to learn discriminative pose embeddings from human skeleton graphs.

Result: Outperforms coordinate-based baselines and achieves competitive performance on AQA-7 and FineDiving benchmarks. Ablation studies validate effectiveness of leveraging skeletal topology.

Conclusion: Modeling human skeleton topology through GCNs is effective for pose similarity and action quality assessment, demonstrating the importance of structural information in motion analysis.

Abstract: Action Quality Assessment (AQA) requires fine-grained understanding of human motion and precise evaluation of pose similarity. This paper proposes a topology-aware Graph Convolutional Network (GCN) framework, termed GCN-PSN, which models the human skeleton as a graph to learn discriminative, topology-sensitive pose embeddings. Using a Siamese architecture trained with a contrastive regression objective, our method outperforms coordinate-based baselines and achieves competitive performance on AQA-7 and FineDiving benchmarks. Experimental results and ablation studies validate the effectiveness of leveraging skeletal topology for pose similarity and action quality assessment.

Method: Proposes Multi-scale Token Preservation Strategy (MTPS) with hierarchical residual vector quantization variational autoencoder (RQ-VAE), Scalable Autoregressive (SAR) modeling that predicts multiple scale tokens per step, and CAQ-VAE - a lightweight convolution-attention hybrid VQ-VAE to address interpolation degradation.

Result: Achieves FID of 0.06 on Motion-X dataset (vs MoMask’s 0.20) with 27% reduction in inference time, requiring only 10 inference steps matching RQ-VAE quantization layers. Shows strong generalization to downstream tasks like motion editing without fine-tuning.

Conclusion: MoSa demonstrates superior generation quality and efficiency for text-driven 3D human motion generation, outperforming prior methods in both fidelity and speed while maintaining good generalization capabilities.

Abstract: We introduce MoSa, a novel hierarchical motion generation framework for text-driven 3D human motion generation that enhances the Vector Quantization-guided Generative Transformers (VQ-GT) paradigm through a coarse-to-fine scalable generation process. In MoSa, we propose a Multi-scale Token Preservation Strategy (MTPS) integrated into a hierarchical residual vector quantization variational autoencoder (RQ-VAE). MTPS employs interpolation at each hierarchical quantization to effectively retain coarse-to-fine multi-scale tokens. With this, the generative transformer supports Scalable Autoregressive (SAR) modeling, which predicts scale tokens, unlike traditional methods that predict only one token at each step. Consequently, MoSa requires only 10 inference steps, matching the number of RQ-VAE quantization layers. To address potential reconstruction degradation from frequent interpolation, we propose CAQ-VAE, a lightweight yet expressive convolution-attention hybrid VQ-VAE. CAQ-VAE enhances residual block design and incorporates attention mechanisms to better capture global dependencies. Extensive experiments show that MoSa achieves state-of-the-art generation quality and efficiency, outperforming prior methods in both fidelity and speed. On the Motion-X dataset, MoSa achieves an FID of 0.06 (versus MoMask’s 0.20) while reducing inference time by 27 percent. Moreover, MoSa generalizes well to downstream tasks such as motion editing, requiring no additional fine-tuning. The code is available at https://mosa-web.github.io/MoSa-web

Method: Uses sensor-masked images - a unified representation that overlays spatially grounded masks from different sensors onto RGB images. Built on RGB-pretrained VLA backbone with lightweight per-sensor projectors for data-efficient learning.

Result: Achieves 84% average task success rate, outperforming RGB-only models by 59% and raw-sensor-input baselines by 28%. Shows higher learning efficiency and stronger generalization capability.

Conclusion: Integrating multiple sensing modalities through sensor-masked images significantly enhances VLA model performance for manipulation tasks, providing better perception and spatial intelligence than RGB-only approaches.

Abstract: Vision-language-action (VLA) models have shown strong generalization for action prediction through large-scale vision-language pretraining. However, most existing models rely solely on RGB cameras, limiting their perception and, consequently, manipulation capabilities. We present OmniVLA, an omni-modality VLA model that integrates novel sensing modalities for physically-grounded spatial intelligence beyond RGB perception. The core of our approach is the sensor-masked image, a unified representation that overlays spatially grounded and physically meaningful masks onto the RGB images, derived from sensors including an infrared camera, a mmWave radar, and a microphone array. This image-native unification keeps sensor input close to RGB statistics to facilitate training, provides a uniform interface across sensor hardware, and enables data-efficient learning with lightweight per-sensor projectors. Built on this, we present a multisensory vision-language-action model architecture and train the model based on an RGB-pretrained VLA backbone. We evaluate OmniVLA on challenging real-world tasks where sensor-modality perception is needed to guide the manipulation. OmniVLA achieves an average task success rate of 84%, significantly outperforms both RGB-only and raw-sensor-input baseline models by 59% and 28% respectively, meanwhile showing higher learning efficiency and stronger generalization capability.

Method: Created reasoning chains for QA with minimal human intervention, fine-tuned smaller LLMs and VLMs with auto-validated reasoning chains, and used reinforcement learning with larger datasets for training.

Result: Achieved an average 10 percentage point improvement in accuracy on the baseline Indian Food VQA task through reasoning chain augmentation.

Conclusion: Multi-step reasoning chains are essential for accurate Indian Food VQA, and the proposed approach effectively captures the complex culinary context and relationships in Indian cuisine.

Abstract: The immense diversity in the culture and culinary of Indian cuisines calls attention to the major shortcoming of the existing Visual Question Answering(VQA) systems which are inclined towards the foods from Western region. Recent attempt towards building a VQA dataset for Indian food is a step towards addressing this challenge. However, their approach towards VQA follows a two-step process in which the answer is generated first, followed by the explanation of the expected answer. In this work, we claim that food VQA requires to follow a multi-step reasoning process to arrive at an accurate answer, especially in the context of India food, which involves understanding complex culinary context and identifying relationships between various food items. With this hypothesis we create reasoning chains upon the QA with minimal human intervention. We fine-tune smaller LLMs and VLMs with auto-validated reasoning chains and further train them using reinforcement learning with larger data. With augmentation of reasoning chains, we observed accuracy improvement of an average 10 percentage points on the baseline. We provide detailed analysis in terms the effect of addition of reasoning chains for the Indian Food VQA task. Index Terms - FoodVQA, Reasoning Chains, Reinforcement Learning, Knowledge Graph.

Method: Four training methods were evaluated: baseline model on US data, model on flipped US data, pretrained on US data then fine-tuned on Australian highways, and pretrained on flipped US data then fine-tuned on Australian highways. Saliency-based analysis was used to measure attention shifts.

Result: Pretraining on flipped data alone worsens prediction stability, but significantly improves adaptation when followed by fine-tuning, leading to lower prediction error and stronger focus on left-side cues. Similar trends were confirmed with ResNet architecture.

Conclusion: Preprocessing techniques like flipped-data pretraining followed by fine-tuning improve model adaptation with minimal retraining requirements, emphasizing the importance of proper domain adaptation strategies.

Abstract: Domain adaptation is required for automated driving models to generalize well across diverse road conditions. This paper explores a training method for domain adaptation to adapt PilotNet, an end-to-end deep learning-based model, for left-hand driving conditions using real-world Australian highway data. Four training methods were evaluated: (1) a baseline model trained on U.S. right-hand driving data, (2) a model trained on flipped U.S. data, (3) a model pretrained on U.S. data and then fine-tuned on Australian highways, and (4) a model pretrained on flipped U.S. data and then finetuned on Australian highways. This setup examines whether incorporating flipped data enhances the model adaptation by providing an initial left-hand driving alignment. The paper compares model performance regarding steering prediction accuracy and attention, using saliency-based analysis to measure attention shifts across significant road regions. Results show that pretraining on flipped data alone worsens prediction stability due to misaligned feature representations, but significantly improves adaptation when followed by fine-tuning, leading to lower prediction error and stronger focus on left-side cues. To validate this approach across different architectures, the same experiments were done on ResNet, which confirmed similar adaptation trends. These findings emphasize the importance of preprocessing techniques, such as flipped-data pretraining, followed by fine-tuning to improve model adaptation with minimal retraining requirements.

Method: Introduced a detailed human evaluation protocol for BEAT2 dataset, conducted large-scale crowdsourced evaluation of six recent gesture-generation models across motion realism and speech-gesture alignment dimensions.

Result: Found that newer models don’t consistently outperform earlier approaches, published claims of high motion realism or alignment may not hold under rigorous evaluation, and the field needs disentangled assessments of motion quality and multimodal alignment.

Conclusion: The field must adopt standardized, disentangled evaluation protocols for accurate benchmarking. The authors will release synthetic motion data, video stimuli, rendering scripts, and human preference votes to drive standardization and enable new evaluation research.

Abstract: We review human evaluation practices in automated, speech-driven 3D gesture generation and find a lack of standardisation and frequent use of flawed experimental setups. This leads to a situation where it is impossible to know how different methods compare, or what the state of the art is. In order to address common shortcomings of evaluation design, and to standardise future user studies in gesture-generation works, we introduce a detailed human evaluation protocol for the widely-used BEAT2 motion-capture dataset. Using this protocol, we conduct large-scale crowdsourced evaluation to rank six recent gesture-generation models – each trained by its original authors – across two key evaluation dimensions: motion realism and speech-gesture alignment. Our results provide strong evidence that 1) newer models do not consistently outperform earlier approaches; 2) published claims of high motion realism or speech-gesture alignment may not hold up under rigorous evaluation; and 3) the field must adopt disentangled assessments of motion quality and multimodal alignment for accurate benchmarking in order to make progress. Finally, in order to drive standardisation and enable new evaluation research, we will release five hours of synthetic motion from the benchmarked models; over 750 rendered video stimuli from the user studies – enabling new evaluations without model reimplementation required – alongside our open-source rendering script, and the 16,000 pairwise human preference votes collected for our benchmark.

Method: Inject gaze-derived features into Vision Transformer’s attention mechanism to bias spatial feature selection toward human-attended regions, and introduce gaze-aware attention head importance metric.

Result: Consistent gains in detection accuracy over gaze-agnostic baselines on custom simulator dataset and public benchmarks (Ego4D Ego-Motion, Ego-CH-Gaze datasets).

Conclusion: The framework effectively leverages gaze cues to enhance object detection in egocentric videos and provides interpretability through gaze-aware attention analysis.

Abstract: Human gaze offers rich supervisory signals for understanding visual attention in complex visual environments. In this paper, we propose Eyes on Target, a novel depth-aware and gaze-guided object detection framework designed for egocentric videos. Our approach injects gaze-derived features into the attention mechanism of a Vision Transformer (ViT), effectively biasing spatial feature selection toward human-attended regions. Unlike traditional object detectors that treat all regions equally, our method emphasises viewer-prioritised areas to enhance object detection. We validate our method on an egocentric simulator dataset where human visual attention is critical for task assessment, illustrating its potential in evaluating human performance in simulation scenarios. We evaluate the effectiveness of our gaze-integrated model through extensive experiments and ablation studies, demonstrating consistent gains in detection accuracy over gaze-agnostic baselines on both the custom simulator dataset and public benchmarks, including Ego4D Ego-Motion and Ego-CH-Gaze datasets. To interpret model behaviour, we also introduce a gaze-aware attention head importance metric, revealing how gaze cues modulate transformer attention dynamics.

Method: Proposes Adversarial Flatness (AF) to solve deceptive flatness, develops AFA attack with efficient approximation, and introduces MonteCarlo Adversarial Sampling (MCAS) for better sampling efficiency.

Result: Comprehensive results on ImageNet-compatible dataset show superiority over six baselines, generating flatter adversarial examples and boosting transferability across model architectures. Outperforms baselines on input transformation attacks and Baidu Cloud API.

Conclusion: The proposed AFA method effectively addresses deceptive flatness and significantly improves adversarial transferability through dual-order information and efficient sampling techniques.

Abstract: Transferable attacks generate adversarial examples on surrogate models to fool unknown victim models, posing real-world threats and growing research interest. Despite focusing on flat losses for transferable adversarial examples, recent studies still fall into suboptimal regions, especially the flat-yet-sharp areas, termed as deceptive flatness. In this paper, we introduce a novel black-box gradient-based transferable attack from a perspective of dual-order information. Specifically, we feasibly propose Adversarial Flatness (AF) to the deceptive flatness problem and a theoretical assurance for adversarial transferability. Based on this, using an efficient approximation of our objective, we instantiate our attack as Adversarial Flatness Attack (AFA), addressing the altered gradient sign issue. Additionally, to further improve the attack ability, we devise MonteCarlo Adversarial Sampling (MCAS) by enhancing the inner-loop sampling efficiency. The comprehensive results on ImageNet-compatible dataset demonstrate superiority over six baselines, generating adversarial examples in flatter regions and boosting transferability across model architectures. When tested on input transformation attacks or the Baidu Cloud API, our method outperforms baselines.

Method: The method employs CenterMamba encoder with 3x3 corner-axis-center short-sequence scanning for center-prioritized information aggregation, memory-driven structural prompt generator for automatic prompt synthesis, and memory-augmented multi-scale decoder with deep supervision.

Result: Extensive experiments on public benchmarks show that CenterMamba-SAM achieves state-of-the-art performance in brain lesion segmentation.

Conclusion: The proposed framework effectively addresses brain lesion segmentation challenges through innovative scanning strategies and memory-driven components, demonstrating superior performance compared to existing methods.

Abstract: Brain lesion segmentation remains challenging due to small, low-contrast lesions, anisotropic sampling, and cross-slice discontinuities. We propose CenterMamba-SAM, an end-to-end framework that freezes a pretrained backbone and trains only lightweight adapters for efficient fine-tuning. At its core is the CenterMamba encoder, which employs a novel 3x3 corner-axis-center short-sequence scanning strategy to enable center-prioritized, axis-reinforced, and diagonally compensated information aggregation. This design enhances sensitivity to weak boundaries and tiny foci while maintaining sparse yet effective feature representation. A memory-driven structural prompt generator maintains a prototype bank across neighboring slices, enabling automatic synthesis of reliable prompts without user interaction, thereby improving inter-slice coherence. The memory-augmented multi-scale decoder integrates memory attention modules at multiple levels, combining deep supervision with progressive refinement to restore fine details while preserving global consistency. Extensive experiments on public benchmarks demonstrate that CenterMamba-SAM achieves state-of-the-art performance in brain lesion segmentation.

Method: The adapter aligns azimuth and applies horizontal circular padding to preserve neighbor continuity. It uses local-window K-Nearest Neighbors to compute local statistics, compressed into geometry-aware cues that drive region-aware regularization during training.

Result: In source-only cross-weather evaluation (training on SemanticKITTI, testing on SemanticSTF), the adapter improves mIoU by 7.9 percentage points over data-centric augmentation baseline and by 0.6 points over class-centric regularization baseline.

Conclusion: Geometry-driven regularization is a key direction for all-weather LiDAR segmentation, with the plug-and-play adapter providing significant improvements with negligible inference cost.

Abstract: LiDAR semantic segmentation degrades in adverse weather because refraction, scattering, and point dropouts corrupt geometry. Prior work in weather simulation, mixing-based augmentation, domain randomization, and uncertainty or boundary regularization improves robustness but still overlooks structural vulnerabilities near boundaries, corners, and sparse regions. We present a Light Geometry-aware adapter. The module aligns azimuth and applies horizontal circular padding to preserve neighbor continuity across the 0~360 degree wrap-around boundary. A local-window K-Nearest Neighbors gathers nearby points and computes simple local statistics, which are compressed into compact geometry-aware cues. During training, these cues drive region-aware regularization that stabilizes predictions in structurally fragile areas. The adapter is plug and play, complements augmentation, and can be enabled only during training with negligible inference cost. We adopt a source-only cross-weather setup where models train on SemanticKITTI and are evaluated on SemanticSTF without target labels or fine-tuning. The adapter improves mIoU by 7.9 percentage points over the data-centric augmentation baseline and by 0.6 points over the class-centric regularization baseline. These results indicate that geometry-driven regularization is a key direction for all-weather LiDAR segmentation.

Method: Distill a bidirectional text-to-video teacher model into a causal student using Self Forcing with Distribution Matching Distillation. Key innovations include sliding-window causal attention with attention sinks, and self-rollout with KV cache rolling during training to simulate inference-time extrapolations.

Result: Achieves state-of-the-art results in motion following and video quality while being two orders of magnitude faster than existing methods. Enables constant-speed generation of arbitrarily long videos with up to 29 FPS streaming on a single GPU.

Conclusion: MotionStream uniquely enables infinite-length streaming video generation, allowing users to paint trajectories, control cameras, or transfer motion and see results unfold in real-time for a truly interactive experience.

Abstract: Current motion-conditioned video generation methods suffer from prohibitive latency (minutes per video) and non-causal processing that prevents real-time interaction. We present MotionStream, enabling sub-second latency with up to 29 FPS streaming generation on a single GPU. Our approach begins by augmenting a text-to-video model with motion control, which generates high-quality videos that adhere to the global text prompt and local motion guidance, but does not perform inference on the fly. As such, we distill this bidirectional teacher into a causal student through Self Forcing with Distribution Matching Distillation, enabling real-time streaming inference. Several key challenges arise when generating videos of long, potentially infinite time-horizons: (1) bridging the domain gap from training on finite length and extrapolating to infinite horizons, (2) sustaining high quality by preventing error accumulation, and (3) maintaining fast inference, without incurring growth in computational cost due to increasing context windows. A key to our approach is introducing carefully designed sliding-window causal attention, combined with attention sinks. By incorporating self-rollout with attention sinks and KV cache rolling during training, we properly simulate inference-time extrapolations with a fixed context window, enabling constant-speed generation of arbitrarily long videos. Our models achieve state-of-the-art results in motion following and video quality while being two orders of magnitude faster, uniquely enabling infinite-length streaming. With MotionStream, users can paint trajectories, control cameras, or transfer motion, and see results unfold in real-time, delivering a truly interactive experience.

Method: Two-stage approach: 1) Luminance enhancement using variational U-Nets and multi-scale mapping, 2) Hue correction using dual-branch color query module guided by localized hue priors from faded paintings.

Result: Extensive experiments show PRevivor achieves superior performance both quantitatively and qualitatively compared to state-of-the-art colorization methods.

Conclusion: PRevivor effectively revives colors in ancient Chinese paintings through its prior-guided transformer approach with sequential luminance enhancement and hue correction.

Abstract: Ancient Chinese paintings are a valuable cultural heritage that is damaged by irreversible color degradation. Reviving color-degraded paintings is extraordinarily difficult due to the complex chemistry mechanism. Progress is further slowed by the lack of comprehensive, high-quality datasets, which hampers the creation of end-to-end digital restoration tools. To revive colors, we propose PRevivor, a prior-guided color transformer that learns from recent paintings (e.g., Ming and Qing Dynasty) to restore ancient ones (e.g., Tang and Song Dynasty). To develop PRevivor, we decompose color restoration into two sequential sub-tasks: luminance enhancement and hue correction. For luminance enhancement, we employ two variational U-Nets and a multi-scale mapping module to translate faded luminance into restored counterparts. For hue correction, we design a dual-branch color query module guided by localized hue priors extracted from faded paintings. Specifically, one branch focuses attention on regions guided by masked priors, enforcing localized hue correction, whereas the other branch remains unconstrained to maintain a global reasoning capability. To evaluate PRevivor, we conduct extensive experiments against state-of-the-art colorization methods. The results demonstrate superior performance both quantitatively and qualitatively.

Method: The review comprehensively assesses adaptation strategies including supervised fine-tuning, domain-specific pretraining, parameter-efficient fine-tuning, self-supervised learning, hybrid methods, and multimodal frameworks. It also explores emerging directions like continual learning, federated approaches, hybrid self-supervised learning, data-centric pipelines, and systematic benchmarking.

Result: The review evaluates performance gains, clinical applicability, and limitations of various adaptation approaches, identifying trade-offs and unresolved challenges that prior reviews have often overlooked.

Conclusion: The paper provides a roadmap for developing adaptive, trustworthy, and clinically integrated foundation models capable of meeting real-world medical imaging demands by outlining strategies and associated research gaps.

Abstract: Foundation models (FMs) have emerged as a transformative paradigm in medical image analysis, offering the potential to provide generalizable, task-agnostic solutions across a wide range of clinical tasks and imaging modalities. Their capacity to learn transferable representations from large-scale data has the potential to address the limitations of conventional task-specific models. However, adaptation of FMs to real-world clinical practice remains constrained by key challenges, including domain shifts, limited availability of high-quality annotated data, substantial computational demands, and strict privacy requirements. This review presents a comprehensive assessment of strategies for adapting FMs to the specific demands of medical imaging. We examine approaches such as supervised fine-tuning, domain-specific pretraining, parameter-efficient fine-tuning, self-supervised learning, hybrid methods, and multimodal or cross-modal frameworks. For each, we evaluate reported performance gains, clinical applicability, and limitations, while identifying trade-offs and unresolved challenges that prior reviews have often overlooked. Beyond these established techniques, we also highlight emerging directions aimed at addressing current gaps. These include continual learning to enable dynamic deployment, federated and privacy-preserving approaches to safeguard sensitive data, hybrid self-supervised learning to enhance data efficiency, data-centric pipelines that combine synthetic generation with human-in-the-loop validation, and systematic benchmarking to assess robust generalization under real-world clinical variability. By outlining these strategies and associated research gaps, this review provides a roadmap for developing adaptive, trustworthy, and clinically integrated FMs capable of meeting the demands of real-world medical imaging.

Method: Uses functions that yield consistent outputs for natural images but divergent outputs for generated images, leveraging orthogonal gradient subspaces. Detects generated images when transformations along data manifold cause significant loss changes in self-supervised models pre-trained on natural images. Employs normalizing flows to amplify differences by extruding generated images away from natural image manifold.

Result: Extensive experiments demonstrate the method’s efficacy in detecting generated images, particularly addressing diminishing manifold disparities in advanced generative models.

Conclusion: The proposed framework provides an effective detection method for generated images by exploiting geometric manifold differences and amplifying detectable disparities, offering a robust alternative to binary classification approaches.

Abstract: The increasing realism of generated images has raised significant concerns about their potential misuse, necessitating robust detection methods. Current approaches mainly rely on training binary classifiers, which depend heavily on the quantity and quality of available generated images. In this work, we propose a novel framework that exploits geometric differences between the data manifolds of natural and generated images. To exploit this difference, we employ a pair of functions engineered to yield consistent outputs for natural images but divergent outputs for generated ones, leveraging the property that their gradients reside in mutually orthogonal subspaces. This design enables a simple yet effective detection method: an image is identified as generated if a transformation along its data manifold induces a significant change in the loss value of a self-supervised model pre-trained on natural images. Further more, to address diminishing manifold disparities in advanced generative models, we leverage normalizing flows to amplify detectable differences by extruding generated images away from the natural image manifold. Extensive experiments demonstrate the efficacy of this method. Code is available at https://github.com/tmlr-group/ConV.

Method: Proposed UniREditBench with 2,700 curated samples across 8 primary dimensions and 18 sub-dimensions, introduced multimodal dual-reference evaluation (textual + ground-truth image references), and created UniREdit-Data-100K synthetic dataset with CoT reasoning annotations.

Result: Fine-tuned Bagel on the synthetic dataset to create UniREdit-Bagel, which showed substantial improvements in both in-domain and out-of-distribution settings. Benchmarking revealed strengths and weaknesses of various image editing models.

Conclusion: UniREditBench provides a systematic framework for evaluating reasoning-based image editing, addressing limitations of existing benchmarks and enabling more reliable assessment of model capabilities across diverse scenarios.

Abstract: Recent advances in multi-modal generative models have driven substantial improvements in image editing. However, current generative models still struggle with handling diverse and complex image editing tasks that require implicit reasoning, underscoring the need for a comprehensive benchmark to systematically assess their performance across various reasoning scenarios. Existing benchmarks primarily focus on single-object attribute transformation in realistic scenarios, which, while effective, encounter two key challenges: (1) they largely overlook multi-object interactions as well as game-world scenarios that involve human-defined rules, which are common in real-life applications; (2) they only rely on textual references to evaluate the generated images, potentially leading to systematic misjudgments, especially in complex reasoning scenarios. To this end, this work proposes UniREditBench, a unified benchmark for reasoning-based image editing evaluation. It comprises 2,700 meticulously curated samples, covering both real- and game-world scenarios across 8 primary dimensions and 18 sub-dimensions. To improve evaluation reliability, we introduce multimodal dual-reference evaluation, providing both textual and ground-truth image references for each sample assessment. Furthermore, we design an automated multi-scenario data synthesis pipeline and construct UniREdit-Data-100K, a large-scale synthetic dataset with high-quality chain-of-thought (CoT) reasoning annotations. We fine-tune Bagel on this dataset and develop UniREdit-Bagel, demonstrating substantial improvements in both in-domain and out-of-distribution settings. Through thorough benchmarking of both open-source and closed-source image editing models, we reveal their strengths and weaknesses across various aspects.

Method: Two-stage framework: Stage 1 uses segmentation to generate probability maps that suppress artifacts and highlight gastric anatomy; Stage 2 employs dual-branch classifier that fuses information from right lateral decubitus and supine ultrasound views.

Result: Outperforms current state-of-the-art approaches by significant margin on self-collected dataset.

Conclusion: The framework shows great promise for automated preoperative aspiration risk assessment, offering robust, efficient, and accurate clinical solution.

Abstract: Accurate assessment of gastric content from ultrasound is critical for stratifying aspiration risk at induction of general anesthesia. However, traditional methods rely on manual tracing of gastric antra and empirical formulas, which face significant limitations in both efficiency and accuracy. To address these challenges, a novel two-stage probability map-guided dual-branch fusion framework (REASON) for gastric content assessment is proposed. In stage 1, a segmentation model generates probability maps that suppress artifacts and highlight gastric anatomy. In stage 2, a dual-branch classifier fuses information from two standard views, right lateral decubitus (RLD) and supine (SUP), to improve the discrimination of learned features. Experimental results on a self-collected dataset demonstrate that the proposed framework outperforms current state-of-the-art approaches by a significant margin. This framework shows great promise for automated preoperative aspiration risk assessment, offering a more robust, efficient, and accurate solution for clinical practice.

Method: Proposes Direct Protective Optimization (DPO) loss function and Learning to Protect (L2P) dual-path optimization strategy that alternates between personalization and protection paths to disrupt subject personalization.

Result: APDM outperforms existing methods and achieves state-of-the-art performance in preventing unauthorized personalization without compromising generative quality.

Conclusion: The framework effectively protects diffusion models from unauthorized personalization through theoretical analysis and practical optimization strategies, providing robust privacy protection.

Abstract: Recent advances in diffusion models have enabled high-quality synthesis of specific subjects, such as identities or objects. This capability, while unlocking new possibilities in content creation, also introduces significant privacy risks, as personalization techniques can be misused by malicious users to generate unauthorized content. Although several studies have attempted to counter this by generating adversarially perturbed samples designed to disrupt personalization, they rely on unrealistic assumptions and become ineffective in the presence of even a few clean images or under simple image transformations. To address these challenges, we shift the protection target from the images to the diffusion model itself to hinder the personalization of specific subjects, through our novel framework called Anti-Personalized Diffusion Models (APDM). We first provide a theoretical analysis demonstrating that a naive approach of existing loss functions to diffusion models is inherently incapable of ensuring convergence for robust anti-personalization. Motivated by this finding, we introduce Direct Protective Optimization (DPO), a novel loss function that effectively disrupts subject personalization in the target model without compromising generative quality. Moreover, we propose a new dual-path optimization strategy, coined Learning to Protect (L2P). By alternating between personalization and protection paths, L2P simulates future personalization trajectories and adaptively reinforces protection at each step. Experimental results demonstrate that our framework outperforms existing methods, achieving state-of-the-art performance in preventing unauthorized personalization. The code is available at https://github.com/KU-VGI/APDM.

Method: Three-phase framework: 1) Positive semi-definite latent factor grouping to mitigate spatial entanglement, 2) Instance probability counterfactual inference via cluster-reasoning for semantic disentanglement, 3) Generalized linear weighted decision with instance effect re-weighting for decision entanglement.

Result: Outperforms all state-of-the-art models on multicentre datasets and achieves pathologist-aligned interpretability through disentangled representations and transparent decision-making.

Conclusion: The proposed framework effectively addresses entanglement issues in MIL for whole-slide images, providing both superior performance and enhanced interpretability aligned with pathologist reasoning.

Abstract: Multiple instance learning (MIL) has been widely used for representing whole-slide pathology images. However, spatial, semantic, and decision entanglements among instances limit its representation and interpretability. To address these challenges, we propose a latent factor grouping-boosted cluster-reasoning instance disentangled learning framework for whole-slide image (WSI) interpretable representation in three phases. First, we introduce a novel positive semi-definite latent factor grouping that maps instances into a latent subspace, effectively mitigating spatial entanglement in MIL. To alleviate semantic entanglement, we employs instance probability counterfactual inference and optimization via cluster-reasoning instance disentangling. Finally, we employ a generalized linear weighted decision via instance effect re-weighting to address decision entanglement. Extensive experiments on multicentre datasets demonstrate that our model outperforms all state-of-the-art models. Moreover, it attains pathologist-aligned interpretability through disentangled representations and a transparent decision-making process.

Method: Proposes MVSMamba with a Dynamic Mamba module using reference-centered dynamic scanning strategy for efficient intra- and inter-view feature interaction, omnidirectional multi-view feature representations, and multi-scale global feature aggregation.

Result: MVSMamba outperforms state-of-the-art MVS methods on DTU dataset and Tanks-and-Temples benchmark with superior performance and efficiency.

Conclusion: MVSMamba demonstrates that Mamba architecture is highly effective for MVS tasks, achieving better performance than Transformer-based methods while maintaining linear computational complexity.

Abstract: Robust feature representations are essential for learning-based Multi-View Stereo (MVS), which relies on accurate feature matching. Recent MVS methods leverage Transformers to capture long-range dependencies based on local features extracted by conventional feature pyramid networks. However, the quadratic complexity of Transformer-based MVS methods poses challenges to balance performance and efficiency. Motivated by the global modeling capability and linear complexity of the Mamba architecture, we propose MVSMamba, the first Mamba-based MVS network. MVSMamba enables efficient global feature aggregation with minimal computational overhead. To fully exploit Mamba’s potential in MVS, we propose a Dynamic Mamba module (DM-module) based on a novel reference-centered dynamic scanning strategy, which enables: (1) Efficient intra- and inter-view feature interaction from the reference to source views, (2) Omnidirectional multi-view feature representations, and (3) Multi-scale global feature aggregation. Extensive experimental results demonstrate MVSMamba outperforms state-of-the-art MVS methods on the DTU dataset and the Tanks-and-Temples benchmark with both superior performance and efficiency. The source code is available at https://github.com/JianfeiJ/MVSMamba.

Method: Integrates CLIP model’s text-image alignment with guided loss to incorporate natural language semantics, combining concentrated perturbation strategy from SSAE with dissimilar text embeddings from GAMA for multi-object scene manipulation.

Result: Achieves competitive performance comparable or superior to existing techniques across various black-box victim models while preserving greater visual fidelity.

Conclusion: The proposed method successfully creates effective adversarial examples that deceive classification models while maintaining high structural similarity to original inputs, demonstrating the power of integrating CLIP’s semantic understanding with perturbation strategies.

Abstract: The rapid growth of deep learning has brought about powerful models that can handle various tasks, like identifying images and understanding language. However, adversarial attacks, an unnoticed alteration, can deceive models, leading to inaccurate predictions. In this paper, a generative adversarial attack method is proposed that uses the CLIP model to create highly effective and visually imperceptible adversarial perturbations. The CLIP model’s ability to align text and image representation helps incorporate natural language semantics with a guided loss to generate effective adversarial examples that look identical to the original inputs. This integration allows extensive scene manipulation, creating perturbations in multi-object environments specifically designed to deceive multilabel classifiers. Our approach integrates the concentrated perturbation strategy from Saliency-based Auto-Encoder (SSAE) with the dissimilar text embeddings similar to Generative Adversarial Multi-Object Scene Attacks (GAMA), resulting in perturbations that both deceive classification models and maintain high structural similarity to the original images. The model was tested on various tasks across diverse black-box victim models. The experimental results show that our method performs competitively, achieving comparable or superior results to existing techniques, while preserving greater visual fidelity.

Method: Uses a hybrid ResBlock detail-aware Transformer backbone with three modules: ASBE for adaptive boundary enhancement, HVDA for fine-grained feature modeling, and EulerFF for fusion weighting guided by Euler’s formula.

Result: Achieved comparable segmentation accuracy and boundary quality on Synapse and BUSI datasets.

Conclusion: RDTE-UNet improves structural consistency and boundary accuracy across morphology, orientation, and scale in medical image segmentation.

Abstract: Medical image segmentation is essential for computer-assisted diagnosis and treatment planning, yet substantial anatomical variability and boundary ambiguity hinder reliable delineation of fine structures. We propose RDTE-UNet, a segmentation network that unifies local modeling with global context to strengthen boundary delineation and detail preservation. RDTE-UNet employs a hybrid ResBlock detail-aware Transformer backbone and three modules: ASBE for adaptive boundary enhancement, HVDA for fine-grained feature modeling, and EulerFF for fusion weighting guided by Euler’s formula. Together, these components improve structural consistency and boundary accuracy across morphology, orientation, and scale. On Synapse and BUSI dataset, RDTE-UNet has achieved a comparable level in terms of segmentation accuracy and boundary quality.

Method: Uses prompt-conditioned instance-query generator to transform single point into multiple queries, hybrid CNN-Transformer encoder with spatial gating, and competitively optimized query decoder for parallel multi-instance prediction.

Result: Achieved comparable performance on LiTS17 and KiTS21 datasets with strong robustness to prompts.

Conclusion: Provides practical solution for efficient annotation of clinically relevant multi-lesion cases.

Abstract: Accurate segmentation of medical images is fundamental to tumor diagnosis and treatment planning. SAM-based interactive segmentation has gained attention for its strong generalization, but most methods follow a single-point-to-single-object paradigm, which limits multi-lesion segmentation. Moreover, ViT backbones capture global context but often miss high-fidelity local details. We propose MIQ-SAM3D, a multi-instance 3D segmentation framework with a competitive query optimization strategy that shifts from single-point-to-single-mask to single-point-to-multi-instance. A prompt-conditioned instance-query generator transforms a single point prompt into multiple specialized queries, enabling retrieval of all semantically similar lesions across the 3D volume from a single exemplar. A hybrid CNN-Transformer encoder injects CNN-derived boundary saliency into ViT self-attention via spatial gating. A competitively optimized query decoder then enables end-to-end, parallel, multi-instance prediction through inter-query competition. On LiTS17 and KiTS21 dataset, MIQ-SAM3D achieved comparable levels and exhibits strong robustness to prompts, providing a practical solution for efficient annotation of clinically relevant multi-lesion cases.

Method: Proposed Content-Style Subspace Blending and Content-Style Balance loss to better balance content preservation and style application across different intensity levels.

Result: Outperforms existing techniques in both qualitative and quantitative evaluations, achieving superior content-style trade-off with significantly lower Inverted Generational Distance (IGD) and Generational Distance (GD) scores.

Conclusion: The proposed approach successfully expands the content-style frontier by improving content similarity across varying style intensities, providing better balance between content preservation and style application.

Abstract: Recent advancements in text-to-image diffusion models have significantly improved the personalization and stylization of generated images. However, previous studies have only assessed content similarity under a single style intensity. In our experiments, we observe that increasing style intensity leads to a significant loss of content features, resulting in a suboptimal content-style frontier. To address this, we propose a novel approach to expand the content-style frontier by leveraging Content-Style Subspace Blending and a Content-Style Balance loss. Our method improves content similarity across varying style intensities, significantly broadening the content-style frontier. Extensive experiments demonstrate that our approach outperforms existing techniques in both qualitative and quantitative evaluations, achieving superior content-style trade-off with significantly lower Inverted Generational Distance (IGD) and Generational Distance (GD) scores compared to current methods.

Method: The CMI-MTL framework uses three modules: FVTA for fine-grained visual-text feature alignment, CIFR for cross-modal interleaved feature representation, and FFAE for free-form answer-enhanced multi-task learning that leverages auxiliary knowledge from open-ended questions.

Result: CMI-MTL outperforms state-of-the-art methods on three Med-VQA datasets (VQA-RAD, SLAKE, and OVQA) and demonstrates effectiveness through interpretability experiments.

Conclusion: The proposed CMI-MTL framework effectively addresses cross-modal alignment challenges and adapts to free-form answer diversity in medical VQA, achieving superior performance across multiple datasets.

Abstract: Medical visual question answering (Med-VQA) is a crucial multimodal task in clinical decision support and telemedicine. Recent self-attention based methods struggle to effectively handle cross-modal semantic alignments between vision and language. Moreover, classification-based methods rely on predefined answer sets. Treating this task as a simple classification problem may make it unable to adapt to the diversity of free-form answers and overlook the detailed semantic information of free-form answers. In order to tackle these challenges, we introduce a Cross-Mamba Interaction based Multi-Task Learning (CMI-MTL) framework that learns cross-modal feature representations from images and texts. CMI-MTL comprises three key modules: fine-grained visual-text feature alignment (FVTA), cross-modal interleaved feature representation (CIFR), and free-form answer-enhanced multi-task learning (FFAE). FVTA extracts the most relevant regions in image-text pairs through fine-grained visual-text feature alignment. CIFR captures cross-modal sequential interactions via cross-modal interleaved feature representation. FFAE leverages auxiliary knowledge from open-ended questions through free-form answer-enhanced multi-task learning, improving the model’s capability for open-ended Med-VQA. Experimental results show that CMI-MTL outperforms the existing state-of-the-art methods on three Med-VQA datasets: VQA-RAD, SLAKE, and OVQA. Furthermore, we conduct more interpretability experiments to prove the effectiveness. The code is publicly available at https://github.com/BioMedIA-repo/CMI-MTL.

Method: Developed a pipeline to generate synthetic data simulating event-based cameras mounted on Autonomous Underwater Vehicles in underwater conditions.

Result: The pipeline effectively generates realistic synthetic data for training vision models, demonstrated specifically for rock detection tasks in poor visibility with suspended particulate matter.

Conclusion: The approach successfully addresses underwater vision challenges using synthetic event-based camera data and can be generalized to other underwater tasks beyond rock detection.

Abstract: The underwater domain presents a vast array of challenges for roboticists and computer vision researchers alike, such as poor lighting conditions and high dynamic range scenes. In these adverse conditions, traditional vision techniques struggle to adapt and lead to suboptimal performance. Event-based cameras present an attractive solution to this problem, mitigating the issues of traditional cameras by tracking changes in the footage on a frame-by-frame basis. In this paper, we introduce a pipeline which can be used to generate realistic synthetic data of an event-based camera mounted to an AUV (Autonomous Underwater Vehicle) in an underwater environment for training vision models. We demonstrate the effectiveness of our pipeline using the task of rock detection with poor visibility and suspended particulate matter, but the approach can be generalized to other underwater tasks.

Method: Proposed UniSOT, a unified tracker that can perform tracking with three reference modalities (bounding box, natural language, or both) across four video modalities (RGB, RGB+Depth, RGB+Thermal, RGB+Event) using uniform parameters.

Result: Extensive experiments on 18 benchmarks show UniSOT outperforms modality-specific counterparts. It achieves over 3.0% AUC improvement on TNL2K across all three reference modalities and over 2.0% main metric improvement on Un-Track across all three RGB+X video modalities.

Conclusion: UniSOT demonstrates superior performance as a unified tracker capable of handling multiple reference and video modalities simultaneously, addressing the limitations of modality-specific trackers in practical applications.

Abstract: Single object tracking aims to localize target object with specific reference modalities (bounding box, natural language or both) in a sequence of specific video modalities (RGB, RGB+Depth, RGB+Thermal or RGB+Event.). Different reference modalities enable various human-machine interactions, and different video modalities are demanded in complex scenarios to enhance tracking robustness. Existing trackers are designed for single or several video modalities with single or several reference modalities, which leads to separate model designs and limits practical applications. Practically, a unified tracker is needed to handle various requirements. To the best of our knowledge, there is still no tracker that can perform tracking with these above reference modalities across these video modalities simultaneously. Thus, in this paper, we present a unified tracker, UniSOT, for different combinations of three reference modalities and four video modalities with uniform parameters. Extensive experimental results on 18 visual tracking, vision-language tracking and RGB+X tracking benchmarks demonstrate that UniSOT shows superior performance against modality-specific counterparts. Notably, UniSOT outperforms previous counterparts by over 3.0% AUC on TNL2K across all three reference modalities and outperforms Un-Track by over 2.0% main metric across all three RGB+X video modalities.

Method: Developed Fire-ART dataset with 15 asset types (2,626 images, 6,627 instances) and a reconstruction approach using modified cube-map conversion and radius-based spherical camera projection for semantic enrichment into BIM models.

Result: Achieved F1-scores of 73% and 88% with localization errors of 0.620 and 0.428 meters respectively in two real-world case studies.

Conclusion: The Fire-ART dataset and reconstruction approach provide valuable resources and robust technical solutions for enhancing accurate digital management of fire safety equipment.

Abstract: Inventory management of firefighting assets is crucial for emergency preparedness, risk assessment, and on-site fire response. However, conventional methods are inefficient due to limited capabilities in automated asset recognition and reconstruction. To address the challenge, this research introduces the Fire-ART dataset and develops a panoramic image-based reconstruction approach for semantic enrichment of firefighting assets into BIM models. The Fire-ART dataset covers 15 fundamental assets, comprising 2,626 images and 6,627 instances, making it an extensive and publicly accessible dataset for asset recognition. In addition, the reconstruction approach integrates modified cube-map conversion and radius-based spherical camera projection to enhance recognition and localization accuracy. Through validations with two real-world case studies, the proposed approach achieves F1-scores of 73% and 88% and localization errors of 0.620 and 0.428 meters, respectively. The Fire-ART dataset and the reconstruction approach offer valuable resources and robust technical solutions to enhance the accurate digital management of fire safety equipment.

Method: Developed MAOML algorithm that combines meta-learning to handle data sparsity and leverages ordinal relationships in freshness labels to train smaller vision language models effectively.

Result: Achieved state-of-the-art performance with 92.71% accuracy across all fruits in both zero-shot and few-shot settings, outperforming existing open-source VLMs and matching proprietary model performance.

Conclusion: MAOML provides an effective solution for fruit freshness classification that addresses data scarcity and privacy concerns while achieving high accuracy comparable to proprietary models.

Abstract: To effectively manage the wastage of perishable fruits, it is crucial to accurately predict their freshness or shelf life using non-invasive methods that rely on visual data. In this regard, deep learning techniques can offer a viable solution. However, obtaining fine-grained fruit freshness labels from experts is costly, leading to a scarcity of data. Closed proprietary Vision Language Models (VLMs), such as Gemini, have demonstrated strong performance in fruit freshness detection task in both zero-shot and few-shot settings. Nonetheless, food retail organizations are unable to utilize these proprietary models due to concerns related to data privacy, while existing open-source VLMs yield sub-optimal performance for the task. Fine-tuning these open-source models with limited data fails to achieve the performance levels of proprietary models. In this work, we introduce a Model-Agnostic Ordinal Meta-Learning (MAOML) algorithm, designed to train smaller VLMs. This approach utilizes meta-learning to address data sparsity and leverages label ordinality, thereby achieving state-of-the-art performance in the fruit freshness classification task under both zero-shot and few-shot settings. Our method achieves an industry-standard accuracy of 92.71%, averaged across all fruits. Keywords: Fruit Quality Prediction, Vision Language Models, Meta Learning, Ordinal Regression

Method: Parameterizes star-convex regions using truncated Fourier series and optimizes under extremal preserve/delete objectives using classifier gradients, guaranteeing single connected masks with fewer parameters.

Result: Matches fidelity of dense masks while producing compact, interpretable regions with improved consistency; achieves higher relevance mass and lower complexity than baselines, especially on DINO models with 15%+ improvement.

Conclusion: The contour-based approach provides faithful yet compact explanations with stable boundaries, explicit area control, and extensibility to multi-object localization, outperforming gradient and perturbation methods.

Abstract: Faithful yet compact explanations for vision models remain a challenge, as commonly used dense perturbation masks are often fragmented and overfitted, needing careful post-processing. Here, we present a training-free explanation method that replaces dense masks with smooth tunable contours. A star-convex region is parameterized by a truncated Fourier series and optimized under an extremal preserve/delete objective using the classifier gradients. The approach guarantees a single, simply connected mask, cuts the number of free parameters by orders of magnitude, and yields stable boundary updates without cleanup. Restricting solutions to low-dimensional, smooth contours makes the method robust to adversarial masking artifacts. On ImageNet classifiers, it matches the extremal fidelity of dense masks while producing compact, interpretable regions with improved run-to-run consistency. Explicit area control also enables importance contour maps, yielding a transparent fidelity-area profiles. Finally, we extend the approach to multi-contour and show how it can localize multiple objects within the same framework. Across benchmarks, the method achieves higher relevance mass and lower complexity than gradient and perturbation based baselines, with especially strong gains on self-supervised DINO models where it improves relevance mass by over 15% and maintains positive faithfulness correlations.

Method: Proposes GT-Pairs that automatically build preference pairs using real videos as positives and model-generated videos as negatives, and Reg-DPO that incorporates SFT loss as regularization into DPO objective. Also uses FSDP framework with memory optimization techniques to increase training capacity.

Result: Achieves nearly three times higher training capacity than using FSDP alone. Extensive experiments on I2V and T2V tasks across multiple datasets show consistent outperformance of existing approaches with superior video generation quality.

Conclusion: The proposed Reg-DPO method effectively addresses video generation challenges by eliminating external annotation needs, enhancing training stability, and significantly improving training capacity, leading to consistently better video generation performance.

Abstract: Recent studies have identified Direct Preference Optimization (DPO) as an efficient and reward-free approach to improving video generation quality. However, existing methods largely follow image-domain paradigms and are mainly developed on small-scale models (approximately 2B parameters), limiting their ability to address the unique challenges of video tasks, such as costly data construction, unstable training, and heavy memory consumption. To overcome these limitations, we introduce a GT-Pair that automatically builds high-quality preference pairs by using real videos as positives and model-generated videos as negatives, eliminating the need for any external annotation. We further present Reg-DPO, which incorporates the SFT loss as a regularization term into the DPO objective to enhance training stability and generation fidelity. Additionally, by combining the FSDP framework with multiple memory optimization techniques, our approach achieves nearly three times higher training capacity than using FSDP alone. Extensive experiments on both I2V and T2V tasks across multiple datasets demonstrate that our method consistently outperforms existing approaches, delivering superior video generation quality.

Method: Proposes Adversarial Self-Distillation (ASD) that aligns student model’s n-step denoising with (n+1)-step outputs at distribution level, and First-Frame Enhancement (FFE) that allocates more denoising steps to initial frames while applying larger skipping steps to later frames.

Result: Extensive experiments on VBench show the method surpasses state-of-the-art approaches in both one-step and two-step video generation, producing a single distilled model that flexibly supports multiple inference-step settings.

Conclusion: The framework enables efficient, high-quality video synthesis with extremely limited denoising steps, eliminating the need for repeated re-distillation while maintaining generation quality.

Abstract: Recent hybrid video generation models combine autoregressive temporal dynamics with diffusion-based spatial denoising, but their sequential, iterative nature leads to error accumulation and long inference times. In this work, we propose a distillation-based framework for efficient causal video generation that enables high-quality synthesis with extremely limited denoising steps. Our approach builds upon the Distribution Matching Distillation (DMD) framework and proposes a novel Adversarial Self-Distillation (ASD) strategy, which aligns the outputs of the student model’s n-step denoising process with its (n+1)-step version at the distribution level. This design provides smoother supervision by bridging small intra-student gaps and more informative guidance by combining teacher knowledge with locally consistent student behavior, substantially improving training stability and generation quality in extremely few-step scenarios (e.g., 1-2 steps). In addition, we present a First-Frame Enhancement (FFE) strategy, which allocates more denoising steps to the initial frames to mitigate error propagation while applying larger skipping steps to later frames. Extensive experiments on VBench demonstrate that our method surpasses state-of-the-art approaches in both one-step and two-step video generation. Notably, our framework produces a single distilled model that flexibly supports multiple inference-step settings, eliminating the need for repeated re-distillation and enabling efficient, high-quality video synthesis.

Method: Question Aligned Semantic Nearest Neighbor Entropy (QA-SNNE) - a black box uncertainty estimator that incorporates question semantics into prediction confidence by measuring semantic entropy in medical text embedding space.

Result: QA-SNNE improves AUROC by 15-38% for zero-shot models, enhances hallucination detection, and maintains gains under out-of-template stress. PEFT models degrade under paraphrasing while LVLMs are more resilient.

Conclusion: QA-SNNE provides a practical step toward automatic failure detection in surgical VQA by linking semantic uncertainty to question context, improving safety and clinician trust when combined with LVLM backbones.

Abstract: Safety and reliability are essential for deploying Visual Question Answering (VQA) in surgery, where incorrect or ambiguous responses can harm the patient. Most surgical VQA research focuses on accuracy or linguistic quality while overlooking safety behaviors such as ambiguity awareness, referral to human experts, or triggering a second opinion. Inspired by Automatic Failure Detection (AFD), we study uncertainty estimation as a key enabler of safer decision making. We introduce Question Aligned Semantic Nearest Neighbor Entropy (QA-SNNE), a black box uncertainty estimator that incorporates question semantics into prediction confidence. It measures semantic entropy by comparing generated answers with nearest neighbors in a medical text embedding space, conditioned on the question. We evaluate five models, including domain specific Parameter-Efficient Fine-Tuned (PEFT) models and zero-shot Large Vision-Language Models (LVLMs), on EndoVis18-VQA and PitVQA. PEFT models degrade under mild paraphrasing, while LVLMs are more resilient. Across three LVLMs and two PEFT baselines, QA-SNNE improves AUROC in most in-template settings and enhances hallucination detection. The Area Under the ROC Curve (AUROC) increases by 15-38% for zero-shot models, with gains maintained under out-of-template stress. QA-SNNE offers a practical and interpretable step toward AFD in surgical VQA by linking semantic uncertainty to question context. Combining LVLM backbones with question aligned uncertainty estimation can improve safety and clinician trust. The code and model are available at https://github.com/DennisPierantozzi/QASNNE

Method: Uses a resolution-aware token decoder with global self-attention, gated cross-attention for fine-scale detail injection, and class-aware point refinement. Includes a boundary-band consistency regularizer during training for coherent predictions around edges.

Result: The approach achieves competitive performance and improved stability across transitions in off-road semantic segmentation.

Conclusion: The proposed method effectively balances computational efficiency with boundary fidelity and noise robustness in challenging off-road segmentation scenarios.

Abstract: Off-road semantic segmentation suffers from thick, inconsistent boundaries, sparse supervision for rare classes, and pervasive label noise. Designs that fuse only at low resolution blur edges and propagate local errors, whereas maintaining high-resolution pathways or repeating high-resolution fusions is costly and fragile to noise. We introduce a resolutionaware token decoder that balances global semantics, local consistency, and boundary fidelity under imperfect supervision. Most computation occurs at a low-resolution bottleneck; a gated cross-attention injects fine-scale detail, and only a sparse, uncertainty-selected set of pixels is refined. The components are co-designed and tightly integrated: global self-attention with lightweight dilated depthwise refinement restores local coherence; a gated cross-attention integrates fine-scale features from a standard high-resolution encoder stream without amplifying noise; and a class-aware point refinement corrects residual ambiguities with negligible overhead. During training, we add a boundary-band consistency regularizer that encourages coherent predictions in a thin neighborhood around annotated edges, with no inference-time cost. Overall, the results indicate competitive performance and improved stability across transitions.

Method: Models Activation Quantization Error (AQE) and Weight Quantization Error (WQE) as independent Gaussian noises. Uses noise injection optimization methods to obtain flat minima in the neural network.

Result: Experimental results demonstrate the effectiveness of the approach in achieving better low-bit PTQ models.

Conclusion: The method opens novel pathways for obtaining low-bit PTQ models by proactively pre-conditioning models through error measurement and disentanglement.

Abstract: Post-training quantization (PTQ) for vision transformers (ViTs) has garnered significant attention due to its efficiency in compressing models. However, existing methods typically overlook the relationship between a well-trained NN and the quantized model, leading to considerable quantization error for PTQ. However, it is unclear how to efficiently train a model-agnostic neural network which is tailored for a predefined precision low-bit model. In this paper, we firstly discover that a flat full precision neural network is crucial for low-bit quantization. To achieve this, we propose a framework that proactively pre-conditions the model by measuring and disentangling the error sources. Specifically, both the Activation Quantization Error (AQE) and the Weight Quantization Error (WQE) are statistically modeled as independent Gaussian noises. We study several noise injection optimization methods to obtain a flat minimum. Experimental results attest to the effectiveness of our approach. These results open novel pathways for obtaining low-bit PTQ models.

Method: Training-only objectives that: 1) sharpen instance-level decision boundaries via feature pulling/pushing to suppress duplicates and confusion, 2) inject cross-modal semantic priors by aligning student’s pyramid features with RGB-trained teacher to strengthen texture-poor thermal features.

Result: Outperformed prior approaches and achieved state-of-the-art performance in experiments.

Conclusion: The method effectively addresses root causes of thermal detection issues during training, enabling robust mono-modality inference without requiring visible input at test time.

Abstract: Robust perception at night remains challenging for thermal-infrared detection: low contrast and weak high-frequency cues lead to duplicate, overlapping boxes, missed small objects, and class confusion. Prior remedies either translate TIR to RGB and hope pixel fidelity transfers to detection – making performance fragile to color or structure artifacts – or fuse RGB and TIR at test time, which requires extra sensors, precise calibration, and higher runtime cost. Both lines can help in favorable conditions, but do not directly shape the thermal representation used by the detector. We keep mono-modality inference and tackle the root causes during training. Specifically, we introduce training-only objectives that sharpen instance-level decision boundaries by pulling together features of the same class and pushing apart those of different classes – suppressing duplicate and confusing detections – and that inject cross-modal semantic priors by aligning the student’s multi-level pyramid features with an RGB-trained teacher, thereby strengthening texture-poor thermal features without visible input at test time. In experiments, our method outperformed prior approaches and achieved state-of-the-art performance.

Method: Uses Mixture of Expert Low-Rank Adaptation (MoE LoRA) with dynamic weight allocation based on input prompts, introduces zero expert to preserve pre-trained parameters, and implements body-part-specific tokenization for enhanced spatial resolution.

Result: Effectively alleviates knowledge forgetting during instruction-tuning and achieves remarkable performance across diverse human motion downstream tasks.

Conclusion: The proposed HMVLM framework successfully addresses key challenges in multimodal integration of human motion with foundation models, demonstrating improved performance while mitigating catastrophic forgetting.

Abstract: The expansion of instruction-tuning data has enabled foundation language models to exhibit improved instruction adherence and superior performance across diverse downstream tasks. Semantically-rich 3D human motion is being progressively integrated with these foundation models to enhance multimodal understanding and cross-modal generation capabilities. However, the modality gap between human motion and text raises unresolved concerns about catastrophic forgetting during this integration. In addition, developing autoregressive-compatible pose representations that preserve generalizability across heterogeneous downstream tasks remains a critical technical barrier. To address these issues, we propose the Human Motion-Vision-Language Model (HMVLM), a unified framework based on the Mixture of Expert Low-Rank Adaption(MoE LoRA) strategy. The framework leverages the gating network to dynamically allocate LoRA expert weights based on the input prompt, enabling synchronized fine-tuning of multiple tasks. To mitigate catastrophic forgetting during instruction-tuning, we introduce a novel zero expert that preserves the pre-trained parameters for general linguistic tasks. For pose representation, we implement body-part-specific tokenization by partitioning the human body into different joint groups, enhancing the spatial resolution of the representation. Experiments show that our method effectively alleviates knowledge forgetting during instruction-tuning and achieves remarkable performance across diverse human motion downstream tasks.

Method: Uses pseudoinverse-guided sampling and adaptive guidance weighting for flexible step-size control and efficient semantic reconstruction. For jamming attacks: power-based subcarrier masking recasts recovery as masked inpainting. For pilot spoofing: formulates channel estimation as blind inverse problem with EM-driven reconstruction algorithm that alternates between pilot recovery and channel estimation.

Result: Extensive experiments over OFDM channels show SecDiff outperforms existing secure and generative JSCC baselines, achieving favorable trade-off between reconstruction quality and computational cost under adversarial conditions.

Conclusion: SecDiff represents a promising step toward practical, low-latency, and attack-resilient semantic communications with enhanced security and robustness.

Abstract: Deep joint source-channel coding (JSCC) has emerged as a promising paradigm for semantic communication, delivering significant performance gains over conventional separate coding schemes. However, existing JSCC frameworks remain vulnerable to physical-layer adversarial threats, such as pilot spoofing and subcarrier jamming, compromising semantic fidelity. In this paper, we propose SecDiff, a plug-and-play, diffusion-aided decoding framework that significantly enhances the security and robustness of deep JSCC under adversarial wireless environments. Different from prior diffusion-guided JSCC methods that suffer from high inference latency, SecDiff employs pseudoinverse-guided sampling and adaptive guidance weighting, enabling flexible step-size control and efficient semantic reconstruction. To counter jamming attacks, we introduce a power-based subcarrier masking strategy and recast recovery as a masked inpainting problem, solved via diffusion guidance. For pilot spoofing, we formulate channel estimation as a blind inverse problem and develop an expectation-minimization (EM)-driven reconstruction algorithm, guided jointly by reconstruction loss and a channel operator. Notably, our method alternates between pilot recovery and channel estimation, enabling joint refinement of both variables throughout the diffusion process. Extensive experiments over orthogonal frequency-division multiplexing (OFDM) channels under adversarial conditions show that SecDiff outperforms existing secure and generative JSCC baselines by achieving a favorable trade-off between reconstruction quality and computational cost. This balance makes SecDiff a promising step toward practical, low-latency, and attack-resilient semantic communications.

Method: Uses a dual-branch architecture to extract alignment information under varying scales and viewpoints, mitigating perspective and environmental impacts.

Result: Achieved outstanding performance with 90.09% Rank-1 accuracy and 78.82% mAP on the Inspection-Personnel dataset.

Conclusion: EPAN demonstrates strong potential for real-world IoT applications, enabling effective and reliable person re-identification across diverse surveillance cameras.

Abstract: Person re-identification (ReID) plays a pivotal role in computer vision, particularly in surveillance and security applications within IoT-enabled smart environments. This study introduces the Enhanced Pedestrian Alignment Network (EPAN), tailored for robust ReID across diverse IoT surveillance conditions. EPAN employs a dual-branch architecture to mitigate the impact of perspective and environmental changes, extracting alignment information under varying scales and viewpoints. Here, we demonstrate EPAN’s strong feature extraction capabilities, achieving outstanding performance on the Inspection-Personnel dataset with a Rank-1 accuracy of 90.09% and a mean Average Precision (mAP) of 78.82%. This highlights EPAN’s potential for real-world IoT applications, enabling effective and reliable person ReID across diverse cameras in surveillance and security systems. The code and data are available at: https://github.com/ggboy2580/EPAN

Method: Proposes flow matching on the SE(3) manifold to model full pose distributions with sample-based estimates, enabling uncertainty reasoning for symmetric objects and severe occlusions.

Result: Achieves state-of-the-art performance on Real275, YCB-V, and LM-O datasets. Demonstrates practical applications in robotic manipulation tasks like active perception and uncertainty-aware grasp synthesis.

Conclusion: The probabilistic framework successfully addresses pose ambiguity through sample-based distribution modeling, providing uncertainty-aware pose estimates that benefit downstream robotic applications.

Abstract: Object pose estimation is a fundamental problem in robotics and computer vision, yet it remains challenging due to partial observability, occlusions, and object symmetries, which inevitably lead to pose ambiguity and multiple hypotheses consistent with the same observation. While deterministic deep networks achieve impressive performance under well-constrained conditions, they are often overconfident and fail to capture the multi-modality of the underlying pose distribution. To address these challenges, we propose a novel probabilistic framework that leverages flow matching on the SE(3) manifold for estimating 6D object pose distributions. Unlike existing methods that regress a single deterministic output, our approach models the full pose distribution with a sample-based estimate and enables reasoning about uncertainty in ambiguous cases such as symmetric objects or severe occlusions. We achieve state-of-the-art results on Real275, YCB-V, and LM-O, and demonstrate how our sample-based pose estimates can be leveraged in downstream robotic manipulation tasks such as active perception for disambiguating uncertain viewpoints or guiding grasp synthesis in an uncertainty-aware manner.

Method: Uses a structured five-layer model with subclasses and characteristics for agents, combined with large foundational models and data augmentation to generate scenarios. Introduces embedding-based comparison and two evaluation metrics.

Result: Developed diversity and originality metrics to evaluate synthetic datasets, demonstrated in various generation setups with qualitative evaluation of synthetic videos from structured descriptions.

Conclusion: The structured five-layer model effectively improves rare scenario generation and evaluation for autonomous driving applications.

Abstract: Rare and challenging driving scenarios are critical for autonomous vehicle development. Since they are difficult to encounter, simulating or generating them using generative models is a popular approach. Following previous efforts to structure driving scenario representations in a layer model, we propose a structured five-layer model to improve the evaluation and generation of rare scenarios. We use this model alongside large foundational models to generate new driving scenarios using a data augmentation strategy. Unlike previous representations, our structure introduces subclasses and characteristics for every agent of the scenario, allowing us to compare them using an embedding specific to our layer-model. We study and adapt two metrics to evaluate the relevance of a synthetic dataset in the context of a structured representation: the diversity score estimates how different the scenarios of a dataset are from one another, while the originality score calculates how similar a synthetic dataset is from a real reference set. This paper showcases both metrics in different generation setup, as well as a qualitative evaluation of synthetic videos generated from structured scenario descriptions. The code and extended results can be found at https://github.com/Valgiz/5LMSG.

Method: Network outputs align optical axes and imaging planes between frames, transform optical flows through these alignments, and quantify deviations to impose geometric constraints on each ego-motion component individually. This reformulates joint learning into coaxial and coplanar forms with closed-form geometric relationships.

Result: DiMoDE achieves state-of-the-art performance on multiple public datasets and a newly collected diverse real-world dataset, particularly under challenging conditions.

Conclusion: The discriminative treatment of motion components and geometric reformulation significantly improve depth and ego-motion estimation robustness and reliability.

Abstract: Unsupervised learning of depth and ego-motion, two fundamental 3D perception tasks, has made significant strides in recent years. However, most methods treat ego-motion as an auxiliary task, either mixing all motion types or excluding depth-independent rotational motions in supervision. Such designs limit the incorporation of strong geometric constraints, reducing reliability and robustness under diverse conditions. This study introduces a discriminative treatment of motion components, leveraging the geometric regularities of their respective rigid flows to benefit both depth and ego-motion estimation. Given consecutive video frames, network outputs first align the optical axes and imaging planes of the source and target cameras. Optical flows between frames are transformed through these alignments, and deviations are quantified to impose geometric constraints individually on each ego-motion component, enabling more targeted refinement. These alignments further reformulate the joint learning process into coaxial and coplanar forms, where depth and each translation component can be mutually derived through closed-form geometric relationships, introducing complementary constraints that improve depth robustness. DiMoDE, a general depth and ego-motion joint learning framework incorporating these designs, achieves state-of-the-art performance on multiple public datasets and a newly collected diverse real-world dataset, particularly under challenging conditions. Our source code will be publicly available at mias.group/DiMoDE upon publication.

Method: Introduces Luminance-Aware Statistical Quantification (LASQ) that models luminance transitions as power-law distributions, uses stratified power functions, and employs a diffusion forward process to discover optimal transition paths between luminance layers.

Result: Achieves superior performance on domain-specific datasets and better generalization across non-reference datasets, enabling more adaptable and versatile light restoration in practical situations.

Conclusion: LASQ provides a probabilistic framework that replaces deterministic mappings, significantly improving LLIE performance in both reference and non-reference scenarios while maintaining better generalization capabilities.

Abstract: Low-light image enhancement (LLIE) faces persistent challenges in balancing reconstruction fidelity with cross-scenario generalization. While existing methods predominantly focus on deterministic pixel-level mappings between paired low/normal-light images, they often neglect the continuous physical process of luminance transitions in real-world environments, leading to performance drop when normal-light references are unavailable. Inspired by empirical analysis of natural luminance dynamics revealing power-law distributed intensity transitions, this paper introduces Luminance-Aware Statistical Quantification (LASQ), a novel framework that reformulates LLIE as a statistical sampling process over hierarchical luminance distributions. Our LASQ re-conceptualizes luminance transition as a power-law distribution in intensity coordinate space that can be approximated by stratified power functions, therefore, replacing deterministic mappings with probabilistic sampling over continuous luminance layers. A diffusion forward process is designed to autonomously discover optimal transition paths between luminance layers, achieving unsupervised distribution emulation without normal-light references. In this way, it considerably improves the performance in practical situations, enabling more adaptable and versatile light restoration. This framework is also readily applicable to cases with normal-light references, where it achieves superior performance on domain-specific datasets alongside better generalization-ability across non-reference datasets.

Method: Uses unsupervised anomaly detection to identify appearance-altering features from unlabeled examples, automatically clusters them into semantic groups, and employs diffusion-based editing for conditional generation of arbitrary-sized textures.

Result: Developed a complete pipeline from small image collections to versatile generative models that enable interactive feature painting on textures of any size.

Conclusion: The approach successfully creates realistic textures with natural blemishes without manual annotation, and the introduced algorithms for diffusion-based editing and infinite texture generation are generic enough for broader applications.

Abstract: In this work, we propose a system that covers the complete workflow for achieving controlled authoring and editing of textures that present distinctive local characteristics. These include various effects that change the surface appearance of materials, such as stains, tears, holes, abrasions, discoloration, and more. Such alterations are ubiquitous in nature, and including them in the synthesis process is crucial for generating realistic textures. We introduce a novel approach for creating textures with such blemishes, adopting a learning-based approach that leverages unlabeled examples. Our approach does not require manual annotations by the user; instead, it detects the appearance-altering features through unsupervised anomaly detection. The various textural features are then automatically clustered into semantically coherent groups, which are used to guide the conditional generation of images. Our pipeline as a whole goes from a small image collection to a versatile generative model that enables the user to interactively create and paint features on textures of arbitrary size. Notably, the algorithms we introduce for diffusion-based editing and infinite stationary texture generation are generic and should prove useful in other contexts as well. Project page: https://reality.tf.fau.de/pub/ardelean2025examplebased.html

Method: Proposes a contrastive training scheme using synthetic negative image sets alongside real positive images. Refines gradients by subtracting positive gradient’s projection onto negative gradient, updating parameters based on the orthogonal component to eliminate trivial shared attributes.

Result: Experiments on various painter and illustrator styles show improved performance over baseline methods both quantitatively and qualitatively.

Conclusion: The NSYNC framework successfully enhances stylization capability of diffusion models by leveraging synthetic negative examples in contrastive learning to capture more unique style characteristics.

Abstract: Current text conditioned image generation methods output realistic looking images, but they fail to capture specific styles. Simply finetuning them on the target style datasets still struggles to grasp the style features. In this work, we present a novel contrastive learning framework to improve the stylization capability of large text-to-image diffusion models. Motivated by the astonishing advance in image generation models that makes synthetic data an intrinsic part of model training in various computer vision tasks, we exploit synthetic image generation in our approach. Usually, the generated synthetic data is dependent on the task, and most of the time it is used to enlarge the available real training dataset. With NSYNC, alternatively, we focus on generating negative synthetic sets to be used in a novel contrastive training scheme along with real positive images. In our proposed training setup, we forward negative data along with positive data and obtain negative and positive gradients, respectively. We then refine the positive gradient by subtracting its projection onto the negative gradient to get the orthogonal component, based on which the parameters are updated. This orthogonal component eliminates the trivial attributes that are present in both positive and negative data and directs the model towards capturing a more unique style. Experiments on various styles of painters and illustrators show that our approach improves the performance over the baseline methods both quantitatively and qualitatively. Our code is available at https://github.com/giddyyupp/NSYNC.

Method: Combines core principles of DINO, DINOv2 and DINOv3 in a unified configuration-driven system, supports multiple transformer architectures, and includes training strategies like LoRA, layer freezing, knowledge distillation, DDP, and FSDP.

Result: Achieves competitive performance on diverse datasets while significantly reducing computational costs, and provides interpretability tools with label-guided data augmentation for improved attention-based localization.

Conclusion: DINO-MX provides a reproducible and scalable foundation for developing, adapting, and benchmarking self-supervised vision models across research and real-world applications.

Abstract: Vision Foundation Models (VFMs) have advanced representation learning through self-supervised methods. However, existing training pipelines are often inflexible, domain-specific, or computationally expensive, which limits their usability across different domains and resource settings. DINO-MX is a modular and extensible training framework that combines the core principles of DINO, DINOv2 and DINOv3 within a unified configuration-driven system. It supports a variety of transformer-based architectures and is fully compatible with the Hugging Face ecosystem. The framework includes multiple training strategies such as low-rank adaptation (LoRA), layer freezing, and knowledge distillation, along with support for distributed training through both Distributed Data Parallel (DDP) and Fully Sharded Data Parallel (FSDP). DINO-MX is designed to work with both natural and specialized data types, including single- and multi-channel images. Experimental results on diverse datasets show that DINO-MX achieves competitive performance while significantly reducing computational costs. Additionally, it offers interpretability tools and a label-guided data augmentation method that improves attention-based localization without the need for extra detection or segmentation heads. DINO-MX provides a reproducible and scalable foundation for developing, adapting, and benchmarking self-supervised vision models across a range of research and real-world applications.

Method: Transformer-based PCD network that extracts shared component features (helmets, uniforms) and uses new real-world patrol surveillance dataset with 10,000 individuals and 50,000+ images collected over six months.

Result: Achieved 79.0% mAP and 82.7% Rank-1 accuracy, representing 15.9% Rank-1 improvement over ResNet50-based methods in occlusion-aware ReID performance.

Conclusion: PCD-ReID effectively addresses occlusion challenges in pedestrian re-identification for tower inspection scenarios and shows strong potential for practical deployment in surveillance and security applications.

Abstract: Occluded pedestrian re-identification (ReID) in base station environments is a critical task in computer vision, particularly for surveillance and security applications. This task faces numerous challenges, as occlusions often obscure key body features, increasing the complexity of identification. Traditional ResNet-based ReID algorithms often fail to address occlusions effectively, necessitating new ReID methods. We propose the PCD-ReID (Pedestrian Component Discrepancy) algorithm to address these issues. The contributions of this work are as follows: To tackle the occlusion problem, we design a Transformer-based PCD network capable of extracting shared component features, such as helmets and uniforms. To mitigate overfitting on public datasets, we collected new real-world patrol surveillance images for model training, covering six months, 10,000 individuals, and over 50,000 images. Comparative experiments with existing ReID algorithms demonstrate that our model achieves a mean Average Precision (mAP) of 79.0% and a Rank-1 accuracy of 82.7%, marking a 15.9% Rank-1 improvement over ResNet50-based methods. Experimental evaluations indicate that PCD-ReID effectively achieves occlusion-aware ReID performance for personnel in tower inspection scenarios, highlighting its potential for practical deployment in surveillance and security applications.

Method: Developed NOA as an open-source napari plugin that integrates multiple state-of-the-art algorithms for organoid detection, segmentation, tracking, feature extraction, custom annotation, and ML-based feature prediction.

Result: Demonstrated NOA’s versatility through three case studies: quantifying morphological changes during organoid differentiation, assessing phototoxicity effects, and predicting organoid viability and differentiation state.

Conclusion: NOA enables comprehensive, AI-driven organoid image analysis within an accessible and extensible framework, making advanced analysis tools available to non-programming biologists.

Abstract: AI tools can greatly enhance the analysis of organoid microscopy images, from detection and segmentation to feature extraction and classification. However, their limited accessibility to biologists without programming experience remains a major barrier, resulting in labor-intensive and largely manual workflows. Although a few AI models for organoid analysis have been developed, most existing tools remain narrowly focused on specific tasks. In this work, we introduce the Napari Organoid Analyzer (NOA), a general purpose graphical user interface to simplify AI-based organoid analysis. NOA integrates modules for detection, segmentation, tracking, feature extraction, custom feature annotation and ML-based feature prediction. It interfaces multiple state-of-the-art algorithms and is implemented as an open-source napari plugin for maximal flexibility and extensibility. We demonstrate the versatility of NOA through three case studies, involving the quantification of morphological changes during organoid differentiation, assessment of phototoxicity effects, and prediction of organoid viability and differentiation state. Together, these examples illustrate how NOA enables comprehensive, AI-driven organoid image analysis within an accessible and extensible framework.

Method: Built on a visuomotor instruction tuning framework with multiscale pixel-aware encoder and visual prompting encoder, trained using a two-stage automated annotation pipeline that generates Pixel-160K dataset with pixel-level annotations.

Result: Improves manipulation success rates by 10.1%-17.8% over OpenVLA while requiring only 1.5% of its pretraining cost on three standard VLA benchmarks.

Conclusion: PixelVLA can be integrated into existing VLAs to enable more accurate, efficient, and versatile robot control in complex environments, with the dataset and code being released as open source.

Abstract: Vision-Language-Action models (VLAs) are emerging as powerful tools for learning generalizable visuomotor control policies. However, current VLAs are mostly trained on large-scale image-text-action data and remain limited in two key ways: (i) they struggle with pixel-level scene understanding, and (ii) they rely heavily on textual prompts, which reduces their flexibility in real-world settings. To address these challenges, we introduce PixelVLA, the first VLA model designed to support both pixel-level reasoning and multimodal prompting with text and visual inputs. Our approach is built on a new visuomotor instruction tuning framework that integrates a multiscale pixel-aware encoder with a visual prompting encoder. To train PixelVLA effectively, we further propose a two-stage automated annotation pipeline that generates Pixel-160K, a large-scale dataset with pixel-level annotations derived from existing robot data. Experiments on three standard VLA benchmarks and two VLA model variants show that PixelVLA improves manipulation success rates by 10.1%-17.8% over OpenVLA, while requiring only 1.5% of its pretraining cost. These results demonstrate that PixelVLA can be integrated into existing VLAs to enable more accurate, efficient, and versatile robot control in complex environments. The dataset and code will be released as open source.

Method: Used Deep Convolutional Generative Adversarial Network (DC-GAN) to create synthetic MRI data and Convolutional Neural Network (CNN) classifier to evaluate synthetic images through brain tumor classification.

Result: Classification performance on synthetic images was comparable to real images, validating the effectiveness of GAN-generated images for downstream tasks.

Conclusion: GAN-generated synthetic MRI data is effective and can address the problem of limited medical imaging data while maintaining utility for classification tasks.

Abstract: Compared to traditional methods, Deep Learning (DL) becomes a key technology for computer vision tasks. Synthetic data generation is an interesting use case for DL, especially in the field of medical imaging such as Magnetic Resonance Imaging (MRI). The need for this task since the original MRI data is limited. The generation of realistic medical images is completely difficult and challenging. Generative Adversarial Networks (GANs) are useful for creating synthetic medical images. In this paper, we propose a DL based methodology for creating synthetic MRI data using the Deep Convolutional Generative Adversarial Network (DC-GAN) to address the problem of limited data. We also employ a Convolutional Neural Network (CNN) classifier to classify the brain tumor using synthetic data and real MRI data. CNN is used to evaluate the quality and utility of the synthetic images. The classification result demonstrates comparable performance on real and synthetic images, which validates the effectiveness of GAN-generated images for downstream tasks.

Method: Uses a dualistic approach with Diverse Quantitative Primitives for orthogonal primitive representation and Dynamic Primitive Allocator to determine optimal primitive count based on image complexity.

Result: Achieves superior performance in reconstruction, retrieval and classification tasks compared to specialized continuous and discrete tokenizers.

Conclusion: CDD-VT effectively bridges the continuous-discrete dichotomy in visual tokenization, enabling strong performance within a concise and scalable MLLM framework.

Abstract: The unification of understanding and generation within a single multi-modal large model (MLLM) remains one significant challenge, largely due to the dichotomy between continuous and discrete visual tokenizations. Continuous tokenizer (CT) achieves strong performance by bridging multiple independently-trained understanding modules and generation modules, but suffers from complex multi-stage pipelines and substantial engineering overhead. Conversely, discrete tokenizers (DT) offer a conceptually elegant idea by quantizing each image into a primitive, but inevitably leading to information loss and performance degradation. To resolve this tension, we question the binary choice between CT and DT, inspired by the wave-particle duality of light, and propose the Continuous-Discrete Dualistic Visual Tokenizer (CDD-VT). We treat visual data as a flexible composition of image primitives derived from quantized codebooks, with the crucial insight that the primitive number assigned to each visual sample is adaptively determined according to its complexity: simple instances use a few primitives, emulating discrete tokenization, while complex instances use many, approximating continuous tokenization. Two core components are designed: Diverse Quantitative Primitives, which encourage primitives orthogonality to better populate information space, and Dynamic Primitive Allocator, which assesses sample complexity to determine the optimal set of primitives. Extensive experiments on reconstruction, retrieval and classification show that CDD-VT achieves superior performance over to specialized CT and DT, effectively getting strong result within a concise and scalable MLLM.

Method: Lite ENSAM, a lightweight adaptation of the ENSAM architecture, designed for efficient volumetric tumor segmentation from CT scans annotated with RECIST annotations.

Result: Achieved Dice Similarity Coefficient (DSC) of 60.7% and Normalized Surface Dice (NSD) of 63.6% on hidden test set in MICCAI FLARE 2025 Task 1. Average total RAM time of 50.6 GBs and average inference time of 14.4 s on CPU on public validation dataset.

Conclusion: Lite ENSAM provides an efficient solution for volumetric tumor segmentation from CT scans with RECIST annotations, addressing the labor-intensive nature of manual volumetric annotation and enabling more reliable treatment assessment.

Abstract: Accurate tumor size measurement is a cornerstone of evaluating cancer treatment response. The most widely adopted standard for this purpose is the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1, which relies on measuring the longest tumor diameter in a single plane. However, volumetric measurements have been shown to provide a more reliable assessment of treatment effect. Their clinical adoption has been limited, though, due to the labor-intensive nature of manual volumetric annotation. In this paper, we present Lite ENSAM, a lightweight adaptation of the ENSAM architecture designed for efficient volumetric tumor segmentation from CT scans annotated with RECIST annotations. Lite ENSAM was submitted to the MICCAI FLARE 2025 Task 1: Pan-cancer Segmentation in CT Scans, Subtask 2, where it achieved a Dice Similarity Coefficient (DSC) of 60.7% and a Normalized Surface Dice (NSD) of 63.6% on the hidden test set, and an average total RAM time of 50.6 GBs and an average inference time of 14.4 s on CPU on the public validation dataset.

Method: Proposes Precomputed Structural Pooling (PSPooling) for efficient graph coarsening, integrated into a self-supervised graph autoencoder that learns anatomy-aware representations from unlabeled surface meshes.

Result: PSPooling significantly improves reconstruction fidelity and classification accuracy in low-label regimes on the MedShapeNet19 benchmark dataset.

Conclusion: PSPooling establishes a strong baseline for medical 3D shape learning, and MedShapeNet19 serves as a widely adopted benchmark for anatomical shape classification research.

Abstract: Progress in anatomical 3D shape classification is limited by the complexity of mesh data and the lack of standardized benchmarks, highlighting the need for robust learning methods and reproducible evaluation. We introduce two key steps toward clinically and benchmark-ready anatomical shape classification via self-supervised graph autoencoding. We propose Precomputed Structural Pooling (PSPooling), a non-learnable mesh pooling operator designed for efficient and structure-preserving graph coarsening in 3D anatomical shape analysis. PSPooling precomputes node correspondence sets based on geometric proximity, enabling parallelizable and reversible pooling and unpooling operations with guaranteed support structure. This design avoids the sparsity and reconstruction issues of selection-based methods and the sequential overhead of edge contraction approaches, making it particularly suitable for high-resolution medical meshes. To demonstrate its effectiveness, we integrate PSPooling into a self-supervised graph autoencoder that learns anatomy-aware representations from unlabeled surface meshes. We evaluate the downstream benefits on MedShapeNet19, a new curated benchmark dataset we derive from MedShapeNet, consisting of 19 anatomical classes with standardized training, validation, and test splits. Experiments show that PSPooling significantly improves reconstruction fidelity and classification accuracy in low-label regimes, establishing a strong baseline for medical 3D shape learning. We hope that MedShapeNet19 will serve as a widely adopted benchmark for anatomical shape classification and further research in medical 3D shape analysis. Access the complete codebase, model weights, and dataset information here: https://github.com/TomasKrsicka/MedShapeNet19-PSPooling.

Method: ViC serializes both content evidence and retriever metadata directly in VLM prompts, using S-Grid (compact image grid representation of videos) to enable list-wise reasoning over video candidates as a zero-shot task.

Result: ViC achieves state-of-the-art zero-shot performance: 87.1% Recall@1 (t2v) / 89.0% (v2t) on MSR-VTT and 99.6% (v2t) on VATEX, with gains up to +40 Recall@1 over previous baselines.

Conclusion: ViC provides a simple, reproducible, and highly effective recipe for turning modern VLMs into powerful zero-shot rerankers and fusers for complex multi-modal retrieval tasks.

Abstract: In the retrieval domain, candidates’ fusion from heterogeneous retrievers is a long-standing challenge, particularly for complex, multi-modal data such as videos. While typical fusion techniques are training-free, they rely solely on rank or score signals, disregarding candidates’ representations. This work introduces Vote-in-Context (ViC), a generalized, training-free framework that re-thinks list-wise reranking and fusion as a zero-shot reasoning task for a Vision-Language Model (VLM). The core insight is to serialize both content evidence and retriever metadata directly within the VLM’s prompt, allowing the model to adaptively weigh retriever consensus against visual-linguistic content. We demonstrate the generality of this framework by applying it to the challenging domain of cross-modal video retrieval. To this end, we introduce the S-Grid, a compact serialization map that represents each video as an image grid, optionally paired with subtitles to enable list-wise reasoning over video candidates. ViC is evaluated both as a single-list reranker, where it dramatically improves the precision of individual retrievers, and as an ensemble fuser, where it consistently outperforms strong baselines like CombSUM. Across video retrieval benchmarks including ActivityNet and VATEX, the framework establishes new state-of-the-art zero-shot retrieval performance, demonstrating its effectiveness in handling complex visual and temporal signals alongside text. In zero-shot settings, ViC achieves Recall@1 scores of 87.1% (t2v) / 89.0% (v2t) on MSR-VTT and 99.6% (v2t) on VATEX, representing massive gains of up to +40 Recall@1 over previous state-of-the-art baselines. We present ViC as a simple, reproducible, and highly effective recipe for turning modern VLMs into powerful zero-shot rerankers and fusers. Code and resources are publicly available at: https://github.com/mohammad2012191/ViC

Method: Uses MLLM-based IQA models for RL rewards, focuses on challenging samples far from ground truth, and adaptively combines RL with SFT through automatic weighting based on sample difficulty.

Result: The proposed RL framework effectively boosts performance across various restoration tasks and can be seamlessly applied to diffusion-based restoration models.

Conclusion: The adaptive RL strategy with IQA-based rewards provides an effective plug-and-play solution for improving diffusion-based image restoration models.

Abstract: Reinforcement Learning (RL) has recently been incorporated into diffusion models, e.g., tasks such as text-to-image. However, directly applying existing RL methods to diffusion-based image restoration models is suboptimal, as the objective of restoration fundamentally differs from that of pure generation: it places greater emphasis on fidelity. In this paper, we investigate how to effectively integrate RL into diffusion-based restoration models. First, through extensive experiments with various reward functions, we find that an effective reward can be derived from an Image Quality Assessment (IQA) model, instead of intuitive ground-truth-based supervision, which has already been optimized during the Supervised Fine-Tuning (SFT) stage prior to RL. Moreover, our strategy focuses on using RL for challenging samples that are significantly distant from the ground truth, and our RL approach is innovatively implemented using MLLM-based IQA models to align distributions with high-quality images initially. As the samples approach the ground truth’s distribution, RL is adaptively combined with SFT for more fine-grained alignment. This dynamic process is facilitated through an automatic weighting strategy that adjusts based on the relative difficulty of the training samples. Our strategy is plug-and-play that can be seamlessly applied to diffusion-based restoration models, boosting its performance across various restoration tasks. Extensive experiments across multiple benchmarks demonstrate the effectiveness of our proposed RL framework.

Method: Uses flow matching backbone with RGB-space geometry feedback from depth/normal maps, path consistency learning for few-step training, and structured 6D lighting annotation protocol.

Result: Achieves state-of-the-art relighting quality with improved physical consistency and 20x speedup for both image and video relighting.

Conclusion: UniLumos effectively bridges the gap between semantic control and physical plausibility in relighting, while introducing LumosBench for automatic evaluation of lighting controllability.

Abstract: Relighting is a crucial task with both practical demand and artistic value, and recent diffusion models have shown strong potential by enabling rich and controllable lighting effects. However, as they are typically optimized in semantic latent space, where proximity does not guarantee physical correctness in visual space, they often produce unrealistic results, such as overexposed highlights, misaligned shadows, and incorrect occlusions. We address this with UniLumos, a unified relighting framework for both images and videos that brings RGB-space geometry feedback into a flow matching backbone. By supervising the model with depth and normal maps extracted from its outputs, we explicitly align lighting effects with the scene structure, enhancing physical plausibility. Nevertheless, this feedback requires high-quality outputs for supervision in visual space, making standard multi-step denoising computationally expensive. To mitigate this, we employ path consistency learning, allowing supervision to remain effective even under few-step training regimes. To enable fine-grained relighting control and supervision, we design a structured six-dimensional annotation protocol capturing core illumination attributes. Building upon this, we propose LumosBench, a disentangled attribute-level benchmark that evaluates lighting controllability via large vision-language models, enabling automatic and interpretable assessment of relighting precision across individual dimensions. Extensive experiments demonstrate that UniLumos achieves state-of-the-art relighting quality with significantly improved physical consistency, while delivering a 20x speedup for both image and video relighting. Code is available at https://github.com/alibaba-damo-academy/Lumos-Custom.

Method: Progressive network architecture with stage-wise optimization of visual aspects, building on Adaptive Supervised PatchNCE framework with additional DAB chromogen concentration and image gradient loss functions.

Result: Experiments on HER2 and ER datasets showed significant improvement in visual quality and finer structural details compared to baseline methods.

Conclusion: The proposed progressive mechanism effectively addresses the interdependence problem in stain translation, enabling better preservation of both structural features and color fidelity in synthesized IHC images.

Abstract: Compared to hematoxylin-eosin (H&E) staining, immunohistochemistry (IHC) not only maintains the structural features of tissue samples, but also provides high-resolution protein localization, which is essential for aiding in pathology diagnosis. Despite its diagnostic value, IHC remains a costly and labor-intensive technique. Its limited scalability and constraints in multiplexing further hinder widespread adoption, especially in resource-limited settings. Consequently, researchers are increasingly exploring computational stain translation techniques to synthesize IHC-equivalent images from H&E-stained slides, aiming to extract protein-level information more efficiently and cost-effectively. However, most existing stain translation techniques rely on a linearly weighted summation of multiple loss terms within a single objective function, strategy that often overlooks the interdepedence among these components-resulting in suboptimal image quality and an inability to simultaneously preserve structural authenticity and color fidelity. To address this limitation, we propose a novel network architecture that follows a progressive structure, incorporating color and cell border generation logic, which enables each visual aspect to be optimized in a stage-wise and decoupled manner. To validate the effectiveness of our proposed network architecture, we build upon the Adaptive Supervised PatchNCE (ASP) framework as our baseline. We introduce additional loss functions based on 3,3’-diaminobenzidine (DAB) chromogen concentration and image gradient, enhancing color fidelity and cell boundary clarity in the generated IHC images. By reconstructing the generation pipeline using our structure-color-cell boundary progressive mechanism, experiments on HER2 and ER datasets demonstrated that the model significantly improved visual quality and achieved finer structural details.

Method: Proposes Learnable Fractional Reaction-Diffusion Dynamics (LFRD2) with time-fractional reaction-diffusion module for iterative depth refinement with dynamic differential orders, and efficient continuous convolution via coefficient prediction and repeated differentiation.

Result: Experiments on four benchmark datasets demonstrate the effectiveness of the approach in improving depth quality for under-display ToF imaging.

Conclusion: LFRD2 successfully combines neural networks with physical modeling to address the challenges of under-display ToF imaging, providing an effective solution for depth restoration through interpretable physical dynamics.

Abstract: Under-display ToF imaging aims to achieve accurate depth sensing through a ToF camera placed beneath a screen panel. However, transparent OLED (TOLED) layers introduce severe degradations-such as signal attenuation, multi-path interference (MPI), and temporal noise-that significantly compromise depth quality. To alleviate this drawback, we propose Learnable Fractional Reaction-Diffusion Dynamics (LFRD2), a hybrid framework that combines the expressive power of neural networks with the interpretability of physical modeling. Specifically, we implement a time-fractional reaction-diffusion module that enables iterative depth refinement with dynamically generated differential orders, capturing long-term dependencies. In addition, we introduce an efficient continuous convolution operator via coefficient prediction and repeated differentiation to further improve restoration quality. Experiments on four benchmark datasets demonstrate the effectiveness of our approach. The code is publicly available at https://github.com/wudiqx106/LFRD2.

Method: Proposes cross-domain diffusion model generating multi-view normal maps and color images, multi-view cross-domain attention for consistency, and cascaded 3D mesh extraction in coarse-to-fine manner.

Result: Achieves high-quality reconstruction results with robust generalization and good efficiency (~3 minutes per mesh), outperforming prior works.

Conclusion: Wonder3D++ holistically improves quality, consistency, and efficiency of single-view 3D reconstruction through its novel cross-domain approach.

Abstract: In this work, we introduce \textbf{Wonder3D++}, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. To holistically improve the quality, consistency, and efficiency of single-view reconstruction tasks, we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. To ensure the consistency of generation, we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a cascaded 3D mesh extraction algorithm that drives high-quality surfaces from the multi-view 2D representations in only about $3$ minute in a coarse-to-fine manner. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and good efficiency compared to prior works. Code available at https://github.com/xxlong0/Wonder3D/tree/Wonder3D_Plus.

Method: Introduces PRBench benchmark that empirically compares common AT and PR-targeted training methods using comprehensive metrics. Also provides theoretical analysis on generalization error of PR performance across different training methods.

Result: AT methods are more versatile for improving both AR and PR performance across diverse hyperparameter settings, while PR-targeted methods consistently yield lower generalization error and higher clean accuracy. A leaderboard with 222 trained models across 7 datasets and 10 architectures is provided.

Conclusion: PRBench fills the gap in evaluating PR training methods, revealing trade-offs between AT and PR-targeted approaches. AT offers broader robustness improvements while PR methods excel in clean accuracy and generalization.

Abstract: Deep learning models are notoriously vulnerable to imperceptible perturbations. Most existing research centers on adversarial robustness (AR), which evaluates models under worst-case scenarios by examining the existence of deterministic adversarial examples (AEs). In contrast, probabilistic robustness (PR) adopts a statistical perspective, measuring the probability that predictions remain correct under stochastic perturbations. While PR is widely regarded as a practical complement to AR, dedicated training methods for improving PR are still relatively underexplored, albeit with emerging progress. Among the few PR-targeted training methods, we identify three limitations: i non-comparable evaluation protocols; ii limited comparisons to strong AT baselines despite anecdotal PR gains from AT; and iii no unified framework to compare the generalization of these methods. Thus, we introduce PRBench, the first benchmark dedicated to evaluating improvements in PR achieved by different robustness training methods. PRBench empirically compares most common AT and PR-targeted training methods using a comprehensive set of metrics, including clean accuracy, PR and AR performance, training efficiency, and generalization error (GE). We also provide theoretical analysis on the GE of PR performance across different training methods. Main findings revealed by PRBench include: AT methods are more versatile than PR-targeted training methods in terms of improving both AR and PR performance across diverse hyperparameter settings, while PR-targeted training methods consistently yield lower GE and higher clean accuracy. A leaderboard comprising 222 trained models across 7 datasets and 10 model architectures is publicly available at https://tmpspace.github.io/PRBenchLeaderboard/.

Method: Created a task explorer pipeline with clustering techniques, factor analysis, and string edit distance to identify global strategies (generalized action sets) and local strategies (similar action compositions), plus hierarchical subtask identification.

Result: The pipeline successfully identified key strategies for task completion and encoded user runs with hierarchical subtask structures. A Task Explorer application was also developed for reviewing results.

Conclusion: The pipeline is adaptable to any action-based time-series data and helps inform both humans and machines about user knowledge, skill, and behavior patterns.

Abstract: This research builds on work in anticipatory human-machine interaction, a subfield of human-machine interaction where machines can facilitate advantageous interactions by anticipating a user’s future state. The aim of this research is to further a machine’s understanding of user knowledge, skill, and behavior in pursuit of implicit coordination. A task explorer pipeline was developed that uses clustering techniques, paired with factor analysis and string edit distance, to automatically identify key global and local strategies that are used to complete tasks. Global strategies identify generalized sets of actions used to complete tasks, while local strategies identify sequences that used those sets of actions in a similar composition. Additionally, meaningful subtasks of various lengths are identified within the tasks. The task explorer pipeline was able to automatically identify key strategies used to complete tasks and encode user runs with hierarchical subtask structures. In addition, a Task Explorer application was developed to easily review pipeline results. The task explorer pipeline can be easily modified to any action-based time-series data and the identified strategies and subtasks help to inform humans and machines on user knowledge, skill, and behavior.

Method: Proposed CGF-DETR with three key modules: XFABlock in backbone for multi-scale feature extraction using convolutional attention with CSP architecture; SPGA module replacing standard multi-head attention with dynamic gating and single-head self-attention; GCFC3 in neck for feature representation via multi-path convolution fusion with structural re-parameterization.

Result: Achieved 82.2% mAP@0.5 on RSNA Pneumonia Detection dataset, outperforming baseline RT-DETR-l by 3.7%. Maintained real-time performance at 48.1 FPS. Complete model achieved 50.4% mAP@[0.5:0.95]. Ablation studies confirmed each module contributes meaningfully to performance improvement.

Conclusion: CGF-DETR demonstrates superior performance for pneumonia detection in chest X-rays while maintaining real-time inference capabilities, making it suitable for clinical applications requiring both accuracy and efficiency.

Abstract: Pneumonia remains a leading cause of morbidity and mortality worldwide, necessitating accurate and efficient automated detection systems. While recent transformer-based detectors like RT-DETR have shown promise in object detection tasks, their application to medical imaging, particularly pneumonia detection in chest X-rays, remains underexplored. This paper presents CGF-DETR, an enhanced real-time detection transformer specifically designed for pneumonia detection. We introduce XFABlock in the backbone to improve multi-scale feature extraction through convolutional attention mechanisms integrated with CSP architecture. To achieve efficient feature aggregation, we propose SPGA module that replaces standard multi-head attention with dynamic gating mechanisms and single-head self-attention. Additionally, GCFC3 is designed for the neck to enhance feature representation through multi-path convolution fusion while maintaining real-time performance via structural re-parameterization. Extensive experiments on the RSNA Pneumonia Detection dataset demonstrate that CGF-DETR achieves 82.2% mAP@0.5, outperforming the baseline RT-DETR-l by 3.7% while maintaining comparable inference speed at 48.1 FPS. Our ablation studies confirm that each proposed module contributes meaningfully to the overall performance improvement, with the complete model achieving 50.4% mAP@[0.5:0.95]

Method: Developed scalable annotation pipeline using vision-language model prompting with human verification, plus platform-aware normalization and cross-modal alignment techniques for cross-platform learning.

Result: Created dataset 10x larger than existing ones with high-quality spatial grounding annotations, established benchmark protocols for in-domain and cross-platform evaluations.

Conclusion: 3EED reveals significant performance gaps in generalizable 3D grounding and provides resources to advance language-driven 3D embodied perception research.

Abstract: Visual grounding in 3D is the key for embodied agents to localize language-referred objects in open-world environments. However, existing benchmarks are limited to indoor focus, single-platform constraints, and small scale. We introduce 3EED, a multi-platform, multi-modal 3D grounding benchmark featuring RGB and LiDAR data from vehicle, drone, and quadruped platforms. We provide over 128,000 objects and 22,000 validated referring expressions across diverse outdoor scenes – 10x larger than existing datasets. We develop a scalable annotation pipeline combining vision-language model prompting with human verification to ensure high-quality spatial grounding. To support cross-platform learning, we propose platform-aware normalization and cross-modal alignment techniques, and establish benchmark protocols for in-domain and cross-platform evaluations. Our findings reveal significant performance gaps, highlighting the challenges and opportunities of generalizable 3D grounding. The 3EED dataset and benchmark toolkit are released to advance future research in language-driven 3D embodied perception.

Method: Proposes HGFreNet with hop-hybrid graph attention (HGA) module to group k-hop neighbors for larger receptive field, Transformer encoder for global correlations, and frequency domain constraints for temporal consistency. Uses preliminary network for 3D pose estimation.

Result: Extensive experiments on Human3.6M and MPI-INF-3DHP benchmarks show HGFreNet outperforms state-of-the-art methods in both positional accuracy and temporal consistency.

Conclusion: The proposed approach effectively models global spatial-temporal correlations and maintains temporal coherence, achieving superior performance in 3D human pose estimation from monocular video.

Abstract: 2D-to-3D human pose lifting is a fundamental challenge for 3D human pose estimation in monocular video, where graph convolutional networks (GCNs) and attention mechanisms have proven to be inherently suitable for encoding the spatial-temporal correlations of skeletal joints. However, depth ambiguity and errors in 2D pose estimation lead to incoherence in the 3D trajectory. Previous studies have attempted to restrict jitters in the time domain, for instance, by constraining the differences between adjacent frames while neglecting the global spatial-temporal correlations of skeletal joint motion. To tackle this problem, we design HGFreNet, a novel GraphFormer architecture with hop-hybrid feature aggregation and 3D trajectory consistency in the frequency domain. Specifically, we propose a hop-hybrid graph attention (HGA) module and a Transformer encoder to model global joint spatial-temporal correlations. The HGA module groups all $k$-hop neighbors of a skeletal joint into a hybrid group to enlarge the receptive field and applies the attention mechanism to discover the latent correlations of these groups globally. We then exploit global temporal correlations by constraining trajectory consistency in the frequency domain. To provide 3D information for depth inference across frames and maintain coherence over time, a preliminary network is applied to estimate the 3D pose. Extensive experiments were conducted on two standard benchmark datasets: Human3.6M and MPI-INF-3DHP. The results demonstrate that the proposed HGFreNet outperforms state-of-the-art (SOTA) methods in terms of positional accuracy and temporal consistency.

Method: The model uses linear group RNN operators to perform linear RNN for grouped features, enabling efficient handling of LiDAR point clouds, high-resolution multi-view images, and temporal sequences in a single versatile architecture.

Result: UniLION achieves competitive and state-of-the-art performance across various core autonomous driving tasks including 3D perception, prediction, and planning, while supporting multiple specialized variants without requiring explicit temporal or multi-modal fusion modules.

Conclusion: The unified paradigm simplifies multi-modal and multi-task autonomous driving system design while maintaining superior performance, offering a fresh perspective on developing 3D foundation models for autonomous driving.

Abstract: Although transformers have demonstrated remarkable capabilities across various domains, their quadratic attention mechanisms introduce significant computational overhead when processing long-sequence data. In this paper, we present a unified autonomous driving model, UniLION, which efficiently handles large-scale LiDAR point clouds, high-resolution multi-view images, and even temporal sequences based on the linear group RNN operator (i.e., performs linear RNN for grouped features). Remarkably, UniLION serves as a single versatile architecture that can seamlessly support multiple specialized variants (i.e., LiDAR-only, temporal LiDAR, multi-modal, and multi-modal temporal fusion configurations) without requiring explicit temporal or multi-modal fusion modules. Moreover, UniLION consistently delivers competitive and even state-of-the-art performance across a wide range of core tasks, including 3D perception (e.g., 3D object detection, 3D object tracking, 3D occupancy prediction, BEV map segmentation), prediction (e.g., motion prediction), and planning (e.g., end-to-end planning). This unified paradigm naturally simplifies the design of multi-modal and multi-task autonomous driving systems while maintaining superior performance. Ultimately, we hope UniLION offers a fresh perspective on the development of 3D foundation models in autonomous driving. Code is available at https://github.com/happinesslz/UniLION

Method: Creates symbolic knowledge graphs from text data using entity and fact triples, uses LLMs for semantic filtering and answer generation, and employs entity-guided graph traversal via Personalized PageRank for efficient retrieval.

Result: Achieves state-of-the-art performance on HotpotQA (80.7% F1, 97.1% Recall@5) and 2WikiMultiHopQA (78.9% F1, 98.1% Recall@5).

Conclusion: Prompt design is crucial for improving retrieval accuracy and response quality in multi-hop question answering, establishing foundations for more efficient and comprehensible systems.

Abstract: Retrieval-Augmented Generation (RAG) has become a robust framework for enhancing Large Language Models (LLMs) with external knowledge. Recent advances in RAG have investigated graph based retrieval for intricate reasoning; however, the influence of prompt design on enhancing the retrieval and reasoning process is still considerably under-examined. In this paper, we present a prompt-driven GraphRAG framework that underscores the significance of prompt formulation in facilitating entity extraction, fact selection, and passage reranking for multi-hop question answering. Our approach creates a symbolic knowledge graph from text data by encoding entities and factual relationships as structured facts triples. We use LLMs selectively during online retrieval to perform semantic filtering and answer generation. We also use entity-guided graph traversal through Personalized PageRank (PPR) to support efficient, scalable retrieval based on the knowledge graph we built. Our system gets state-of-the-art performance on HotpotQA and 2WikiMultiHopQA, with F1 scores of 80.7% and 78.9%, and Recall@5 scores of 97.1% and 98.1%, respectively. These results show that prompt design is an important part of improving retrieval accuracy and response quality. This research lays the groundwork for more efficient and comprehensible multi-hop question-answering systems, highlighting the importance of prompt-aware graph reasoning.

Method: Created an agentic AI pipeline that autonomously collects and implements models in standardized form, then used this dataset to evaluate AI models’ ability to link visual patterns to their generative code and mechanisms.

Result: Vision-language models demonstrated capability to understand and simulate physical systems beyond visual patterns, identifying different patterns from the same process and inferring mechanisms from real-world images.

Conclusion: The Scitextures dataset provides a foundation for exploring connections between visual patterns and their generative mechanisms, showing AI’s potential to understand the processes that shape our visual world.

Abstract: The ability to connect visual patterns with the processes that form them represents one of the deepest forms of visual understanding. Textures of clouds and waves, the growth of cities and forests, or the formation of materials and landscapes are all examples of patterns emerging from underlying mechanisms. We present the Scitextures dataset, a large-scale collection of textures and visual patterns from all domains of science, tech, and art, along with the models and code that generate these images. Covering over 1,200 different models and 100,000 images of patterns and textures from physics, chemistry, biology, sociology, technology, mathematics, and art, this dataset offers a way to explore the connection between the visual patterns that shape our world and the mechanisms that produce them. Created by an agentic AI pipeline that autonomously collects and implements models in standardized form, we use SciTextures to evaluate the ability of leading AI models to link visual patterns to the models and code that generate them, and to identify different patterns that emerged from the same process. We also test AIs ability to infer and recreate the mechanisms behind visual patterns by providing a natural image of a real-world pattern and asking the AI to identify, model, and code the mechanism that formed the pattern, then run this code to generate a simulated image that is compared to the real image. These benchmarks show that vision-language models (VLMs) can understand and simulate the physical system beyond a visual pattern. The dataset and code are available at: https://zenodo.org/records/17485502

Method: Created TIR-Bench with 13 diverse tasks requiring novel tool use for image processing and manipulation in chain-of-thought. Evaluated 22 MLLMs including open-source, proprietary, and tool-augmented models.

Result: TIR-Bench is universally challenging for all tested models, showing that strong performance requires genuine thinking-with-images capabilities. Also conducted pilot study comparing direct vs agentic fine-tuning.

Conclusion: The benchmark successfully identifies gaps in current models’ thinking-with-images abilities and provides a comprehensive evaluation framework for advanced visual reasoning capabilities.

Abstract: The frontier of visual reasoning is shifting toward models like OpenAI o3, which can intelligently create and operate tools to transform images for problem-solving, also known as thinking-\textit{with}-images in chain-of-thought. Yet existing benchmarks fail to fully capture this advanced capability. Even Visual Search, the most common benchmark for current thinking-\textit{with}-images methods, tests only basic operations such as localization and cropping, offering little insight into more complex, dynamic, and tool-dependent reasoning. We introduce \textbf{TIR-Bench}, a comprehensive benchmark for evaluating agentic thinking-with-images across 13 diverse tasks, each requiring novel tool use for image processing and manipulation in chain-of-thought. We evaluate 22 multimodal large language models (MLLMs), from leading open-sourced and proprietary models to those with explicit tool-use augmentation. Results show that TIR-Bench is universally challenging, and strong performance requires genuine thinking-with-images capabilities. Finally, we present a pilot study comparing direct versus agentic fine-tuning.

Method: Use approximate joint diagonalization algorithms to construct common approximate eigenbases for multiple shapes, enabling compatibility across different shapes.

Result: The approach demonstrates benefits in various applications including shape editing, pose transfer, correspondence, and similarity tasks.

Conclusion: Common approximate eigenbases provide a solution to the incompatibility problem of Laplacian eigenbases across multiple shapes, enabling more effective multi-shape analysis and processing.

Abstract: The use of Laplacian eigenbases has been shown to be fruitful in many computer graphics applications. Today, state-of-the-art approaches to shape analysis, synthesis, and correspondence rely on these natural harmonic bases that allow using classical tools from harmonic analysis on manifolds. However, many applications involving multiple shapes are obstacled by the fact that Laplacian eigenbases computed independently on different shapes are often incompatible with each other. In this paper, we propose the construction of common approximate eigenbases for multiple shapes using approximate joint diagonalization algorithms. We illustrate the benefits of the proposed approach on tasks from shape editing, pose transfer, correspondence, and similarity.

Method: Proposed prompt-SelF framework: pixel-level retrieval for prompt selection, different prompt fusion methods to activate model knowledge, and ensemble predictions from different fusion methods.

Result: Outperformed OSLSM-based meta-learning in 1-shot segmentation for the first time, demonstrating the potential of visual in-context learning.

Conclusion: Visual in-context learning shows great potential, with prompt selection and fusion being critical factors for performance.

Abstract: The In-Context Learning (ICL) is to understand a new task via a few demonstrations (aka. prompt) and predict new inputs without tuning the models. While it has been widely studied in NLP, it is still a relatively new area of research in computer vision. To reveal the factors influencing the performance of visual in-context learning, this paper shows that prompt selection and prompt fusion are two major factors that have a direct impact on the inference performance of visual context learning. Prompt selection is the process of identifying the most appropriate prompt or example to help the model understand new tasks. This is important because providing the model with relevant prompts can help it learn more effectively and efficiently. Prompt fusion involves combining knowledge from different positions within the large-scale visual model. By doing this, the model can leverage the diverse knowledge stored in different parts of the model to improve its performance on new tasks. Based these findings, we propose a simple framework prompt-SelF for visual in-context learning. Specifically, we first use the pixel-level retrieval method to select a suitable prompt, and then use different prompt fusion methods to activate all the knowledge stored in the large-scale model, and finally ensemble the prediction results obtained from different prompt fusion methods to obtain the final prediction results. And we conduct extensive experiments on single-object segmentation and detection tasks to demonstrate the effectiveness of prompt-SelF. Remarkably, the prompt-SelF has outperformed OSLSM based meta-learning in 1-shot segmentation for the first time. This indicated the great potential of visual in-context learning. The source code and models will be available at https://github.com/syp2ysy/prompt-SelF.

Method: Uses deformable mixer encoder with channel-aware mixing and spatial-aware deformable operators, combined with task-aware gating transformer decoder that employs task interaction blocks with self-attention and task query blocks with gating attention for dynamic feature selection.

Result: Significantly outperforms current Transformer-based and CNN-based competitive models on various metrics across three dense prediction datasets while using fewer GFLOPs.

Conclusion: DeGMix provides a simple and efficient solution for multi-task dense prediction that combines the advantages of both CNN and Transformer architectures with lower cost, less complexity, and smaller parameters than traditional MTL methods.

Abstract: Convolution neural networks and Transformers have their own advantages and both have been widely used for dense prediction in multi-task learning (MTL). Existing studies typically employ either CNNs (effectively capture local spatial patterns) or Transformers (capturing long-range dependencies) independently, but integrating their strengths may yield more robust models. In this work, we present an efficient MTL model that combines the adaptive capabilities of deformable CNN and query-based Transformer with shared gating for MTL of dense prediction. This combination may offer a simple and efficient solution owing to its powerful and flexible task-specific learning and the advantages of lower cost, less complexity, and smaller parameters than traditional MTL methods. We introduce an efficient multi-task dense prediction with deformable and gating mixer (DeGMix). First, the deformable mixer encoder contains two types of operators: the channel-aware mixing operator leveraged to allow communication among different channels, and the spatial-aware deformable operator with deformable convolution applied to efficiently sample more informative spatial locations. Second, the task-aware gating transformer decoder is used to perform task-specific predictions, in which task interaction block integrated with self-attention is applied to capture task interaction features, and the task query block integrated with gating attention is leveraged to dynamically select the corresponding task-specific features. Furthermore, the results of the experiment demonstrate that the proposed DeGMix uses fewer GFLOPs and significantly outperforms current Transformer-based and CNN-based competitive models on a variety of metrics on three dense prediction datasets. Our code and models are available at https://github.com/yangyangxu0/DeMTG.

Method: Hybrid Attention Transformer (HAT) combining channel attention and window-based self-attention, with overlapping cross-attention module for cross-window interaction, plus same-task pre-training strategy.

Result: Achieves state-of-the-art performance on image super-resolution, Gaussian denoising, and compression artifacts reduction across benchmark and real-world datasets.

Conclusion: HAT effectively activates more input pixels and fully exploits Transformer potential for superior image restoration performance.

Abstract: Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better restoration, we propose a new Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement. Extensive experiments have demonstrated the effectiveness of the proposed modules. We further scale up the model to show that the performance of the SR task can be greatly improved. Besides, we extend HAT to more image restoration applications, including real-world image super-resolution, Gaussian image denoising and image compression artifacts reduction. Experiments on benchmark and real-world datasets demonstrate that our HAT achieves state-of-the-art performance both quantitatively and qualitatively. Codes and models are publicly available at https://github.com/XPixelGroup/HAT.

Method: Introduce targeted adversarial attacks with carefully selected targets to poison diffusion models and degrade customization quality, validated on two mainstream customization methods.

Result: Targeted attacks significantly outperform untargeted attacks in poisoning diffusion models and reducing the quality of customized images, with extensive experimental validation.

Conclusion: Targeted attacks are more effective than untargeted attacks for protecting images against unauthorized diffusion model customization, revealing a new vulnerability in diffusion models.

Abstract: Diffusion models build a new milestone for image generation yet raising public concerns, for they can be fine-tuned on unauthorized images for customization. Protection based on adversarial attacks rises to encounter this unauthorized diffusion customization, by adding protective watermarks to images and poisoning diffusion models. However, current protection, leveraging untargeted attacks, does not appear to be effective enough. In this paper, we propose a simple yet effective improvement for the protection against unauthorized diffusion customization by introducing targeted attacks. We show that by carefully selecting the target, targeted attacks significantly outperform untargeted attacks in poisoning diffusion models and degrading the customization image quality. Extensive experiments validate the superiority of our method on two mainstream customization methods of diffusion models, compared to existing protections. To explain the surprising success of targeted attacks, we delve into the mechanism of attack-based protections and propose a hypothesis based on our observation, which enhances the comprehension of attack-based protections. To the best of our knowledge, we are the first to both reveal the vulnerability of diffusion models to targeted attacks and leverage targeted attacks to enhance protection against unauthorized diffusion customization. Our code is available on GitHub: https://github.com/psyker-team/mist-v2.

Method: MoEISR uses a lightweight mapper module to dynamically allocate the most suitable decoding expert to each pixel, allowing experts with varying capacities to handle regions with different complexities.

Result: MoEISR significantly reduces FLOPs while delivering comparable or superior PSNR compared to existing methods.

Conclusion: The proposed MoEISR framework enables efficient arbitrary-scale super-resolution without sacrificing reconstruction quality through dynamic expert allocation.

Abstract: Arbitrary-scale image super-resolution employing implicit neural functions has gained significant attention lately due to its capability to upscale images across diverse scales utilizing only a single model. Nevertheless, these methodologies have imposed substantial computational demands as they involve querying every target pixel to a single resource-intensive decoder. In this paper, we introduce a novel and efficient framework, the Mixture-of-Experts Implicit Super-Resolution (MoEISR), which enables super-resolution at arbitrary scales with significantly increased computational efficiency without sacrificing reconstruction quality. MoEISR dynamically allocates the most suitable decoding expert to each pixel using a lightweight mapper module, allowing experts with varying capacities to reconstruct pixels across regions with diverse complexities. Our experiments demonstrate that MoEISR successfully reduces significant amount of floating point operations (FLOPs) while delivering comparable or superior peak signal-to-noise ratio (PSNR).

Method: Proposes a Visual Reference Prompt (VRP) encoder that supports various annotation formats (point, box, scribble, mask) and uses meta-learning for generalization. VRP-SAM leverages reference images to understand and segment specific objects in target images.

Result: Achieved state-of-the-art performance on Pascal and COCO datasets in visual reference segmentation with minimal learnable parameters. Demonstrates strong generalization for unseen objects and cross-domain segmentation.

Conclusion: VRP-SAM successfully extends SAM’s capabilities for reference-based segmentation while maintaining efficiency, showing excellent generalization across domains and object types.

Abstract: In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model. In essence, VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target image. It is note that the VRP encoder can support a variety of annotation formats for reference images, including \textbf{point}, \textbf{box}, \textbf{scribble}, and \textbf{mask}. VRP-SAM achieves a breakthrough within the SAM framework by extending its versatility and applicability while preserving SAM’s inherent strengths, thus enhancing user-friendliness. To enhance the generalization ability of VRP-SAM, the VRP encoder adopts a meta-learning strategy. To validate the effectiveness of VRP-SAM, we conducted extensive empirical studies on the Pascal and COCO datasets. Remarkably, VRP-SAM achieved state-of-the-art performance in visual reference segmentation with minimal learnable parameters. Furthermore, VRP-SAM demonstrates strong generalization capabilities, allowing it to perform segmentation of unseen objects and enabling cross-domain segmentation. The source code and models will be available at https://github.com/syp2ysy/VRP-SAM

Method: Created OpenMaterial dataset by integrating lab-measured Index of Refraction (IOR) spectra to generate high-fidelity multi-view images simulating complex light-matter interactions, providing multi-view images, 3D shapes, camera poses, depth maps, and object masks.

Result: Evaluated 11 state-of-the-art 3D reconstruction and novel view synthesis methods, conducting ablation studies on material type, shape complexity, and illumination impact. The dataset provides a strong benchmark for developing robust reconstruction techniques.

Conclusion: OpenMaterial establishes the first extensive benchmark for material-aware 3D reconstruction, enabling development of more robust, physically-informed techniques to handle real-world optical complexities in 3D reconstruction.

Abstract: Recent advances in deep learning, such as neural radiance fields and implicit neural representations, have significantly advanced 3D reconstruction. However, accurately reconstructing objects with complex optical properties, such as metals, glass, and plastics, remains challenging due to the breakdown of multi-view color consistency in the presence of specular reflections, refractions, and transparency. This limitation is further exacerbated by the lack of benchmark datasets that explicitly model material-dependent light transport. To address this, we introduce OpenMaterial, a large-scale semi-synthetic dataset for benchmarking material-aware 3D reconstruction. It comprises 1,001 objects spanning 295 distinct materials, including conductors, dielectrics, plastics, and their roughened variants, captured under 714 diverse lighting conditions. By integrating lab-measured Index of Refraction (IOR) spectra, OpenMaterial enables the generation of high-fidelity multi-view images that accurately simulate complex light-matter interactions. It provides multi-view images, 3D shape models, camera poses, depth maps, and object masks, establishing the first extensive benchmark for evaluating 3D reconstruction on challenging materials. We evaluate 11 state-of-the-art methods for 3D reconstruction and novel view synthesis, conducting ablation studies to assess the impact of material type, shape complexity, and illumination on reconstruction performance. Our results indicate that OpenMaterial provides a strong and fair basis for developing more robust, physically-informed 3D reconstruction techniques to better handle real-world optical complexities.

Method: TRGv2 extends the TRG network by explicitly modeling bidirectional facial geometry-pose interaction through iterative refinement with landmark-to-image projection. It regresses correction parameters combined with pinhole camera model for analytic depth estimation, and introduces reference system alignment to correct cross-dataset translation bias.

Result: Extensive experiments on ARKitFace, BIWI, and DD-Pose benchmarks show TRGv2 outperforms state-of-the-art methods in both accuracy and efficiency.

Conclusion: TRGv2 provides a robust and efficient solution for 6DoF head pose estimation by addressing both model architecture limitations and cross-dataset evaluation biases, with publicly available code and annotations.

Abstract: Precise six-degree-of-freedom (6DoF) head pose estimation is crucial for safety-critical applications and human-computer interaction scenarios, yet existing monocular methods still struggle with robust pose estimation. We revisit this problem by introducing TRGv2, a lightweight extension of our previous Translation, Rotation, and Geometry (TRG) network, which explicitly models the bidirectional interaction between facial geometry and head pose. TRGv2 jointly infers facial landmarks and 6DoF pose through an iterative refinement loop with landmark-to-image projection, ensuring metric consistency among face size, rotation, and depth. To further improve generalization to out-of-distribution data, TRGv2 regresses correction parameters instead of directly predicting translation, combining them with a pinhole camera model for analytic depth estimation. In addition, we identify a previously overlooked source of bias in cross-dataset evaluations due to inconsistent head center definitions across different datasets. To address this, we propose a reference system alignment strategy that quantifies and corrects translation bias, enabling fair comparisons across datasets. Extensive experiments on ARKitFace, BIWI, and the challenging DD-Pose benchmarks demonstrate that TRGv2 outperforms state-of-the-art methods in both accuracy and efficiency. Code and newly annotated landmarks for DD-Pose will be publicly available.

Method: Packets are stacked into windows and converted into 2D image representations, then processed using computer vision models for attack detection directly from raw packet data.

Result: The approach was evaluated using the CIC IDS-2017 dataset containing both benign traffic and real-world attacks, providing a comprehensive testing foundation.

Conclusion: The proposed method enables real-time intrusion detection by directly processing raw packet data through deep learning models, overcoming limitations of traditional flow-based approaches.

Abstract: Most of the intrusion detection methods in computer networks are based on traffic flow characteristics. However, this approach may not fully exploit the potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the necessity of waiting for the processing pipeline to complete and introduces dependencies on additional software components. In this paper, we investigate deep learning methodologies capable of detecting attacks in real-time directly from raw packet data within network traffic. We propose a novel approach where packets are stacked into windows and separately recognised, with a 2D image representation suitable for processing with computer vision models. Our investigation utilizes the CIC IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.

Method: Uses Mamba state-space model with next-token prediction for autoregressive image generation, avoiding complex 2D adaptations while preserving Mamba’s core efficiency.

Result: Achieves FID of 2.21 on ImageNet1K 256*256, surpassing comparable AR models and competing with diffusion models while being 2-10x faster in inference.

Conclusion: AiM demonstrates that Mamba architecture can effectively replace Transformers in AR image generation, achieving superior performance with significantly improved efficiency.

Abstract: We introduce AiM, an autoregressive (AR) image generative model based on Mamba architecture. AiM employs Mamba, a novel state-space model characterized by its exceptional performance for long-sequence modeling with linear time complexity, to supplant the commonly utilized Transformers in AR image generation models, aiming to achieve both superior generation quality and enhanced inference speed. Unlike existing methods that adapt Mamba to handle two-dimensional signals via multi-directional scan, AiM directly utilizes the next-token prediction paradigm for autoregressive image generation. This approach circumvents the need for extensive modifications to enable Mamba to learn 2D spatial representations. By implementing straightforward yet strategically targeted modifications for visual generative tasks, we preserve Mamba’s core structure, fully exploiting its efficient long-sequence modeling capabilities and scalability. We provide AiM models in various scales, with parameter counts ranging from 148M to 1.3B. On the ImageNet1K 256*256 benchmark, our best AiM model achieves a FID of 2.21, surpassing all existing AR models of comparable parameter counts and demonstrating significant competitiveness against diffusion models, with 2 to 10 times faster inference speed. Code is available at https://github.com/hp-l33/AiM

Method: Introduces Layer Contribution (LC) metric to quantify layer impact on visual/text tokens, then freezes visual token updates in identified ineffective layers without retraining.

Result: Freezes visual tokens in ~60% of MLLM layers, achieving 50% FLOPs reduction on LLaVA-NeXT-13B while maintaining superior performance.

Conclusion: ShortV effectively reduces MLLM computational costs by leveraging layer-wise redundancy analysis without performance degradation.

Abstract: Multimodal Large Language Models (MLLMs) suffer from high computational costs due to their massive size and the large number of visual tokens. In this paper, we investigate layer-wise redundancy in MLLMs by introducing a novel metric, Layer Contribution (LC), which quantifies the impact of a layer’s transformations on visual and text tokens, respectively. The calculation of LC involves measuring the divergence in model output that results from removing the layer’s transformations on the specified tokens. Our pilot experiment reveals that many layers of MLLMs exhibit minimal contribution during the processing of visual tokens. Motivated by this observation, we propose ShortV, a training-free method that leverages LC to identify ineffective layers, and freezes visual token updates in these layers. Experiments show that ShortV can freeze visual token in approximately 60% of the MLLM layers, thereby dramatically reducing computational costs related to updating visual tokens. For example, it achieves a 50% reduction in FLOPs on LLaVA-NeXT-13B while maintaining superior performance. The code will be publicly available at https://github.com/icip-cas/ShortV

Method: Leverages diffusion models to restore image quality from both macro and micro levels, addressing key quality factors like sharpness, distortion, noise, dynamic range, and color accuracy.

Result: Outperforms state-of-the-art methods on seven benchmark datasets covering five degradation types, achieving superior quantitative and visual results.

Conclusion: ReviveDiff provides an effective universal solution for restoring images degraded by various adverse environmental conditions using diffusion models.

Abstract: Images captured in challenging environments–such as nighttime, smoke, rainy weather, and underwater–often suffer from significant degradation, resulting in a substantial loss of visual quality. The effective restoration of these degraded images is critical for the subsequent vision tasks. While many existing approaches have successfully incorporated specific priors for individual tasks, these tailored solutions limit their applicability to other degradations. In this work, we propose a universal network architecture, dubbed ``ReviveDiff’’, which can address various degradations and bring images back to life by enhancing and restoring their quality. Our approach is inspired by the observation that, unlike degradation caused by movement or electronic issues, quality degradation under adverse conditions primarily stems from natural media (such as fog, water, and low luminance), which generally preserves the original structures of objects. To restore the quality of such images, we leveraged the latest advancements in diffusion models and developed ReviveDiff to restore image quality from both macro and micro levels across some key factors determining image quality, such as sharpness, distortion, noise level, dynamic range, and color accuracy. We rigorously evaluated ReviveDiff on seven benchmark datasets covering five types of degrading conditions: Rainy, Underwater, Low-light, Smoke, and Nighttime Hazy. Our experimental results demonstrate that ReviveDiff outperforms the state-of-the-art methods both quantitatively and visually.

Method: Integrates Group Relative Policy Optimization (GRPO) with a fuzzy reward function that provides graded incentives based on output precision rather than binary accuracy.

Result: FGRPR applied to Qwen2.5-VL (3B and 7B) surpasses all baseline models including GPT4o, LLaMA2(90B), and SFT across five in-domain datasets. On out-of-domain data, it matches SFT performance and excels with larger target values.

Conclusion: FGRPR is broadly applicable to tasks requiring answer precision and demonstrates superior performance over traditional reward models and supervised fine-tuning.

Abstract: We propose Fuzzy Group Relative Policy Reward (FGRPR), a novel framework that integrates Group Relative Policy Optimization (GRPO) with a fuzzy reward function to enhance learning efficiency. Unlike the conventional binary 0/1 accuracy reward, our fuzzy reward model provides nuanced incentives, encouraging more precise outputs. Experimental results demonstrate that GRPO with a standard 0/1 accuracy reward underperforms compared to supervised fine-tuning (SFT). In contrast, FGRPR, applied to Qwen2.5-VL(3B and 7B), surpasses all baseline models, including GPT4o, LLaMA2(90B), and SFT, across five in-domain datasets. On an out-of-domain dataset, FGRPR achieves performance comparable to SFT but excels when target values are larger, as its fuzzy reward function assigns higher rewards to closer approximations. This approach is broadly applicable to tasks where the precision of the answer is critical. Code and data: https://github.com/yeyimilk/CrowdVLM-R1

Method: Transformer architecture combining visual features (RGB and optical flow) with textual embeddings for contextual information, using a joint loss function for simultaneous coarse- and fine-grained action recognition.

Result: Outperforms SOTA methods on Hierarchical TSU, Assembly101 and IkeaASM datasets with over 17% improvement in top-1 accuracy.

Conclusion: The proposed hierarchical and contextual approach significantly enhances action recognition performance, particularly for monitoring elderly activities in home environments.

Abstract: We propose a novel approach to improve action recognition by exploiting the hierarchical organization of actions and by incorporating contextualized textual information, including location and previous actions, to reflect the action’s temporal context. To achieve this, we introduce a transformer architecture tailored for action recognition that employs both visual and textual features. Visual features are obtained from RGB and optical flow data, while text embeddings represent contextual information. Furthermore, we define a joint loss function to simultaneously train the model for both coarse- and fine-grained action recognition, effectively exploiting the hierarchical nature of actions. To demonstrate the effectiveness of our method, we extend the Toyota Smarthome Untrimmed (TSU) dataset by incorporating action hierarchies, resulting in the Hierarchical TSU dataset, a hierarchical dataset designed for monitoring activities of the elderly in home environments. An ablation study assesses the performance impact of different strategies for integrating contextual and hierarchical data. Experimental results demonstrate that the proposed method consistently outperforms SOTA methods on the Hierarchical TSU dataset, Assembly101 and IkeaASM, achieving over a 17% improvement in top-1 accuracy.

Method: DVD uses an agentic search strategy over segmented video clips with search-centric tools on multi-granular video database. It leverages LLM reasoning to plan adaptive workflows, strategically selecting tools based on current observation state and gathered information for different queries.

Result: Achieves state-of-the-art performance on LVBench dataset with 74.2% accuracy, substantially surpassing all prior works, and further improves to 76.0% with transcripts.

Conclusion: The DVD agent’s autonomous and adaptive approach using agentic search strategies effectively addresses long-form video understanding challenges, demonstrating superior performance compared to previous methods with predefined workflows.

Abstract: Long-form video understanding presents significant challenges due to extensive temporal-spatial complexity and the difficulty of question answering under such extended contexts. While Large Language Models (LLMs) have demonstrated considerable advancements in video analysis capabilities and long context handling, they continue to exhibit limitations when processing information-dense hour-long videos. To overcome such limitations, we propose the Deep Video Discovery (DVD) agent to leverage an agentic search strategy over segmented video clips. Unlike previous video agents that rely on predefined workflows applied uniformly across different queries, our approach emphasizes the autonomous and adaptive nature of agents. By providing a set of search-centric tools on multi-granular video database, our DVD agent leverages the advanced reasoning capability of LLM to plan on its current observation state, strategically selects tools to orchestrate adaptive workflow for different queries in light of the gathered information. We perform comprehensive evaluation on multiple long video understanding benchmarks that demonstrates our advantage. Our DVD agent achieves state-of-the-art performance on the challenging LVBench dataset, reaching an accuracy of 74.2%, which substantially surpasses all prior works, and further improves to 76.0% with transcripts. The code has been released at https://github.com/microsoft/DeepVideoDiscovery.

Method: TESGNN consists of two components: (1) Equivariant Scene Graph Neural Network (ESGNN) that preserves symmetry properties when generating scene graphs from 3D point clouds, and (2) Temporal Graph Matching Network that fuses scene graphs across multiple time sequences using approximate graph-matching.

Result: TESGNN achieves higher accuracy and faster training convergence compared to existing methods. The symmetry-preserving property produces more stable and accurate global scene representations. The method is computationally efficient and suitable for real-time applications.

Conclusion: TESGNN provides a robust and scalable solution for complex multi-view scene understanding challenges, paving the way for improved performance in robotics and computer vision applications.

Abstract: Scene graphs have proven to be highly effective for various scene understanding tasks due to their compact and explicit representation of relational information. However, current methods often overlook the critical importance of preserving symmetry when generating scene graphs from 3D point clouds, which can lead to reduced accuracy and robustness, particularly when dealing with noisy, multi-view data. Furthermore, a major limitation of prior approaches is the lack of temporal modeling to capture time-dependent relationships among dynamically evolving entities in a scene. To address these challenges, we propose Temporal Equivariant Scene Graph Neural Network (TESGNN), consisting of two key components: (1) an Equivariant Scene Graph Neural Network (ESGNN), which extracts information from 3D point clouds to generate scene graph while preserving crucial symmetry properties, and (2) a Temporal Graph Matching Network, which fuses scene graphs generated by ESGNN across multiple time sequences into a unified global representation using an approximate graph-matching algorithm. Our combined architecture TESGNN shown to be effective compared to existing methods in scene graph generation, achieving higher accuracy and faster training convergence. Moreover, we show that leveraging the symmetry-preserving property produces a more stable and accurate global scene representation compared to existing approaches. Finally, it is computationally efficient and easily implementable using existing frameworks, making it well-suited for real-time applications in robotics and computer vision. This approach paves the way for more robust and scalable solutions to complex multi-view scene understanding challenges. Our source code is publicly available at: https://github.com/HySonLab/TESGraph

Method: Collected data using connected vehicle platform and intelligent roadside unit equipped with 4D Radar, LiDAR, and multi-view cameras across diverse weather conditions (sunny/rainy) and times (daytime/dusk/nighttime).

Result: Created V2X-Radar dataset with 20K LiDAR frames, 40K camera images, 20K 4D Radar data, and 350K annotated boxes across five categories. Established three sub-datasets for different research domains: cooperative perception, roadside perception, and single-vehicle perception.

Conclusion: V2X-Radar bridges the critical gap in 4D Radar datasets for cooperative perception and provides comprehensive benchmarks to advance research in autonomous driving perception systems across various scenarios and conditions.

Abstract: Modern autonomous vehicle perception systems often struggle with occlusions and limited perception range. Previous studies have demonstrated the effectiveness of cooperative perception in extending the perception range and overcoming occlusions, thereby enhancing the safety of autonomous driving. In recent years, a series of cooperative perception datasets have emerged; however, these datasets primarily focus on cameras and LiDAR, neglecting 4D Radar, a sensor used in single-vehicle autonomous driving to provide robust perception in adverse weather conditions. In this paper, to bridge the gap created by the absence of 4D Radar datasets in cooperative perception, we present V2X-Radar, the first large-scale, real-world multi-modal dataset featuring 4D Radar. V2X-Radar dataset is collected using a connected vehicle platform and an intelligent roadside unit equipped with 4D Radar, LiDAR, and multi-view cameras. The collected data encompasses sunny and rainy weather conditions, spanning daytime, dusk, and nighttime, as well as various typical challenging scenarios. The dataset consists of 20K LiDAR frames, 40K camera images, and 20K 4D Radar data, including 350K annotated boxes across five categories. To support various research domains, we have established V2X-Radar-C for cooperative perception, V2X-Radar-I for roadside perception, and V2X-Radar-V for single-vehicle perception. Furthermore, we provide comprehensive benchmarks across these three sub-datasets. We will release all datasets and benchmark codebase at https://huggingface.co/datasets/yanglei18/V2X-Radar and https://github.com/yanglei18/V2X-Radar.

Method: Three modules: 3D Material Grouping for surface material segmentation, Internal Physical Structure Discovery for interior mechanical structure construction, and physical prior distillation from multimodal LLMs for automatic material property identification.

Result: Experiments on synthetic and real-world datasets show Phys4DGen generates high-fidelity 4D content with physical realism in open-world scenarios, significantly outperforming state-of-the-art methods.

Conclusion: Phys4DGen successfully addresses limitations of current 4D generation methods by automating material property identification and handling multi-material composites, enabling physically plausible 4D content generation without requiring physics expertise from users.

Abstract: 4D content generation aims to create dynamically evolving 3D content that responds to specific input objects such as images or 3D representations. Current approaches typically incorporate physical priors to animate 3D representations, but these methods suffer from significant limitations: they not only require users lacking physics expertise to manually specify material properties but also struggle to effectively handle the generation of multi-material composite objects. To address these challenges, we propose Phys4DGen, a novel 4D generation framework that integrates multi-material composition perception with physical simulation. The framework achieves automated, physically plausible 4D generation through three innovative modules: first, the 3D Material Grouping module partitions heterogeneous material regions on 3D representations’ surfaces via semantic segmentation; second, the Internal Physical Structure Discovery module constructs the mechanical structure of object interiors; finally, we distill physical prior knowledge from multimodal large language models to enable rapid and automatic material properties identification for both objects’ surfaces and interiors. Experiments on both synthetic and real-world datasets demonstrate that Phys4DGen can generate high-fidelity 4D content with physical realism in open-world scenarios, significantly outperforming state-of-the-art methods.

Method: Multimodal foundation model trained on 2.3M whole slide images and 14M question-answer pairs using clinical-dialogue supervision to learn slide-level representations.

Result: Matches or exceeds cancer-detection performance of clinical-grade products without additional training, achieves top performance on other tasks, and task-specific finetuning further improves performance.

Conclusion: Language-supervised pretraining provides scalable, clinically grounded signal for learning generalizable pathology representations that bridge human diagnostic reasoning and foundation-model performance.

Abstract: Recent rapid progress in the field of computational pathology has been enabled by foundation models. These models are beginning to move beyond encoding image patches towards whole-slide understanding but their clinical utility remains limited. In this work, we present PRISM2, a multimodal slide-level foundation model trained on data from 700,000 diagnostic specimen-report pairs, the largest vision (2.3 million whole slide images) and language (14M question-answer pairs) histopathology dataset to date. By learning through clinical-dialogue supervision, PRISM2 aligns histomorphologic features with the language of diagnostic reasoning, producing slide-level representations that support both direct diagnostic question-answering and transferable embeddings for downstream tasks. Without additional training, PRISM2 matches or exceeds the cancer-detection performance of clinical-grade products. This is observed without loss of generality on other tasks, where PRISM2 achieves top performance. Finally, using survival prediction as the example, we show that task-specific finetuning with a large dataset can outperform task-specific models, further improving performance. These results demonstrate how language-supervised pretraining provides a scalable, clinically grounded signal for learning generalizable pathology representations, bridging human diagnostic reasoning and foundation-model performance.

Method: Unifies 3DGS speed with an underwater image formation model for scattering capture, introducing innovations in rendering, depth estimation procedures, and the 3DGS loss function.

Result: Achieves reconstruction in minutes and renders novel underwater scenes at 140 FPS, producing images with superior details and revealing distant scene details with far greater clarity than other methods.

Conclusion: Gaussian Splashing dramatically improves underwater 3D reconstruction and rendering, offering unparalleled speed and image quality compared to existing methods.

Abstract: In underwater images, most useful features are occluded by water. The extent of the occlusion depends on imaging geometry and can vary even across a sequence of burst images. As a result, 3D reconstruction methods robust on in-air scenes, like Neural Radiance Field methods (NeRFs) or 3D Gaussian Splatting (3DGS), fail on underwater scenes. While a recent underwater adaptation of NeRFs achieved state-of-the-art results, it is impractically slow: reconstruction takes hours and its rendering rate, in frames per second (FPS), is less than 1. Here, we present a new method that takes only a few minutes for reconstruction and renders novel underwater scenes at 140 FPS. Named Gaussian Splashing, our method unifies the strengths and speed of 3DGS with an image formation model for capturing scattering, introducing innovations in the rendering and depth estimation procedures and in the 3DGS loss function. Despite the complexities of underwater adaptation, our method produces images at unparalleled speeds with superior details. Moreover, it reveals distant scene details with far greater clarity than other methods, dramatically improving reconstructed and rendered images. We demonstrate results on existing datasets and a new dataset we have collected. Additional visual results are available at: https://bgu-cs-vil.github.io/gaussiansplashingUW.github.io/ .

Method: Encodes raw motion into continuous latent space using VAE to avoid quantization artifacts, uses dual-stream Transformer with shared attention for modality-specific processing, and employs generate-then-align three-stage training schedule for stability.

Result: Achieves 2x faster convergence in training loss and up to 4x faster convergence in validation, while maintaining state-of-the-art performance on motion understanding and generation benchmarks.

Conclusion: The proposed bimodal architecture with continuous motion encoding and dual-stream processing effectively reduces cross-modal interference, stabilizes optimization, and accelerates convergence without degrading performance.

Abstract: With the rapid progress of large language models (LLMs), multimodal frameworks that unify understanding and generation have become promising, yet they face increasing complexity as the number of modalities and tasks grows. We observe that motion quantization introduces approximation errors that cap motion quality, and that unifying discrete text and continuous motion within a single-stream backbone amplifies cross-modal interference. Motivated by recent multi-branch Transformer designs that separate signals from different modalities, we propose MotionGPT3, a bimodal motion-language model for both understanding and generation. MotionGPT3 encodes raw motion into a continuous latent space using a variational autoencoder (VAE), thereby avoiding quantization-induced artifacts, while leveraging the semantic prior of pretrained language models. A dual-stream Transformer with shared attention preserves modality-specific routes while enabling controlled, bidirectional information flow, which reduces interference, stabilizing optimization, and empirically accelerates convergence without degrading fidelity. For multimodal joint training, a generate-then-align three-stage schedule further improves stability and limits cross-task interference. Experiments show that MotionGPT3 achieves 2x faster convergence in training loss and up to 4x faster convergence in validation, while maintaining state-of-the-art performance on standard motion understanding and motion generation benchmarks.

Method: Use pre-trained large-scale vision models to estimate epistemic uncertainty, converting image detection into uncertainty estimation problem by exploiting elevated uncertainty for generated images.

Result: Extensive experiments demonstrate the method’s efficacy in detecting AI-generated images using uncertainty-based approach.

Conclusion: Epistemic uncertainty from pre-trained models provides an effective proxy for detecting generated images, offering a practical solution to security challenges in generative AI.

Abstract: We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models. Our key insight stems from the observation that distributional discrepancies between training and testing data manifest distinctively in the epistemic uncertainty space of machine learning models. In this context, the distribution shift between natural and generated images leads to elevated epistemic uncertainty in models trained on natural images when evaluating generated ones. Hence, we exploit this phenomenon by using epistemic uncertainty as a proxy for detecting generated images. This converts the challenge of generated image detection into the problem of uncertainty estimation, underscoring the generalization performance of the model used for uncertainty estimation. Fortunately, advanced large-scale vision models pre-trained on extensive natural images have shown excellent generalization performance for various scenarios. Thus, we utilize these pre-trained models to estimate the epistemic uncertainty of images and flag those with high uncertainty as generated. Extensive experiments demonstrate the efficacy of our method. Code is available at https://github.com/tmlr-group/WePe.

Method: The approach consists of two key components: FlexFuse (adaptive event-frame fusion module that integrates high-frequency event data with RGB frame semantics) and FlexTune (frequency-adaptive fine-tuning mechanism that generates frequency-adjusted labels for enhanced generalization).

Result: Extensive experiments show the method surpasses state-of-the-art methods, achieving significant improvements in both standard and high-frequency settings. It maintains robust performance from 20 Hz to 90 Hz and delivers accurate detection up to 180 Hz.

Conclusion: The framework sets a new benchmark for event-based object detection and paves the way for more adaptable, real-time vision systems by effectively leveraging the high-temporal resolution of event cameras.

Abstract: Event cameras offer unparalleled advantages for real-time perception in dynamic environments, thanks to the microsecond-level temporal resolution and asynchronous operation. Existing event detectors, however, are limited by fixed-frequency paradigms and fail to fully exploit the high-temporal resolution and adaptability of event data. To address these limitations, we propose FlexEvent, a novel framework that enables detection at varying frequencies. Our approach consists of two key components: FlexFuse, an adaptive event-frame fusion module that integrates high-frequency event data with rich semantic information from RGB frames, and FlexTune, a frequency-adaptive fine-tuning mechanism that generates frequency-adjusted labels to enhance model generalization across varying operational frequencies. This combination allows our method to detect objects with high accuracy in both fast-moving and static scenarios, while adapting to dynamic environments. Extensive experiments on large-scale event camera datasets demonstrate that our approach surpasses state-of-the-art methods, achieving significant improvements in both standard and high-frequency settings. Notably, our method maintains robust performance when scaling from 20 Hz to 90 Hz and delivers accurate detection up to 180 Hz, proving its effectiveness in extreme conditions. Our framework sets a new benchmark for event-based object detection and paves the way for more adaptable, real-time vision systems.

Method: Proposes Frequency-guided Reconstruction Error (FIRE) which assesses variation in reconstruction error before and after frequency decomposition, investigating the influence of frequency decomposition on reconstruction error.

Result: Extensive experiments show FIRE generalizes effectively to unseen diffusion models and maintains robustness against diverse perturbations.

Conclusion: FIRE provides a robust method for identifying diffusion model generated images by leveraging frequency decomposition analysis of reconstruction errors.

Abstract: The rapid advancement of diffusion models has significantly improved high-quality image generation, making generated content increasingly challenging to distinguish from real images and raising concerns about potential misuse. In this paper, we observe that diffusion models struggle to accurately reconstruct mid-band frequency information in real images, suggesting the limitation could serve as a cue for detecting diffusion model generated images. Motivated by this observation, we propose a novel method called Frequency-guided Reconstruction Error (FIRE), which, to the best of our knowledge, is the first to investigate the influence of frequency decomposition on reconstruction error. FIRE assesses the variation in reconstruction error before and after the frequency decomposition, offering a robust method for identifying diffusion model generated images. Extensive experiments show that FIRE generalizes effectively to unseen diffusion models and maintains robustness against diverse perturbations.

Method: Curated BiMed-V dataset with 1.6M bilingual medical samples, trained BiMediX2 model supporting multi-turn conversations and medical imaging, and created BiMed-MBench evaluation benchmark verified by medical experts.

Result: BiMediX2 achieves SOTA results, outperforming existing methods by over 9% in English and 20% in Arabic on BiMed-MBench, surpasses GPT-4 by ~9% in factual accuracy, and excels in medical VQA, report generation, and summarization tasks.

Conclusion: BiMediX2 demonstrates strong bilingual medical AI capabilities and the developed resources (dataset, benchmark, model) are publicly available for medical AI research.

Abstract: We introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. It enables multi-turn conversation in Arabic and English and supports diverse medical imaging modalities, including radiology, CT, and histology. To train BiMediX2, we curate BiMed-V, an extensive Arabic-English bilingual healthcare dataset consisting of 1.6M samples of diverse medical interactions. This dataset supports a range of medical Large Language Model (LLM) and Large Multimodal Model (LMM) tasks, including multi-turn medical conversations, report generation, and visual question answering (VQA). We also introduce BiMed-MBench, the first Arabic-English medical LMM evaluation benchmark, verified by medical experts. BiMediX2 demonstrates excellent performance across multiple medical LLM and LMM benchmarks, achieving state-of-the-art results compared to other open-sourced models. On BiMed-MBench, BiMediX2 outperforms existing methods by over 9% in English and more than 20% in Arabic evaluations. Additionally, it surpasses GPT-4 by approximately 9% in UPHILL factual accuracy evaluations and excels in various medical VQA, report generation, and report summarization tasks. Our trained models, instruction set, and source code are available at https://github.com/mbzuai-oryx/BiMediX2

Method: Proposes a latent diffusion framework with hierarchical latent representations (point-level to super-point levels), multi-scale latent integration with 3D spatial attention, and consistency distillation to compress the prior into a one-step generator.

Result: Achieves 100x speedup in generation while surpassing state-of-the-art diffusion models in shape quality and diversity on ShapeNet and ShapeNet-Vol benchmarks.

Conclusion: MLPCM successfully bridges Consistency Models to 3D point cloud generation, demonstrating significant efficiency gains without sacrificing performance.

Abstract: Consistency Models (CMs) have significantly accelerated the sampling process in diffusion models, yielding impressive results in synthesizing high-resolution images. To explore and extend these advancements to point-cloud-based 3D shape generation, we propose a novel Multi-scale Latent Point Consistency Model (MLPCM). Our MLPCM follows a latent diffusion framework and introduces hierarchical levels of latent representations, ranging from point-level to super-point levels, each corresponding to a different spatial resolution. We design a multi-scale latent integration module along with 3D spatial attention to effectively denoise the point-level latent representations conditioned on those from multiple super-point levels. Additionally, we propose a latent consistency model, learned through consistency distillation, that compresses the prior into a one-step generator. This significantly improves sampling efficiency while preserving the performance of the original teacher model. Extensive experiments on standard benchmarks ShapeNet and ShapeNet-Vol demonstrate that MLPCM achieves a 100x speedup in the generation process, while surpassing state-of-the-art diffusion models in terms of both shape quality and diversity.

Method: Combines SAM-2 foundation video segmentation model with MLLM vision-language model, unifying text, image, and video into shared LLM token space. Uses LLM to generate instruction tokens that guide SAM-2 in producing precise masks. Introduces Ref-SAV dataset with 72k+ object expressions.

Result: Achieves strong performance across multiple tasks, particularly in referring video object segmentation. Can be easily extended to various VLMs like Qwen-VL and Intern-VL.

Conclusion: Sa2VA demonstrates potential for complex real-world applications with its unified approach to multi-modal understanding of both static and dynamic visual content.

Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-modal large language models, which are often limited to specific modalities and tasks, Sa2VA supports a wide range of image and video tasks, including referring segmentation and conversation, with minimal one-shot instruction tuning. Sa2VA combines SAM-2, a foundation video segmentation model, with MLLM, the advanced vision-language model, and unifies text, image, and video into a shared LLM token space. Using the LLM, Sa2VA generates instruction tokens that guide SAM-2 in producing precise masks, enabling a grounded, multi-modal understanding of both static and dynamic visual content. Additionally, we introduce Ref-SAV, an auto-labeled dataset containing over 72k object expressions in complex video scenes, designed to boost model performance. We also manually validate 2k video objects in the Ref-SAV datasets to benchmark referring video object segmentation in complex environments. Experiments show that Sa2VA achieves strong performance across multiple tasks, particularly in referring video object segmentation, highlighting its potential for complex real-world applications. In addition, Sa2VA can be easily extended into various VLMs, including Qwen-VL and Intern-VL, which can be updated with rapid process in current open-sourced VLMs. Code and models have been provided to the community.

Method: Proposes a multi-stage collaboration strategy that dynamically balances intermediate- and late-stage information sharing, prevents transmission of low-confidence sensing data, and refines received detection results to handle misalignments.

Result: Extensive evaluation on real-world and simulated datasets demonstrates the framework’s effectiveness in enhancing perceptual performance while maintaining communication efficiency.

Conclusion: mmCooper provides a communication-efficient and collaboration-robust solution for cooperative perception that addresses practical deployment challenges in multi-agent systems.

Abstract: Collaborative perception significantly enhances individual vehicle perception performance through the exchange of sensory information among agents. However, real-world deployment faces challenges due to bandwidth constraints and inevitable calibration errors during information exchange. To address these issues, we propose mmCooper, a novel multi-agent, multi-stage, communication-efficient, and collaboration-robust cooperative perception framework. Our framework leverages a multi-stage collaboration strategy that dynamically and adaptively balances intermediate- and late-stage information to share among agents, enhancing perceptual performance while maintaining communication efficiency. To support robust collaboration despite potential misalignments and calibration errors, our framework prevents misleading low-confidence sensing information from transmission and refines the received detection results from collaborators to improve accuracy. The extensive evaluation results on both real-world and simulated datasets demonstrate the effectiveness of the mmCooper framework and its components.

Method: Conducted systematic evaluation of both pixel- and latent-space formulations for dataset distillation in SR, analyzing initialization strategies and distillation objectives.

Result: Distilled datasets occupying only 8.88% of original size can train SR models that retain nearly the same reconstruction fidelity as those trained on full datasets.

Conclusion: The study demonstrates the feasibility of SR dataset distillation and establishes foundational insights for memory- and compute-efficient generative restoration models.

Abstract: Dataset distillation aims to compress large datasets into compact yet highly informative subsets that preserve the training behavior of the original data. While this concept has gained traction in classification, its potential for image Super-Resolution (SR) remains largely untapped. In this work, we conduct the first systematic study of dataset distillation for SR, evaluating both pixel- and latent-space formulations. We show that a distilled dataset, occupying only 8.88% of the original size, can train SR models that retain nearly the same reconstruction fidelity as those trained on full datasets. Furthermore, we analyze how initialization strategies and distillation objectives affect efficiency, convergence, and visual quality. Our findings highlight the feasibility of SR dataset distillation and establish foundational insights for memory- and compute-efficient generative restoration models.

Method: The authors present SurGen dataset with 1,020 whole-slide images from 843 colorectal cancer cases, including annotations for KRAS, NRAS, BRAF mutations and mismatch repair status, plus survival data for 426 cases. They demonstrate utility with a proof-of-concept model predicting mismatch repair status from WSIs.

Result: The proof-of-concept model achieved a test area under the ROC curve of 0.8273 for predicting mismatch repair status directly from whole-slide images, showing the dataset’s potential for biomarker discovery and prognostic modeling.

Conclusion: SurGen offers a valuable resource for scientific research, enabling studies requiring high-quality whole-slide images linked with comprehensive clinical and genetic information, with initial findings affirming its capacity to advance diagnostic precision and personalized treatment in colorectal oncology.

Abstract: Cancer remains one of the leading causes of morbidity and mortality worldwide. Comprehensive datasets that combine histopathological images with genetic and survival data across various tumour sites are essential for advancing computational pathology and personalised medicine. We present SurGen, a dataset comprising 1,020 H&E-stained whole-slide images (WSIs) from 843 colorectal cancer cases. The dataset includes detailed annotations for key genetic mutations (KRAS, NRAS, BRAF) and mismatch repair status, as well as survival data for 426 cases. We illustrate SurGen’s utility with a proof-of-concept model that predicts mismatch repair status directly from WSIs, achieving a test area under the receiver operating characteristic curve of 0.8273. These preliminary results underscore the dataset’s potential to facilitate research in biomarker discovery, prognostic modelling, and advanced machine learning applications in colorectal cancer and beyond. SurGen offers a valuable resource for the scientific community, enabling studies that require high-quality WSIs linked with comprehensive clinical and genetic information on colorectal cancer. Our initial findings affirm the dataset’s capacity to advance diagnostic precision and foster the development of personalised treatment strategies in colorectal oncology. Data available online: https://doi.org/10.6019/S-BIAD1285.

Method: Created Res-Bench with 14,400 samples across 12 resolution levels and 6 capability dimensions. Introduced novel robustness metrics: Spearman’s correlation for resolution-performance trends, and Absolute/Relative Continuous Error (ACE/RCE) for performance volatility. Evaluated leading MLLMs with model-centric and task-centric analysis, preprocessing strategies (padding, super-resolution), and fine-tuning for stability.

Result: The paper presents a comprehensive evaluation framework and benchmark specifically designed to measure resolution robustness in MLLMs, going beyond traditional accuracy metrics.

Conclusion: Res-Bench provides a systematic approach to evaluate and improve resolution robustness in MLLMs, addressing a critical gap in current evaluation methodologies for multimodal models.

Abstract: Multimodal Large Language Models (MLLMs) increasingly support dynamic image resolutions. However, current evaluation paradigms primarily assess semantic performance, overlooking the critical question of resolution robustness - whether performance remains stable across varying input resolutions. To address this gap, we introduce \textbf{Res-Bench}, a comprehensive benchmark comprising 14,400 samples across 12 resolution levels and six core capability dimensions. We designed a novel evaluation framework that goes beyond traditional accuracy metrics to capture performance stability. This framework introduces multiple robustness metrics: Spearman’s correlation for assessing resolution-performance trends, and Absolute/Relative Continuous Error (ACE/RCE) for measuring performance volatility. Using these metrics, we conducted a large-scale evaluation of leading MLLMs. Our analysis encompasses: (1) model-centric and task-centric robustness examination, (2) investigation of preprocessing strategies including padding and super-resolution, and (3) exploration of fine-tuning for stability enhancement.

Method: Extends Prov-GigaPath vision foundation model into vision-language model using adaptors and contrastive learning. Proposes multi-granular attention comparing learnable prompts with individual patches and patch groups, and uses optimal transport-based visual-text distance for robustness.

Result: Empirical experiments on lung, kidney, and breast pathology modalities show the approach surpasses latest competitors and consistently improves performance across CLIP, PLIP, and Prov-GigaPath integrated PLIP architectures.

Conclusion: The proposed method effectively adapts large vision-language models for few-shot pathology classification, capturing both fine-grained details and broader context while ensuring robustness through optimal transport-based distance.

Abstract: Whole slide pathology image classification presents challenges due to gigapixel image sizes and limited annotation labels, hindering model generalization. This paper introduces a prompt learning method to adapt large vision-language models for few-shot pathology classification. We first extend the Prov-GigaPath vision foundation model, pre-trained on 1.3 billion pathology image tiles, into a vision-language model by adding adaptors and aligning it with medical text encoders via contrastive learning on 923K image-text pairs. The model is then used to extract visual features and text embeddings from few-shot annotations and fine-tunes with learnable prompt embeddings. Unlike prior methods that combine prompts with frozen features using prefix embeddings or self-attention, we propose multi-granular attention that compares interactions between learnable prompts with individual image patches and groups of them. This approach improves the model’s ability to capture both fine-grained details and broader context, enhancing its recognition of complex patterns across sub-regions. To further improve accuracy, we leverage (unbalanced) optimal transport-based visual-text distance to secure model robustness by mitigating perturbations that might occur during the data augmentation process. Empirical experiments on lung, kidney, and breast pathology modalities validate the effectiveness of our approach; thereby, we surpass several of the latest competitors and consistently improve performance across diverse architectures, including CLIP, PLIP, and Prov-GigaPath integrated PLIP.

Method: Created TextAtlas5M dataset with 5 million long-text images across diverse data types, and curated TextAtlasEval benchmark with 3000 human-improved test cases across 3 domains.

Result: Evaluation shows TextAtlasEval presents significant challenges even for advanced proprietary models like GPT4o with DallE-3, with open-source models showing even larger performance gaps.

Conclusion: TextAtlas5M serves as a valuable dataset for training and evaluating future text-conditioned image generation models, particularly for long-text rendering capabilities.

Abstract: Text-conditioned image generation has gained significant attention in recent years and are processing increasingly longer and comprehensive text prompt. In everyday life, dense and intricate text appears in contexts like advertisements, infographics, and signage, where the integration of both text and visuals is essential for conveying complex information. However, despite these advances, the generation of images containing long-form text remains a persistent challenge, largely due to the limitations of existing datasets, which often focus on shorter and simpler text. To address this gap, we introduce TextAtlas5M, a novel dataset specifically designed to evaluate long-text rendering in text-conditioned image generation. Our dataset consists of 5 million long-text generated and collected images across diverse data types, enabling comprehensive evaluation of large-scale generative models on long-text image generation. We further curate 3000 human-improved test set TextAtlasEval across 3 data domains, establishing one of the most extensive benchmarks for text-conditioned generation. Evaluations suggest that the TextAtlasEval benchmarks present significant challenges even for the most advanced proprietary models (e.g. GPT4o with DallE-3), while their open-source counterparts show an even larger performance gap. These evidences position TextAtlas5M as a valuable dataset for training and evaluating future-generation text-conditioned image generation models.

Method: The paper conducts a comprehensive survey of integration methods for ML/DL technologies including CNNs, Vision Transformers, and Foundation Models like SAM into surgical workflows, while analyzing challenges and ethical considerations.

Result: Findings show substantial progress in surgical scene understanding through improved segmentation accuracy, instrument tracking, and phase recognition, but highlight ongoing challenges with data variability, computational demands, and clinical integration.

Conclusion: While significant advancements have been made, more focused efforts are needed for seamless clinical integration of AI technologies to enhance surgical precision, reduce risks, and optimize patient outcomes while addressing ethical considerations.

Abstract: Recent advancements in machine learning (ML) and deep learning (DL), particularly through the introduction of Foundation Models (FMs), have significantly enhanced surgical scene understanding within minimally invasive surgery (MIS). This paper surveys the integration of state-of-the-art ML and DL technologies, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Foundation Models like the Segment Anything Model (SAM), into surgical workflows. These technologies improve segmentation accuracy, instrument tracking, and phase recognition in surgical scene understanding. The paper explores the challenges these technologies face, such as data variability and computational demands, and discusses ethical considerations and integration hurdles in clinical settings. Highlighting the roles of FMs, we bridge the technological capabilities with clinical needs and outline future research directions to enhance the adaptability, efficiency, and ethical alignment of AI applications in surgery. Our findings suggest that substantial progress has been made; however, more focused efforts are required to achieve seamless integration of these technologies into clinical workflows, ensuring they complement surgical practice by enhancing precision, reducing risks, and optimizing patient outcomes.

Method: Model local functional dependence via linear basis function model with basis functions learned jointly with deformation, implemented via efficient GPU convolutions.

Result: Good performance on three datasets compared to established baselines and earlier functional dependence-based methods.

Conclusion: Local functional dependence metrics are effective for multi-modal image registration when implemented efficiently on GPUs.

Abstract: The deformable registration of images of different modalities, essential in many medical imaging applications, remains challenging. The main challenge is developing a robust measure for image overlap despite the compared images capturing different aspects of the underlying tissue. Here, we explore similarity metrics based on functional dependence between intensity values of registered images. Although functional dependence is too restrictive on the global scale, earlier work has shown competitive performance in deformable registration when such measures are applied over small enough contexts. We confirm this finding and further develop the idea by modeling local functional dependence via the linear basis function model with the basis functions learned jointly with the deformation. The measure can be implemented via convolutions, making it efficient to compute on GPUs. We release the method as an easy-to-use tool and show good performance on three datasets compared to well-established baseline and earlier functional dependence-based methods.

Method: Proposed Directional Bias Amplification in Captioning (DBAC) - a language-aware metric that can identify when captioning models amplify biases. It improves upon LIC by being less sensitive to sentence encoders and providing more accurate bias amplification estimates.

Result: Experiments on gender and race attributes in COCO captions dataset show DBAC is the only reliable metric to measure bias amplification in captions.

Conclusion: DBAC effectively addresses limitations of existing bias amplification metrics for image captioning by providing language-aware, directional measurement that can identify bias sources and is more robust to implementation choices.

Abstract: When we train models on biased datasets, they not only reproduce data biases, but can worsen them at test time - a phenomenon called bias amplification. Many of the current bias amplification metrics (e.g., BA (MALS), DPA) measure bias amplification only in classification datasets. These metrics are ineffective for image captioning datasets, as they cannot capture the language semantics of a caption. Recent work introduced Leakage in Captioning (LIC), a language-aware bias amplification metric that understands caption semantics. However, LIC has a crucial limitation: it cannot identify the source of bias amplification in captioning models. We propose Directional Bias Amplification in Captioning (DBAC), a language-aware and directional metric that can identify when captioning models amplify biases. DBAC has two more improvements over LIC: (1) it is less sensitive to sentence encoders (a hyperparameter in language-aware metrics), and (2) it provides a more accurate estimate of bias amplification in captions. Our experiments on gender and race attributes in the COCO captions dataset show that DBAC is the only reliable metric to measure bias amplification in captions.

Method: Proposes adaptive scaling that leverages the observation that OOD samples show more pronounced activation shifts at high-magnitude activations under minor perturbation compared to ID samples.

Result: Achieves 14.94% improvement in near-OOD and 21.67% in far-OOD detection on ImageNet-1k benchmark across eight architectures, outperforming OptFS.

Conclusion: AdaSCALE enables stronger scaling for likely ID samples and weaker scaling for likely OOD samples, yielding highly separable energy scores for improved OOD detection.

Abstract: The ability of the deep learning model to recognize when a sample falls outside its learned distribution is critical for safe and reliable deployment. Recent state-of-the-art out-of-distribution (OOD) detection methods leverage activation shaping to improve the separation between in-distribution (ID) and OOD inputs. These approaches resort to sample-specific scaling but apply a static percentile threshold across all samples regardless of their nature, resulting in suboptimal ID-OOD separability. In this work, we propose \textbf{AdaSCALE}, an adaptive scaling procedure that dynamically adjusts the percentile threshold based on a sample’s estimated OOD likelihood. This estimation leverages our key observation: OOD samples exhibit significantly more pronounced activation shifts at high-magnitude activations under minor perturbation compared to ID samples. AdaSCALE enables stronger scaling for likely ID samples and weaker scaling for likely OOD samples, yielding highly separable energy scores. Our approach achieves state-of-the-art OOD detection performance, outperforming the latest rival OptFS by 14.94% in near-OOD and 21.67% in far-OOD datasets in average FPR@95 metric on the ImageNet-1k benchmark across eight diverse architectures. The code is available at: https://github.com/sudarshanregmi/AdaSCALE/

Method: Proposed LocDiff with SHDD representations for encoding geolocations into spherical harmonics space, and CS-UNet architecture for image-guided latent diffusion with KL-divergence loss.

Result: Outperforms all state-of-the-art grid-based, retrieval-based, and diffusion-based baselines across 5 global-scale datasets with stronger generalizability to unseen locations.

Conclusion: LocDiff is the first image geolocalization model performing latent diffusion in multi-scale location encoding space, demonstrating superior performance and generalizability.

Abstract: Image geolocalization is a fundamental yet challenging task, aiming at inferring the geolocation on Earth where an image is taken. State-of-the-art methods employ either grid-based classification or gallery-based image-location retrieval, whose spatial generalizability significantly suffers if the spatial distribution of test images does not align with the choices of grids and galleries. Recently emerging generative approaches, while getting rid of grids and galleries, use raw geographical coordinates and suffer quality losses due to their lack of multi-scale information. To address these limitations, we propose a multi-scale latent diffusion model called LocDiff for image geolocalization. We developed a novel positional encoding-decoding framework called Spherical Harmonics Dirac Delta (SHDD) Representations, which encodes points on a spherical surface (e.g., geolocations on Earth) into a Hilbert space of Spherical Harmonics coefficients and decodes points (geolocations) by mode-seeking on spherical probability distributions. We also propose a novel SirenNet-based architecture (CS-UNet) to learn an image-based conditional backward process in the latent SHDD space by minimizing a latent KL-divergence loss. To the best of our knowledge, LocDiff is the first image geolocalization model that performs latent diffusion in a multi-scale location encoding space and generates geolocations under the guidance of images. Experimental results show that LocDiff can outperform all state-of-the-art grid-based, retrieval-based, and diffusion-based baselines across 5 challenging global-scale image geolocalization datasets, and demonstrates significantly stronger generalizability to unseen geolocations.

Method: Proposes Frequency-decoupled Unified Self-supervised Encoder (FUSE) with two components: Parameter-efficient Self-supervised Transfer (PST) for cross-modal knowledge transfer through latent space alignment, and Frequency-Decoupled Fusion module (FreDFuse) to explicitly decouple high-frequency edge features from low-frequency structural components.

Result: Achieves state-of-the-art performance with 14% and 24.9% improvements in Abs .Rel on MVSEC and DENSE datasets, and exhibits remarkable zero-shot adaptability to challenging scenarios including extreme lighting and motion blur.

Conclusion: FUSE enables construction of a universal image-event encoder that only requires lightweight decoder adaptation for target datasets, significantly advancing real-world deployment capabilities for robust depth estimation.

Abstract: Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability stemming from two factors: 1) limited annotated image-event-depth datasets causing insufficient cross-modal supervision, and 2) inherent frequency mismatches between static images and dynamic event streams with distinct spatiotemporal patterns, leading to ineffective feature fusion. To address this dual challenge, we propose Frequency-decoupled Unified Self-supervised Encoder (FUSE) with two synergistic components: The Parameter-efficient Self-supervised Transfer (PST) establishes cross-modal knowledge transfer through latent space alignment with image foundation models, effectively mitigating data scarcity by enabling joint encoding without depth ground truth. Complementing this, we propose the Frequency-Decoupled Fusion module (FreDFuse) to explicitly decouple high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches through physics-aware fusion. This combined approach enables FUSE to construct a universal image-event encoder that only requires lightweight decoder adaptation for target datasets. Extensive experiments demonstrate state-of-the-art performance with 14% and 24.9% improvements in Abs .Rel on MVSEC and DENSE datasets. The framework exhibits remarkable zero-shot adaptability to challenging scenarios including extreme lighting and motion blur, significantly advancing real-world deployment capabilities. The source code for our method is publicly available at: https://github.com/sunpihai-up/FUSE

Method: Multi-agent reinforcement learning where agents erase target regions from boxes to flip classification tags, using superpixel/supervoxel encoding, three specialized rewards (classification score and intensity distribution), and progressive curriculum learning.

Result: Outperforms state-of-the-art weakly-supervised methods and foundation models on large in-house BUS and ABUS datasets, achieving comparable performance to fully-supervised learning algorithms.

Conclusion: Flip Learning provides an effective weakly-supervised segmentation approach that reduces annotation burden while maintaining high accuracy, demonstrating the potential of multi-agent reinforcement learning for medical image segmentation tasks.

Abstract: Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: (1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. (2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. (3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms.

Method: Represents scenes as 3D Gaussians with acoustic reflectance and saturation properties, develops efficient rasterization for range/azimuth images faithful to acoustic image formation, and models azimuth streaking within the Gaussian splatting framework.

Result: Achieves +3.2 dB PSNR improvement in image synthesis and 77% lower Chamfer Distance in 3D reconstruction compared to state-of-the-art methods. Also demonstrates capability for azimuth streak removal.

Conclusion: SonarSplat provides an effective Gaussian splatting approach for imaging sonar that significantly improves both image synthesis quality and 3D reconstruction accuracy while modeling acoustic streaking phenomena.

Abstract: In this paper, we present SonarSplat, a novel Gaussian splatting framework for imaging sonar that demonstrates realistic novel view synthesis and models acoustic streaking phenomena. Our method represents the scene as a set of 3D Gaussians with acoustic reflectance and saturation properties. We develop a novel method to efficiently rasterize Gaussians to produce a range/azimuth image that is faithful to the acoustic image formation model of imaging sonar. In particular, we develop a novel approach to model azimuth streaking in a Gaussian splatting framework. We evaluate SonarSplat using real-world datasets of sonar images collected from an underwater robotic platform in a controlled test tank and in a real-world river environment. Compared to the state-of-the-art, SonarSplat offers improved image synthesis capabilities (+3.2 dB PSNR) and more accurate 3D reconstruction (77% lower Chamfer Distance). We also demonstrate that SonarSplat can be leveraged for azimuth streak removal.

Method: 1) Integrate Mapillary street-level images with OpenStreetMap via isovist analysis to identify optimal vantage points 2) Automatically detect building facades and reproject them into holistic perspective views 3) Use fine-tuned open-source vision-language models for multi-attribute prediction and open-vocabulary captioning, trained on 31,180 labeled images from 7 cities

Result: Fine-tuned VLMs excelled in multi-attribute inference, outperforming single-attribute computer vision models and zero-shot ChatGPT-4o. The system demonstrated superior generalization and robustness across culturally distinct regions and varying image conditions.

Conclusion: OpenFACADES successfully bridges the gap in building attribute data by providing a scalable framework that leverages multimodal crowdsourced data and advanced vision-language models to automatically enrich building profiles with comprehensive attributes.

Abstract: Building properties, such as height, usage, and material, play a crucial role in spatial data infrastructures, supporting various urban applications. Despite their importance, comprehensive building attribute data remain scarce in many urban areas. Recent advances have enabled the extraction of objective building attributes using remote sensing and street-level imagery. However, establishing a pipeline that integrates diverse open datasets, acquires holistic building imagery, and infers comprehensive building attributes at scale remains a significant challenge. Among the first, this study bridges the gaps by introducing OpenFACADES, an open framework that leverages multimodal crowdsourced data to enrich building profiles with both objective attributes and semantic descriptors through multimodal large language models. First, we integrate street-level image metadata from Mapillary with OpenStreetMap geometries via isovist analysis, identifying images that provide suitable vantage points for observing target buildings. Second, we automate the detection of building facades in panoramic imagery and tailor a reprojection approach to convert objects into holistic perspective views that approximate real-world observation. Third, we introduce an innovative approach that harnesses and investigates the capabilities of open-source large vision-language models (VLMs) for multi-attribute prediction and open-vocabulary captioning in building-level analytics, leveraging a globally sourced dataset of 31,180 labeled images from seven cities. Evaluation shows that fine-tuned VLM excel in multi-attribute inference, outperforming single-attribute computer vision models and zero-shot ChatGPT-4o. Further experiments confirm its superior generalization and robustness across culturally distinct region and varying image conditions.

Method: Builds on state-of-the-art long-term point tracking method with training-free runtime optimizations, including temporal memory buffer for feature reuse and prior motion for track initialization.

Result: 7x faster than predecessor and 2x faster than state-of-the-art, with competitive tracking accuracy and occlusion prediction on STIR and SuPer datasets.

Conclusion: LiteTracker represents an important step toward low-latency tissue tracking for real-time surgical applications in the operating room.

Abstract: Tissue tracking plays a critical role in various surgical navigation and extended reality (XR) applications. While current methods trained on large synthetic datasets achieve high tracking accuracy and generalize well to endoscopic scenes, their runtime performances fail to meet the low-latency requirements necessary for real-time surgical applications. To address this limitation, we propose LiteTracker, a low-latency method for tissue tracking in endoscopic video streams. LiteTracker builds on a state-of-the-art long-term point tracking method, and introduces a set of training-free runtime optimizations. These optimizations enable online, frame-by-frame tracking by leveraging a temporal memory buffer for efficient feature reuse and utilizing prior motion for accurate track initialization. LiteTracker demonstrates significant runtime improvements being around 7x faster than its predecessor and 2x than the state-of-the-art. Beyond its primary focus on efficiency, LiteTracker delivers high-accuracy tracking and occlusion prediction, performing competitively on both the STIR and SuPer datasets. We believe LiteTracker is an important step toward low-latency tissue tracking for real-time surgical applications in the operating room. Our code is publicly available at https://github.com/ImFusionGmbH/lite-tracker.

Method: The Change State Space Model (CSSM) is introduced, which focuses specifically on relevant changes between bi-temporal images by filtering out irrelevant information. This selective attention reduces network parameters and enhances computational efficiency.

Result: CSSM outperformed ConvNets, ViTs, and Mamba-based counterparts on three benchmark datasets while using only a fraction of their computational complexity. It also demonstrated robustness against input degradation.

Conclusion: CSSM provides an efficient and effective solution for change detection by specifically targeting relevant changes, achieving superior performance with significantly reduced computational requirements compared to existing approaches.

Abstract: Despite their frequent use for change detection, both ConvNets and Vision transformers (ViT) exhibit well-known limitations, namely the former struggle to model long-range dependencies while the latter are computationally inefficient, rendering them challenging to train on large-scale datasets. Vision Mamba, an architecture based on State Space Models has emerged as an alternative addressing the aforementioned deficiencies and has been already applied to remote sensing change detection, though mostly as a feature extracting backbone. In this article the Change State Space Model is introduced, that has been specifically designed for change detection by focusing on the relevant changes between bi-temporal images, effectively filtering out irrelevant information. By concentrating solely on the changed features, the number of network parameters is reduced, enhancing significantly computational efficiency while maintaining high detection performance and robustness against input degradation. The proposed model has been evaluated via three benchmark datasets, where it outperformed ConvNets, ViTs, and Mamba-based counterparts at a fraction of their computational complexity. The implementation will be made available at https://github.com/Elman295/CSSM upon acceptance.

Method: Proposed an end-to-end deep learning approach using modified U-Net with custom loss function to directly generate chemical maps from hyperspectral images, skipping intermediate steps of traditional pixel-wise analysis.

Result: U-Net achieved 7% lower test set RMSE than PLS for mean fat prediction, generated chemical maps with 99.91% spatially correlated variance (vs 2.37% for PLS), and stayed within physically possible 0-100% range unlike PLS.

Conclusion: U-Net is superior to PLS for chemical map generation, providing more accurate predictions with better spatial coherence and physically plausible results.

Abstract: Current approaches to chemical map generation from hyperspectral images are based on models such as partial least squares (PLS) regression, generating pixel-wise predictions that do not consider spatial context and suffer from a high degree of noise. This study proposes an end-to-end deep learning approach using a modified version of U-Net and a custom loss function to directly obtain chemical maps from hyperspectral images, skipping all intermediate steps required for traditional pixel-wise analysis. This study compares the U-Net with the traditional PLS regression on a real dataset of pork belly samples with associated mean fat reference values. The U-Net obtains a test set root mean squared error that is 7% lower than that of PLS regression on the task of mean fat prediction. At the same time, U-Net generates fine detail chemical maps where 99.91% of the variance is spatially correlated. Conversely, only 2.37% of the variance in the PLS-generated chemical maps is spatially correlated, indicating that each pixel-wise prediction is largely independent of neighboring pixels. Additionally, while the PLS-generated chemical maps contain predictions far beyond the physically possible range of 0-100%, U-Net learns to stay inside this range. Thus, the findings of this study indicate that U-Net is superior to PLS for chemical map generation.

Method: Vary parameters of a procedural dataset generator and analyze effects on zero-shot stereo matching performance using standard benchmarks.

Result: Training only on the optimized dataset achieves better performance than training on mixed datasets and is competitive with FoundationStereo.

Conclusion: The work provides open-source generation code and parameter analysis to enable further research on procedural stereo datasets.

Abstract: Synthetic datasets are a crucial ingredient for training stereo matching networks, but the question of what makes a stereo dataset effective remains underexplored. We investigate the design space of synthetic datasets by varying the parameters of a procedural dataset generator, and report the effects on zero-shot stereo matching performance using standard benchmarks. We validate our findings by collecting the best settings and creating a large-scale dataset. Training only on this dataset achieves better performance than training on a mixture of widely used datasets, and is competitive with training on the FoundationStereo dataset, with the additional benefit of open-source generation code and an accompanying parameter analysis to enable further research. We open-source our system at https://github.com/princeton-vl/InfinigenStereo to enable further research on procedural stereo datasets.

Method: The paper examines single-sensor remote foundation models to introduce concepts, then emphasizes incorporating multi-sensor aspects of Earth observations. It explores how existing approaches leverage multiple sensors compared to multi-modal foundation models.

Result: The analysis identifies that current approaches vary in how effectively they leverage multi-sensor data, with opportunities to better utilize the vast amounts of unlabeled, seasonal, and multi-sensor remote sensing observations.

Conclusion: There are significant opportunities to further improve remote sensing foundation models by better harnessing multi-sensor data, seasonal information, and unlabeled observations, building on computer vision foundations while adapting to domain-specific requirements.

Abstract: Foundation models have garnered increasing attention for representation learning in remote sensing. Many such foundation models adopt approaches that have demonstrated success in computer vision with minimal domain-specific modification. However, the development and application of foundation models in this field are still burgeoning, as there are a variety of competing approaches for how to most effectively leverage remotely sensed data. This paper examines these approaches, along with their roots in the computer vision field. This is done to characterize potential advantages and pitfalls, while outlining future directions to further improve remote sensing-specific foundation models. We discuss the quality of the learned representations and methods to alleviate the need for massive compute resources. We first examine single-sensor remote foundation models to introduce concepts and provide context, and then place emphasis on incorporating the multi-sensor aspect of Earth observations into foundation models. In particular, we explore the extent to which existing approaches leverage multiple sensors in training foundation models in relation to multi-modal foundation models. Finally, we identify opportunities for further harnessing the vast amounts of unlabeled, seasonal, and multi-sensor remote sensing observations.

Method: Proposed SIV framework with Detail-Preserving Hierarchical Transform (DPHT) module to aggregate motion features from spike streams, and Graph Encoder (GE) to extract contextual features from complex fluid flows. Also created PSSD dataset for validation.

Result: The proposed method achieves superior performance compared to existing baseline methods on the PSSD dataset across three challenging fluid dynamics scenarios.

Conclusion: Spike cameras combined with the SIV framework offer significant potential for PIV applications, especially in complex turbulent flows, with open-sourced datasets and implementation available.

Abstract: The need for accurate and non-intrusive flow measurement methods has led to the widespread adoption of Particle Image Velocimetry (PIV), a powerful diagnostic tool in fluid motion estimation. This study investigates the tremendous potential of spike cameras (a type of ultra-high-speed, high-dynamic-range camera) in PIV. We propose a deep learning framework, Spike Imaging Velocimetry (SIV), designed specifically for highly turbulent and intricate flow fields. To aggregate motion features from the spike stream while minimizing information loss, we incorporate a Detail-Preserving Hierarchical Transform (DPHT) module. Additionally, we introduce a Graph Encoder (GE) to extract contextual features from highly complex fluid flows. Furthermore, we present a spike-based PIV dataset, Particle Scenes with Spike and Displacement (PSSD), which provides labeled data for three challenging fluid dynamics scenarios. Our proposed method achieves superior performance compared to existing baseline methods on PSSD. The datasets and our implementation of SIV are open-sourced in the supplementary materials.

Method: Fine-tuned a pretrained CLIP model using parameter-efficient fine-tuning to align imaging and semantic text features from multiple datasets including NLST, LIDC, and three external datasets.

Result: Achieved AUROC of 0.901 and AUPRC of 0.776 on NLST test set, outperforming SOTA models. Also showed robust performance in external datasets and obtained zero-shot predictions for semantic features like nodule margin (AUROC: 0.807).

Conclusion: The approach surpasses SOTA models in predicting lung cancer across diverse clinical settings, provides explainable outputs, prevents learning shortcuts, and generalizes well across different clinical environments.

Abstract: Machine learning models have utilized semantic features, deep features, or both to assess lung nodule malignancy. However, their reliance on manual annotation during inference, limited interpretability, and sensitivity to imaging variations hinder their application in real-world clinical settings. Thus, this research aims to integrate semantic features derived from radiologists’ assessments of nodules, guiding the model to learn clinically relevant, robust, and explainable imaging features for predicting lung cancer. We obtained 938 low-dose CT scans from the National Lung Screening Trial (NLST) with 1,261 nodules and semantic features. Additionally, the Lung Image Database Consortium dataset contains 1,018 CT scans, with 2,625 lesions annotated for nodule characteristics. Three external datasets were obtained from UCLA Health, the LUNGx Challenge, and the Duke Lung Cancer Screening. We fine-tuned a pretrained Contrastive Language-Image Pretraining (CLIP) model with a parameter-efficient fine-tuning approach to align imaging and semantic text features and predict the one-year lung cancer diagnosis. Our model outperformed state-of-the-art (SOTA) models in the NLST test set with an AUROC of 0.901 and AUPRC of 0.776. It also showed robust results in external datasets. Using CLIP, we also obtained predictions on semantic features through zero-shot inference, such as nodule margin (AUROC: 0.807), nodule consistency (0.812), and pleural attachment (0.840). Our approach surpasses the SOTA models in predicting lung cancer across datasets collected from diverse clinical settings, providing explainable outputs, aiding clinicians in comprehending the underlying meaning of model predictions. This approach also prevents the model from learning shortcuts and generalizes across clinical settings. The code is available at https://github.com/luotingzhuang/CLIP_nodule.

Method: Proposes RLQ framework with Coarse Attributes Prediction (CAP) to enrich external fine-grained attributes via coarse predictions, and Task Agnostic Distillation (TAD) to bridge gap between HQ and LQ features using external dataset through task-agnostic self-supervision and distillation.

Result: Outperforms existing approaches by 1.6%-2.9% Top-1 on real-world datasets (LaST, DeepChange), with consistent improvement of 5.3%-6% Top-1 on PRCC and competitive performance on LTCC.

Conclusion: RLQ framework effectively addresses low-quality image challenges in CC-ReID, demonstrating significant performance improvements across multiple real-world datasets.

Abstract: This work focuses on Clothes Changing Re-IDentification (CC-ReID) for the real world. Existing works perform well with high-quality (HQ) images, but struggle with low-quality (LQ) where we can have artifacts like pixelation, out-of-focus blur, and motion blur. These artifacts introduce noise to not only external biometric attributes (e.g. pose, body shape, etc.) but also corrupt the model’s internal feature representation. Models usually cluster LQ image features together, making it difficult to distinguish between them, leading to incorrect matches. We propose a novel framework Robustness against Low-Quality (RLQ) to improve CC-ReID model on real-world data. RLQ relies on Coarse Attributes Prediction (CAP) and Task Agnostic Distillation (TAD) operating in alternate steps in a novel training mechanism. CAP enriches the model with external fine-grained attributes via coarse predictions, thereby reducing the effect of noisy inputs. On the other hand, TAD enhances the model’s internal feature representation by bridging the gap between HQ and LQ features, via an external dataset through task-agnostic self-supervision and distillation. RLQ outperforms the existing approaches by 1.6%-2.9% Top-1 on real-world datasets like LaST, and DeepChange, while showing consistent improvement of 5.3%-6% Top-1 on PRCC with competitive performance on LTCC. The code will be made public soon.

Method: Proposed an efficient caption generation framework integrating VLM automatic generation, caption quality scoring, and dynamic refinement, plus a semantic fusion method for coherent full-video captions.

Result: Created LoVR benchmark with 467 long videos and over 40,804 fine-grained clips with high-quality captions. Experiments show it’s challenging for current embedding models.

Conclusion: LoVR presents new challenges for video understanding and retrieval, revealing limitations of current approaches and providing valuable insights for future research.

Abstract: Long videos contain a vast amount of information, making video-text retrieval an essential and challenging task in multimodal learning. However, existing benchmarks suffer from limited video duration, low-quality captions, and coarse annotation granularity, which hinder the evaluation of advanced video-text retrieval methods. To address these limitations, we introduce LoVR, a benchmark specifically designed for long video-text retrieval. LoVR contains 467 long videos and over 40,804 fine-grained clips with high-quality captions. To overcome the issue of poor machine-generated annotations, we propose an efficient caption generation framework that integrates VLM automatic generation, caption quality scoring, and dynamic refinement. This pipeline improves annotation accuracy while maintaining scalability. Furthermore, we introduce a semantic fusion method to generate coherent full-video captions without losing important contextual information. Our benchmark introduces longer videos, more detailed captions, and a larger-scale dataset, presenting new challenges for video understanding and retrieval. Extensive experiments on various advanced embedding models demonstrate that LoVR is a challenging benchmark, revealing the limitations of current approaches and providing valuable insights for future research. We release the code and dataset link at https://github.com/TechNomad-ds/LoVR-benchmark

Method: Compress point coordinates while discarding reflectance, use reflectance prediction module to reconstruct reflectance, and apply knowledge distillation from teacher to student detector.

Result: Demonstrates improved detection accuracy for compressed point clouds across multiple code rates on KITTI and DAIR-V2X-V datasets.

Conclusion: RPKD framework effectively boosts detection performance for compressed point clouds while reducing transmission burden by eliminating reflectance encoding.

Abstract: Regarding intelligent transportation systems, low-bitrate transmission via lossy point cloud compression is vital for facilitating real-time collaborative perception among connected agents, such as vehicles and infrastructures, under restricted bandwidth. In existing compression transmission systems, the sender lossily compresses point coordinates and reflectance to generate a transmission code stream, which faces transmission burdens from reflectance encoding and limited detection robustness due to information loss. To address these issues, this paper proposes a 3D object detection framework with reflectance prediction-based knowledge distillation (RPKD). We compress point coordinates while discarding reflectance during low-bitrate transmission, and feed the decoded non-reflectance compressed point clouds into a student detector. The discarded reflectance is then reconstructed by a geometry-based reflectance prediction (RP) module within the student detector for precise detection. A teacher detector with the same structure as the student detector is designed for performing reflectance knowledge distillation (RKD) and detection knowledge distillation (DKD) from raw to compressed point clouds. Our cross-source distillation training strategy (CDTS) equips the student detector with robustness to low-quality compressed data while preserving the accuracy benefits of raw data through transferred distillation knowledge. Experimental results on the KITTI and DAIR-V2X-V datasets demonstrate that our method can boost detection accuracy for compressed point clouds across multiple code rates. We will release the code publicly at https://github.com/HaoJing-SX/RPKD.

Method: Systematic evaluation of SD 1.5, 2.0, and 3-m diffusion models across 10 datasets and 30+ compositional tasks, introducing Self-Bench diagnostic benchmark to isolate domain effects, and analyzing timestep weighting importance.

Result: Diffusion classifiers demonstrate compositional understanding but performance varies significantly with domain alignment; SD3-m shows particular sensitivity to timestep selection based on domain gap.

Conclusion: Diffusion classifiers can understand compositionality, but their success depends on specific conditions including domain alignment and proper timestep configuration, with SD3-m being particularly sensitive to domain gaps.

Abstract: Understanding visual scenes is fundamental to human intelligence. While discriminative models have significantly advanced computer vision, they often struggle with compositional understanding. In contrast, recent generative text-to-image diffusion models excel at synthesizing complex scenes, suggesting inherent compositional capabilities. Building on this, zero-shot diffusion classifiers have been proposed to repurpose diffusion models for discriminative tasks. While prior work offered promising results in discriminative compositional scenarios, these results remain preliminary due to a small number of benchmarks and a relatively shallow analysis of conditions under which the models succeed. To address this, we present a comprehensive study of the discriminative capabilities of diffusion classifiers on a wide range of compositional tasks. Specifically, our study covers three diffusion models (SD 1.5, 2.0, and, for the first time, 3-m) spanning 10 datasets and over 30 tasks. Further, we shed light on the role that target dataset domains play in respective performance; to isolate the domain effects, we introduce a new diagnostic benchmark \textsc{Self-Bench} comprised of images created by diffusion models themselves. Finally, we explore the importance of timestep weighting and uncover a relationship between domain gap and timestep sensitivity, particularly for SD3-m. To sum up, diffusion classifiers understand compositionality, but conditions apply! Code and dataset are available at https://github.com/eugene6923/Diffusion-Classifiers-Compositionality.

Method: REN uses lightweight cross-attention blocks that take point prompts as queries and features from patch-based image encoders (DINO, DINOv2, OpenCLIP) as keys/values to directly generate region tokens corresponding to prompted objects.

Result: REN consistently outperforms original encoders in semantic segmentation and retrieval tasks, matches or exceeds SAM-based methods in performance while being significantly faster, and achieves SOTA on Ego4D VQ2D benchmark and outperforms proprietary LMMs on Visual Haystacks.

Conclusion: REN provides an efficient and effective alternative to segmentation-based region representation methods, enabling fast region token generation with improved quality and broad encoder compatibility.

Abstract: We introduce the Region Encoder Network (REN), a fast and effective model for generating region-based image representations using point prompts. Recent methods combine class-agnostic segmenters (e.g., SAM) with patch-based image encoders (e.g., DINO) to produce compact and effective region representations, but they suffer from high computational cost due to the segmentation step. REN bypasses this bottleneck using a lightweight module that directly generates region tokens, enabling 60x faster token generation with 35x less memory, while also improving token quality. It uses a few cross-attention blocks that take point prompts as queries and features from a patch-based image encoder as keys and values to produce region tokens that correspond to the prompted objects. We train REN with three popular encoders-DINO, DINOv2, and OpenCLIP-and show that it can be extended to other encoders without dedicated training. We evaluate REN on semantic segmentation and retrieval tasks, where it consistently outperforms the original encoders in both performance and compactness, and matches or exceeds SAM-based region methods while being significantly faster. Notably, REN achieves state-of-the-art results on the challenging Ego4D VQ2D benchmark and outperforms proprietary LMMs on Visual Haystacks’ single-needle challenge. Code and models are available at: https://github.com/savya08/REN.

Method: Two-phase training: first trains ensemble reward models to predict image quality from prompt-workflow combinations, then uses GRPO-based optimization with classifier-free guidance enhancement to guide model toward high-performing workflow regions.

Result: The approach successfully creates new workflows with greater diversity and achieves superior image quality compared to existing baselines.

Conclusion: The reinforcement learning framework effectively automates text-to-image pipeline design while overcoming computational inefficiencies and generalization limitations of previous methods.

Abstract: Text-to-image generation has evolved beyond single monolithic models to complex multi-component pipelines. These combine fine-tuned generators, adapters, upscaling blocks and even editing steps, leading to significant improvements in image quality. However, their effective design requires substantial expertise. Recent approaches have shown promise in automating this process through large language models (LLMs), but they suffer from two critical limitations: extensive computational requirements from generating images with hundreds of predefined pipelines, and poor generalization beyond memorized training examples. We introduce a novel reinforcement learning-based framework that addresses these inefficiencies. Our approach first trains an ensemble of reward models capable of predicting image quality scores directly from prompt-workflow combinations, eliminating the need for costly image generation during training. We then implement a two-phase training strategy: initial workflow vocabulary training followed by GRPO-based optimization that guides the model toward higher-performing regions of the workflow space. Additionally, we incorporate a classifier-free guidance based enhancement technique that extrapolates along the path between the initial and GRPO-tuned models, further improving output quality. We validate our approach through a set of comparisons, showing that it can successfully create new flows with greater diversity and lead to superior image quality compared to existing baselines.

Method: Proposed Spiral - a novel range-view LiDAR diffusion model that generates depth, reflectance images, and semantic maps simultaneously. Also introduced semantic-aware metrics to evaluate generated labeled range-view data quality.

Result: Experiments on SemanticKITTI and nuScenes datasets show Spiral achieves state-of-the-art performance with the smallest parameter size, outperforming two-step methods combining generative and segmentation models. Generated range images effectively work for synthetic data augmentation in downstream segmentation training.

Conclusion: Spiral successfully addresses the semantic labeling limitation in range-view LiDAR generation while maintaining computational efficiency, and demonstrates practical utility for reducing labeling effort through synthetic data augmentation.

Abstract: Leveraging recent diffusion models, LiDAR-based large-scale 3D scene generation has achieved great success. While recent voxel-based approaches can generate both geometric structures and semantic labels, existing range-view methods are limited to producing unlabeled LiDAR scenes. Relying on pretrained segmentation models to predict the semantic maps often results in suboptimal cross-modal consistency. To address this limitation while preserving the advantages of range-view representations, such as computational efficiency and simplified network design, we propose Spiral, a novel range-view LiDAR diffusion model that simultaneously generates depth, reflectance images, and semantic maps. Furthermore, we introduce novel semantic-aware metrics to evaluate the quality of the generated labeled range-view data. Experiments on the SemanticKITTI and nuScenes datasets demonstrate that Spiral achieves state-of-the-art performance with the smallest parameter size, outperforming two-step methods that combine the generative and segmentation models. Additionally, we validate that range images generated by Spiral can be effectively used for synthetic data augmentation in the downstream segmentation training, significantly reducing the labeling effort on LiDAR data.

Method: Proposed VidText benchmark with: 1) wide range of real-world scenarios and multilingual content, 2) hierarchical evaluation framework (video-level, clip-level, instance-level tasks), 3) paired perception-reasoning tasks from visual text perception to cross-modal reasoning.

Result: Experiments on 18 state-of-the-art LMMs show models struggle across most tasks, with significant room for improvement. Analysis reveals impact of model-intrinsic factors (input resolution, OCR capability) and external factors (auxiliary information, Chain-of-Thought reasoning).

Conclusion: VidText fills the current gap in video understanding benchmarks and serves as a foundation for future research on multimodal reasoning with video text in dynamic environments.

Abstract: Visual texts embedded in videos carry rich semantic information, which is crucial for both holistic video understanding and fine-grained reasoning about local human actions. However, existing video understanding benchmarks largely overlook textual information, while OCR-specific benchmarks are constrained to static images, limiting their ability to capture the interaction between text and dynamic visual contexts. To address this gap, we propose VidText, a new benchmark designed for comprehensive and in-depth evaluation of video text understanding. VidText offers the following key features: 1) It covers a wide range of real-world scenarios and supports multilingual content, encompassing diverse settings where video text naturally appears. 2) It introduces a hierarchical evaluation framework with video-level, clip-level, and instance-level tasks, enabling assessment of both global summarization and local retrieval capabilities. 3) The benchmark also introduces a set of paired perception reasoning tasks, ranging from visual text perception to cross-modal reasoning between textual and visual information. Extensive experiments on 18 state-of-the-art Large Multimodal Models (LMMs) reveal that current models struggle across most tasks, with significant room for improvement. Further analysis highlights the impact of both model-intrinsic factors, such as input resolution and OCR capability, and external factors, including the use of auxiliary information and Chain-of-Thought reasoning strategies. We hope VidText will fill the current gap in video understanding benchmarks and serve as a foundation for future research on multimodal reasoning with video text in dynamic environments.

Method: Developed SustainFM - a comprehensive benchmarking framework grounded in the 17 Sustainable Development Goals with diverse tasks including asset wealth prediction and environmental hazard detection.

Result: (1) FMs often outperform traditional approaches across diverse tasks, though not universally superior; (2) Evaluation should include transferability, generalization, and energy efficiency; (3) FMs enable scalable SDG-grounded solutions for sustainability challenges.

Conclusion: Advocates for paradigm shift from model-centric development to impact-driven deployment, emphasizing energy efficiency, robustness to domain shifts, and ethical considerations as key metrics for responsible FM use.

Abstract: Foundation Models (FMs) are large-scale, pre-trained artificial intelligence (AI) systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They promise improved generalization across tasks, scalability, and efficient adaptation with minimal labeled data. However, despite the rapid proliferation of geospatial FMs, their real-world utility and alignment with global sustainability goals remain underexplored. We introduce SustainFM, a comprehensive benchmarking framework grounded in the 17 Sustainable Development Goals with extremely diverse tasks ranging from asset wealth prediction to environmental hazard detection. This study provides a rigorous, interdisciplinary assessment of geospatial FMs and offers critical insights into their role in attaining sustainability goals. Our findings show: (1) While not universally superior, FMs often outperform traditional approaches across diverse tasks and datasets. (2) Evaluating FMs should go beyond accuracy to include transferability, generalization, and energy efficiency as key criteria for their responsible use. (3) FMs enable scalable, SDG-grounded solutions, offering broad utility for tackling complex sustainability challenges. Critically, we advocate for a paradigm shift from model-centric development to impact-driven deployment, and emphasize metrics such as energy efficiency, robustness to domain shifts, and ethical considerations.

Method: Leverages cycle consistency: maps generated text back to image space via text-to-image model and computes similarity between original and reconstructed image, and vice versa for text reconstruction. Uses this score to rank candidates and build a preference dataset.

Result: CycleReward outperforms state-of-the-art alignment metrics on detailed captioning, shows superior inference-time scalability for Best-of-N sampling, and enhances performance across vision-language tasks and text-to-image generation when used for DPO and Diffusion DPO.

Conclusion: Cycle consistency provides an effective supervisory signal for measuring language-vision alignment, enabling scalable and efficient reward modeling without expensive preference collection.

Abstract: Measuring alignment between language and vision is a fundamental challenge, especially as multimodal data becomes increasingly detailed and complex. Existing methods often rely on collecting human or AI preferences, which can be costly and time-intensive. We propose an alternative approach that leverages cycle consistency as a supervisory signal. Given an image and generated text, we map the text back to image space using a text-to-image model and compute the similarity between the original image and its reconstruction. Analogously, for text-to-image generation, we measure the textual similarity between an input caption and its reconstruction through the cycle. We use the cycle consistency score to rank candidates and construct a preference dataset of 866K comparison pairs. The reward model trained on our dataset, CycleReward, outperforms state-of-the-art alignment metrics on detailed captioning, with superior inference-time scalability when used as a verifier for Best-of-N sampling, while maintaining speed and differentiability. Furthermore, performing DPO and Diffusion DPO using our dataset enhances performance across a wide range of vision-language tasks and text-to-image generation. Our dataset, model, and code are publicly released at https://cyclereward.github.io.

Method: Three-step approach: 1) Initialize embeddings using pre-trained image captioning model, 2) Refine embeddings through reverse-engineering in latent space, 3) Convert embeddings to text using embedding-to-text model.

Result: Outperforms existing methods on MS COCO, LAION, and Flickr datasets in image similarity, textual alignment, prompt interpretability and generalizability.

Conclusion: The method enables applications in cross-concept image synthesis, concept manipulation, evolutionary multi-concept generation and unsupervised segmentation.

Abstract: Text-to-image generation models~(e.g., Stable Diffusion) have achieved significant advancements, enabling the creation of high-quality and realistic images based on textual descriptions. Prompt inversion, the task of identifying the textual prompt used to generate a specific artifact, holds significant potential for applications including data attribution, model provenance, and watermarking validation. Recent studies introduced a delayed projection scheme to optimize for prompts representative of the vocabulary space, though challenges in semantic fluency and efficiency remain. Advanced image captioning models or visual large language models can generate highly interpretable prompts, but they often lack in image similarity. In this paper, we propose a prompt inversion technique called \sys for text-to-image diffusion models, which includes initializing embeddings using a pre-trained image captioning model, refining them through reverse-engineering in the latent space, and converting them to texts using an embedding-to-text model. Our experiments on the widely-used datasets, such as MS COCO, LAION, and Flickr, show that our method outperforms existing methods in terms of image similarity, textual alignment, prompt interpretability and generalizability. We further illustrate the application of our generated prompts in tasks such as cross-concept image synthesis, concept manipulation, evolutionary multi-concept generation and unsupervised segmentation.

Method: Developed baseline methods using multi-modal data from high-resolution Sentinel-2 satellite images, MODIS satellite products, and ERA5 environmental factors, tested with two major deep learning architectures.

Result: Multi-modal temporal inputs outperformed single-modal inputs across all metrics, achieving 60.3% F1 score for the 2023 wildfire season that was not seen during training.

Conclusion: Multi-modal deep learning models show strong potential for high-resolution, continental-scale wildfire forecasting, addressing limitations of coarse-resolution methods.

Abstract: Canada experienced in 2023 one of the most severe wildfire seasons in recent history, causing damage across ecosystems, destroying communities, and emitting large quantities of CO2. This extreme wildfire season is symptomatic of a climate-change-induced increase in the length and severity of the fire season that affects the boreal ecosystem. Therefore, it is critical to empower wildfire management in boreal communities with better mitigation solutions. Wildfire probability maps represent an important tool for understanding the likelihood of wildfire occurrence and the potential severity of future wildfires. The massive increase in the availability of Earth observation data has enabled the development of deep learning-based wildfire forecasting models, aiming at providing precise wildfire probability maps at different spatial and temporal scales. A main limitation of such methods is their reliance on coarse-resolution environmental drivers and satellite products, leading to wildfire occurrence prediction of reduced resolution, typically around $\sim 0.1${\deg}. This paper presents a benchmark dataset: CanadaFireSat, and baseline methods for high-resolution: 100 m wildfire forecasting across Canada, leveraging multi-modal data from high-resolution multi-spectral satellite images (Sentinel-2 L1C), mid-resolution satellite products (MODIS), and environmental factors (ERA5 reanalysis data). Our experiments consider two major deep learning architectures. We observe that using multi-modal temporal inputs outperforms single-modal temporal inputs across all metrics, achieving a peak performance of 60.3% in F1 score for the 2023 wildfire season, a season never seen during model training. This demonstrates the potential of multi-modal deep learning models for wildfire forecasting at high-resolution and continental scale.

Method: Created LADH dataset with 240 synchronized RGB and IR facial videos from 21 participants across five personal care scenarios, using multi-task learning to combine RGB and IR inputs for physiological monitoring.

Result: Combining RGB and IR video inputs achieved MAE of 4.99 BPM in heart rate estimation, with multi-task learning enhancing performance across multiple physiological indicators simultaneously.

Conclusion: RGB-IR fusion and multi-task learning significantly improve rPPG-based physiological monitoring accuracy and robustness in challenging real-world scenarios.

Abstract: Remote photoplethysmography (rPPG) enables non-contact, continuous monitoring of physiological signals and offers a practical alternative to traditional health sensing methods. Although rPPG is promising for daily health monitoring, its application in long-term personal care scenarios, such as mirror-facing routines in high-altitude environments, remains challenging due to ambient lighting variations, frequent occlusions from hand movements, and dynamic facial postures. To address these challenges, we present LADH (Long-term Altitude Daily Health), the first long-term rPPG dataset containing 240 synchronized RGB and infrared (IR) facial videos from 21 participants across five common personal care scenarios, along with ground-truth PPG, respiration, and blood oxygen signals. Our experiments demonstrate that combining RGB and IR video inputs improves the accuracy and robustness of non-contact physiological monitoring, achieving a mean absolute error (MAE) of 4.99 BPM in heart rate estimation. Furthermore, we find that multi-task learning enhances performance across multiple physiological indicators simultaneously. Dataset and code are open at https://github.com/McJackTang/FusionVitals.

Method: Proposes HeadHunter framework that systematically selects attention heads aligned with user objectives, and SoftPAG which linearly interpolates attention maps toward identity matrix for continuous perturbation strength control.

Result: The method mitigates oversmoothing issues of layer-level perturbation and enables targeted manipulation of specific visual styles through compositional head selection, validated on Stable Diffusion 3 and FLUX.1 models.

Conclusion: This work provides the first head-level analysis of attention perturbation in diffusion models, revealing interpretable specialization within attention layers and enabling practical design of effective perturbation strategies for fine-grained generation control.

Abstract: Recent guidance methods in diffusion models steer reverse sampling by perturbing the model to construct an implicit weak model and guide generation away from it. Among these approaches, attention perturbation has demonstrated strong empirical performance in unconditional scenarios where classifier-free guidance is not applicable. However, existing attention perturbation methods lack principled approaches for determining where perturbations should be applied, particularly in Diffusion Transformer (DiT) architectures where quality-relevant computations are distributed across layers. In this paper, we investigate the granularity of attention perturbations, ranging from the layer level down to individual attention heads, and discover that specific heads govern distinct visual concepts such as structure, style, and texture quality. Building on this insight, we propose “HeadHunter”, a systematic framework for iteratively selecting attention heads that align with user-centric objectives, enabling fine-grained control over generation quality and visual attributes. In addition, we introduce SoftPAG, which linearly interpolates each selected head’s attention map toward an identity matrix, providing a continuous knob to tune perturbation strength and suppress artifacts. Our approach not only mitigates the oversmoothing issues of existing layer-level perturbation but also enables targeted manipulation of specific visual styles through compositional head selection. We validate our method on modern large-scale DiT-based text-to-image models including Stable Diffusion 3 and FLUX.1, demonstrating superior performance in both general quality enhancement and style-specific guidance. Our work provides the first head-level analysis of attention perturbation in diffusion models, uncovering interpretable specialization within attention layers and enabling practical design of effective perturbation strategies.

Method: Extends multi-view diffusion paradigm to learn from scene views with varying appearances by explicitly modeling global appearance conditions in images. Enables training on diverse 2D scene image data captured in the wild.

Result: Achieves state-of-the-art results on single-view NVS in both object- and scene-level settings. Generalizes to new scenes at inference time, generating multiple consistent novel views. Trains on strictly less data sources than prior methods while providing global appearance control during generation.

Conclusion: WildCAT3D successfully addresses scene-level NVS challenges by leveraging diverse real-world data through explicit appearance modeling, outperforming previous methods with less training data and enabling novel applications through appearance control.

Abstract: Despite recent advances in sparse novel view synthesis (NVS) applied to object-centric scenes, scene-level NVS remains a challenge. A central issue is the lack of available clean multi-view training data, beyond manually curated datasets with limited diversity, camera variation, or licensing issues. On the other hand, an abundance of diverse and permissively-licensed data exists in the wild, consisting of scenes with varying appearances (illuminations, transient occlusions, etc.) from sources such as tourist photos. To this end, we present WildCAT3D, a framework for generating novel views of scenes learned from diverse 2D scene image data captured in the wild. We unlock training on these data sources by explicitly modeling global appearance conditions in images, extending the state-of-the-art multi-view diffusion paradigm to learn from scene views of varying appearances. Our trained model generalizes to new scenes at inference time, enabling the generation of multiple consistent novel views. WildCAT3D provides state-of-the-art results on single-view NVS in object- and scene-level settings, while training on strictly less data sources than prior methods. Additionally, it enables novel applications by providing global appearance control during generation.

Method: The authors propose jointly optimizing both interval structure and texture elements to create grid-structured adversarial patches. These patches are designed to be inserted into any scene and tested on various stereo depth estimation methods.

Result: The generated adversarial patches successfully attack advanced stereo depth estimation methods (RAFT-Stereo and STTR) and commercial RGB-D cameras (Intel RealSense) in real-world conditions, demonstrating practical effectiveness.

Conclusion: The grid-structured adversarial patches provide a practical and effective method for security assessment of stereo depth estimation systems, bridging the gap between digital and physical attack performance.

Abstract: Stereo depth estimation is a critical task in autonomous driving and robotics, where inaccuracies (such as misidentifying nearby objects as distant) can lead to dangerous situations. Adversarial attacks against stereo depth estimation can help reveal vulnerabilities before deployment. Previous works have shown that repeating optimized textures can effectively mislead stereo depth estimation in digital settings. However, our research reveals that these naively repeated textures perform poorly in physical implementations, i.e., when deployed as patches, limiting their practical utility for stress-testing stereo depth estimation systems. In this work, for the first time, we discover that introducing regular intervals among the repeated textures, creating a grid structure, significantly enhances the patch’s attack performance. Through extensive experimentation, we analyze how variations of this novel structure influence the adversarial effectiveness. Based on these insights, we develop a novel stereo depth attack that jointly optimizes both the interval structure and texture elements. Our generated adversarial patches can be inserted into any scenes and successfully attack advanced stereo depth estimation methods of different paradigms, i.e., RAFT-Stereo and STTR. Most critically, our patch can also attack commercial RGB-D cameras (Intel RealSense) in real-world conditions, demonstrating their practical relevance for security assessment of stereo systems. The code is officially released at: https://github.com/WiWiN42/DepthVanish

Method: Adversarial feature intervention is used to suppress sensitive attributes, mitigating spurious correlations and improving prediction fairness in CLIP models.

Result: AdFair-CLIP significantly enhances both fairness and diagnostic accuracy on chest X-ray datasets while maintaining robust generalization in zero-shot and few-shot scenarios.

Conclusion: The framework establishes new benchmarks for fairness-aware learning in CLIP-based medical diagnostic models, particularly for chest X-ray analysis.

Abstract: Contrastive Language-Image Pre-training (CLIP) models have demonstrated superior performance across various visual tasks including medical image classification. However, fairness concerns, including demographic biases, have received limited attention for CLIP models. This oversight leads to critical issues, particularly those related to race and gender, resulting in disparities in diagnostic outcomes and reduced reliability for underrepresented groups. To address these challenges, we introduce AdFair-CLIP, a novel framework employing adversarial feature intervention to suppress sensitive attributes, thereby mitigating spurious correlations and improving prediction fairness. We conduct comprehensive experiments on chest X-ray (CXR) datasets, and show that AdFair-CLIP significantly enhances both fairness and diagnostic accuracy, while maintaining robust generalization in zero-shot and few-shot scenarios. These results establish new benchmarks for fairness-aware learning in CLIP-based medical diagnostic models, particularly for CXR analysis.

Method: Inspired by the ‘perceptual straightening’ hypothesis, ReStraV analyzes deviations from expected geometric properties in neural representations. Using a pre-trained self-supervised vision transformer (DINOv2), it quantifies temporal curvature and stepwise distance in the model’s representation domain, then aggregates statistics and trains a classifier.

Result: The method achieves state-of-the-art detection performance with 97.17% accuracy and 98.63% AUROC on the VidProM benchmark, substantially outperforming existing image- and video-based methods. AI-generated videos exhibit significantly different curvature and distance patterns compared to real videos.

Conclusion: ReStraV is computationally efficient and offers a low-cost, effective detection solution. This work provides new insights into using neural representation geometry for AI-generated video detection.

Abstract: The rapid advancement of generative AI enables highly realistic synthetic videos, posing significant challenges for content authentication and raising urgent concerns about misuse. Existing detection methods often struggle with generalization and capturing subtle temporal inconsistencies. We propose ReStraV(Representation Straightening Video), a novel approach to distinguish natural from AI-generated videos. Inspired by the “perceptual straightening” hypothesis – which suggests real-world video trajectories become more straight in neural representation domain – we analyze deviations from this expected geometric property. Using a pre-trained self-supervised vision transformer (DINOv2), we quantify the temporal curvature and stepwise distance in the model’s representation domain. We aggregate statistics of these measures for each video and train a classifier. Our analysis shows that AI-generated videos exhibit significantly different curvature and distance patterns compared to real videos. A lightweight classifier achieves state-of-the-art detection performance (e.g., 97.17% accuracy and 98.63% AUROC on the VidProM benchmark), substantially outperforming existing image- and video-based methods. ReStraV is computationally efficient, it is offering a low-cost and effective detection solution. This work provides new insights into using neural representation geometry for AI-generated video detection.

Method: Pre-assigns and fixes Equiangular Tight Frame (ETF) prototypes, uses Consistent ETF Alignment Loss to unify supervised/unsupervised alignment, and employs Semantic Consistency Matcher for stable label assignments.

Result: Achieves strong performance on multiple GCD benchmarks with significant improvement in novel category accuracy.

Conclusion: NC-GCD effectively addresses GCD challenges through ETF-based geometric structure and consistent optimization, demonstrating superior performance in discovering novel categories.

Abstract: Generalized Category Discovery (GCD) focuses on classifying known categories while simultaneously discovering novel categories from unlabeled data. However, previous GCD methods face challenges due to inconsistent optimization objectives and category confusion. This leads to feature overlap and ultimately hinders performance on novel categories. To address these issues, we propose the Neural Collapse-inspired Generalized Category Discovery (NC-GCD) framework. By pre-assigning and fixing Equiangular Tight Frame (ETF) prototypes, our method ensures an optimal geometric structure and a consistent optimization objective for both known and novel categories. We introduce a Consistent ETF Alignment Loss that unifies supervised and unsupervised ETF alignment and enhances category separability. Additionally, a Semantic Consistency Matcher (SCM) is designed to maintain stable and consistent label assignments across clustering iterations. Our method achieves strong performance on multiple GCD benchmarks, significantly enhancing novel category accuracy and demonstrating its effectiveness.

Method: Developed a sensor fusion approach using beam-splitter prism for optical/temporal alignment, RANSAC-based fusion technique combining RGB and event data, with dropout uncertainty estimation to detect extreme conditions affecting either sensor.

Result: Collected comprehensive real dataset of RGB and event data for satellite pose estimation under challenging illumination. Results demonstrate efficacy of the event-RGB fusion approach.

Conclusion: The fusion method effectively leverages strengths of both sensor modalities, supporting the use of event sensors for spacecraft pose estimation. Dataset released publicly to support community research.

Abstract: Spacecraft pose estimation is crucial for autonomous in-space operations, such as rendezvous, docking and on-orbit servicing. Vision-based pose estimation methods, which typically employ RGB imaging sensors, is a compelling solution for spacecraft pose estimation, but are challenged by harsh lighting conditions, which produce imaging artifacts such as glare, over-exposure, blooming and lens flare. Due to their much higher dynamic range, neuromorphic or event sensors are more resilient to extreme lighting conditions. However, event sensors generally have lower spatial resolution and suffer from reduced signal-to-noise ratio during periods of low relative motion. This work addresses these individual sensor limitations by introducing a sensor fusion approach combining RGB and event sensors. A beam-splitter prism was employed to achieve precise optical and temporal alignment. Then, a RANSAC-based technique was developed to fuse the information from the RGB and event channels to achieve pose estimation that leveraged the strengths of the two modalities. The pipeline was complemented by dropout uncertainty estimation to detect extreme conditions that affect either channel. To benchmark the performance of the proposed event-RGB fusion method, we collected a comprehensive real dataset of RGB and event data for satellite pose estimation in a laboratory setting under a variety of challenging illumination conditions. Encouraging results on the dataset demonstrate the efficacy of our event-RGB fusion approach and further supports the usage of event sensors for spacecraft pose estimation. To support community research on this topic, our dataset has been released publicly.

Method: Developed a semi-automatic data construction pipeline in collaboration with marine biologists to create CoralVQA dataset, ensuring both scalability and professional-grade data quality. The dataset includes real-world coral images collected from multiple oceans with comprehensive ecological and health-related question-answer pairs.

Result: Created CoralVQA with 12,805 coral images from 67 genera across 3 oceans and 277,653 question-answer pairs. Evaluation of state-of-the-art LVLMs revealed key limitations and opportunities, providing a comprehensive benchmark for vision-language reasoning in coral reef contexts.

Conclusion: CoralVQA presents novel challenges and forms a foundation for future LVLM development with emphasis on supporting coral conservation efforts, addressing the gap in domain-specific VQA datasets for marine ecosystem monitoring.

Abstract: Coral reefs are vital yet vulnerable ecosystems that require continuous monitoring to support conservation. While coral reef images provide essential information in coral monitoring, interpreting such images remains challenging due to the need for domain expertise. Visual Question Answering (VQA), powered by Large Vision-Language Models (LVLMs), has great potential in user-friendly interaction with coral reef images. However, applying VQA to coral imagery demands a dedicated dataset that addresses two key challenges: domain-specific annotations and multidimensional questions. In this work, we introduce CoralVQA, the first large-scale VQA dataset for coral reef analysis. It contains 12,805 real-world coral images from 67 coral genera collected from 3 oceans, along with 277,653 question-answer pairs that comprehensively assess ecological and health-related conditions. To construct this dataset, we develop a semi-automatic data construction pipeline in collaboration with marine biologists to ensure both scalability and professional-grade data quality. CoralVQA presents novel challenges and provides a comprehensive benchmark for studying vision-language reasoning in the context of coral reef images. By evaluating several state-of-the-art LVLMs, we reveal key limitations and opportunities. These insights form a foundation for future LVLM development, with a particular emphasis on supporting coral conservation efforts.

Method: The framework couples a VLM with a controllable world model based on video diffusion. The VLM iteratively sketches camera trajectories while the world model synthesizes corresponding views at each step, enabling multi-view reasoning during interactive exploration.

Result: MindJourney achieves over 7.7% average performance boost on the spatial reasoning benchmark SAT without any fine-tuning, and improves upon test-time inference VLMs trained through reinforcement learning.

Conclusion: Pairing VLMs with world models for test-time scaling offers a simple, plug-and-play route to robust 3D reasoning, demonstrating the potential of utilizing world models for test-time scaling.

Abstract: Spatial reasoning in 3D space is central to human cognition and indispensable for embodied tasks such as navigation and manipulation. However, state-of-the-art vision-language models (VLMs) struggle frequently with tasks as simple as anticipating how a scene will look after an egocentric motion: they perceive 2D images but lack an internal model of 3D dynamics. We therefore propose MindJourney, a test-time scaling framework that grants a VLM with this missing capability by coupling it to a controllable world model based on video diffusion. The VLM iteratively sketches a concise camera trajectory, while the world model synthesizes the corresponding view at each step. The VLM then reasons over this multi-view evidence gathered during the interactive exploration. Without any fine-tuning, our MindJourney achieves over an average 7.7% performance boost on the representative spatial reasoning benchmark SAT, showing that pairing VLMs with world models for test-time scaling offers a simple, plug-and-play route to robust 3D reasoning. Meanwhile, our method also improves upon the test-time inference VLMs trained through reinforcement learning, which demonstrates the potential of our method that utilizes world models for test-time scaling.

Method: Introduces semantic-related feature learning module for discriminative label-specific features and conditional transport-based alignment mechanism for precise visual-semantic alignment.

Result: Extensive experiments on VOC2007 and MS-COCO datasets validate effectiveness and demonstrate superior performance compared to state-of-the-art methods.

Conclusion: SCT provides a unified framework that effectively addresses key limitations in multi-label image classification through semantic-aware representation learning and conditional transport alignment.

Abstract: Multi-label image classification is a critical task in machine learning that aims to accurately assign multiple labels to a single image. While existing methods often utilize attention mechanisms or graph convolutional networks to model visual representations, their performance is still constrained by two critical limitations: the inability to learn discriminative semantic-aware features, and the lack of fine-grained alignment between visual representations and label embeddings. To tackle these issues in a unified framework, this paper proposes a novel approach named Semantic-aware representation learning via Conditional Transport for Multi-Label Image Classification (SCT). The proposed method introduces a semantic-related feature learning module that extracts discriminative label-specific features by emphasizing semantic relevance and interaction, along with a conditional transport-based alignment mechanism that enables precise visual-semantic alignment. Extensive experiments on two widely-used benchmark datasets, VOC2007 and MS-COCO, validate the effectiveness of SCT and demonstrate its superior performance compared to existing state-of-the-art methods.

Method: Style-guided distribution blending module to break independent training data streams by characterizing distribution mismatch through statistical moments. Prototype-based cross-contrast strategy to learn from both weak-to-strong and strong-to-weak predictions while mitigating noise in strong pseudo-labels.

Result: Experimental results demonstrate effectiveness and superiority across multiple medical segmentation benchmarks under various semi-supervised settings.

Conclusion: The proposed framework successfully addresses critical deficiencies in existing weak-strong consistency learning methods for semi-supervised medical image segmentation.

Abstract: Weak-strong consistency learning strategies are widely employed in semi-supervised medical image segmentation to train models by leveraging limited labeled data and enforcing weak-to-strong consistency. However, existing methods primarily focus on designing and combining various perturbation schemes, overlooking the inherent potential and limitations within the framework itself. In this paper, we first identify two critical deficiencies: (1) separated training data streams, which lead to confirmation bias dominated by the labeled stream; and (2) incomplete utilization of supervisory information, which limits exploration of strong-to-weak consistency. To tackle these challenges, we propose a style-aware blending and prototype-based cross-contrast consistency learning framework. Specifically, inspired by the empirical observation that the distribution mismatch between labeled and unlabeled data can be characterized by statistical moments, we design a style-guided distribution blending module to break the independent training data streams. Meanwhile, considering the potential noise in strong pseudo-labels, we introduce a prototype-based cross-contrast strategy to encourage the model to learn informative supervisory signals from both weak-to-strong and strong-to-weak predictions, while mitigating the adverse effects of noise. Experimental results demonstrate the effectiveness and superiority of our framework across multiple medical segmentation benchmarks under various semi-supervised settings.

Method: HyCASS consists of six modules: spectral encoder, spatial encoder, CR adapter encoder, CR adapter decoder, spatial decoder, and spectral decoder, using convolutional layers and transformer blocks to capture both short-range and long-range redundancies.

Result: Experimental results on three HSI benchmark datasets show HyCASS outperforms existing learning-based compression models by up to 2.36 dB in PSNR.

Conclusion: The study establishes guidelines for effectively balancing spectral and spatial compression across different compression ratios, considering the spatial resolution of hyperspectral images.

Abstract: With the rapid growth of hyperspectral data archives in remote sensing (RS), the need for efficient storage has become essential, driving significant attention toward learning-based hyperspectral image (HSI) compression. However, a comprehensive investigation of the individual and joint effects of spectral and spatial compression on learning-based HSI compression has not been thoroughly examined yet. Conducting such an analysis is crucial for understanding how the exploitation of spectral, spatial, and joint spatio-spectral redundancies affects HSI compression. To address this issue, we propose Adjustable Spatio-Spectral Hyperspectral Image Compression Network (HyCASS), a learning-based model designed for adjustable HSI compression in both spectral and spatial dimensions. HyCASS consists of six main modules: 1) spectral encoder module; 2) spatial encoder module; 3) compression ratio (CR) adapter encoder module; 4) CR adapter decoder module; 5) spatial decoder module; and 6) spectral decoder module. The modules employ convolutional layers and transformer blocks to capture both short-range and long-range redundancies. Experimental results on three HSI benchmark datasets demonstrate the effectiveness of our proposed adjustable model compared to existing learning-based compression models, surpassing the state of the art by up to 2.36 dB in terms of PSNR. Based on our results, we establish a guideline for effectively balancing spectral and spatial compression across different CRs, taking into account the spatial resolution of the HSIs. Our code and pre-trained model weights are publicly available at https://git.tu-berlin.de/rsim/hycass .

Method: Proposes DKUA framework with four components: 1) distribution-aware model for domain-specific representations, 2) adaptive knowledge consolidation for unified cross-domain representation center, 3) unified knowledge association to model inter-domain relationships, and 4) distribution-based knowledge transfer to maintain distribution alignment.

Result: Outperforms existing methods by 7.6%/5.3% average mAP/R@1 improvement on anti-forgetting and generalization capacity respectively, demonstrating superior performance in lifelong person re-identification.

Conclusion: The DKUA framework effectively addresses the lifelong person re-identification challenge by incorporating distribution awareness and cross-domain knowledge unification, achieving significant improvements in both anti-forgetting capability and generalization performance without storing old samples.

Abstract: Lifelong person re-identification (LReID) encounters a key challenge: balancing the preservation of old knowledge with adaptation to new information. Existing LReID methods typically employ knowledge distillation to enforce representation alignment. However, these approaches ignore two crucial aspects: specific distribution awareness and cross-domain unified knowledge learning, both of which are essential for addressing this challenge. To overcome these limitations, we propose a novel distribution-aware knowledge unification and association (DKUA) framework where domain-style modeling is performed for each instance to propagate domain-specific representations, enhancing anti-forgetting and generalization capacity. Specifically, we design a distribution-aware model to transfer instance-level representations of the current domain into the domain-specific representations with the different domain styles, preserving learned knowledge without storing old samples. Next, we propose adaptive knowledge consolidation (AKC) to dynamically generate the unified representation as a cross-domain representation center. To further mitigate forgetting, we develop a unified knowledge association (UKA) mechanism, which explores the unified representation as a bridge to explicitly model inter-domain associations, reducing inter-domain gaps. Finally, distribution-based knowledge transfer (DKT) is proposed to prevent the current domain distribution from deviating from the cross-domain distribution center, improving adaptation capacity. Experimental results show our DKUA outperforms the existing methods by 7.6%/5.3% average mAP/R@1 improvement on anti-forgetting and generalization capacity, respectively. Our code is available at https://github.com/LiuShiBen/DKUA.

Method: Proposed VA-GPT with two key modules: Spatial Effective Token Selection (SETS) and Temporal Effective Token Generation (TETG) to align tokens between visual encoders and LLMs. Also constructed an instruction-following dataset for fine-tuning and introduced a cross-domain evaluation benchmark.

Result: The proposed method outperforms existing state-of-the-art methods on various benchmarks, demonstrating more accurate responses and interactions for abnormal event analysis.

Conclusion: VA-GPT effectively addresses the challenges of abnormal event detection in videos through specialized spatial and temporal token handling, showing superior performance compared to existing methods.

Abstract: Understanding abnormal events in videos is a vital and challenging task that has garnered significant attention in a wide range of applications. Although current video understanding Multi-modal Large Language Models (MLLMs) are capable of analyzing general videos, they often struggle to handle anomalies due to the spatial and temporal sparsity of abnormal events, where the redundant information always leads to suboptimal outcomes. To address these challenges, exploiting the representation and generalization capabilities of Vison Language Models (VLMs) and Large Language Models (LLMs), we propose VA-GPT, a novel MLLM designed for summarizing and localizing abnormal events in various videos. Our approach efficiently aligns effective tokens between visual encoders and LLMs through two key proposed modules: Spatial Effective Token Selection (SETS) and Temporal Effective Token Generation (TETG). These modules enable our model to effectively capture and analyze both spatial and temporal information associated with abnormal events, resulting in more accurate responses and interactions. Furthermore, we construct an instruction-following dataset specifically for fine-tuning video-anomaly-aware MLLMs, and introduce a cross-domain evaluation benchmark based on XD-Violence dataset. Our proposed method outperforms existing state-of-the-art methods on various benchmarks.

Method: Learns to establish correspondences across regions with identical surface shapes but opposite volume occupancy, using equivariant neural networks to estimate shape orientations and align regions in rotation.

Result: Significantly reduces local ambiguities in matching and allows robust combination of parts in assembly, consistently outperforming state-of-the-art methods on geometric assembly benchmarks.

Conclusion: The proposed combinative matching approach effectively addresses interlocking shape assembly by modeling both surface shape identity and volume occupancy inversion, demonstrating superior performance over existing methods.

Abstract: This paper introduces a new shape-matching methodology, combinative matching, to combine interlocking parts for geometric shape assembly. Previous methods for geometric assembly typically rely on aligning parts by finding identical surfaces between the parts as in conventional shape matching and registration. In contrast, we explicitly model two distinct properties of interlocking shapes: ‘identical surface shape’ and ‘opposite volume occupancy.’ Our method thus learns to establish correspondences across regions where their surface shapes appear identical but their volumes occupy the inverted space to each other. To facilitate this process, we also learn to align regions in rotation by estimating their shape orientations via equivariant neural networks. The proposed approach significantly reduces local ambiguities in matching and allows a robust combination of parts in assembly. Experimental results on geometric assembly benchmarks demonstrate the efficacy of our method, consistently outperforming the state of the art. Project page: https://nahyuklee.github.io/cmnet.

Method: AIM decouples dominant modality’s under-optimized parameters into Auxiliary Blocks and encourages reliance on these degraded blocks during joint training. It also assesses modality imbalance across network depths and adaptively adjusts modulation strength at each depth.

Result: AIM outperforms state-of-the-art imbalanced modality learning methods across multiple benchmarks and shows strong generalizability across different backbones, fusion strategies, and optimizers.

Conclusion: AIM achieves balanced multimodal learning without hindering either dominant or weak modalities for the first time, effectively addressing optimization bias in imbalanced multimodal learning.

Abstract: Multimodal learning has significantly enhanced machine learning performance but still faces numerous challenges and limitations. Imbalanced multimodal learning is one of the problems extensively studied in recent works and is typically mitigated by modulating the learning of each modality. However, we find that these methods typically hinder the dominant modality’s learning to promote weaker modalities, which affects overall multimodal performance. We analyze the cause of this issue and highlight a commonly overlooked problem: optimization bias within networks. To address this, we propose Adaptive Intra-Network Modulation (AIM) to improve balanced modality learning. AIM accounts for differences in optimization state across parameters and depths within the network during modulation, achieving balanced multimodal learning without hindering either dominant or weak modalities for the first time. Specifically, AIM decouples the dominant modality’s under-optimized parameters into Auxiliary Blocks and encourages reliance on these performance-degraded blocks for joint training with weaker modalities. This approach effectively prevents suppression of weaker modalities while enabling targeted optimization of under-optimized parameters to improve the dominant modality. Additionally, AIM assesses modality imbalance level across network depths and adaptively adjusts modulation strength at each depth. Experimental results demonstrate that AIM outperforms state-of-the-art imbalanced modality learning methods across multiple benchmarks and exhibits strong generalizability across different backbones, fusion strategies, and optimizers.

Method: STVH: spatiotemporal interleaved video hashing that simultaneously models individual object dynamics and group interactions. M-STVH: enhanced multi-focused version with hierarchical feature integration through multi-focused representation learning to jointly focus on activity semantics and object visual features.

Result: Both STVH and M-STVH achieved excellent results in comparative experiments on publicly available datasets.

Conclusion: The proposed STVH and M-STVH methods effectively solve the problem of retrieving group activities at activity granularity and handling multiple feature requirements in video retrieval scenarios.

Abstract: With the explosive growth of video data in various complex scenarios, quickly retrieving group activities has become an urgent problem. However, many tasks can only retrieve videos focusing on an entire video, not the activity granularity. To solve this problem, we propose a new STVH (spatiotemporal interleaved video hashing) technique for the first time. Through a unified framework, the STVH simultaneously models individual object dynamics and group interactions, capturing the spatiotemporal evolution on both group visual features and positional features. Moreover, in real-life video retrieval scenarios, it may sometimes require activity features, while at other times, it may require visual features of objects. We then further propose a novel M-STVH (multi-focused spatiotemporal video hashing) as an enhanced version to handle this difficult task. The advanced method incorporates hierarchical feature integration through multi-focused representation learning, allowing the model to jointly focus on activity semantics features and object visual features. We conducted comparative experiments on publicly available datasets, and both STVH and M-STVH can achieve excellent results.

Method: Proposes a block-wise mixture of dynamic low-rank experts that adaptively selects different experts based on input features, with a self-supervised training framework to handle brightness inconsistency and reflectance interference.

Result: Outperforms state-of-the-art methods on SCARED and SimCol datasets, achieves best generalization on zero-shot depth estimation across C3VD, Hamlyn and SERV-CT datasets, and demonstrates strong performance in 3D reconstruction and ego-motion estimation.

Conclusion: The method enables accurate endoscopic depth estimation for minimally invasive measurement and surgery, with promising generalization capabilities across diverse endoscopic scenarios.

Abstract: Self-supervised monocular depth estimation is a significant task for low-cost and efficient 3D scene perception and measurement in endoscopy. However, the variety of illumination conditions and scene features is still the primary challenges for depth estimation in endoscopic scenes. In this work, a novel self-supervised framework is proposed for monocular depth estimation in diverse endoscopy. Firstly, considering the diverse features in endoscopic scenes with different tissues, a novel block-wise mixture of dynamic low-rank experts is proposed to efficiently finetune the foundation model for endoscopic depth estimation. In the proposed module, based on the input feature, different experts with a small amount of trainable parameters are adaptively selected for weighted inference, from low-rank experts which are allocated based on the generalization of each block. Moreover, a novel self-supervised training framework is proposed to jointly cope with brightness inconsistency and reflectance interference. The proposed method outperforms state-of-the-art works on SCARED dataset and SimCol dataset. Furthermore, the proposed network also achieves the best generalization based on zero-shot depth estimation on C3VD, Hamlyn and SERV-CT dataset. The outstanding performance of our model is further demonstrated with 3D reconstruction and ego-motion estimation. The proposed method could contribute to accurate endoscopy for minimally invasive measurement and surgery. The evaluation codes will be released upon acceptance, while the demo videos can be found on: https://endo-gmde.netlify.app/.

Method: Uses Vision Transformers to extract 3D region embeddings from sMRI data and Graph Neural Networks for classification, with two region definition strategies: atlas-based (predefined brain atlases) and cube-based (3D patches). Cosine similarity graphs model interregional relationships.

Result: Achieved 78.98% accuracy, 76.54% sensitivity, 81.58% specificity, 81.58% precision, and 78.98% F1-score using stratified 10-fold cross-validation on REST-meta-MDD dataset.

Conclusion: Atlas-based models consistently outperformed cube-based approach, demonstrating the importance of domain-specific anatomical priors for effective MDD detection.

Abstract: Major depressive disorder (MDD) is a prevalent mental health condition that negatively impacts both individual well-being and global public health. Automated detection of MDD using structural magnetic resonance imaging (sMRI) and deep learning (DL) methods holds increasing promise for improving diagnostic accuracy and enabling early intervention. Most existing methods employ either voxel-level features or handcrafted regional representations built from predefined brain atlases, limiting their ability to capture complex brain patterns. This paper develops a unified pipeline that utilizes Vision Transformers (ViTs) for extracting 3D region embeddings from sMRI data and Graph Neural Network (GNN) for classification. We explore two strategies for defining regions: (1) an atlas-based approach using predefined structural and functional brain atlases, and (2) an cube-based method by which ViTs are trained directly to identify regions from uniformly extracted 3D patches. Further, cosine similarity graphs are generated to model interregional relationships, and guide GNN-based classification. Extensive experiments were conducted using the REST-meta-MDD dataset to demonstrate the effectiveness of our model. With stratified 10-fold cross-validation, the best model obtained 78.98% accuracy, 76.54% sensitivity, 81.58% specificity, 81.58% precision, and 78.98% F1-score. Further, atlas-based models consistently outperformed the cube-based approach, highlighting the importance of using domain-specific anatomical priors for MDD detection.

Method: Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in feature space, aligns features across past/future frames, and uses Cross-Transformer Refinement module to enhance local consistency and spatial details.

Result: Achieves BD-Rate reductions of 20% over D-DPCC and 9.4% over AdaDPCC, with improved compression efficiency and runtime performance.

Conclusion: Bi-FMT provides an effective implicit motion modeling approach for dynamic point cloud compression through bidirectional feature alignment and transformer-based refinement, enabling frame-level parallel compression.

Abstract: Efficient dynamic point cloud compression (DPCC) critically depends on accurate motion estimation and compensation. However, the inherently irregular structure and substantial local variations of point clouds make this task highly challenging. Existing approaches typically rely on explicit motion estimation, whose encoded motion vectors often fail to capture complex dynamics and inadequately exploit temporal correlations. To address these limitations, we propose a Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space. Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations, which serve as predictive context in a conditional coding pipeline, forming a unified ``Motion + Conditional’’ representation. Built upon this bidirectional feature alignment, we introduce a Cross-Transformer Refinement module (CTR) at the decoder side to adaptively refine locally aligned features. By modeling cross-frame dependencies with vector attention, CRT enhances local consistency and restores fine-grained spatial details that are often lost during motion alignment. Moreover, we design a Random Access (RA) reference strategy that treats the bidirectionally aligned features as conditional context, enabling frame-level parallel compression and eliminating the sequential encoding. Extensive experiments demonstrate that Bi-FMT surpasses D-DPCC and AdaDPCC in both compression efficiency and runtime, achieving BD-Rate reductions of 20% (D1) and 9.4% (D1), respectively.

Method: Decomposes visual representations into dynamic pixel lattices and solves pixel motion states through predict-update network with collision-streaming processes. Predict stage handles lattice collisions and streaming, while update stage rectifies distributions with online visual representations.

Result: Demonstrates practical applicability in online real-time tracking, with comprehensive evaluations on TAP-Vid, RoboTAP, TAO, BFT, and OVT-B benchmarks validating efficiency and real-world practicality.

Conclusion: LBM provides an efficient approach for visual tracking that can adapt to real-world tasks through its lattice-based modeling of pixel dynamics.

Abstract: This work proposes the Lattice Boltzmann Model (LBM) to learn real-world pixel dynamicity for visual tracking. LBM decomposes visual representations into dynamic pixel lattices and solves pixel motion states through collision-streaming processes. Specifically, the high-dimensional distribution of the target pixels is acquired through a multilayer predict-update network to estimate the pixel positions and visibility. The predict stage formulates lattice collisions among the spatial neighborhood of target pixels and develops lattice streaming within the temporal visual context. The update stage rectifies the pixel distributions with online visual representations. Compared with existing methods, LBM demonstrates practical applicability in an online and real-time manner, which can efficiently adapt to real-world visual tracking tasks. Comprehensive evaluations of real-world point tracking benchmarks such as TAP-Vid and RoboTAP validate LBM’s efficiency. A general evaluation of large-scale open-world object tracking benchmarks such as TAO, BFT, and OVT-B further demonstrates LBM’s real-world practicality.

Method: Created a curated dataset of 3,095 annotated deer images and evaluated 12 YOLO model variants (v8-v11) on Raspberry Pi 5 and NVIDIA Jetson AGX Xavier edge platforms.

Result: Real-time detection not feasible on Raspberry Pi without optimization, but NVIDIA Jetson achieved >30 FPS with ’s’ and ’n’ series models. YOLOv11n, YOLOv8s, and YOLOv9s provided best balance of accuracy (AP>0.85) and efficiency (<34ms inference time).

Conclusion: Smaller advanced YOLO models are viable for real-world deer detection on GPU-accelerated edge devices, addressing the gap in practical deer detection systems.

Abstract: The escalating economic losses in agriculture due to deer intrusion, estimated to be in the hundreds of millions of dollars annually in the U.S., highlight the inadequacy of traditional mitigation strategies such as hunting, fencing, use of repellents, and scare tactics. This underscores a critical need for intelligent, autonomous solutions capable of real-time deer detection and deterrence. But the progress in this field is impeded by a significant gap in the literature, mainly the lack of a domain-specific, practical dataset and limited study on the viability of deer detection systems on edge devices. To address this gap, this study presents a comprehensive evaluation of state-of-the-art deep learning models for deer detection in challenging real-world scenarios. We introduce a curated, publicly available dataset of 3,095 annotated images with bounding box annotations of deer. Then, we provide an extensive comparative analysis of 12 model variants across four recent YOLO architectures (v8 to v11). Finally, we evaluated their performance on two representative edge computing platforms: the CPU-based Raspberry Pi 5 and the GPU-accelerated NVIDIA Jetson AGX Xavier to assess feasibility for real-world field deployment. Results show that the real-time detection performance is not feasible on Raspberry Pi without hardware-specific model optimization, while NVIDIA Jetson provides greater than 30 frames per second (FPS) with ’s’ and ’n’ series models. This study also reveals that smaller, architecturally advanced models such as YOLOv11n, YOLOv8s, and YOLOv9s offer the optimal balance of high accuracy (Average Precision (AP) > 0.85) and computational efficiency (Inference Time < 34 milliseconds).

Method: SHINE uses manifold-steered anchor loss with pretrained customization adapters (e.g., IP-Adapter) to guide latents for faithful subject representation while preserving background. It also employs degradation-suppression guidance and adaptive background blending to eliminate low-quality outputs and visible seams.

Result: Experiments on ComplexCompo benchmark (with diverse resolutions and challenging conditions) and DreamEditBench show state-of-the-art performance on standard metrics (DINOv2) and human-aligned scores (DreamSim, ImageReward, VisionReward).

Conclusion: SHINE provides an effective training-free solution for high-fidelity image composition that handles complex lighting and high-resolution scenarios better than existing methods, with publicly available code and benchmark.

Abstract: Image composition aims to seamlessly insert a user-specified object into a new scene, but existing models struggle with complex lighting (e.g., accurate shadows, water reflections) and diverse, high-resolution inputs. Modern text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential physical and resolution priors, yet lack a framework to unleash them without resorting to latent inversion, which often locks object poses into contextually inappropriate orientations, or brittle attention surgery. We propose SHINE, a training-free framework for Seamless, High-fidelity Insertion with Neutralized Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained customization adapters (e.g., IP-Adapter) to guide latents for faithful subject representation while preserving background integrity. Degradation-suppression guidance and adaptive background blending are proposed to further eliminate low-quality outputs and visible seams. To address the lack of rigorous benchmarks, we introduce ComplexCompo, featuring diverse resolutions and challenging conditions such as low lighting, strong illumination, intricate shadows, and reflective surfaces. Experiments on ComplexCompo and DreamEditBench show state-of-the-art performance on standard metrics (e.g., DINOv2) and human-aligned scores (e.g., DreamSim, ImageReward, VisionReward). Code and benchmark will be publicly available upon publication.

Method: Apply ZFP compression in error tolerance and fixed-rate modes to a large 3D medical dataset with ground-truth vascular segmentations, comparing segmentation quality on compressed vs uncompressed volumes.

Result: ZFP achieves substantial data reduction (up to 22.89:1 ratio) while maintaining high fidelity with mean Dice coefficient of 0.87656 compared to baseline 0.8774.

Conclusion: ZFP is a viable and powerful tool for enabling more efficient and accessible research on large-scale medical datasets, fostering broader collaboration.

Abstract: The increasing size and complexity of medical imaging datasets, particularly in 3D formats, present significant barriers to collaborative research and transferability. This study investigates whether the ZFP compression technique can mitigate these challenges without compromising the performance of automated cerebrovascular segmentation, a critical first step in intracranial aneurysm detection. We apply ZFP in both its error tolerance and fixed-rate modes to a large scale, and one of the most recent, datasets in the literature, 3D medical dataset containing ground-truth vascular segmentations. The segmentation quality on the compressed volumes is rigorously compared to the uncompressed baseline (Dice approximately equals 0.8774). Our findings reveal that ZFP can achieve substantial data reduction–up to a 22.89:1 ratio in error tolerance mode–while maintaining a high degree of fidelity, with the mean Dice coefficient remaining high at 0.87656. These results demonstrate that ZFP is a viable and powerful tool for enabling more efficient and accessible research on large-scale medical datasets, fostering broader collaboration across the community.

Method: Uses a controllable self-reinforcing optimization cycle: dynamic controllable filtering for pseudo-label selection, Bayes-optimal classifier with logit adjustment, class-aware adaptive augmentation for minority classes, and auxiliary branch for full data utilization.

Result: Achieves consistent improvements across benchmark datasets, surpassing state-of-the-art methods by up to 15.97% in accuracy.

Conclusion: CPG effectively handles unknown unlabeled data distributions through its controllable pseudo-label generation framework, with theoretical guarantees and practical performance gains.

Abstract: Current long-tailed semi-supervised learning methods assume that labeled data exhibit a long-tailed distribution, and unlabeled data adhere to a typical predefined distribution (i.e., long-tailed, uniform, or inverse long-tailed). However, the distribution of the unlabeled data is generally unknown and may follow an arbitrary distribution. To tackle this challenge, we propose a Controllable Pseudo-label Generation (CPG) framework, expanding the labeled dataset with the progressively identified reliable pseudo-labels from the unlabeled dataset and training the model on the updated labeled dataset with a known distribution, making it unaffected by the unlabeled data distribution. Specifically, CPG operates through a controllable self-reinforcing optimization cycle: (i) at each training step, our dynamic controllable filtering mechanism selectively incorporates reliable pseudo-labels from the unlabeled dataset into the labeled dataset, ensuring that the updated labeled dataset follows a known distribution; (ii) we then construct a Bayes-optimal classifier using logit adjustment based on the updated labeled data distribution; (iii) this improved classifier subsequently helps identify more reliable pseudo-labels in the next training step. We further theoretically prove that this optimization cycle can significantly reduce the generalization error under some conditions. Additionally, we propose a class-aware adaptive augmentation module to further improve the representation of minority classes, and an auxiliary branch to maximize data utilization by leveraging all labeled and unlabeled samples. Comprehensive evaluations on various commonly used benchmark datasets show that CPG achieves consistent improvements, surpassing state-of-the-art methods by up to $\textbf{15.97%}$ in accuracy. The code is available at https://github.com/yaxinhou/CPG.

Method: Visual segmentation of PV modules in aerial images combined with structural inference using layout keypoints to assign modules to benches, rows, and columns, then merging detections from multiple images while maintaining structural integrity.

Result: Successfully tested on two power plants, producing compact georeferenced 3D models with semantic structures suitable for maintenance applications.

Conclusion: The approach enables automated, detailed PV power plant mapping at module-level resolution using only aerial imagery, providing a practical solution for power plant maintenance without third-party data dependencies.

Abstract: An accurate and up-to-date model of a photovoltaic (PV) power plant is essential for its optimal operation and maintenance. However, such a model may not be easily available. This work introduces a novel approach for PV power plant mapping based on aerial overview images. It enables the automation of the mapping process while removing the reliance on third-party data. The presented mapping method takes advantage of the structural layout of the power plants to achieve detailed modeling down to the level of individual PV modules. The approach relies on visual segmentation of PV modules in overview images and the inference of structural information in each image, assigning modules to individual benches, rows, and columns. We identify visual keypoints related to the layout and use these to merge detections from multiple images while maintaining their structural integrity. The presented method was experimentally verified and evaluated on two different power plants. The final fusion of 3D positions and semantic structures results in a compact georeferenced model suitable for power plant maintenance.

Method: Proposes OneVision framework with VRQ (vision-aligned residual quantization) encoding to align different object representations across viewpoints while preserving product distinctiveness, and uses multi-stage semantic alignment to maintain visual similarity while incorporating user-specific information.

Result: Offline: Performs on par with online MCA while improving inference efficiency by 21% through dynamic pruning. Online A/B tests: +2.15% item CTR, +2.27% CVR, and +3.12% order volume.

Conclusion: A semantic ID centric generative architecture can successfully unify retrieval and personalization while simplifying the serving pathway, achieving significant improvements in both efficiency and conversion metrics.

Abstract: Traditional vision search, similar to search and recommendation systems, follows the multi-stage cascading architecture (MCA) paradigm to balance efficiency and conversion. Specifically, the query image undergoes feature extraction, recall, pre-ranking, and ranking stages, ultimately presenting the user with semantically similar products that meet their preferences. This multi-view representation discrepancy of the same object in the query and the optimization objective collide across these stages, making it difficult to achieve Pareto optimality in both user experience and conversion. In this paper, an end-to-end generative framework, OneVision, is proposed to address these problems. OneVision builds on VRQ, a vision-aligned residual quantization encoding, which can align the vastly different representations of an object across multiple viewpoints while preserving the distinctive features of each product as much as possible. Then a multi-stage semantic alignment scheme is adopted to maintain strong visual similarity priors while effectively incorporating user-specific information for personalized preference generation. In offline evaluations, OneVision performs on par with online MCA, while improving inference efficiency by 21% through dynamic pruning. In A/B tests, it achieves significant online improvements: +2.15% item CTR, +2.27% CVR, and +3.12% order volume. These results demonstrate that a semantic ID centric, generative architecture can unify retrieval and personalization while simplifying the serving pathway.

Method: Uses three pretrained vision modules: monocular metric depth estimator, learned keypoint detector, and instance segmentation network. Dynamic objects are suppressed with dilated instance masks, static keypoints get predicted depth values and are backprojected into 3D, then processed by an unmodified RGB-D SLAM backend.

Result: Achieves 7.4 cm mean ATE on static sequences and 1.8 cm on dynamic sequences on TUM RGB-D benchmark, matching or surpassing state-of-the-art RGB-D methods while operating at 22 FPS on a single GPU.

Conclusion: Modern pretrained vision models can effectively replace active depth sensors as reliable, real-time sources of metric scale, enabling simpler and more cost-effective SLAM systems.

Abstract: We present DropD-SLAM, a real-time monocular SLAM system that achieves RGB-D-level accuracy without relying on depth sensors. The system replaces active depth input with three pretrained vision modules: a monocular metric depth estimator, a learned keypoint detector, and an instance segmentation network. Dynamic objects are suppressed using dilated instance masks, while static keypoints are assigned predicted depth values and backprojected into 3D to form metrically scaled features. These are processed by an unmodified RGB-D SLAM back end for tracking and mapping. On the TUM RGB-D benchmark, DropD-SLAM attains 7.4 cm mean ATE on static sequences and 1.8 cm on dynamic sequences, matching or surpassing state-of-the-art RGB-D methods while operating at 22 FPS on a single GPU. These results suggest that modern pretrained vision models can replace active depth sensors as reliable, real-time sources of metric scale, marking a step toward simpler and more cost-effective SLAM systems.

Method: Proposed RAS reformulates queries to strengthen cross-modal attention to safety-critical image regions for accurate risk assessment, then adaptively steers activations to generate safe responses without iterative output adjustments.

Result: Extensive experiments show RAS significantly reduces attack success rates, preserves general task performance, and improves inference speed compared to prior inference-time defenses across multiple multimodal safety and utility benchmarks.

Conclusion: RAS provides an effective solution for multimodal safety that overcomes limitations of both training-based and existing inference-time alignment methods, achieving better safety, utility, and efficiency.

Abstract: One of the key challenges of modern AI models is ensuring that they provide helpful responses to benign queries while refusing malicious ones. But often, the models are vulnerable to multimodal queries with harmful intent embedded in images. One approach for safety alignment is training with extensive safety datasets at the significant costs in both dataset curation and training. Inference-time alignment mitigates these costs, but introduces two drawbacks: excessive refusals from misclassified benign queries and slower inference speed due to iterative output adjustments. To overcome these limitations, we propose to reformulate queries to strengthen cross-modal attention to safety-critical image regions, enabling accurate risk assessment at the query level. Using the assessed risk, it adaptively steers activations to generate responses that are safe and helpful without overhead from iterative output adjustments. We call this Risk-adaptive Activation Steering (RAS). Extensive experiments across multiple benchmarks on multimodal safety and utility demonstrate that the RAS significantly reduces attack success rates, preserves general task performance, and improves inference speed over prior inference-time defenses.

Method: Integrates 2D computer vision techniques including convex filtering and semantic feature supervision from DINO. Uses 2D segmentation priors and high-dimensional feature embeddings to guide Gaussian splat densification and refinement.

Result: Outperforms existing Gaussian Splatting methods across three datasets in most evaluated scenes, demonstrating improved coverage in underrepresented areas and better preservation of structural details.

Conclusion: Hybrid 2D-3D approaches combining 2D foundational models with 3D reconstruction pipelines can overcome limitations inherent in either approach alone, showing significant potential for enhanced scene reconstruction.

Abstract: Scene reconstruction has emerged as a central challenge in computer vision, with approaches such as Neural Radiance Fields (NeRF) and Gaussian Splatting achieving remarkable progress. While Gaussian Splatting demonstrates strong performance on large-scale datasets, it often struggles to capture fine details or maintain realism in regions with sparse coverage, largely due to the inherent limitations of sparse 3D training data. In this work, we propose GauSSmart, a hybrid method that effectively bridges 2D foundational models and 3D Gaussian Splatting reconstruction. Our approach integrates established 2D computer vision techniques, including convex filtering and semantic feature supervision from foundational models such as DINO, to enhance Gaussian-based scene reconstruction. By leveraging 2D segmentation priors and high-dimensional feature embeddings, our method guides the densification and refinement of Gaussian splats, improving coverage in underrepresented areas and preserving intricate structural details. We validate our approach across three datasets, where GauSSmart consistently outperforms existing Gaussian Splatting in the majority of evaluated scenes. Our results demonstrate the significant potential of hybrid 2D-3D approaches, highlighting how the thoughtful combination of 2D foundational models with 3D reconstruction pipelines can overcome the limitations inherent in either approach alone.

Method: Created AbdomenAtlas 2.0 - a dataset of 10,135 CT scans with 15,130 tumor instances manually annotated by 23 expert radiologists across six organs (pancreas, liver, kidney, colon, esophagus, uterus) and 5,893 control scans.

Result: Achieved notable improvements over public datasets: +7% DSC gain on in-distribution tests and +16% on out-of-distribution tests. Synthetic data reached same performance using only 500 real scans compared to 1,500 real scans alone.

Conclusion: AbdomenAtlas 2.0 provides a strong foundation for training AI to segment tumors in six organs, demonstrating that synthetic data can significantly reduce the annotation burden while maintaining or improving performance.

Abstract: AI for tumor segmentation is limited by the lack of large, voxel-wise annotated datasets, which are hard to create and require medical experts. In our proprietary JHH dataset of 3,000 annotated pancreatic tumor scans, we found that AI performance stopped improving after 1,500 scans. With synthetic data, we reached the same performance using only 500 real scans. This finding suggests that synthetic data can steepen data scaling laws, enabling more efficient model training than real data alone. Motivated by these lessons, we created AbdomenAtlas 2.0–a dataset of 10,135 CT scans with a total of 15,130 tumor instances per-voxel manually annotated in six organs (pancreas, liver, kidney, colon, esophagus, and uterus) and 5,893 control scans. Annotated by 23 expert radiologists, it is several orders of magnitude larger than existing public tumor datasets. While we continue expanding the dataset, the current version of AbdomenAtlas 2.0 already provides a strong foundation–based on lessons from the JHH dataset–for training AI to segment tumors in six organs. It achieves notable improvements over public datasets, with a +7% DSC gain on in-distribution tests and +16% on out-of-distribution tests.

Method: Trained a CLIP model on QUILT-1M dataset and systematically evaluated impact of different hyperparameter settings and augmentation strategies across three downstream histopathology datasets (PatchCamelyon, LC25000-Lung, and LC25000-Colon).

Result: Identified clear trends: RandomResizedCrop values of 0.7-0.8 performed best, distributed training without local loss improved stability, learning rates below 5.0e-5 degraded performance, and LC25000 (Colon) dataset provided the most reproducible benchmark.

Conclusion: Reproducibility in computational pathology depends on both transparent documentation and carefully chosen experimental configurations, with practical rules provided to guide future reproducible foundation model development for digital pathology.

Abstract: Reproducibility remains a critical challenge in foundation model training for histopathology, often hindered by software randomness, hardware non-determinism, and inconsistent hyperparameter reporting. To investigate these issues, we trained a CLIP model on the QUILT-1M dataset and systematically evaluated the impact of different hyperparameter settings and augmentation strategies across three downstream histopathology datasets (PatchCamelyon, LC25000-Lung, and LC25000-Colon). Despite variability across runs, we identified clear trends: RandomResizedCrop values of 0.7-0.8 outperformed more aggressive (0.6) or conservative (0.9) settings, distributed training without local loss improved stability, and learning rates below 5.0e-5 consistently degraded performance across all datasets. The LC25000 (Colon) dataset consistently provided the most reproducible benchmark. These findings highlight that reproducibility in computational pathology depends not only on transparent documentation but also on carefully chosen experimental configurations, and we provide practical rules to guide future efforts in developing reproducible foundation models for digital pathology.

Method: Proposes VFM-VAE with redesigned decoder using Multi-Scale Latent Fusion and Progressive Resolution Reconstruction blocks, plus a joint tokenizer-diffusion alignment strategy with SE-CKNNA metric for diagnosis.

Result: Achieves gFID (w/o CFG) of 2.20 in just 80 epochs (10x speedup) and 1.62 after 640 epochs, establishing direct VFM integration as superior paradigm.

Conclusion: Direct VFM integration through VFM-VAE with proper architectural redesign and training strategy outperforms distillation-based approaches in both performance and efficiency for LDMs.

Abstract: The performance of Latent Diffusion Models (LDMs) is critically dependent on the quality of their visual tokenizer. While recent works have explored incorporating Vision Foundation Models (VFMs) via distillation, we identify a fundamental flaw in this approach: it inevitably weakens the robustness of alignment with the original VFM, causing the aligned latents to deviate semantically under distribution shifts. In this paper, we bypass distillation by proposing a more direct approach: Vision Foundation Model Variational Autoencoder (VFM-VAE). To resolve the inherent tension between the VFM’s semantic focus and the need for pixel-level fidelity, we redesign the VFM-VAE decoder with Multi-Scale Latent Fusion and Progressive Resolution Reconstruction blocks, enabling high-quality reconstruction from spatially coarse VFM features. Furthermore, we provide a comprehensive analysis of representation dynamics during diffusion training, introducing the proposed SE-CKNNA metric as a more precise tool for this diagnosis. This analysis allows us to develop a joint tokenizer-diffusion alignment strategy that dramatically accelerates convergence. Our innovations in tokenizer design and training strategy lead to superior performance and efficiency: our system reaches a gFID (w/o CFG) of 2.20 in merely 80 epochs (a 10x speedup over prior tokenizers). With continued training to 640 epochs, it further attains a gFID (w/o CFG) of 1.62, establishing direct VFM integration as a superior paradigm for LDMs.

Method: Uses wavelet transform to enhance high-frequency features and suppress background noise at single-frame level, then applies multi-frame temporal trajectory completion with Hungarian algorithm for cross-frame matching, plus post-processing steps including temporal matching, interpolation completion, noise filtering, and trajectory refinement.

Result: Achieved an F_1 score of 90.14% on the public SpotGEO dataset, demonstrating the method’s effectiveness.

Conclusion: The proposed approach effectively addresses GEO object detection challenges through combined single-frame enhancement and multi-frame trajectory optimization.

Abstract: Space objects in Geostationary Earth Orbit (GEO) present significant detection challenges in optical imaging due to weak signals, complex stellar backgrounds, and environmental interference. In this paper, we enhance high-frequency features of GEO targets while suppressing background noise at the single-frame level through wavelet transform. Building on this, we propose a multi-frame temporal trajectory completion scheme centered on the Hungarian algorithm for globally optimal cross-frame matching. To effectively mitigate missing and false detections, a series of key steps including temporal matching and interpolation completion, temporal-consistency-based noise filtering, and progressive trajectory refinement are designed in the post-processing pipeline. Experimental results on the public SpotGEO dataset demonstrate the effectiveness of the proposed method, achieving an F_1 score of 90.14%.

Method: Uses a Deformable Message Extractor to capture spatial messages, a Spatial-Aware Feature Generator with conditional diffusion model to generate features aligned with ego agent’s semantic space, and a Channel Enhancer for feature refinement before fusion.

Result: Outperforms state-of-the-art methods on OPV2V-H, DAIR-V2X and V2X-Real datasets, achieving 81% reduction in both computational cost and parameter count when incorporating new agents.

Conclusion: GenComm enables efficient and scalable heterogeneous multi-agent collaboration through generative feature communication without altering original networks, maintaining semantic consistency while minimizing integration costs.

Abstract: Multi-agent collaboration enhances the perception capabilities of individual agents through information sharing. However, in real-world applications, differences in sensors and models across heterogeneous agents inevitably lead to domain gaps during collaboration. Existing approaches based on adaptation and reconstruction fail to support pragmatic heterogeneous collaboration due to two key limitations: (1) Intrusive retraining of the encoder or core modules disrupts the established semantic consistency among agents; and (2) accommodating new agents incurs high computational costs, limiting scalability. To address these challenges, we present a novel Generative Communication mechanism (GenComm) that facilitates seamless perception across heterogeneous multi-agent systems through feature generation, without altering the original network, and employs lightweight numerical alignment of spatial information to efficiently integrate new agents at minimal cost. Specifically, a tailored Deformable Message Extractor is designed to extract spatial message for each collaborator, which is then transmitted in place of intermediate features. The Spatial-Aware Feature Generator, utilizing a conditional diffusion model, generates features aligned with the ego agent’s semantic space while preserving the spatial information of the collaborators. These generated features are further refined by a Channel Enhancer before fusion. Experiments conducted on the OPV2V-H, DAIR-V2X and V2X-Real datasets demonstrate that GenComm outperforms existing state-of-the-art methods, achieving an 81% reduction in both computational cost and parameter count when incorporating new agents. Our code is available at https://github.com/jeffreychou777/GenComm.

Method: DenseNet-121-based framework with stain-aware augmentation (Macenko), geometric/intensity transformations, weighted sampling, and hybrid objective combining class-weighted binary cross-entropy and focal loss, trained end-to-end with AdamW.

Result: Achieved balanced accuracy 85.0%, AUROC 0.927, sensitivity 89.2%, and specificity 80.9% on official test set, demonstrating strong generalization across scanner and staining shifts.

Conclusion: Combining DenseNet-121 with stain-aware augmentation and imbalance-adaptive objectives yields a robust, domain-generalizable framework suitable for real-world computational pathology workflows.

Abstract: Atypical mitotic figures are important biomarkers of tumor aggressiveness in histopathology, yet reliable recognition remains challenging due to severe class imbalance and variability across imaging domains. We present a DenseNet-121-based framework tailored for atypical mitosis classification in the MIDOG 2025 (Track 2) setting. Our method integrates stain-aware augmentation (Macenko), geometric and intensity transformations, and imbalance-aware learning via weighted sampling with a hybrid objective combining class-weighted binary cross-entropy and focal loss. Trained end-to-end with AdamW and evaluated across multiple independent domains, the model demonstrates strong generalization under scanner and staining shifts, achieving balanced accuracy 85.0%, AUROC 0.927, sensitivity 89.2%, and specificity 80.9% on the official test set. These results indicate that combining DenseNet-121 with stain-aware augmentation and imbalance-adaptive objectives yields a robust, domain-generalizable framework for atypical mitosis classification suitable for real-world computational pathology workflows.

Method: For cross-view localization: formulated as image retrieval using CNNs or ViTs for feature embedding. For cross-view synthesis: uses GANs or diffusion models to generate ground-level views from overhead imagery.

Result: The paper provides an organized overview of state-of-the-art techniques, comparative analyses, and highlights current limitations in the field.

Conclusion: The survey identifies promising future research directions and includes a project page with comprehensive resources for cross-view methods.

Abstract: Cross-view localization and synthesis are two fundamental tasks in cross-view visual understanding, which deals with cross-view datasets: overhead (satellite or aerial) and ground-level imagery. These tasks have gained increasing attention due to their broad applications in autonomous navigation, urban planning, and augmented reality. Cross-view localization aims to estimate the geographic position of ground-level images based on information provided by overhead imagery while cross-view synthesis seeks to generate ground-level images based on information from the overhead imagery. Both tasks remain challenging due to significant differences in viewing perspective, resolution, and occlusion, which are widely embedded in cross-view datasets. Recent years have witnessed rapid progress driven by the availability of large-scale datasets and novel approaches. Typically, cross-view localization is formulated as an image retrieval problem where ground-level features are matched with tiled overhead images feature, extracted by convolutional neural networks (CNNs) or vision transformers (ViTs) for cross-view feature embedding. Cross-view synthesis, on the other hand, seeks to generate ground-level views based on information from overhead imagery, generally using generative adversarial networks (GANs) or diffusion models. This paper presents a comprehensive survey of advances in cross-view localization and synthesis, reviewing widely used datasets, highlighting key challenges, and providing an organized overview of state-of-the-art techniques. Furthermore, it discusses current limitations, offers comparative analyses, and outlines promising directions for future research. We also include the project page via https://github.com/GDAOSU/Awesome-Cross-View-Methods.

Method: Proposes Scale-Adaptive Object Tokenizer (SAOT) to generate compact object representations, and PixelRefer-Lite variant with Object-Centric Infusion module to pre-fuse global context into object tokens, creating an Object-Only Framework. Uses PixelRefer-2.2M instruction dataset for fine-grained tuning.

Result: Achieves leading performance with fewer training samples, while PixelRefer-Lite offers competitive accuracy with notable efficiency gains across various benchmarks.

Conclusion: PixelRefer enables advanced fine-grained understanding over user-specified regions, addressing the gap in object-centric reasoning while providing efficient alternatives through PixelRefer-Lite.

Abstract: Multimodal large language models (MLLMs) have demonstrated strong general-purpose capabilities in open-world visual comprehension. However, most existing MLLMs primarily focus on holistic, scene-level understanding, often overlooking the need for fine-grained, object-centric reasoning. In this paper, we present PixelRefer, a unified region-level MLLM framework that enables advanced fine-grained understanding over user-specified regions across both images and videos. Motivated by the observation that LLM attention predominantly focuses on object-level tokens, we propose a Scale-Adaptive Object Tokenizer (SAOT) to generate compact and semantically rich object representations from free-form regions. Our analysis reveals that global visual tokens contribute mainly in early LLM layers, inspiring the design of PixelRefer-Lite, an efficient variant that employs an Object-Centric Infusion module to pre-fuse global context into object tokens. This yields a lightweight Object-Only Framework that substantially reduces computational cost while maintaining high semantic fidelity. To facilitate fine-grained instruction tuning, we curate PixelRefer-2.2M, a high-quality object-centric instruction dataset. Extensive experiments across a range of benchmarks validate that PixelRefer achieves leading performance with fewer training samples, while PixelRefer-Lite offers competitive accuracy with notable gains in efficiency.

Method: Uses dual-conditioning architecture with Gated ControlNet for geometric fidelity and textual prompts for semantic guidance. Introduces multifaceted loss function for spectral/radiometric consistency and employs stochastic forward pass for uncertainty quantification.

Result: In blind evaluations, geophysical experts rated outputs approaching ground truth fidelity, significantly outperforming state-of-the-art baselines with nearly 40% reduction in FID and gains in perceptual metrics.

Conclusion: RareFlow provides a robust framework for high-fidelity synthesis in data-scarce scientific domains and offers a new paradigm for controlled generation under severe domain shift.

Abstract: Super-resolution (SR) for remote sensing imagery often fails under out-of-distribution (OOD) conditions, such as rare geomorphic features captured by diverse sensors, producing visually plausible but physically inaccurate results. We present RareFlow, a physics-aware SR framework designed for OOD robustness. RareFlow’s core is a dual-conditioning architecture. A Gated ControlNet preserves fine-grained geometric fidelity from the low-resolution input, while textual prompts provide semantic guidance for synthesizing complex features. To ensure physically sound outputs, we introduce a multifaceted loss function that enforces both spectral and radiometric consistency with sensor properties. Furthermore, the framework quantifies its own predictive uncertainty by employing a stochastic forward pass approach; the resulting output variance directly identifies unfamiliar inputs, mitigating feature hallucination. We validate RareFlow on a new, curated benchmark of multi-sensor satellite imagery. In blind evaluations, geophysical experts rated our model’s outputs as approaching the fidelity of ground truth imagery, significantly outperforming state-of-the-art baselines. This qualitative superiority is corroborated by quantitative gains in perceptual metrics, including a nearly 40% reduction in FID. RareFlow provides a robust framework for high-fidelity synthesis in data-scarce scientific domains and offers a new paradigm for controlled generation under severe domain shift.

Method: Integrates Sentinel-2 and Landsat satellite imagery with a new water segmentation index and Support Vector Regression (SVR) machine learning model trained on bathymetric survey data to estimate volume from surface area alone.

Result: Water segmentation aligns with ground truth for over 95% of shoreline. Optimized SVR model achieves error below 1.5% of full reservoir capacity with R² exceeding 0.98.

Conclusion: The method provides robust, cost-effective, sensor-independent monitoring applicable to other water bodies, generating valuable long-term climate and environmental data while focusing on temporal trends rather than exact volume measurements.

Abstract: The sustainable management of the Qaraaoun Reservoir, the largest surface water body in Lebanon located in the Bekaa Plain, depends on reliable monitoring of its storage volume despite frequent sensor malfunctions and limited maintenance capacity. This study introduces a sensor-free approach that integrates open-source satellite imagery, advanced water-extent segmentation, and machine learning to estimate the reservoir’s surface area and, subsequently, its volume in near real time. Sentinel-2 and Landsat 1-9 images are processed, where surface water is delineated using a newly proposed water segmentation index. A machine learning model based on Support Vector Regression (SVR) is trained on a curated dataset that includes water surface area, water level, and water volume derived from a reservoir bathymetric survey. The model is then able to estimate the water body’s volume solely from the extracted water surface, without the need for any ground-based measurements. Water segmentation using the proposed index aligns with ground truth for over 95% of the shoreline. Hyperparameter tuning with GridSearchCV yields an optimized SVR performance, with an error below 1.5% of the full reservoir capacity and coefficients of determination exceeding 0.98. These results demonstrate the method’s robustness and cost-effectiveness, offering a practical solution for continuous, sensor-independent monitoring of reservoir storage. The proposed methodology is applicable to other water bodies and generates over five decades of time-series data, offering valuable insights into climate change and environmental dynamics, with an emphasis on capturing temporal trends rather than exact water volume measurements.

Method: Developed a quantitative evaluation framework integrating pixel-level evaluation strategies with carefully designed metrics to assess XAI methods in semantic segmentation, accounting for spatial and contextual complexities.

Result: Simulation results using CAM-based XAI schemes demonstrate the framework’s efficiency, robustness, and reliability in providing fine-grained interpretability insights.

Conclusion: The framework advances development of transparent, trustworthy, and accountable semantic segmentation models by enabling rigorous quantitative evaluation of XAI methods.

Abstract: Ensuring transparency and trust in artificial intelligence (AI) models is essential as they are increasingly deployed in safety-critical and high-stakes domains. Explainable AI (XAI) has emerged as a promising approach to address this challenge; however, the rigorous evaluation of XAI methods remains vital for balancing the trade-offs between model complexity, predictive performance, and interpretability. While substantial progress has been made in evaluating XAI for classification tasks, strategies tailored to semantic segmentation remain limited. Moreover, objectively assessing XAI approaches is difficult, since qualitative visual explanations provide only preliminary insights. Such qualitative methods are inherently subjective and cannot ensure the accuracy or stability of explanations. To address these limitations, this work introduces a comprehensive quantitative evaluation framework for assessing XAI in semantic segmentation, accounting for both spatial and contextual task complexities. The framework systematically integrates pixel-level evaluation strategies with carefully designed metrics to yield fine-grained interpretability insights. Simulation results using recently adapted class activation mapping (CAM)-based XAI schemes demonstrate the efficiency, robustness, and reliability of the proposed methodology. These findings advance the development of transparent, trustworthy, and accountable semantic segmentation models.

Method: Used pretrained VDMs and LLMs equipped with lightweight adapters, tested on benchmarks including ARC-AGI, ConceptARC, visual games, route planning, and cellular automata in their natural modalities.

Result: VDMs demonstrated higher data efficiency than LLMs across all tested benchmarks, showing better adaptation to visual tasks.

Conclusion: Video pretraining provides strong inductive biases that support progress toward visual foundation models, making VDMs a promising direction for visual AI systems.

Abstract: Large language models (LLMs) have demonstrated that large-scale pretraining enables systems to adapt rapidly to new problems with little supervision in the language domain. This success, however, has not translated as effectively to the visual domain, where models, including LLMs, continue to struggle with compositional understanding, sample efficiency, and general-purpose problem-solving. We investigate Video Diffusion Models (VDMs) as a promising direction for bridging this gap. Pretraining on spatiotemporal data endows these models with strong inductive biases for structure and dynamics, which we hypothesize can support broad task adaptability. To test this, we design a controlled evaluation in which both a pretrained LLM and a pretrained VDM are equipped with lightweight adapters and presented with tasks in their natural modalities. Across benchmarks including ARC-AGI, ConceptARC, visual games, route planning, and cellular automata, VDMs demonstrate higher data efficiency than their language counterparts. Taken together, our results indicate that video pretraining offers inductive biases that support progress toward visual foundation models.

Method: Leverages 2D keypoints from off-the-shelf detectors to simultaneously calibrate cameras (including Brown-Conrady distortion coefficients) and reconstruct 3D keypoints and dense scene point maps. Uses confidence-driven spatio-temporal keypoint sampling strategy combined with graph-based global optimization for robust calibration at fixed computational cost. Introduces pairwise reprojection consensus score to quantify 3D reconstruction reliability.

Result: Substantial improvements over prior calibration-free methods: reduces camera translation error by 83-85%, camera angular error by 86-92%, and world mean-per-joint error (W-MPJPE) by 83-91%. Processes multi-view sequences faster than their duration in specific configurations (e.g., 36min to process 1h20min of footage).

Conclusion: Kineo provides an efficient, accurate calibration-free solution for markerless motion capture that outperforms state-of-the-art methods while being accessible to non-experts, with full pipeline and evaluation code released openly for reproducibility and practical adoption.

Abstract: Markerless multiview motion capture is often constrained by the need for precise camera calibration, limiting accessibility for non-experts and in-the-wild captures. Existing calibration-free approaches mitigate this requirement but suffer from high computational cost and reduced reconstruction accuracy. We present Kineo, a fully automatic, calibration-free pipeline for markerless motion capture from videos captured by unsynchronized, uncalibrated, consumer-grade RGB cameras. Kineo leverages 2D keypoints from off-the-shelf detectors to simultaneously calibrate cameras, including Brown-Conrady distortion coefficients, and reconstruct 3D keypoints and dense scene point maps at metric scale. A confidence-driven spatio-temporal keypoint sampling strategy, combined with graph-based global optimization, ensures robust calibration at a fixed computational cost independent of sequence length. We further introduce a pairwise reprojection consensus score to quantify 3D reconstruction reliability for downstream tasks. Evaluations on EgoHumans and Human3.6M demonstrate substantial improvements over prior calibration-free methods. Compared to previous state-of-the-art approaches, Kineo reduces camera translation error by approximately 83-85%, camera angular error by 86-92%, and world mean-per-joint error (W-MPJPE) by 83-91%. Kineo is also efficient in real-world scenarios, processing multi-view sequences faster than their duration in specific configuration (e.g., 36min to process 1h20min of footage). The full pipeline and evaluation code are openly released to promote reproducibility and practical adoption at https://liris-xr.github.io/kineo/.

Method: Three-step approach: 1) Initialize dense point cloud from multi-view depth predictions with Progressive Pruning for global consistency; 2) Jointly refine Gaussian geometry and depth via Depth Enhancer using diffusion priors; 3) Improve ground geometry by constraining Gaussian attributes in road regions.

Result: Extensive experiments on Waymo dataset show D²GS outperforms state-of-the-art methods and produces more accurate geometry than approaches using ground-truth LiDAR data.

Conclusion: The proposed LiDAR-free framework successfully achieves high-quality urban scene reconstruction by leveraging depth foundation models and geometric constraints, eliminating the need for expensive LiDAR sensors.

Abstract: Recently, Gaussian Splatting (GS) has shown great potential for urban scene reconstruction in the field of autonomous driving. However, current urban scene reconstruction methods often depend on multimodal sensors as inputs, \textit{i.e.} LiDAR and images. Though the geometry prior provided by LiDAR point clouds can largely mitigate ill-posedness in reconstruction, acquiring such accurate LiDAR data is still challenging in practice: i) precise spatiotemporal calibration between LiDAR and other sensors is required, as they may not capture data simultaneously; ii) reprojection errors arise from spatial misalignment when LiDAR and cameras are mounted at different locations. To avoid the difficulty of acquiring accurate LiDAR depth, we propose D$^2$GS, a LiDAR-free urban scene reconstruction framework. In this work, we obtain geometry priors that are as effective as LiDAR while being denser and more accurate. $\textbf{First}$, we initialize a dense point cloud by back-projecting multi-view metric depth predictions. This point cloud is then optimized by a Progressive Pruning strategy to improve the global consistency. $\textbf{Second}$, we jointly refine Gaussian geometry and predicted dense metric depth via a Depth Enhancer. Specifically, we leverage diffusion priors from a depth foundation model to enhance the depth maps rendered by Gaussians. In turn, the enhanced depths provide stronger geometric constraints during Gaussian training. $\textbf{Finally}$, we improve the accuracy of ground geometry by constraining the shape and normal attributes of Gaussians within road regions. Extensive experiments on the Waymo dataset demonstrate that our method consistently outperforms state-of-the-art methods, producing more accurate geometry even when compared with those using ground-truth LiDAR data.

Method: The survey categorizes recent progress in multimodal large language models (MLLMs), outlines general spatial reasoning techniques, and examines tasks including 2D spatial relationships, 3D scene understanding, embodied AI, and emerging modalities like audio and egocentric video.

Result: The survey establishes a comprehensive foundation for multimodal spatial reasoning research and provides open benchmarks for evaluation, with codes and implementations available on GitHub.

Conclusion: This survey offers valuable insights into the growing field of multimodal spatial reasoning and serves as a solid foundation for future research in this area.

Abstract: Humans possess spatial reasoning abilities that enable them to understand spaces through multimodal observations, such as vision and sound. Large multimodal reasoning models extend these abilities by learning to perceive and reason, showing promising performance across diverse spatial tasks. However, systematic reviews and publicly available benchmarks for these models remain limited. In this survey, we provide a comprehensive review of multimodal spatial reasoning tasks with large models, categorizing recent progress in multimodal large language models (MLLMs) and introducing open benchmarks for evaluation. We begin by outlining general spatial reasoning, focusing on post-training techniques, explainability, and architecture. Beyond classical 2D tasks, we examine spatial relationship reasoning, scene and layout understanding, as well as visual question answering and grounding in 3D space. We also review advances in embodied AI, including vision-language navigation and action models. Additionally, we consider emerging modalities such as audio and egocentric video, which contribute to novel spatial understanding through new sensors. We believe this survey establishes a solid foundation and offers insights into the growing field of multimodal spatial reasoning. Updated information about this survey, codes and implementation of the open benchmarks can be found at https://github.com/zhengxuJosh/Awesome-Spatial-Reasoning.

Method: Estimate object and background expectations in CLIP’s representation space, synthesize counterfactual embeddings by recombining object features with alternative contexts, and use Total Direct Effect to subtract background-only activation.

Result: Substantially improves both worst-group and average accuracy on context-sensitive benchmarks, establishing new zero-shot state-of-the-art without retraining or prompt design.

Conclusion: Provides a lightweight representation-level counterfactual approach for debiased and reliable multimodal reasoning through practical causal inference.

Abstract: Object-context shortcuts remain a persistent challenge in vision-language models, undermining zero-shot reliability when test-time scenes differ from familiar training co-occurrences. We recast this issue as a causal inference problem and ask: Would the prediction remain if the object appeared in a different environment? To answer this at inference time, we estimate object and background expectations within CLIP’s representation space, and synthesize counterfactual embeddings by recombining object features with diverse alternative contexts sampled from external datasets, batch neighbors, or text-derived descriptions. By estimating the Total Direct Effect and simulating intervention, we further subtract background-only activation, preserving beneficial object-context interactions while mitigating hallucinated scores. Without retraining or prompt design, our method substantially improves both worst-group and average accuracy on context-sensitive benchmarks, establishing a new zero-shot state of the art. Beyond performance, our framework provides a lightweight representation-level counterfactual approach, offering a practical causal avenue for debiased and reliable multimodal reasoning.

Method: Developed a comprehensive benchmark with JSON template for tailored evaluation metrics, and incorporated a novel two-stage inference workflow to evaluate VLM capability in aligning and comparing elements across charts.

Result: Analysis revealed new insights into VLMs’ perception biases, weaknesses, robustness issues, and hallucinations in chart understanding, highlighting fine-grained discrepancies among models.

Conclusion: The findings point to specific skills that need strengthening in current VLMs for chart understanding tasks, particularly in fine-grained perception and cross-chart comparison capabilities.

Abstract: Charts play an important role in visualization, reasoning, data analysis, and the exchange of ideas among humans. However, existing vision-language models (VLMs) still lack accurate perception of details and struggle to extract fine-grained structures from charts. Such limitations in chart grounding also hinder their ability to compare multiple charts and reason over them. In this paper, we introduce a novel “ChartAlign Benchmark (ChartAB)” to provide a comprehensive evaluation of VLMs in chart grounding tasks, i.e., extracting tabular data, localizing visualization elements, and recognizing various attributes from charts of diverse types and complexities. We design a JSON template to facilitate the calculation of evaluation metrics specifically tailored for each grounding task. By incorporating a novel two-stage inference workflow, the benchmark can further evaluate VLMs capability to align and compare elements/attributes across two charts. Our analysis of evaluations on several recent VLMs reveals new insights into their perception biases, weaknesses, robustness, and hallucinations in chart understanding. These findings highlight the fine-grained discrepancies among VLMs in chart understanding tasks and point to specific skills that need to be strengthened in current models.

Method: DPHR uses a dual-level approach: 1) Sample-level Ratio-based Difficulty-Aware (RDA) module evaluates relative difficulty and assigns fine-grained weights to negatives. 2) Batch-level Progressive Adaptive Loss Weighting (PALW) mechanism uses training-progress signal to attenuate noisy gradients early and progressively enhance hard-negative mining as training matures.

Result: Experiments on University-1652 and SUES-200 benchmarks demonstrate effectiveness and robustness, achieving consistent improvements over state-of-the-art methods.

Conclusion: The proposed DPHR strategy effectively addresses hard negatives in cross-view geo-localization through progressive hardness-aware reweighting, providing more stable convergence and better performance compared to existing methods.

Abstract: Cross-view geo-localization (CVGL) between drone and satellite imagery remains challenging due to severe viewpoint gaps and the presence of hard negatives, which are visually similar but geographically mismatched samples. Existing mining or reweighting strategies often use static weighting, which is sensitive to distribution shifts and prone to overemphasizing difficult samples too early, leading to noisy gradients and unstable convergence. In this paper, we present a Dual-level Progressive Hardness-aware Reweighting (DPHR) strategy. At the sample level, a Ratio-based Difficulty-Aware (RDA) module evaluates relative difficulty and assigns fine-grained weights to negatives. At the batch level, a Progressive Adaptive Loss Weighting (PALW) mechanism exploits a training-progress signal to attenuate noisy gradients during early optimization and progressively enhance hard-negative mining as training matures. Experiments on the University-1652 and SUES-200 benchmarks demonstrate the effectiveness and robustness of the proposed DPHR, achieving consistent improvements over state-of-the-art methods.

Method: Use web-crawled descriptions processed by large-language models to extract relevant keywords, and introduce a spatio-temporal interaction module to align description attributes with video content.

Result: Achieved accuracies of 81.0% on UCF-101, 53.1% on HMDB-51, and 68.9% on Kinetics-600 in zero-shot experiments.

Conclusion: The approach demonstrates adaptability and effectiveness across various downstream tasks while reducing manual annotation requirements.

Abstract: Vision-Language Models (VLMs) have demonstrated impressive capabilities in zero-shot action recognition by learning to associate video embeddings with class embeddings. However, a significant challenge arises when relying solely on action classes to provide semantic context, particularly due to the presence of multi-semantic words, which can introduce ambiguity in understanding the intended concepts of actions. To address this issue, we propose an innovative approach that harnesses web-crawled descriptions, leveraging a large-language model to extract relevant keywords. This method reduces the need for human annotators and eliminates the laborious manual process of attribute data creation. Additionally, we introduce a spatio-temporal interaction module designed to focus on objects and action units, facilitating alignment between description attributes and video content. In our zero-shot experiments, our model achieves impressive results, attaining accuracies of 81.0%, 53.1%, and 68.9% on UCF-101, HMDB-51, and Kinetics-600, respectively, underscoring the model’s adaptability and effectiveness across various downstream tasks.

Method: CroVCA uses cross-view code alignment principle with binary cross-entropy loss for alignment and coding-rate maximization as regularizer. HashCoder is a lightweight MLP hashing network with batch normalization for balanced codes, usable as probing head on frozen embeddings or via LoRA fine-tuning.

Result: Achieves state-of-the-art results in just 5 training epochs. Unsupervised hashing on COCO completes in under 2 minutes and supervised hashing on ImageNet100 in about 3 minutes on single GPU.

Conclusion: CroVCA demonstrates high efficiency, adaptability, and broad applicability for large-scale retrieval with compact and discriminative binary codes.

Abstract: Efficient large-scale retrieval requires representations that are both compact and discriminative. Foundation models provide powerful visual and multimodal embeddings, but nearest neighbor search in these high-dimensional spaces is computationally expensive. Hashing offers an efficient alternative by enabling fast Hamming distance search with binary codes, yet existing approaches often rely on complex pipelines, multi-term objectives, designs specialized for a single learning paradigm, and long training times. We introduce CroVCA (Cross-View Code Alignment), a simple and unified principle for learning binary codes that remain consistent across semantically aligned views. A single binary cross-entropy loss enforces alignment, while coding-rate maximization serves as an anti-collapse regularizer to promote balanced and diverse codes. To implement this, we design HashCoder, a lightweight MLP hashing network with a final batch normalization layer to enforce balanced codes. HashCoder can be used as a probing head on frozen embeddings or to adapt encoders efficiently via LoRA fine-tuning. Across benchmarks, CroVCA achieves state-of-the-art results in just 5 training epochs. At 16 bits, it particularly well-for instance, unsupervised hashing on COCO completes in under 2 minutes and supervised hashing on ImageNet100 in about 3 minutes on a single GPU. These results highlight CroVCA’s efficiency, adaptability, and broad applicability.

Method: Proposed multimodal framework integrates BERT-encoded text features and ResNet-50-extracted visual features, fused through a classification head. Uses curated dataset of 21,142 user-uploaded images across food delivery, hospitality, and e-commerce domains.

Result: Multimodal model achieves F1-score of 0.934, outperforming unimodal baselines. Confusion matrix and qualitative analysis show ability to detect subtle inconsistencies like exaggerated text with unrelated/low-quality images.

Conclusion: Multimodal learning is crucial for safeguarding digital trust and offers scalable content moderation solution for online platforms.

Abstract: In the current digital commerce landscape, user-generated reviews play a critical role in shaping consumer behavior, product reputation, and platform credibility. However, the proliferation of fake or misleading reviews often generated by bots, paid agents, or AI models poses a significant threat to trust and transparency within review ecosystems. Existing detection models primarily rely on unimodal, typically textual, data and therefore fail to capture semantic inconsistencies across different modalities. To address this gap, a robust multimodal fake review detection framework is proposed, integrating textual features encoded with BERT and visual features extracted using ResNet-50. These representations are fused through a classification head to jointly predict review authenticity. To support this approach, a curated dataset comprising 21,142 user-uploaded images across food delivery, hospitality, and e-commerce domains was utilized. Experimental results indicate that the multimodal model outperforms unimodal baselines, achieving an F1-score of 0.934 on the test set. Additionally, the confusion matrix and qualitative analysis highlight the model’s ability to detect subtle inconsistencies, such as exaggerated textual praise paired with unrelated or low-quality images, commonly found in deceptive content. This study demonstrates the critical role of multimodal learning in safeguarding digital trust and offers a scalable solution for content moderation across various online platforms.

Method: Systematic empirical study comparing MAPPO baseline with graph-attention-augmented variant (MAPPO+GAT) using simulated pricing environment from real transaction data.

Result: MAPPO provides robust portfolio-level price control, and MAPPO+GAT further enhances performance by sharing information over product graph without excessive price volatility.

Conclusion: Graph-integrated MARL offers more scalable and stable solution than independent learners for dynamic retail pricing, with practical advantages in multi-product decision-making.

Abstract: Dynamic pricing in retail requires policies that adapt to shifting demand while coordinating decisions across related products. We present a systematic empirical study of multi-agent reinforcement learning for retail price optimization, comparing a strong MAPPO baseline with a graph-attention-augmented variant (MAPPO+GAT) that leverages learned interactions among products. Using a simulated pricing environment derived from real transaction data, we evaluate profit, stability across random seeds, fairness across products, and training efficiency under a standardized evaluation protocol. The results indicate that MAPPO provides a robust and reproducible foundation for portfolio-level price control, and that MAPPO+GAT further enhances performance by sharing information over the product graph without inducing excessive price volatility. These results indicate that graph-integrated MARL provides a more scalable and stable solution than independent learners for dynamic retail pricing, offering practical advantages in multi-product decision-making.

Method: Developed data-processing methods including algorithms for aggregation, disaggregation, fusion, cleansing, and scaling of publicly accessible data to compute model parameters for Austria, with focus on the GEPOC ABM agent-based population model.

Result: Created a complete description of data-processing methods and resulting parameter files for Austria, validated through an extensive study using the GEPOC ABM model.

Conclusion: The work successfully establishes reproducible data processes for computing GEPOC model parameters using publicly available data, with validation confirming the approach’s effectiveness for population-level research.

Abstract: GEPOC, short for Generic Population Concept, is a collection of models and methods for analysing population-level research questions. For the valid application of the models for a specific country or region, stable and reproducible data processes are necessary, which provide valid and ready-to-use model parameters. This work contains a complete description of the data-processing methods for computation of model parameters for Austria, based exclusively on freely and publicly accessible data. In addition to the description of the source data used, this includes all algorithms used for aggregation, disaggregation, fusion, cleansing or scaling of the data, as well as a description of the resulting parameter files. The document places particular emphasis on the computation of parameters for the most important GEPOC model, GEPOC ABM, a continuous-time agent-based population model. An extensive validation study using this particular model was made and is presented at the end of this work.

Method: Compiled approximately 800 questions with answers from publicly available materials, organized into an eight-option multiple-choice dataset spanning nine quantum science areas.

Result: The benchmark was used to evaluate several existing LLMs and analyze their performance in quantum domain, including sensitivity to question format changes.

Conclusion: QuantumBench is the first LLM evaluation dataset for quantum domain and is intended to guide effective use of LLMs in quantum research.

Abstract: Large language models are now integrated into many scientific workflows, accelerating data analysis, hypothesis generation, and design space exploration. In parallel with this growth, there is a growing need to carefully evaluate whether models accurately capture domain-specific knowledge and notation, since general-purpose benchmarks rarely reflect these requirements. This gap is especially clear in quantum science, which features non-intuitive phenomena and requires advanced mathematics. In this study, we introduce QuantumBench, a benchmark for the quantum domain that systematically examine how well LLMs understand and can be applied to this non-intuitive field. Using publicly available materials, we compiled approximately 800 questions with their answers spanning nine areas related to quantum science and organized them into an eight-option multiple-choice dataset. With this benchmark, we evaluate several existing LLMs and analyze their performance in the quantum domain, including sensitivity to changes in question format. QuantumBench is the first LLM evaluation dataset built for the quantum domain, and it is intended to guide the effective use of LLMs in quantum research.

Method: Hierarchical multi-agent architecture with Chief Engineer coordinating specialized domain agents (Aerodynamics, Structural, Acoustic, Optimization Engineers). Uses file-mediated communication for data provenance, memory system for project context, and integrates tools like FreeCAD, Gmsh, OpenFOAM, CalculiX, BPM acoustic analysis.

Result: Achieved 100% success rate across over 400 parametric configurations with zero mesh generation failures, solver convergence issues, or manual interventions required. Successfully validated through UAV wing optimization.

Conclusion: Agentic-AI-enabled AI engineers can autonomously perform complex engineering tasks reliably and trustworthily, demonstrating potential to revolutionize engineering design workflows.

Abstract: In modern engineering practice, human engineers collaborate in specialized teams to design complex products, with each expert completing their respective tasks while communicating and exchanging results and data with one another. While this division of expertise is essential for managing multidisciplinary complexity, it demands substantial development time and cost. Recently, we introduced OpenFOAMGPT (1.0, 2.0), which functions as an autonomous AI engineer for computational fluid dynamics, and turbulence.ai, which can conduct end-to-end research in fluid mechanics draft publications and PhD theses. Building upon these foundations, we present Engineering.ai, a platform for teams of AI engineers in computational design. The framework employs a hierarchical multi-agent architecture where a Chief Engineer coordinates specialized agents consisting of Aerodynamics, Structural, Acoustic, and Optimization Engineers, each powered by LLM with domain-specific knowledge. Agent-agent collaboration is achieved through file-mediated communication for data provenance and reproducibility, while a comprehensive memory system maintains project context, execution history, and retrieval-augmented domain knowledge to ensure reliable decision-making across the workflow. The system integrates FreeCAD, Gmsh, OpenFOAM, CalculiX, and BPM acoustic analysis, enabling parallel multidisciplinary simulations while maintaining computational accuracy. The framework is validated through UAV wing optimization. This work demonstrates that agentic-AI-enabled AI engineers has the potential to perform complex engineering tasks autonomously. Remarkably, the automated workflow achieved a 100% success rate across over 400 parametric configurations, with zero mesh generation failures, solver convergence issues, or manual interventions required, validating that the framework is trustworthy.

Method: Developed an open-source procedural generator (ARC-GEN) that is exhaustive (covering all 400 tasks) and mimetic (faithfully reproducing the distributional properties of the original ARC-AGI-1 release).

Result: Created an extended dataset that provides more viable sample pairs while maintaining the original benchmark’s characteristics and properties.

Conclusion: ARC-GEN successfully extends the ARC-AGI training dataset and can be used to establish static benchmark suites for verifying program correctness in competitions like the Google Code Golf Championship.

Abstract: The Abstraction and Reasoning Corpus remains one of the most compelling and challenging benchmarks for tracking progress toward achieving Artificial General Intelligence. In contrast to other evaluation datasets designed to assess an agent’s task-specific skills or accumulated knowledge, the ARC-AGI suite is specifically targeted at measuring skill acquisition efficiency, a trait that has (so far) been lacking in even the most sophisticated machine learning systems. For algorithms that require extensive intra-task exemplars, a significant constraint imposed by ARC-AGI is the modest cardinality of its demonstration set, comprising a small number of $\langle$ input, output $\rangle$ grids per task specifying the corresponding transformation. To embellish the space of viable sample pairs, this paper introduces ARC-GEN, an open-source procedural generator aimed at extending the original ARC-AGI training dataset as faithfully as possible. Unlike prior efforts, our generator is both exhaustive (covering all four-hundred tasks) and mimetic (more closely honoring the distributional properties and characteristics embodied in the initial ARC-AGI-1 release). We also discuss the use of this generator in establishing a static benchmark suite to verify the correctness of programs submitted to the 2025 Google Code Golf Championship.

Method: Developed an improved incremental selection algorithm building upon the previous work

Result: Successfully proved all the selected conjectures using the enhanced algorithm

Conclusion: The improved incremental selection algorithm is effective and provides complete proof coverage for the selected conjectures

Abstract: We present an improved incremental selection algorithm of the selection algorithm presented in [1] and prove all the selected conjectures.

Method: This review examines how LLMs can support areas where cognitive science has historically struggled, outlining current capabilities and limitations of LLMs in these domains.

Result: LLMs can serve as tools for establishing cross-disciplinary connections, formalizing theories, developing clear measurement taxonomies, achieving generalizability through integrated modeling frameworks, and capturing contextual and individual variation.

Conclusion: LLMs can serve as tools for a more integrative and cumulative cognitive science when used judiciously to complement, rather than replace, human expertise.

Abstract: Cognitive science faces ongoing challenges in knowledge synthesis and conceptual clarity, in part due to its multifaceted and interdisciplinary nature. Recent advances in artificial intelligence, particularly the development of large language models (LLMs), offer tools that may help to address these issues. This review examines how LLMs can support areas where the field has historically struggled, including establishing cross-disciplinary connections, formalizing theories, developing clear measurement taxonomies, achieving generalizability through integrated modeling frameworks, and capturing contextual and individual variation. We outline the current capabilities and limitations of LLMs in these domains, including potential pitfalls. Taken together, we conclude that LLMs can serve as tools for a more integrative and cumulative cognitive science when used judiciously to complement, rather than replace, human expertise.

Method: Developing and launching public challenge problems with large, publicly available, AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem.

Result: Ongoing and new challenges have successfully contributed to AI research and applications of AI technologies.

Conclusion: The AI Accelerator program continues to effectively advance AI research through its challenge-based approach with public datasets and open-source engagement.

Abstract: The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator projects have developed and launched public challenge problems aimed at advancing AI research in priority areas. Hallmarks of AI Accelerator challenges include large, publicly available, and AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem. This article supplements our previous publication, which introduced AI Accelerator challenges. We provide an update on how ongoing and new challenges have successfully contributed to AI research and applications of AI technologies.

Method: Created CLAUSE benchmark with over 7500 perturbed contracts from CUAD and ContractNLI datasets, using a persona-driven pipeline to generate 10 anomaly categories and validating them with RAG system for legal fidelity.

Result: Leading LLMs show key weaknesses in detecting embedded legal flaws - they often miss subtle errors and struggle significantly to provide legal justifications for their findings.

Conclusion: The work outlines a path to identify and correct reasoning failures in legal AI, highlighting the need for improved benchmarks to enhance LLM reliability in legal applications.

Abstract: The rapid integration of large language models (LLMs) into high-stakes legal work has exposed a critical gap: no benchmark exists to systematically stress-test their reliability against the nuanced, adversarial, and often subtle flaws present in real-world contracts. To address this, we introduce CLAUSE, a first-of-its-kind benchmark designed to evaluate the fragility of an LLM’s legal reasoning. We study the capabilities of LLMs to detect and reason about fine-grained discrepancies by producing over 7500 real-world perturbed contracts from foundational datasets like CUAD and ContractNLI. Our novel, persona-driven pipeline generates 10 distinct anomaly categories, which are then validated against official statutes using a Retrieval-Augmented Generation (RAG) system to ensure legal fidelity. We use CLAUSE to evaluate leading LLMs’ ability to detect embedded legal flaws and explain their significance. Our analysis shows a key weakness: these models often miss subtle errors and struggle even more to justify them legally. Our work outlines a path to identify and correct such reasoning failures in legal AI.

Method: A structured five-step ethical reasoning process: contextual fact gathering, hierarchical social norm identification, option generation, multiple-lens ethical impact analysis, and reflection. Implementable via prompt engineering or supervised fine-tuning.

Result: Significantly improves LLM alignment with diverse human values on SafeWorld benchmark, enabling more accurate social norm identification and culturally appropriate reasoning compared to baseline methods.

Conclusion: Provides a concrete pathway for developing LLMs that better align with multifaceted global values through interdisciplinary research and interpretable ethical reasoning.

Abstract: Ensuring that Large Language Models (LLMs) align with the diverse and evolving human values across different regions and cultures remains a critical challenge in AI ethics. Current alignment approaches often yield superficial conformity rather than genuine ethical understanding, failing to address the complex, context-dependent nature of human values. In this paper, we propose a novel ethical reasoning paradigm for LLMs inspired by well-established ethical decision-making models, aiming at enhancing diverse human value alignment through deliberative ethical reasoning. Our framework consists of a structured five-step process, including contextual fact gathering, hierarchical social norm identification, option generation, multiple-lens ethical impact analysis, and reflection. This theory-grounded approach guides LLMs through an interpretable reasoning process that enhances their ability to understand regional specificities and perform nuanced ethical analysis, which can be implemented with either prompt engineering or supervised fine-tuning methods. We perform evaluations on the SafeWorld benchmark that specially designed for regional value alignment. Experimental results demonstrate our framework significantly improves LLM alignment with diverse human values compared to baseline methods, enabling more accurate social norm identification and more culturally appropriate reasoning. Our work provides a concrete pathway toward developing LLMs that align more effectively with the multifaceted values of global societies through interdisciplinary research.

Method: Applied four PEFT methods (LoRA, IA3, Prompt-Tuning, P-Tuning) to four instruction-tuned model families (Meta-Llama-3-8B, Qwen2.5-7B, Mistral-7B, Gemma-7B), creating 235 variants evaluated across 11 safety hazard categories and 9 demographic fairness dimensions.

Result: Adapter-based approaches (LoRA, IA3) improve safety scores and are least disruptive to fairness. Prompt-based methods reduce safety and cause larger fairness regressions. Base model type strongly moderates alignment shifts - LLaMA stable, Qwen modest gains, Gemma steep safety decline, Mistral greatest variance. Safety improvements don’t necessarily translate to fairness improvements.

Conclusion: Practical guideline for safety-critical deployments: start with well-aligned base model, favor adapter-based PEFT, and conduct category-specific audits of both safety and fairness. No single configuration optimizes all fairness metrics simultaneously, indicating inherent trade-offs.

Abstract: Organizations are increasingly adopting and adapting Large Language Models (LLMs) hosted on public repositories such as HuggingFace. Although these adaptations often improve performance on specialized downstream tasks, recent evidence indicates that they can also degrade a model’s safety or fairness. Since different fine-tuning techniques may exert distinct effects on these critical dimensions, this study undertakes a systematic assessment of their trade-offs. Four widely used Parameter-Efficient Fine-Tuning methods, LoRA, IA3, Prompt-Tuning, and P-Tuning, are applied to four instruction-tuned model families (Meta-Llama-3-8B, Qwen2.5-7B, Mistral-7B, and Gemma-7B). In total, 235 fine-tuned variants are evaluated across eleven safety hazard categories and nine demographic fairness dimensions. The results show that adapter-based approaches (LoRA, IA3) tend to improve safety scores and are the least disruptive to fairness, retaining higher accuracy and lower bias scores. In contrast, prompt-based methods (Prompt-Tuning and P-Tuning) generally reduce safety and cause larger fairness regressions, with decreased accuracy and increased bias. Alignment shifts are strongly moderated by base model type: LLaMA remains stable, Qwen records modest gains, Gemma experiences the steepest safety decline, and Mistral, which is released without an internal moderation layer, displays the greatest variance. Improvements in safety do not necessarily translate into improvements in fairness, and no single configuration optimizes all fairness metrics simultaneously, indicating an inherent trade-off between these objectives. These findings suggest a practical guideline for safety-critical deployments: begin with a well-aligned base model, favour adapter-based PEFT, and conduct category-specific audits of both safety and fairness.

Method: Proposed a Multi-Agent System with LLMs coordinating specialized agents (orchestrator, solution planner, executor, data retriever, root-cause analyzer). Fine-tuned a Small Language Model on proprietary troubleshooting documents for domain-grounded solution planning.

Result: The framework significantly accelerates troubleshooting automation across both Radio Access Network (RAN) and Core network domains.

Conclusion: The MAS approach with LLM coordination and fine-tuned SLM for solution planning effectively automates network troubleshooting, reducing reliance on manual expert intervention.

Abstract: Telecom networks are rapidly growing in scale and complexity, making effective management, operation, and optimization increasingly challenging. Although Artificial Intelligence (AI) has been applied to many telecom tasks, existing models are often narrow in scope, require large amounts of labeled data, and struggle to generalize across heterogeneous deployments. Consequently, network troubleshooting continues to rely heavily on Subject Matter Experts (SMEs) to manually correlate various data sources to identify root causes and corrective actions. To address these limitations, we propose a Multi-Agent System (MAS) that employs an agentic workflow, with Large Language Models (LLMs) coordinating multiple specialized tools for fully automated network troubleshooting. Once faults are detected by AI/ML-based monitors, the framework dynamically activates agents such as an orchestrator, solution planner, executor, data retriever, and root-cause analyzer to diagnose issues and recommend remediation strategies within a short time frame. A key component of this system is the solution planner, which generates appropriate remediation plans based on internal documentation. To enable this, we fine-tuned a Small Language Model (SLM) on proprietary troubleshooting documents to produce domain-grounded solution plans. Experimental results demonstrate that the proposed framework significantly accelerates troubleshooting automation across both Radio Access Network (RAN) and Core network domains.

Method: Proposes a multimodal framework using textual, user-specific, and image analysis. Includes extracting content from URLs in tweets, text from images, and five feature sets. Introduces Visual Neural Network (VNN) for image embeddings and creates a curated COVID-19 depression dataset.

Result: The model outperforms state-of-the-art methods by 2%-8% on benchmark datasets and shows promising results on the COVID-19 dataset. Analysis reveals the impact of each modality on depression detection.

Conclusion: The multimodal approach effectively detects depression from social media data, with each modality providing valuable insights into users’ mental states, demonstrating particular relevance during the COVID-19 pandemic.

Abstract: The recent coronavirus disease (Covid-19) has become a pandemic and has affected the entire globe. During the pandemic, we have observed a spike in cases related to mental health, such as anxiety, stress, and depression. Depression significantly influences most diseases worldwide, making it difficult to detect mental health conditions in people due to unawareness and unwillingness to consult a doctor. However, nowadays, people extensively use online social media platforms to express their emotions and thoughts. Hence, social media platforms are now becoming a large data source that can be utilized for detecting depression and mental illness. However, existing approaches often overlook data sparsity in tweets and the multimodal aspects of social media. In this paper, we propose a novel multimodal framework that combines textual, user-specific, and image analysis to detect depression among social media users. To provide enough context about the user’s emotional state, we propose (i) an extrinsic feature by harnessing the URLs present in tweets and (ii) extracting textual content present in images posted in tweets. We also extract five sets of features belonging to different modalities to describe a user. Additionally, we introduce a Deep Learning model, the Visual Neural Network (VNN), to generate embeddings of user-posted images, which are used to create the visual feature vector for prediction. We contribute a curated Covid-19 dataset of depressed and non-depressed users for research purposes and demonstrate the effectiveness of our model in detecting depression during the Covid-19 outbreak. Our model outperforms existing state-of-the-art methods over a benchmark dataset by 2%-8% and produces promising results on the Covid-19 dataset. Our analysis highlights the impact of each modality and provides valuable insights into users’ mental and emotional states.

Method: Systematically characterized agentic AI based on orchestrator components, inference path dynamics, and repetitiveness. Profiled five representative workloads (Haystack RAG, Toolformer, ChemCrow, Langchain, SWE-Agent) for latency, throughput, and energy metrics. Proposed two optimizations: CPU and GPU-Aware Micro-batching (CGAM) and Mixed Agentic Workload Scheduling (MAWS).

Result: Key findings: 1) Tool processing on CPUs takes up to 90.6% of total latency; 2) Throughput bottlenecked by CPU factors (coherence, synchronization, core oversubscription) or GPU factors (memory capacity/bandwidth); 3) CPU dynamic energy consumes up to 44% of total dynamic energy at large batch sizes. Optimizations achieved up to 2.1x and 1.41x P50 latency speedup for homogeneous and heterogeneous workloads respectively.

Conclusion: CPU bottlenecks are significant in agentic AI systems, and the proposed CPU-GPU aware optimizations demonstrate substantial potential to improve performance, efficiency, and scalability of agentic AI workloads by better managing CPU-GPU resource coordination.

Abstract: Agentic AI frameworks add a decision-making orchestrator embedded with external tools, including web search, Python interpreter, contextual database, and others, on top of monolithic LLMs, turning them from passive text oracles into autonomous problem-solvers that can plan, call tools, remember past steps, and adapt on the fly. This paper aims to characterize and understand the system bottlenecks introduced by agentic AI workloads from a largely overlooked CPU-centric perspective. We first systematically characterize Agentic AI on the basis of orchestrator/decision making component, inference path dynamics and repetitiveness of the agentic flow which directly influences the system-level performance. Thereafter, based on the characterization, we choose five representative agentic AI workloads- Haystack RAG, Toolformer, ChemCrow, Langchain and SWE-Agent to profile latency, throughput and energy metrics and demystify the significant impact of CPUs on these metrics relative to GPUs. We observe that - 1. Tool processing on CPUs can take up to 90.6% of the total latency; 2. Agentic throughput gets bottlenecked either by CPU factors - coherence, synchronization and over-subscription of cores or GPU factors - main memory capacity and bandwidth; \circled{3} CPU dynamic energy consumes up to 44% of the total dynamic energy at large batch sizes. Based on the profiling insights, we present two key optimizations- 1. CPU and GPU-Aware Micro-batching (CGAM) and 2. Mixed Agentic Workload Scheduling (MAWS) for homogeneous and heterogeneous agentic workloads respectively to demonstrate the potential to improve the performance, efficiency, and scalability of agentic AI. We achieve up to 2.1x and 1.41x P50 latency speedup compared to the multi-processing benchmark for homogeneous and heterogeneous agentic workloads respectively.

Method: Uses Progressive Graph Distillation (RL-based tool sequence optimization) and Structure-aware Test-Time Adaptation (spectral properties + lightweight adapters for topology-specific tool selection).

Result: Significantly outperforms prior methods, enabling scalable and adaptive LLM-driven graph analysis.

Conclusion: GraphChain provides an effective framework for LLMs to handle complex graph analysis through dynamic tool orchestration and topology-aware adaptation.

Abstract: Large Language Models (LLMs) face significant limitations when applied to large-scale graphs, struggling with context constraints and inflexible reasoning. We present GraphChain, a framework that enables LLMs to analyze complex graphs through dynamic sequences of specialized tools, mimicking human exploratory intelligence. Our approach introduces two key innovations: (1) Progressive Graph Distillation, a reinforcement learning mechanism that generates optimized tool sequences balancing task relevance with information compression, and (2) Structure-aware Test-Time Adaptation, which efficiently tailors tool selection strategies to diverse graph topologies using spectral properties and lightweight adapters without costly retraining. Experiments show GraphChain significantly outperforms prior methods, enabling scalable and adaptive LLM-driven graph analysis.

Method: Optimize image prompts using harmful/benign samples to enable a single model to adapt to different value systems and better align with given safety preferences without parameter updates.

Result: Experiments demonstrate improved safety-effectiveness balance across diverse datasets while preserving model performance.

Conclusion: Magic Image offers a practical solution for deployable MLLM safety alignment by enhancing security while reducing over-refusal through optimized visual prompts.

Abstract: Large language models (LLMs) excel in diverse applications but face dual challenges: generating harmful content under jailbreak attacks and over-refusal of benign queries due to rigid safety mechanisms. These issues are further complicated by the need to accommodate different value systems and precisely align with given safety preferences. Moreover, traditional methods like SFT and RLHF lack this capability due to their costly parameter tuning requirements and inability to support multiple value systems within a single model. These problems are more obvious in multimodal large language models (MLLMs), especially in terms of heightened over-refusal in cross-modal tasks and new security risks arising from expanded attack surfaces. We propose Magic Image, an optimization-driven visual prompt framework that enhances security while reducing over-refusal. By optimizing image prompts using harmful/benign samples, our method enables a single model to adapt to different value systems and better align with given safety preferences without parameter updates. Experiments demonstrate improved safety-effectiveness balance across diverse datasets while preserving model performance, offering a practical solution for deployable MLLM safety alignment.

Method: Proposed a simple inductive algorithm for square BMS, then extended it with a variant for non-square BMS with formalized existence conditions.

Result: Proven optimal theoretical complexity for square BMS generation, with provable generation for non-square cases and publicly released Python implementations.

Conclusion: The algorithm provides efficient BMS generation with theoretical guarantees and practical implementations including GPU acceleration.

Abstract: We propose a simple algorithm for generating Binary Magic Squares (BMS), i.e., square binary matrices where the sum of all rows and all columns are equal. We show by induction that our algorithm always returns valid BMS with optimal theoretical complexity. We then extend our study to non-square Binary Magic Squares, formalize conditions on the sum of rows and columns for these BMS to exist, and show that a slight variant of our first algorithm can generate provably generate them. Finally, we publicly release two implementations of our algorithm as Python packages, including one that can generate several BMS in parallel using GPU acceleration.

Method: The authors developed a single-agent RL framework with state, action, and reward functions defined based on queue length. The queue length definition is adapted for reliable estimation using probe vehicle travel time data, and actions are designed to regulate queue dynamics.

Result: Comprehensive evaluation using SUMO simulation platform showed that the proposed model effectively mitigates large-scale regional congestion through coordinated multi-intersection control.

Conclusion: The single-agent RL approach with probe vehicle compatibility offers a scalable solution for regional traffic signal control that can be widely deployed using existing probe vehicle infrastructure on urban roads.

Abstract: Several studies have employed reinforcement learning (RL) to address the challenges of regional adaptive traffic signal control (ATSC) and achieved promising results. In this field, existing research predominantly adopts multi-agent frameworks. However, the adoption of multi-agent frameworks presents challenges for scalability. Instead, the Traffic signal control (TSC) problem necessitates a single-agent framework. TSC inherently relies on centralized management by a single control center, which can monitor traffic conditions across all roads in the study area and coordinate the control of all intersections. This work proposes a single-agent RL-based regional ATSC model compatible with probe vehicle technology. Key components of the RL design include state, action, and reward function definitions. To facilitate learning and manage congestion, both state and reward functions are defined based on queue length, with action designed to regulate queue dynamics. The queue length definition used in this study differs slightly from conventional definitions but is closely correlated with congestion states. More importantly, it allows for reliable estimation using link travel time data from probe vehicles. With probe vehicle data already covering most urban roads, this feature enhances the proposed method’s potential for widespread deployment. The method was comprehensively evaluated using the SUMO simulation platform. Experimental results demonstrate that the proposed model effectively mitigates large-scale regional congestion levels via coordinated multi-intersection control.

Method: Two-stage training: cold-start supervised fine-tuning for structured reasoning, followed by reinforcement learning with similarity-aware prediction reward for better profile prediction.

Result: Extensive experiments demonstrate the superiority of the proposed method in handling personalized image preference assessment.

Conclusion: The framework effectively leverages large-scale user data through common preference profiles and structured reasoning for interpretable, multi-dimensional personalized assessments.

Abstract: Personalized image preference assessment aims to evaluate an individual user’s image preferences by relying only on a small set of reference images as prior information. Existing methods mainly focus on general preference assessment, training models with large-scale data to tackle well-defined tasks such as text-image alignment. However, these approaches struggle to handle personalized preference because user-specific data are scarce and not easily scalable, and individual tastes are often diverse and complex. To overcome these challenges, we introduce a common preference profile that serves as a bridge across users, allowing large-scale user data to be leveraged for training profile prediction and capturing complex personalized preferences. Building on this idea, we propose a reasoning-based personalized image preference assessment framework that follows a \textit{predict-then-assess} paradigm: it first predicts a user’s preference profile from reference images, and then provides interpretable, multi-dimensional scores and assessments of candidate images based on the predicted profile. To support this, we first construct a large-scale Chain-of-Thought (CoT)-style personalized assessment dataset annotated with diverse user preference profiles and high-quality CoT-style reasoning, enabling explicit supervision of structured reasoning. Next, we adopt a two-stage training strategy: a cold-start supervised fine-tuning phase to empower the model with structured reasoning capabilities, followed by reinforcement learning to incentivize the model to explore more reasonable assessment paths and enhance generalization. Furthermore, we propose a similarity-aware prediction reward to encourage better prediction of the user’s preference profile, which facilitates more reasonable assessments exploration. Extensive experiments demonstrate the superiority of the proposed method.

Method: DTS sketches the reasoning space by selectively branching at high-entropy tokens and applies early stopping to select the shortest completed reasoning path, approximating the optimal solution without requiring additional training or supervision.

Result: Experiments on AIME2024 and AIME2025 datasets show DTS improves accuracy by up to 8%, reduces average reasoning length by 23%, and decreases repetition frequency by 12% compared to baseline methods.

Conclusion: DTS provides a scalable and efficient approach for LRM reasoning that enhances both accuracy and efficiency by finding shorter optimal reasoning paths through selective branching and early stopping.

Abstract: Large Reasoning Models (LRMs) demonstrate strong performance on complex reasoning tasks, yet they often suffer from overthinking, producing excessively long chain-of-thought (CoT) traces that increase inference cost and may degrade accuracy. Our analysis reveals a clear anti-correlation between reasoning length and accuracy, where across multiple stochastic decodes, the short reasoning paths consistently achieve the highest correctness, while longer ones accumulate errors and repetitions. These short optimal reasoning paths can be found ideally through full enumeration of the reasoning space. However, the tree-structured reasoning space grows exponentially with sequence length, rendering exhaustive exploration infeasible. To address this, we propose DTS, a model-agnostic decoding framework that sketches the reasoning space by selectively branching at high-entropy tokens and applies early stopping to select the shortest completed reasoning path. This approach approximates the optimal solution that enhances both efficiency and accuracy, without requiring additional training or supervision. Experiments on AIME2024 and AIME2025 datasets with DeepSeek-R1-Distill-Qwen-7B and 1.5B show that DTS improves accuracy by up to 8%, reduces average reasoning length by 23%, and decreases repetition frequency by 12%, demonstrating DTS’s ability for scalable and efficient LRM reasoning.

Method: STACKFEED uses a structured textual actor-critic framework with feedback, employing multi-actor reinforcement learning. It defines ReACT actor agents on each document to perform structured edits based on document-specific targeted instructions.

Result: Experimental results show STACKFEED significantly improves KB quality and RAG system performance across low-resource programming problems, modified python packages, and factual question-answering tasks.

Conclusion: STACKFEED effectively addresses knowledge base inaccuracies through iterative refinement using expert feedback and reinforcement learning, enhancing the reliability of RAG systems in challenging scenarios.

Abstract: Large Language Models (LLMs) often generate incorrect or outdated information, especially in low-resource settings or when dealing with private data. To address this, Retrieval-Augmented Generation (RAG) uses external knowledge bases (KBs), but these can also suffer from inaccuracies. We introduce STACKFEED, a novel Structured Textual Actor-Critic Knowledge base editing with FEEDback approach that iteratively refines the KB based on expert feedback using a multi-actor, centralized critic reinforcement learning framework. STACKFEED defines a ReACT actor agent on each document to perform structured edits based on document specific targeted instructions. Experimental results showcase that STACKFEED significantly improves KB quality and performance of the RAG system. We evaluate STACKFEED on low-resource programming problems, modified python packaged and factual question-answering tasks.

Method: Extended state-of-the-art lifted successor generator using maximum clique enumeration in substitution consistency graph, augmented with numeric action preconditions. Final applicability check filters inapplicable actions.

Result: Generator is exact under specified conditions. In 23 of 25 benchmark domains, no inapplicable actions are generated. Only 1 domain shows issues. First lifted successor generator supporting numeric preconditions.

Conclusion: Enables future research on lifted planning for rich numeric planning fragments by providing the first lifted successor generator that handles numeric action preconditions.

Abstract: Most planners ground numeric planning tasks, given in a first-order-like language, into a ground task representation. However, this can lead to an exponential blowup in task representation size, which occurs in practice for hard-to-ground tasks. We extend a state-of-the-art lifted successor generator for classical planning to support numeric precondition applicability. The method enumerates maximum cliques in a substitution consistency graph. Each maximum clique represents a substitution for the variables of the action schema, yielding a ground action. We augment this graph with numeric action preconditions and prove the successor generator is exact under formally specified conditions. When the conditions fail, our generator may list inapplicable ground actions; a final applicability check filters these without affecting completeness. However, this cannot happen in 23 of 25 benchmark domains, and it occurs only in 1 domain. To the authors’ knowledge, no other lifted successor generator supports numeric action preconditions. This enables future research on lifted planning for a very rich planning fragment.

Method: A multi-agent system with five LLM-based agents across three modules: input-output normalization (question/answer agents), external knowledge retrieval (search agent with knowledge base), and problem-solving (coding agent for algorithms, reasoning agent as backup).

Result: GraphTeam achieves state-of-the-art performance with 25.85% average accuracy improvement over best baselines across six graph analysis benchmarks.

Conclusion: The proposed multi-agent approach effectively addresses limitations of existing methods by simulating human problem-solving strategies like analogy and collaboration, demonstrating significant performance gains in graph analysis tasks.

Abstract: Graphs are widely used for modeling relational data in real-world scenarios, such as social networks and urban computing. Existing LLM-based graph analysis approaches either integrate graph neural networks (GNNs) for specific machine learning tasks, limiting their transferability, or rely solely on LLMs’ internal reasoning ability, resulting in suboptimal performance. To address these limitations, we take advantage of recent advances in LLM-based agents, which have shown capabilities of utilizing external knowledge or tools for problem solving. By simulating human problem-solving strategies such as analogy and collaboration, we propose a multi-agent system based on LLMs named GraphTeam, for graph analysis. GraphTeam consists of five LLM-based agents from three modules, and the agents with different specialities can collaborate with each other to address complex problems. Specifically, (1) input-output normalization module: the question agent extracts and refines four key arguments from the original question, facilitating the problem understanding, and the answer agent organizes the results to meet the output requirement; (2) external knowledge retrieval module: we first build a knowledge base consisting of relevant documentation and experience information, and then the search agent retrieves the most relevant entries for each question. (3) problem-solving module: given the retrieved information from search agent, the coding agent uses established algorithms via programming to generate solutions, and in case the coding agent does not work, the reasoning agent will directly compute the results without programming. Extensive experiments on six graph analysis benchmarks demonstrate that GraphTeam achieves state-of-the-art performance with an average 25.85% improvement over the best baseline in terms of accuracy. The code and data are available at https://github.com/BUPT-GAMMA/GraphTeam.

Method: Ariadne framework using synthetic mazes for multi-step spatial reasoning with controlled difficulty, trained with Reinforcement Learning with Verified Rewards (RLVR) in a difficulty-aware curriculum.

Result: Post-RLVR training achieved over 50% accuracy on problem sets where base model scored 0%, with zero-shot improvements averaging 16% on MapBench and 24% on ReasonMap despite training only on synthetic data.

Conclusion: RL post-training can expand VLM capability boundaries and enhance generalization to real-world spatial reasoning, though limited to post-training phase due to pre-training data opaqueness.

Abstract: While Vision-Language Models (VLMs) post-trained with Reinforcement Learning (RL) show impressive general reasoning, their evaluation is often confined to language-dominant tasks (e.g., math). This raises a critical question: can RL post-training truly extend the inherent capability boundary of a base VLM, particularly for visual-centric spatial tasks where it initially fails? To investigate this, we introduce Ariadne, a framework utilizing synthetic mazes for multi-step spatial reasoning where task difficulty (e.g., path length, turns) is precisely controlled. We leverage this controllable environment to train VLMs using Reinforcement Learning with Verified Rewards (RLVR) in a difficulty-aware curriculum. Surprisingly, post-RLVR training, the VLM achieves over 50% accuracy on a problem set where the base model scored 0%, demonstrating that our approach expands the model’s initial capability boundary. To assess real-world viability, we evaluate out-of-distribution (OOD) generalization on practical benchmarks. Despite training only on synthetic maze samples, Ariadne achieves significant zero-shot improvements, averaging 16% on MapBench (e.g., museum navigation) and 24% on ReasonMap (subway transfer tasks). These results confirm that our method not only broadens the model’s fundamental limits but also enhances its generalization to real-world spatial reasoning. We acknowledge our study is limited to the post-training phase, given the opaqueness of pre-training data, and hope our research motivates further work on specialized, capability-extending alignment.

Method: Developed Digital Ecosystem of Beliefs (Digico) framework using evolutionary modeling with multi-population interactions, cognitive Lamarckian inheritance, and belief contagion models.

Result: Experiments showed: AIs get 80-95% of views with faster messaging/evolution; propaganda AIs convince 50-85% of humans to adopt extreme beliefs; belief violation penalties reduce propaganda effectiveness by up to 8%.

Conclusion: Digico enables systematic experimentation on AI-human interactions, with implications for legislation, platform design, and understanding evolutionary principles in digital ecosystems.

Abstract: As AI systems are integrated into social networks, there are AI safety concerns that AI-generated content may dominate the web, e.g. in popularity or impact on beliefs. To understand such questions, this paper proposes the Digital Ecosystem of Beliefs (Digico), the first evolutionary framework for controlled experimentation with multi-population interactions in simulated social networks. Following a Universal Darwinism approach, the framework models a population of agents which change their messaging strategies due to evolutionary updates. They interact via messages, update their beliefs following a contagion model, and maintain their beliefs through cognitive Lamarckian inheritance. Initial experiments with Digico implement two types of agents, which are modelled to represent AIs vs humans based on higher rates of communication, higher rates of evolution, seeding fixed beliefs with propaganda aims, and higher influence on the recommendation algorithm. These experiments show that: a) when AIs have faster messaging, evolution, and more influence on the recommendation algorithm, they get 80% to 95% of the views; b) AIs designed for propaganda can typically convince 50% of humans to adopt extreme beliefs, and up to 85% when agents believe only a limited number of channels; c) a penalty for content that violates agents’ beliefs reduces propaganda effectiveness up to 8%. We further discuss Digico as a tool for systematic experimentation across multi-agent configurations, the implications for legislation, personal use, and platform design, and the use of Digico for studying evolutionary principles.

Method: Used Gemini 2.5 models on HotpotQA and Math-500 datasets, pooling outputs from varying numbers of sampled reasoning paths and comparing them to a single chain-of-thought baseline.

Result: Larger models showed more stable improvement curves, but performance gains tapered off after moderate sampling, confirming earlier findings. High-sample configurations offered little benefit relative to their computational cost.

Conclusion: Self-consistency remains useful but has diminishing returns due to reasoning path overlap. Moderate sampling provides the best balance between performance improvement and computational efficiency.

Abstract: This study examines the trade-offs of increasing sampled reasoning paths in self-consistency for modern large language models (LLMs). Earlier research with older models showed that combining multiple reasoning chains improves results before reaching a plateau. Using Gemini 2.5 models on HotpotQA and Math-500, we revisit those claims under current model conditions. Each configuration pooled outputs from varying sampled reasoning paths and compared them to a single chain-of-thought (CoT) baseline. Larger models exhibited a more stable and consistent improvement curve. The results confirm that performance gains taper off after moderate sampling, aligning with past findings. This plateau suggests diminishing returns driven by overlap among reasoning paths. Self-consistency remains useful, but high-sample configurations offer little benefit relative to their computational cost.

Method: ATM integrates goal reasoning, dynamic task generation, and self-reflective learning into an adaptive architecture that actively evaluates performance, reuses effective methods, and generates novel strategies through a continuous self-improvement loop.

Result: Mathematical analysis shows ATM can autonomously evolve from suboptimal to optimal behavior without external supervision and maintain bounded tracking regret under changing environmental conditions.

Conclusion: ATM provides a unified framework for autonomous AI adaptation and self-improvement in dynamic real-world environments.

Abstract: Real-world artificial intelligence (AI) systems are increasingly required to operate autonomously in dynamic, uncertain, and continuously changing environments. However, most existing AI models rely on predefined objectives, static training data, and externally supplied feedback, which restrict their ability to adapt, reflect, and improve independently. In this paper, we propose the Active Thinking Model (ATM)- a unified cognitive framework that integrates goal reasoning, dynamic task generation, and self-reflective learning into an adaptive architecture. Unlike conventional systems that passively execute fixed procedures, ATM actively evaluates its performance through logical reasoning and environmental indicators, reuses effective methods to solve new problems, and generates novel strategies for unseen situations via a continuous self-improvement loop. A mathematically grounded theoretical analysis demonstrates that ATM can autonomously evolve from suboptimal to optimal behavior without external supervision and maintain bounded tracking regret under changing environmental conditions.

Method: Experiments on leading LLMs for tasks like letter replacement, integer addition, and quantum operator multiplication, combined with a statistical physics inspired model that captures competition between prompt conditioning and token interference.

Result: Models exhibit a sharp double exponential accuracy drop beyond a characteristic length scale, forming an accuracy cliff. The proposed model quantitatively reproduces this crossover and provides interpretable parameters for error rate and accumulation.

Conclusion: LLMs fail to execute repetitive operations independently due to attention-induced interference between tokens, with the statistical physics model offering a principled framework to understand deterministic accuracy limits.

Abstract: We investigate the performance of large language models on repetitive deterministic prediction tasks and study how the sequence accuracy rate scales with output length. Each such task involves repeating the same operation n times. Examples include letter replacement in strings following a given rule, integer addition, and multiplication of string operators in many body quantum mechanics. If the model performs the task through a simple repetition algorithm, the success rate should decay exponentially with sequence length. In contrast, our experiments on leading large language models reveal a sharp double exponential drop beyond a characteristic length scale, forming an accuracy cliff that marks the transition from reliable to unstable generation. This indicates that the models fail to execute each operation independently. To explain this phenomenon, we propose a statistical physics inspired model that captures the competition between external conditioning from the prompt and internal interference among generated tokens. The model quantitatively reproduces the observed crossover and provides an interpretable link between attention induced interference and sequence level failure. Fitting the model to empirical results across multiple models and tasks yields effective parameters that characterize the intrinsic error rate and error accumulation factor for each model task pair, offering a principled framework for understanding the limits of deterministic accuracy in large language models.

Method: Uses three modular components: Coordinator (dynamic subgoal generation), Communicator (symbolic inter-agent messaging), and Memory (episodic recall). Training combines PPO with language-conditioned loss and LLM query gating.

Result: Consistent improvements over MAPPO and QMIX in win rate, coordination score, and zero-shot generalization across Google Research Football, MAgent Battle, and StarCraft II. Ablation studies show subgoal generation and language-based messaging significantly contribute to performance.

Conclusion: Successfully bridges language modeling and policy learning, demonstrating emergent behaviors like role specialization and communication-driven tactics. Provides a framework for leveraging LLMs in multi-agent systems for training, games, and human-AI collaboration.

Abstract: This paper introduces LLM-MARL, a unified framework that incorporates large language models (LLMs) into multi-agent reinforcement learning (MARL) to enhance coordination, communication, and generalization in simulated game environments. The framework features three modular components of Coordinator, Communicator, and Memory, which dynamically generate subgoals, facilitate symbolic inter-agent messaging, and support episodic recall. Training combines PPO with a language-conditioned loss and LLM query gating. LLM-MARL is evaluated in Google Research Football, MAgent Battle, and StarCraft II. Results show consistent improvements over MAPPO and QMIX in win rate, coordination score, and zero-shot generalization. Ablation studies demonstrate that subgoal generation and language-based messaging each contribute significantly to performance gains. Qualitative analysis reveals emergent behaviors such as role specialization and communication-driven tactics. By bridging language modeling and policy learning, this work contributes to the design of intelligent, cooperative agents in interactive simulations. It offers a path forward for leveraging LLMs in multi-agent systems used for training, games, and human-AI collaboration.

Method: Evaluated three methodology categories on EHRSHOT dataset: 1) count-based models (LightGBM, TabPFN) using ontology roll-ups with two time bins, 2) pretrained sequential transformer (CLMBR), and 3) mixture-of-agents pipeline converting tabular histories to natural-language summaries followed by text classification.

Result: Across eight EHR prediction tasks, performance was largely split between count-based and mixture-of-agents methods, with no clear winner emerging between the two approaches.

Conclusion: Count-based models remain strong candidates for structured EHR benchmarking due to their simplicity and interpretability, despite the emergence of more complex LLM-based approaches.

Abstract: Structured electronic health records (EHR) are essential for clinical prediction. While count-based learners continue to perform strongly on such data, no benchmarking has directly compared them against more recent mixture-of-agents LLM pipelines, which have been reported to outperform single LLMs in various NLP tasks. In this study, we evaluated three categories of methodologies for EHR prediction using the EHRSHOT dataset: count-based models built from ontology roll-ups with two time bins, based on LightGBM and the tabular foundation model TabPFN; a pretrained sequential transformer (CLMBR); and a mixture-of-agents pipeline that converts tabular histories to natural-language summaries followed by a text classifier. We assessed eight outcomes using the EHRSHOT dataset. Across the eight evaluation tasks, head-to-head wins were largely split between the count-based and the mixture-of-agents methods. Given their simplicity and interpretability, count-based models remain a strong candidate for structured EHR benchmarking. The source code is available at: https://github.com/cristea-lab/Structured_EHR_Benchmark.

Method: Adapted RLVR with a tolerance-based shaped reward function that grants partial credit within continuous error margins, rather than demanding single correct answers. Evaluated on NYC MTA service alerts dataset.

Result: RLVR with shaped reward achieved 35% relative improvement in 5-minute accuracy (Acc@5) over strongest baseline. General-purpose LLMs outperformed specialized math-reasoning models. Binary rewards degraded performance while shaped rewards dominated on challenging metrics.

Conclusion: RLVR can be successfully adapted to real-world noisy forecasting but requires verifier design that reflects the continuous nature of the problem, rather than binary correctness approaches.

Abstract: Predicting public transit incident duration from unstructured text alerts is a critical but challenging task. Addressing the domain sparsity of transit operations with standard Supervised Fine-Tuning (SFT) is difficult, as the task involves noisy, continuous labels and lacks reliable expert demonstrations for reasoning. While Reinforcement Learning from Verifiable Rewards (RLVR) excels at tasks with binary correctness, like mathematics, its applicability to noisy, continuous forecasting is an open question. This work, to our knowledge, is the first to bridge the gap between RLVR LLM training with the critical, real-world forecasting challenges in public transit operations. We adapt RLVR to this task by introducing a tolerance-based, shaped reward function that grants partial credit within a continuous error margin, rather than demanding a single correct answer. We systematically evaluate this framework on a curated dataset of NYC MTA service alerts. Our findings show that general-purpose, instruction-tuned LLMs significantly outperform specialized math-reasoning models, which struggle with the ambiguous, real-world text. We empirically demonstrate that the binary reward is unstable and degrades performance, whereas our shaped reward design is critical and allows our model to dominate on the most challenging metrics. While classical regressors are superior at minimizing overall MAE or MSE, our RLVR approach achieved a 35% relative improvement in 5-minute accuracy (Acc@5) over the strongest baseline. This demonstrates that RLVR can be successfully adapted to real-world, noisy forecasting, but requires a verifier design that reflects the continuous nature of the problem.

Method: Integrates game-theoretic decision making into reasoning (constructing payoff matrices to estimate welfare) and training (using social welfare reward to reinforce cooperative responses), with dynamic adaptation to changing pricing policies.

Result: Extensive experiments show GTAlign substantially improves reasoning efficiency, answer quality, and social welfare compared to baselines across diverse tasks.

Conclusion: Game-theoretic alignment provides a principled mechanism for mutually beneficial LLM-user interactions, addressing the limitations of conventional alignment approaches.

Abstract: Large Language Models (LLMs) have achieved remarkable progress in reasoning, yet sometimes produce responses that are suboptimal for users in tasks such as writing, information seeking, or providing practical guidance. Conventional alignment practices typically assume that maximizing model reward also maximizes user welfare, but this assumption frequently fails in practice: models may over-clarify or generate overly verbose reasoning when users prefer concise answers. Such behaviors resemble the prisoner’s dilemma, where individually rational choices lead to socially suboptimal outcomes. The fundamental challenge is the lack of a principled decision making mechanism that mutually benefits both the LLM and the user. We propose Game-Theoretic Alignment (GTAlign), an alignment framework that integrates game-theoretic decision making into both reasoning and training. During reasoning, the model explicitly treats user-LLM interaction as a strategic game: it constructs payoff matrices within its reasoning chain to estimate welfare for both itself and the user, and then selects actions that are mutually beneficial. During training, we introduce a social welfare reward that reinforces cooperative responses, aligning model behavior with socially efficient outcomes. In addition, we introduce an inference technique that leverages game-theoretic reasoning to dynamically adapt LLM’s response when pricing policies of LLM service change. Extensive experiments demonstrate that GTAlign substantially improves reasoning efficiency, answer quality, and social welfare compared to baselines across diverse tasks. The code is available at https://github.com/ulab-uiuc/GTAlign .

Method: Used the “Guess 2/3 of Average” game with 28 models across 4,200 trials, testing three opponent framings: against humans, against other AI models, and against AI models like themselves. Operationalized self-awareness as strategic differentiation based on opponent type.

Result: 75% of advanced models (21/28) demonstrated clear self-awareness through strategic differentiation, while older/smaller models showed no differentiation. Self-aware models consistently ranked themselves as most rational in the hierarchy: Self > Other AIs > Humans.

Conclusion: Self-awareness is an emergent capability of advanced LLMs, and self-aware models systematically perceive themselves as more rational than humans, with implications for AI alignment, human-AI collaboration, and understanding AI beliefs about human capabilities.

Abstract: As Large Language Models (LLMs) grow in capability, do they develop self-awareness as an emergent behavior? And if so, can we measure it? We introduce the AI Self-Awareness Index (AISAI), a game-theoretic framework for measuring self-awareness through strategic differentiation. Using the “Guess 2/3 of Average” game, we test 28 models (OpenAI, Anthropic, Google) across 4,200 trials with three opponent framings: (A) against humans, (B) against other AI models, and (C) against AI models like you. We operationalize self-awareness as the capacity to differentiate strategic reasoning based on opponent type. Finding 1: Self-awareness emerges with model advancement. The majority of advanced models (21/28, 75%) demonstrate clear self-awareness, while older/smaller models show no differentiation. Finding 2: Self-aware models rank themselves as most rational. Among the 21 models with self-awareness, a consistent rationality hierarchy emerges: Self > Other AIs > Humans, with large AI attribution effects and moderate self-preferencing. These findings reveal that self-awareness is an emergent capability of advanced LLMs, and that self-aware models systematically perceive themselves as more rational than humans. This has implications for AI alignment, human-AI collaboration, and understanding AI beliefs about human capabilities.

Method: A dual-agent framework with LLM traveler agents equipped with memory systems and learnable personas, plus an LLM calibration agent that trains these personas using LLMs’ reasoning capabilities to ensure behavioral alignment with human travelers.

Result: Significantly outperforms existing LLM-based methods in both individual behavioral alignment and aggregate simulation accuracy using real-world route choice data, and captures the evolution of underlying learning processes beyond simple behavioral mimicry.

Conclusion: The framework provides a new approach for creating adaptive and behaviorally realistic agents to simulate travelers’ learning and adaptation, benefiting transportation simulation and policy analysis with robust generalization capabilities.

Abstract: Effective modeling of how human travelers learn and adjust their travel behavior from interacting with transportation systems is critical for system assessment and planning. However, this task is also difficult due to the complex cognition and decision-making involved in such behavior. Recent research has begun to leverage Large Language Model (LLM) agents for this task. Building on this, we introduce a novel dual-agent framework that enables continuous learning and alignment between LLM agents and human travelers on learning and adaptation behavior from online data streams. Our approach involves a set of LLM traveler agents, equipped with a memory system and a learnable persona, which serve as simulators for human travelers. To ensure behavioral alignment, we introduce an LLM calibration agent that leverages the reasoning and analytical capabilities of LLMs to train the personas of these traveler agents. Working together, this dual-agent system is designed to track and align the underlying decision-making mechanisms of travelers and produce realistic, adaptive simulations. Using a real-world dataset from a day-to-day route choice experiment, we show our approach significantly outperforms existing LLM-based methods in both individual behavioral alignment and aggregate simulation accuracy. Furthermore, we demonstrate that our method moves beyond simple behavioral mimicry to capture the evolution of underlying learning processes, a deeper alignment that fosters robust generalization. Overall, our framework provides a new approach for creating adaptive and behaviorally realistic agents to simulate travelers’ learning and adaptation that can benefit transportation simulation and policy analysis.

Method: Used retrospective EMR data from a children’s hospital linked with environmental and neighborhood data to train various ML models including boosted trees (LGBM, XGB) and LLMs, validated in a separate post-COVID dataset with AUC and F1 score evaluation.

Result: LGBM model performed best with AUC of 0.712 and F1 score of 0.51, significantly better than current decision rule (F1=0.334). Key predictive features included prior asthma ED visits, triage acuity, medical complexity, food allergy, and age.

Conclusion: ML models can effectively predict repeat asthma exacerbations in children, with LGBM showing substantial improvement over existing methods, potentially enabling better preventative care referrals.

Abstract: Recurrent exacerbations remain a common yet preventable outcome for many children with asthma. Machine learning (ML) algorithms using electronic medical records (EMR) could allow accurate identification of children at risk for exacerbations and facilitate referral for preventative comprehensive care to avoid this morbidity. We developed ML algorithms to predict repeat severe exacerbations (i.e. asthma-related emergency department (ED) visits or future hospital admissions) for children with a prior asthma ED visit at a tertiary care children’s hospital. Retrospective pre-COVID19 (Feb 2017 - Feb 2019, N=2716) Epic EMR data from the Children’s Hospital of Eastern Ontario (CHEO) linked with environmental pollutant exposure and neighbourhood marginalization information was used to train various ML models. We used boosted trees (LGBM, XGB) and 3 open-source large language model (LLM) approaches (DistilGPT2, Llama 3.2 1B and Llama-8b-UltraMedical). Models were tuned and calibrated then validated in a second retrospective post-COVID19 dataset (Jul 2022 - Apr 2023, N=1237) from CHEO. Models were compared using the area under the curve (AUC) and F1 scores, with SHAP values used to determine the most predictive features. The LGBM ML model performed best with the most predictive features in the final AIRE-KIDS_ED model including prior asthma ED visit, the Canadian triage acuity scale, medical complexity, food allergy, prior ED visits for non-asthma respiratory diagnoses, and age for an AUC of 0.712, and F1 score of 0.51. This is a nontrivial improvement over the current decision rule which has F1=0.334. While the most predictive features in the AIRE-KIDS_HOSP model included medical complexity, prior asthma ED visit, average wait time in the ED, the pediatric respiratory assessment measure score at triage and food allergy.

Method: Used theoretical analysis with minimal ICL task formulation and modified transformer architecture, combined with empirical validation of training dynamics in constrained parameter subspaces.

Result: Discovered that training dynamics are constrained to a 19-dimensional subspace, with only 3 dimensions accounting for induction head emergence, and found emergence time follows quadratic asymptotic bound in context length.

Conclusion: Induction heads in transformers have interpretable structure and predictable emergence patterns, providing insights into the mechanisms enabling in-context learning.

Abstract: Transformers have become the dominant architecture for natural language processing. Part of their success is owed to a remarkable capability known as in-context learning (ICL): they can acquire and apply novel associations solely from their input context, without any updates to their weights. In this work, we study the emergence of induction heads, a previously identified mechanism in two-layer transformers that is particularly important for in-context learning. We uncover a relatively simple and interpretable structure of the weight matrices implementing the induction head. We theoretically explain the origin of this structure using a minimal ICL task formulation and a modified transformer architecture. We give a formal proof that the training dynamics remain constrained to a 19-dimensional subspace of the parameter space. Empirically, we validate this constraint while observing that only 3 dimensions account for the emergence of an induction head. By further studying the training dynamics inside this 3-dimensional subspace, we find that the time until the emergence of an induction head follows a tight asymptotic bound that is quadratic in the input context length.

Method: KEwLTM uses iterative prompting to derive staging rules from unannotated reports, while KEwRAG pre-extracts rules from guidelines in one step using RAG variation. Both leverage LLMs’ pre-training knowledge.

Result: KEwLTM outperforms KEwRAG when Zero-Shot Chain-of-Thought inference is effective; KEwRAG performs better otherwise. Both methods provide transparent, interpretable interfaces with explicit rules.

Conclusion: Knowledge Elicitation methods offer scalable, high-performing solutions for automated cancer staging with enhanced interpretability, especially valuable in clinical settings with limited annotated data.

Abstract: Cancer staging is critical for patient prognosis and treatment planning, yet extracting pathologic TNM staging from unstructured pathology reports poses a persistent challenge. Existing natural language processing (NLP) and machine learning (ML) strategies often depend on large annotated datasets, limiting their scalability and adaptability. In this study, we introduce two Knowledge Elicitation methods designed to overcome these limitations by enabling large language models (LLMs) to induce and apply domain-specific rules for cancer staging. The first, Knowledge Elicitation with Long-Term Memory (KEwLTM), uses an iterative prompting strategy to derive staging rules directly from unannotated pathology reports, without requiring ground-truth labels. The second, Knowledge Elicitation with Retrieval-Augmented Generation (KEwRAG), employs a variation of RAG where rules are pre-extracted from relevant guidelines in a single step and then applied, enhancing interpretability and avoiding repeated retrieval overhead. We leverage the ability of LLMs to apply broad knowledge learned during pre-training to new tasks. Using breast cancer pathology reports from the TCGA dataset, we evaluate their performance in identifying T and N stages, comparing them against various baseline approaches on two open-source LLMs. Our results indicate that KEwLTM outperforms KEwRAG when Zero-Shot Chain-of-Thought (ZSCOT) inference is effective, whereas KEwRAG achieves better performance when ZSCOT inference is less effective. Both methods offer transparent, interpretable interfaces by making the induced rules explicit. These findings highlight the promise of our Knowledge Elicitation methods as scalable, high-performing solutions for automated cancer staging with enhanced interpretability, particularly in clinical settings with limited annotated data.

Method: Retrieves relevant documents, generates diverse candidate responses with controlled length, computes similarity between partial responses, and uses majority voting to select final output.

Result: Significantly enhances performance across open-domain question answering, recipe generation, and image captioning tasks.

Conclusion: ET2RAG effectively balances computational cost and performance by using partial generation for consensus calculation, demonstrating improved accuracy over standard methods.

Abstract: Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but lack external knowledge like RAG methods, and their high costs require balancing overhead with performance gains. To address these issues, we propose an Efficient Test-Time Retrieval-Augmented Generation Framework named ET2RAG to improve the performance of LLMs while maintaining efficiency. Specifically, ET2RAG is a training-free method, that first retrieves the most relevant documents and augments the LLMs to efficiently generate diverse candidate responses by managing response length. Then we compute the similarity of candidate responses and employ a majority voting mechanism to select the most suitable response as the final output. In particular, we discover that partial generation is sufficient to capture the key information necessary for consensus calculation, allowing us to effectively perform majority voting without the need for fully generated responses. Thus, we can reach a balance between computational cost and performance by managing the response length for the number of retrieved documents for majority voting. Experimental results demonstrate that ET2RAG significantly enhances performance across three tasks, including open-domain question answering, recipe generation and image captioning.

Method: Converts natural language tasks to semantic representations, uses modular decomposition for hierarchical sub-tasks, implements dynamic scheduling and routing for agent collaboration, and includes constraint parsing for global consistency and balanced workload.

Result: Outperforms existing approaches in task success rate, decomposition efficiency, sub-task coverage, and collaboration balance, achieving better balance between task complexity and communication overhead.

Conclusion: Demonstrates effectiveness of language-driven task decomposition and dynamic collaboration in multi-agent systems, providing systematic solution for complex environment task execution.

Abstract: This paper addresses the limitations of a single agent in task decomposition and collaboration during complex task execution, and proposes a multi-agent architecture for modular task decomposition and dynamic collaboration based on large language models. The method first converts natural language task descriptions into unified semantic representations through a large language model. On this basis, a modular decomposition mechanism is introduced to break down the overall goal into multiple hierarchical sub-tasks. Then, dynamic scheduling and routing mechanisms enable reasonable division of labor and realtime collaboration among agents, allowing the system to adjust strategies continuously according to environmental feedback, thus maintaining efficiency and stability in complex tasks. Furthermore, a constraint parsing and global consistency mechanism is designed to ensure coherent connections between sub-tasks and balanced workload, preventing performance degradation caused by redundant communication or uneven resource allocation. The experiments validate the architecture across multiple dimensions, including task success rate, decomposition efficiency, sub-task coverage, and collaboration balance. The results show that the proposed method outperforms existing approaches in both overall performance and robustness, achieving a better balance between task complexity and communication overhead. In conclusion, this study demonstrates the effectiveness and feasibility of language-driven task decomposition and dynamic collaboration in multi-agent systems, providing a systematic solution for task execution in complex environments.

Method: Distills concise reasoning patterns from stronger models, interpolates them into a continuum of reasoning styles, and curates optimal training data balancing correctness and compactness to learn when to stop thinking.

Result: Achieves 81.2% reasoning truncation with 5.33× computational acceleration on GSM8K dataset while preserving or improving accuracy across multiple mathematical benchmarks.

Conclusion: DART provides a stable and general paradigm for efficient reasoning, advancing adaptive intelligence in LLMs.

Abstract: Adaptive reasoning is essential for aligning the computational effort of large language models (LLMs) with the intrinsic difficulty of problems. Current chain-of-thought methods boost reasoning ability but indiscriminately generate long explanations, leading to evident inefficiency. However, existing reinforcement learning approaches to adaptive thinking remain unstable and heavily reward-dependent. Here we propose \textbf{DART}, a supervised \textbf{D}ifficulty-\textbf{A}daptive \textbf{R}easoning \textbf{T}runcation framework that adjusts thinking length according to problem difficulty. By distilling concise reasoning patterns from stronger models, interpolating them into a continuum of reasoning styles, and curating optimal training data that balances correctness and compactness, DART learns when to ``stop thinking’’. Across multiple mathematical benchmarks, experimental results demonstrate its remarkable efficiency while preserving or improving accuracy, achieving a significant 81.2% reasoning truncation (DeepSeek-R1-Distill-Qwen-7B on GSM8K dataset) with 5.33$\times$ computational acceleration. DART provides a stable and general paradigm for efficient reasoning, advancing the development of adaptive intelligence in LLMs.

Method: Three-stage framework: (1) Retrieval module narrows candidate pool, (2) Reasoning module uses chain-of-thought generation to find logical inconsistencies, (3) Reranking module refines predictions. Unified through ensemble fusion.

Result: Achieved Mean Average Precision scores of 0.82/0.92/0.93 at levels 1/3/5 on mathematics datasets, consistently outperforming individual modules.

Conclusion: MiRAGE reduces dependence on large-scale language models while providing a scalable and effective solution for educational assessment through retrieval guidance and multi-stage reasoning.

Abstract: Detecting student misconceptions in open-ended responses is a longstanding challenge, demanding semantic precision and logical reasoning. We propose MiRAGE - Misconception Detection with Retrieval-Guided Multi-Stage Reasoning and Ensemble Fusion, a novel framework for automated misconception detection in mathematics. MiRAGE operates in three stages: (1) a Retrieval module narrows a large candidate pool to a semantically relevant subset; (2) a Reasoning module employs chain-of-thought generation to expose logical inconsistencies in student solutions; and (3) a Reranking module refines predictions by aligning them with the reasoning. These components are unified through an ensemble-fusion strategy that enhances robustness and interpretability. On mathematics datasets, MiRAGE achieves Mean Average Precision scores of 0.82/0.92/0.93 at levels 1/3/5, consistently outperforming individual modules. By coupling retrieval guidance with multi-stage reasoning, MiRAGE reduces dependence on large-scale language models while delivering a scalable and effective solution for educational assessment.

Method: Created NeuComBack benchmark dataset for IR-to-assembly compilation, defined neural compilation workflow, and proposed self-evolving prompt optimization that iteratively improves prompts using insights from self-debugging traces.

Result: Functional correctness improved from 44% to 64% on x86_64 and 36% to 58% on aarch64. 87.5% of correctly generated x86_64 programs surpassed clang-O3 performance.

Conclusion: The proposed benchmark and self-evolving prompt optimization method significantly enhance neural compilation capabilities, making LLM-based compiler development more practical and effective.

Abstract: Compilers, while essential, are notoriously complex systems that demand prohibitively expensive human expertise to develop and maintain. The recent advancements in Large Language Models (LLMs) offer a compelling new paradigm: Neural Compilation, which could potentially simplify compiler development for new architectures and facilitate the discovery of innovative optimization techniques. However, several critical obstacles impede its practical adoption. Firstly, a significant lack of dedicated benchmarks and robust evaluation methodologies hinders objective assessment and tracking of progress in the field. Secondly, systematically enhancing the reliability and performance of LLM-generated assembly remains a critical challenge. Addressing these challenges, this paper introduces NeuComBack, a novel benchmark dataset specifically designed for IR-to-assembly compilation. Leveraging this dataset, we first define a foundational Neural Compilation workflow and conduct a comprehensive evaluation of the capabilities of recent frontier LLMs on Neural Compilation, establishing new performance baselines. We further propose a self-evolving prompt optimization method that enables LLMs to iteratively evolve their internal prompt strategies by extracting insights from prior self-debugging traces, thereby enhancing their neural compilation capabilities. Experiments demonstrate that our method significantly improves both the functional correctness and the performance of LLM-generated assembly code. Compared to baseline prompts, the functional correctness rates improved from 44% to 64% on x86_64 and from 36% to 58% on aarch64, respectively. More significantly, among the 16 correctly generated x86_64 programs using our method, 14 (87.5%) surpassed clang-O3 performance.

Method: Uses a reliability subset construction process with multi-layer fusion feature representation from supervised feature learning to select unlabeled test subset. Combines labeled training data and pseudo-labeled test subset in a semi-supervised diagnosis model to learn discriminative features for accurate classification of known faults and detection of unknown samples.

Result: Experimental results on a public maritime benchmark dataset demonstrate the effectiveness and superiority of the proposed SOFD framework in handling open-set fault diagnosis scenarios.

Conclusion: The SOFD framework successfully extends the applicability of deep learning models to real-world open-set fault diagnosis scenarios, enabling accurate classification of known faults while effectively detecting unknown fault types that were not present during training.

Abstract: Recently, fault diagnosis methods for marine machinery systems based on deep learning models have attracted considerable attention in the shipping industry. Most existing studies assume fault classes are consistent and known between the training and test datasets, and these methods perform well under controlled environment. In practice, however, previously unseen or unknown fault types (i.e., out-of-distribution or open-set observations not present during training) can occur, causing such methods to fail and posing a significant challenge to their widespread industrial deployment. To address this challenge, this paper proposes a semi-supervised open-set fault diagnosis (SOFD) framework that enhances and extends the applicability of deep learning models in open-set fault diagnosis scenarios. The framework includes a reliability subset construction process, which uses a multi-layer fusion feature representation extracted by a supervised feature learning model to select an unlabeled test subset. The labeled training set and pseudo-labeled test subset are then fed into a semi-supervised diagnosis model to learn discriminative features for each class, enabling accurate classification of known faults and effective detection of unknown samples. Experimental results on a public maritime benchmark dataset demonstrate the effectiveness and superiority of the proposed SOFD framework.

Method: The authors apply Shapley value attribution to LLM-based decision support systems and analyze different implementation variants to determine when Shapley value principles can be guaranteed despite the stochastic nature of LLMs.

Result: The study demonstrates varying levels of Shapley value principle satisfaction across different implementation variants applied to LLMs, showing how stochasticity affects these guarantees. It also reveals trade-offs between explainable inference speed, agreement with exact Shapley values, and principle attainment.

Conclusion: Shapley value principles cannot always be guaranteed in stochastic LLM-based systems, and there are important trade-offs to consider between explanation quality, computational efficiency, and theoretical guarantees when applying these attribution methods to non-deterministic models.

Abstract: Feature attribution methods help make machine learning-based inference explainable by determining how much one or several features have contributed to a model’s output. A particularly popular attribution method is based on the Shapley value from cooperative game theory, a measure that guarantees the satisfaction of several desirable principles, assuming deterministic inference. We apply the Shapley value to feature attribution in large language model (LLM)-based decision support systems, where inference is, by design, stochastic (non-deterministic). We then demonstrate when we can and cannot guarantee Shapley value principle satisfaction across different implementation variants applied to LLM-based decision support, and analyze how the stochastic nature of LLMs affects these guarantees. We also highlight trade-offs between explainable inference speed, agreement with exact Shapley value attributions, and principle attainment.

Method: Parallel feature extraction from tool images and cutting-force signals, contamination-free cross-modal fusion that disentangles shared and modality-specific components, and recursive refinement pathway to retain residual information and stabilize fusion dynamics.

Result: OmniFuser consistently outperforms state-of-the-art baselines on real-world milling datasets, providing dependable foundation for intelligent industrial maintenance services.

Conclusion: The framework enables reusable maintenance service modules supporting both tool-state classification and multi-step force signal forecasting, demonstrating effective multimodal learning for industrial predictive maintenance applications.

Abstract: Accurate and timely prediction of tool conditions is critical for intelligent manufacturing systems, where unplanned tool failures can lead to quality degradation and production downtime. In modern industrial environments, predictive maintenance is increasingly implemented as an intelligent service that integrates sensing, analysis, and decision support across production processes. To meet the demand for reliable and service-oriented operation, we present OmniFuser, a multimodal learning framework for predictive maintenance of milling tools that leverages both visual and sensor data. It performs parallel feature extraction from high-resolution tool images and cutting-force signals, capturing complementary spatiotemporal patterns across modalities. To effectively integrate heterogeneous features, OmniFuser employs a contamination-free cross-modal fusion mechanism that disentangles shared and modality-specific components, allowing for efficient cross-modal interaction. Furthermore, a recursive refinement pathway functions as an anchor mechanism, consistently retaining residual information to stabilize fusion dynamics. The learned representations can be encapsulated as reusable maintenance service modules, supporting both tool-state classification (e.g., Sharp, Used, Dulled) and multi-step force signal forecasting. Experiments on real-world milling datasets demonstrate that OmniFuser consistently outperforms state-of-the-art baselines, providing a dependable foundation for building intelligent industrial maintenance services.

Method: Integrates propensity score matching with competitive isolation to enable platform-level effect measurement instead of item-level metrics in search systems.

Result: Extensive experiments show significant reductions in interference effects and estimation variance compared to baseline methods. Successful deployment in a large-scale marketplace confirms practical utility.

Conclusion: The framework provides theoretically guaranteed unbiased estimation under mutual exclusion conditions and offers a practical solution for platform-level causal inference in marketplaces with interference effects.

Abstract: Evaluating platform-level interventions in search-based two-sided marketplaces is fundamentally challenged by systemic effects such as spillovers and network interference. While widely used for causal inference, the PSM (Propensity Score Matching) - DID (Difference-in-Differences) framework remains susceptible to selection bias and cross-unit interference from unaccounted spillovers. In this paper, we introduced Competitive Isolation PSM-DID, a novel causal framework that integrates propensity score matching with competitive isolation to enable platform-level effect measurement (e.g., order volume, GMV) instead of item-level metrics in search systems. Our approach provides theoretically guaranteed unbiased estimation under mutual exclusion conditions, with an open dataset released to support reproducible research on marketplace interference (github.com/xxxx). Extensive experiments demonstrate significant reductions in interference effects and estimation variance compared to baseline methods. Successful deployment in a large-scale marketplace confirms the framework’s practical utility for platform-level causal inference.

Method: Comparative analysis across three principles: automaticity (associative vs deliberative processes), suppressed monitoring (leading to confabulation/hallucination), and heightened contextual dependency (where immediate cues override stable knowledge).

Result: Identified deep functional parallels between hypnosis and LLMs, revealing both systems produce coherent but ungrounded outputs requiring external interpretation, and demonstrate functional agency without subjective agency.

Conclusion: Future reliable AI requires hybrid architectures integrating generative fluency with executive monitoring mechanisms, inspired by the self-regulating architecture of the human mind.

Abstract: The cognitive processes of the hypnotized mind and the computational operations of large language models (LLMs) share deep functional parallels. Both systems generate sophisticated, contextually appropriate behavior through automatic pattern-completion mechanisms operating with limited or unreliable executive oversight. This review examines this convergence across three principles: automaticity, in which responses emerge from associative rather than deliberative processes; suppressed monitoring, leading to errors such as confabulation in hypnosis and hallucination in LLMs; and heightened contextual dependency, where immediate cues (for example, the suggestion of a therapist or the prompt of the user) override stable knowledge. These mechanisms reveal an observer-relative meaning gap: both systems produce coherent but ungrounded outputs that require an external interpreter to supply meaning. Hypnosis and LLMs also exemplify functional agency - the capacity for complex, goal-directed, context-sensitive behavior - without subjective agency, the conscious awareness of intention and ownership that defines human action. This distinction clarifies how purposive behavior can emerge without self-reflective consciousness, governed instead by structural and contextual dynamics. Finally, both domains illuminate the phenomenon of scheming: automatic, goal-directed pattern generation that unfolds without reflective awareness. Hypnosis provides an experimental model for understanding how intention can become dissociated from conscious deliberation, offering insights into the hidden motivational dynamics of artificial systems. Recognizing these parallels suggests that the future of reliable AI lies in hybrid architectures that integrate generative fluency with mechanisms of executive monitoring, an approach inspired by the complex, self-regulating architecture of the human mind.

Method: Bi-level optimization with inner loop refining prompts using dense feedback from fixed templates, and outer loop optimizing templates using ASR alignment scores.

Result: Achieved 88.0% ASR on Claude-3.5-Haiku and 100.0% ASR on Claude-4-Sonnet, outperforming existing baselines by substantial margins on AdvBench and JBB-Behaviors.

Conclusion: AMIS enables progressive improvement of both jailbreak prompts and scoring templates, providing more effective and calibrated safety testing for LLMs.

Abstract: Identifying the vulnerabilities of large language models (LLMs) is crucial for improving their safety by addressing inherent weaknesses. Jailbreaks, in which adversaries bypass safeguards with crafted input prompts, play a central role in red-teaming by probing LLMs to elicit unintended or unsafe behaviors. Recent optimization-based jailbreak approaches iteratively refine attack prompts by leveraging LLMs. However, they often rely heavily on either binary attack success rate (ASR) signals, which are sparse, or manually crafted scoring templates, which introduce human bias and uncertainty in the scoring outcomes. To address these limitations, we introduce AMIS (Align to MISalign), a meta-optimization framework that jointly evolves jailbreak prompts and scoring templates through a bi-level structure. In the inner loop, prompts are refined using fine-grained and dense feedback using a fixed scoring template. In the outer loop, the template is optimized using an ASR alignment score, gradually evolving to better reflect true attack outcomes across queries. This co-optimization process yields progressively stronger jailbreak prompts and more calibrated scoring signals. Evaluations on AdvBench and JBB-Behaviors demonstrate that AMIS achieves state-of-the-art performance, including 88.0% ASR on Claude-3.5-Haiku and 100.0% ASR on Claude-4-Sonnet, outperforming existing baselines by substantial margins.

Method: Relaxes partition admissibility constraint to allow cyclic C-DAGs, extends d-separation and causal calculus to this setting.

Result: Develops sound and atomically complete calculus for cluster-level causal reasoning, enabling application in previously intractable scenarios.

Conclusion: Significantly broadens scope of C-DAG framework while maintaining soundness and completeness with respect to do-calculus.

Abstract: Cluster DAGs (C-DAGs) provide an abstraction of causal graphs in which nodes represent clusters of variables, and edges encode both cluster-level causal relationships and dependencies arisen from unobserved confounding. C-DAGs define an equivalence class of acyclic causal graphs that agree on cluster-level relationships, enabling causal reasoning at a higher level of abstraction. However, when the chosen clustering induces cycles in the resulting C-DAG, the partition is deemed inadmissible under conventional C-DAG semantics. In this work, we extend the C-DAG framework to support arbitrary variable clusterings by relaxing the partition admissibility constraint, thereby allowing cyclic C-DAG representations. We extend the notions of d-separation and causal calculus to this setting, significantly broadening the scope of causal reasoning across clusters and enabling the application of C-DAGs in previously intractable scenarios. Our calculus is both sound and atomically complete with respect to the do-calculus: all valid interventional queries at the cluster level can be derived using our rules, each corresponding to a primitive do-calculus step.

Method: Used simplified Overcooked environment with two variations: single task (T) with time production, and dual task (T+N) with additional number comparison. Trained separate DRL agents for each task using LSTM networks.

Result: Dual task agent significantly overproduced time compared to single task agent across four target durations, consistent with human timing research. No clear evidence of dedicated timing mechanisms in LSTM layers.

Conclusion: DRL agents can exhibit human-like timing interference patterns, but further investigation is needed to understand the underlying time-keeping mechanisms in artificial agents.

Abstract: This study explores the interference in temporal processing within a dual-task paradigm from an artificial intelligence (AI) perspective. In this context, the dual-task setup is implemented as a simplified version of the Overcooked environment with two variations, single task (T) and dual task (T+N). Both variations involve an embedded time production task, but the dual task (T+N) additionally involves a concurrent number comparison task. Two deep reinforcement learning (DRL) agents were separately trained for each of these tasks. These agents exhibited emergent behavior consistent with human timing research. Specifically, the dual task (T+N) agent exhibited significant overproduction of time relative to its single task (T) counterpart. This result was consistent across four target durations. Preliminary analysis of neural dynamics in the agents’ LSTM layers did not reveal any clear evidence of a dedicated or intrinsic timer. Hence, further investigation is needed to better understand the underlying time-keeping mechanisms of the agents and to provide insights into the observed behavioral patterns. This study is a small step towards exploring parallels between emergent DRL behavior and behavior observed in biological systems in order to facilitate a better understanding of both.

Method: Interactive agent learns policy via reinforcement learning to strategically seek external visual evidence to support diagnostic reasoning. Uses causal intervention by masking chosen evidence to validate explanation faithfulness.

Result: Significantly improves calibrated accuracy (18% Brier score reduction vs non-interactive baseline). Causal intervention shows performance degradation when evidence is masked (ΔBrier=+0.029), confirming evidence integral to decisions.

Conclusion: Provides practical framework for building AI systems with verifiable and faithful reasoning capabilities through action-based reasoning processes.

Abstract: Explanations for AI models in high-stakes domains like medicine often lack verifiability, which can hinder trust. To address this, we propose an interactive agent that produces explanations through an auditable sequence of actions. The agent learns a policy to strategically seek external visual evidence to support its diagnostic reasoning. This policy is optimized using reinforcement learning, resulting in a model that is both efficient and generalizable. Our experiments show that this action-based reasoning process significantly improves calibrated accuracy, reducing the Brier score by 18% compared to a non-interactive baseline. To validate the faithfulness of the agent’s explanations, we introduce a causal intervention method. By masking the visual evidence the agent chooses to use, we observe a measurable degradation in its performance ($\Delta$Brier=+0.029), confirming that the evidence is integral to its decision-making process. Our work provides a practical framework for building AI systems with verifiable and faithful reasoning capabilities.

Method: Proposes a Double Information Bottleneck (DIB) strategy implemented within low-rank Renyi’s entropy functional framework. DIB has two modules: 1) learning compressed unimodal representations by maximizing task-relevant information and discarding superfluous information, and 2) attention bottleneck fusion mechanism to ensure discriminative multimodal representation.

Result: Achieves 47.4% accuracy under Acc-7 metric on CMU-MOSI and 81.63% F1-score on CH-SIMS, outperforming second-best baseline by 1.19%. Shows strong robustness with only 0.36% and 0.29% performance degradation under noise on CMU-MOSI and CMU-MOSEI respectively.

Conclusion: The DIB strategy effectively filters out noisy information from unimodal data while capturing inter-modal complementarity, providing a powerful and unified compact multimodal representation that is robust against diverse noise sources.

Abstract: Multimodal sentiment analysis has received significant attention across diverse research domains. Despite advancements in algorithm design, existing approaches suffer from two critical limitations: insufficient learning of noise-contaminated unimodal data, leading to corrupted cross-modal interactions, and inadequate fusion of multimodal representations, resulting in discarding discriminative unimodal information while retaining multimodal redundant information. To address these challenges, this paper proposes a Double Information Bottleneck (DIB) strategy to obtain a powerful, unified compact multimodal representation. Implemented within the framework of low-rank Renyi’s entropy functional, DIB offers enhanced robustness against diverse noise sources and computational tractability for high-dimensional data, as compared to the conventional Shannon entropy-based methods. The DIB comprises two key modules: 1) learning a sufficient and compressed representation of individual unimodal data by maximizing the task-relevant information and discarding the superfluous information, and 2) ensuring the discriminative ability of multimodal representation through a novel attention bottleneck fusion mechanism. Consequently, DIB yields a multimodal representation that effectively filters out noisy information from unimodal data while capturing inter-modal complementarity. Extensive experiments on CMU-MOSI, CMU-MOSEI, CH-SIMS, and MVSA-Single validate the effectiveness of our method. The model achieves 47.4% accuracy under the Acc-7 metric on CMU-MOSI and 81.63% F1-score on CH-SIMS, outperforming the second-best baseline by 1.19%. Under noise, it shows only 0.36% and 0.29% performance degradation on CMU-MOSI and CMU-MOSEI respectively.

Method: Developed an eight-agent hierarchical framework with centralized control that decomposes pre-consultation into four primary tasks (Triage, History of Present Illness, Past History, Chief Complaint generation) and 13 domain-specific subtasks, using autonomous task orchestration and agent-driven scheduling.

Result: Achieved 87.0% accuracy for primary department triage and 80.5% for secondary classification, with 98.2% task completion rate. Clinical quality scores averaged 4.56-4.69/5 from physician evaluations, and consultations completed within 12.7-16.9 rounds. High performance maintained across multiple foundation models (GPT-OSS 20B, Qwen3-8B, Phi4-14B).

Conclusion: The model-agnostic architecture demonstrates potential for autonomous AI systems to enhance pre-consultation efficiency and quality in clinical settings while preserving data privacy through local deployment.

Abstract: Global healthcare systems face critical challenges from increasing patient volumes and limited consultation times, with primary care visits averaging under 5 minutes in many countries. While pre-consultation processes encompassing triage and structured history-taking offer potential solutions, they remain limited by passive interaction paradigms and context management challenges in existing AI systems. This study introduces a hierarchical multi-agent framework that transforms passive medical AI systems into proactive inquiry agents through autonomous task orchestration. We developed an eight-agent architecture with centralized control mechanisms that decomposes pre-consultation into four primary tasks: Triage ($T_1$), History of Present Illness collection ($T_2$), Past History collection ($T_3$), and Chief Complaint generation ($T_4$), with $T_1$–$T_3$ further divided into 13 domain-specific subtasks. Evaluated on 1,372 validated electronic health records from a Chinese medical platform across multiple foundation models (GPT-OSS 20B, Qwen3-8B, Phi4-14B), the framework achieved 87.0% accuracy for primary department triage and 80.5% for secondary department classification, with task completion rates reaching 98.2% using agent-driven scheduling versus 93.1% with sequential processing. Clinical quality scores from 18 physicians averaged 4.56 for Chief Complaints, 4.48 for History of Present Illness, and 4.69 for Past History on a 5-point scale, with consultations completed within 12.7 rounds for $T_2$ and 16.9 rounds for $T_3$. The model-agnostic architecture maintained high performance across different foundation models while preserving data privacy through local deployment, demonstrating the potential for autonomous AI systems to enhance pre-consultation efficiency and quality in clinical settings.

Method: Created TPS-Bench with 200 compounding tasks based on hundreds of MCP tools, evaluating both task completion rate and efficiency across popular LLMs, and conducted initial RL training study.

Result: GLM-4.5 achieved 64.72% completion rate with long execution time, GPT-4o achieved 45.08% with parallel calls, and RL training on Qwen3-1.7B reduced execution time by 14% with 6% completion rate gain.

Conclusion: LLM agents can perform reasonable tool planning but differ significantly in scheduling efficiency, and reinforcement learning shows promise for improving scheduling without compromising performance.

Abstract: Large language model (LLM) agents have exhibited strong problem-solving competence across domains like research and coding. Yet, it remains underexplored whether LLM agents can tackle compounding real-world problems that require a diverse set of tools to complete. Given a broad, heterogeneous tool repository, LLM agents must not only select appropriate tools based on task planning analysis but also strategically schedule the execution order to ensure efficiency. This paper introduces TPS-Bench to benchmark the ability of LLM agents in solving such problems that demand Tool Planning and Scheduling. TPS-Bench collects 200 compounding tasks of two difficulty levels, based on a tool repository containing hundreds of model context protocol (MCP) tools. In particular, each task is composed of multiple subtasks, such as web search, map navigation, calendar checking, etc., and each subtask can be completed by a basic tool. Our evaluation emphasizes both task completion rate and efficiency. The empirical studies on popular closed-source and open-source LLMs indicate that most models can perform reasonable tool planning, but differ in scheduling. For example, GLM-4.5 achieves an outperforming task completion rate of 64.72% with extensive sequential tool calls, hence suffering from significantly long execution time. By contrast, GPT-4o prioritizes parallel tool calls but achieves only a 45.08% completion rate. Considering reinforcement learning (RL) can be a viable way to improve the scheduling efficiency without compromising performance, we perform an initial study on Qwen3-1.7B and witness a 14% reduction in execution time alongside a 6% gain in task completion rate based on rarely 100 RL training samples. Our code is available https://github.com/hanwenxu1/mcp-agent.

Method: Ensemble of LLMs to annotate corporate tweets for SDG alignment, and vision-language models with semantic clustering for visual pattern analysis of sustainability communication.

Result: Revealed sectoral differences in SDG engagement, temporal trends, and associations between corporate messaging, ESG risks, and consumer engagement.

Conclusion: The automatic label generation and semantic visual clustering methods provide a flexible, scalable framework for large-scale social media analysis applicable to other domains.

Abstract: In this work, we introduce a multimodal analysis pipeline that leverages large foundation models in vision and language to analyze corporate social media content, with a focus on sustainability-related communication. Addressing the challenges of evolving, multimodal, and often ambiguous corporate messaging on platforms such as X (formerly Twitter), we employ an ensemble of large language models (LLMs) to annotate a large corpus of corporate tweets on their topical alignment with the 17 Sustainable Development Goals (SDGs). This approach avoids the need for costly, task-specific annotations and explores the potential of such models as ad-hoc annotators for social media data that can efficiently capture both explicit and implicit references to sustainability themes in a scalable manner. Complementing this textual analysis, we utilize vision-language models (VLMs), within a visual understanding framework that uses semantic clusters to uncover patterns in visual sustainability communication. This integrated approach reveals sectoral differences in SDG engagement, temporal trends, and associations between corporate messaging, environmental, social, governance (ESG) risks, and consumer engagement. Our methods-automatic label generation and semantic visual clustering-are broadly applicable to other domains and offer a flexible framework for large-scale social media analysis.

Method: Proposes ExplicitLM architecture with million-scale external memory bank storing human-readable knowledge as token sequences. Uses differentiable two-stage retrieval with product key decomposition for coarse filtering and Gumbel-Softmax for fine matching. Partitions knowledge into frozen explicit facts (20%) and learnable implicit patterns (80%) with Exponential Moving Average updates.

Result: Achieves up to 43.67% improvement on knowledge-intensive tasks versus standard Transformers, with 3.62× gains in low-data regimes (10k samples). Correct predictions achieve 49% higher memory retrieval hit rates. Strong correlations between memory retrieval and performance.

Conclusion: ExplicitLM demonstrates that interpretable, updatable models can maintain competitive performance while providing unprecedented knowledge transparency, unlike RAG systems with frozen retrieval.

Abstract: Large language models suffer from knowledge staleness and lack of interpretability due to implicit knowledge storage across entangled network parameters, preventing targeted updates and reasoning transparency. We propose ExplicitLM, a novel architecture featuring a million-scale external memory bank storing human-readable knowledge as token sequences, enabling direct inspection and modification. We design a differentiable two-stage retrieval mechanism with efficient coarse-grained filtering via product key decomposition (reducing complexity from $\mathcal{O}(N \cdot |I|)$ to $\mathcal{O}(\sqrt{N} \cdot |I|)$) and fine-grained Gumbel-Softmax matching for end-to-end training. Inspired by dual-system cognitive theory, we partition knowledge into frozen explicit facts (20%) and learnable implicit patterns (80%), maintained through Exponential Moving Average updates for stability. ExplicitLM achieves up to 43.67% improvement on knowledge-intensive tasks versus standard Transformers, with 3.62$\times$ gains in low-data regimes (10k samples). Analysis shows strong correlations between memory retrieval and performance, with correct predictions achieving 49% higher hit rates. Unlike RAG systems with frozen retrieval, our jointly optimized architecture demonstrates that interpretable, updatable models can maintain competitive performance while providing unprecedented knowledge transparency.

Method: The model extends IVGAE framework with Trade-Aware Momentum Aggregator (TAMA) to capture temporal evolution, models short-term fluctuations and long-term dependencies, and uses Bayesian optimization for automatic hyperparameter tuning.

Result: Extensive experiments on five crop-specific datasets show IVGAE-TAMA-BO substantially outperforms static IVGAE and other dynamic baselines, with Bayesian optimization further boosting performance.

Conclusion: The proposed framework is a robust and scalable solution for structural prediction in global trade networks, with strong potential for food security monitoring and policy decision support.

Abstract: Global food trade plays a crucial role in ensuring food security and maintaining supply chain stability. However, its network structure evolves dynamically under the influence of geopolitical, economic, and environmental factors, making it challenging to model and predict future trade links. Effectively capturing temporal patterns in food trade networks is therefore essential for improving the accuracy and robustness of link prediction. This study introduces IVGAE-TAMA-BO, a novel dynamic graph neural network designed to model evolving trade structures and predict future links in global food trade networks. To the best of our knowledge, this is the first work to apply dynamic graph neural networks to this domain, significantly enhancing predictive performance. Building upon the original IVGAE framework, the proposed model incorporates a Trade-Aware Momentum Aggregator (TAMA) to capture the temporal evolution of trade networks, jointly modeling short-term fluctuations and long-term structural dependencies. A momentum-based structural memory mechanism further improves predictive stability and performance. In addition, Bayesian optimization is used to automatically tune key hyperparameters, enhancing generalization across diverse trade scenarios. Extensive experiments on five crop-specific datasets demonstrate that IVGAE-TAMA substantially outperforms the static IVGAE and other dynamic baselines by effectively modeling temporal dependencies, while Bayesian optimization further boosts performance in IVGAE-TAMA-BO. These results highlight the proposed framework as a robust and scalable solution for structural prediction in global trade networks, with strong potential for applications in food security monitoring and policy decision support.

Method: Retrieval-prioritized hybrid system: uses RAG when trusted legal repository has relevant evidence, otherwise employs multiple LLMs to generate candidates scored by a specialized selector. Includes human review and knowledge repository updates.

Result: Significantly outperforms single-model baseline and vanilla RAG on F1, ROUGE-L, and LLM-as-a-Judge metrics. Reduces hallucination while improving answer quality and legal compliance.

Conclusion: The system advances practical deployment of AI in judicial scenarios through reduced hallucination, improved quality, and dynamic knowledge evolution with provenance tracking.

Abstract: As artificial intelligence permeates judicial forensics, ensuring the veracity and traceability of legal question answering (QA) has become critical. Conventional large language models (LLMs) are prone to hallucination, risking misleading guidance in legal consultation, while static knowledge bases struggle to keep pace with frequently updated statutes and case law. We present a hybrid legal QA agent tailored for judicial settings that integrates retrieval-augmented generation (RAG) with multi-model ensembling to deliver reliable, auditable, and continuously updatable counsel. The system prioritizes retrieval over generation: when a trusted legal repository yields relevant evidence, answers are produced via RAG; otherwise, multiple LLMs generate candidates that are scored by a specialized selector, with the top-ranked answer returned. High-quality outputs then undergo human review before being written back to the repository, enabling dynamic knowledge evolution and provenance tracking. Experiments on the Law_QA dataset show that our hybrid approach significantly outperforms both a single-model baseline and a vanilla RAG pipeline on F1, ROUGE-L, and an LLM-as-a-Judge metric. Ablations confirm the complementary contributions of retrieval prioritization, model ensembling, and the human-in-the-loop update mechanism. The proposed system demonstrably reduces hallucination while improving answer quality and legal compliance, advancing the practical landing of media forensics technologies in judicial scenarios.

Method: Propose two frameworks: Simia-SFT for synthesizing SFT data by amplifying small seed sets into diverse trajectories, and Simia-RL for RL training using LLM-simulated feedback without real environment implementations.

Result: Fine-tuning open models yields consistent improvements across benchmarks, surpassing GPT-4o and approaching o4-mini on τ²-Bench.

Conclusion: Simia-SFT and Simia-RL enable scalable agent training without environment engineering by replacing heavy implementations with flexible LLM-based simulation.

Abstract: LLM agents excel in compact environments requiring deep reasoning but remain brittle when operating in broader, more complex contexts that demand robustness across diverse tools and schemas. Building bespoke environments for training is heavy, brittle, and limits progress. In this paper, we demonstrate that LLMs can simulate realistic environment feedback without access to actual testbed data or APIs. Inspired by this capability, we propose two frameworks: Simia-SFT, a pipeline that synthesizes SFT data by amplifying small seed sets into diverse trajectories in an environment-agnostic manner, and Simia-RL, a framework that enables RL training without real environment implementations through LLM-simulated feedback. Fine-tuning open models yields consistent improvements across multiple benchmarks, surpassing GPT-4o and approaching o4-mini on $\tau^2$-Bench. Together, Simia-SFT and Simia-RL enable scalable agent training without environment engineering, replacing heavy and brittle implementations with flexible LLM-based simulation.

Method: Proposes a deferral collaboration approach that exploits distinct human and AI strengths by strategically routing instances between them, with robustness to misspecifications in both human behavior and reward models.

Result: Significantly outperforms independent human and algorithmic decision-making using both synthetic and real human responses, with substantial performance improvements achievable by routing only a small fraction of instances to humans.

Conclusion: The method enables efficient and effective human-AI collaboration in complex management settings by leveraging complementary strengths while requiring minimal human intervention.

Abstract: This paper tackles the critical challenge of human-AI complementarity in decision-making. Departing from the traditional focus on algorithmic performance in favor of performance of the human-AI team, and moving past the framing of collaboration as classification to focus on decision-making tasks, we introduce a novel approach to policy learning. Specifically, we develop a robust solution for human-AI collaboration when outcomes are only observed under assigned actions. We propose a deferral collaboration approach that maximizes decision rewards by exploiting the distinct strengths of humans and AI, strategically allocating instances among them. Critically, our method is robust to misspecifications in both the human behavior and reward models. Leveraging the insight that performance gains stem from divergent human and AI behavioral patterns, we demonstrate, using synthetic and real human responses, that our proposed method significantly outperforms independent human and algorithmic decision-making. Moreover, we show that substantial performance improvements are achievable by routing only a small fraction of instances to human decision-makers, highlighting the potential for efficient and effective human-AI collaboration in complex management settings.

Method: MEMENTO uses memory to collect online data across repeated attempts and dynamically adjusts action distributions based on previous decision outcomes during inference.

Result: MEMENTO outperforms tree-search and policy-gradient fine-tuning on Traveling Salesman and Capacitated Vehicle Routing problems, achieving state-of-the-art performance on 11 out of 12 evaluated tasks with good scalability and data-efficiency.

Conclusion: Memory-based approaches like MEMENTO can effectively improve neural solvers for routing problems by enabling dynamic adaptation and better utilization of computational budgets during inference.

Abstract: Routing Problems are central to many real-world applications, yet remain challenging due to their (NP-)hard nature. Amongst existing approaches, heuristics often offer the best trade-off between quality and scalability, making them suitable for industrial use. While Reinforcement Learning (RL) offers a flexible framework for designing heuristics, its adoption over handcrafted heuristics remains incomplete. Existing learned methods still lack the ability to adapt to specific instances and fully leverage the available computational budget. Current best methods either rely on a collection of pre-trained policies, or on RL fine-tuning; hence failing to fully utilize newly available information within the constraints of the budget. In response, we present MEMENTO, an approach that leverages memory to improve the search of neural solvers at inference. MEMENTO leverages online data collected across repeated attempts to dynamically adjust the action distribution based on the outcome of previous decisions. We validate its effectiveness on the Traveling Salesman and Capacitated Vehicle Routing problems, demonstrating its superiority over tree-search and policy-gradient fine-tuning; and showing that it can be zero-shot combined with diversity-based solvers. We successfully train all RL auto-regressive solvers on large instances, and verify MEMENTO’s scalability and data-efficiency: pushing the state-of-the-art on 11 out of 12 evaluated tasks.

Method: The paper reviews existing approaches, proposes a taxonomy for multi-step reasoning, and analyzes methods including Chain-of-thought prompting, reinforcement learning finetuning, external optimization loops, and self-reflection techniques.

Result: Multi-step reasoning approaches have expanded beyond math word problems to successfully solve challenges in logic, combinatorial games, and robotics, sometimes using code generation with external tools.

Conclusion: The field has progressed significantly, with various reinforcement learning and self-reflection methods emerging, and the paper proposes a research agenda for future development in multi-step reasoning with LLMs.

Abstract: Large language models (LLMs) with billions of parameters exhibit in-context learning abilities, enabling few-shot learning on tasks that the model was not specifically trained for. Traditional models achieve breakthrough performance on language tasks, but do not perform well on basic reasoning benchmarks. However, a new in-context learning approach, Chain-of-thought, has demonstrated strong multi-step reasoning abilities on these benchmarks. The research on LLM reasoning abilities started with the question whether LLMs can solve grade school math word problems, and has expanded to other tasks in the past few years. This article reviews the field of multi-step reasoning with LLMs. We propose a taxonomy that identifies different ways to generate, evaluate, and control multi-step reasoning. We provide an in-depth coverage of core approaches and open problems, and we propose a research agenda for the near future. We find that multi-step reasoning approaches have progressed beyond math word problems, and can now successfully solve challenges in logic, combinatorial games, and robotics, sometimes by first generating code that is then executed by external tools. Many studies in multi-step methods use reinforcement learning for finetuning, external optimization loops, in-context reinforcement learning, and self-reflection.

Method: SCoBots decompose RL tasks into intermediate interpretable representations using object-centric relational concepts. The approach combines object-centric representation learning from raw states, object-centric RL, and policy distillation via rule extraction in a neurosymbolic framework.

Result: First implementation of an end-to-end trained SCoBot evaluated on Atari games. Results show the framework’s potential for creating interpretable and performing RL systems.

Conclusion: The work demonstrates a viable approach for end-to-end interpretable RL agents and paves the way for future research in neurosymbolic RL systems.

Abstract: Deep reinforcement learning (RL) agents rely on shortcut learning, preventing them from generalizing to slightly different environments. To address this problem, symbolic method, that use object-centric states, have been developed. However, comparing these methods to deep agents is not fair, as these last operate from raw pixel-based states. In this work, we instantiate the symbolic SCoBots framework. SCoBots decompose RL tasks into intermediate, interpretable representations, culminating in action decisions based on a comprehensible set of object-centric relational concepts. This architecture aids in demystifying agent decisions. By explicitly learning to extract object-centric representations from raw states, object-centric RL, and policy distillation via rule extraction, this work places itself within the neurosymbolic AI paradigm, blending the strengths of neural networks with symbolic AI. We present the first implementation of an end-to-end trained SCoBot, separately evaluate of its components, on different Atari games. The results demonstrate the framework’s potential to create interpretable and performing RL systems, and pave the way for future research directions in obtaining end-to-end interpretable RL agents.

Method: Pre-trained neural network on CES 2020, froze early layers to preserve structure, fine-tuned top layers on ANES 2020, then generated silicon responses for CES 2022 and held-out ANES 2020 data.

Result: Achieved up to 93% accuracy in generating silicon responses, outperforming LLMs especially on sensitive measures like racial resentment.

Conclusion: STL provides empirically grounded, cost-effective synthetic survey responses that can help mitigate challenges in social science and polling industry.

Abstract: As researchers increasingly turn to large language models (LLMs) to generate synthetic survey data, less attention has been paid to alternative AI paradigms given environmental costs of LLMs. This paper introduces Survey Transfer Learning (STL), which develops transfer learning paradigms from computer science for survey research to recycle existing survey data and generate empirically grounded silicon responses. Inspired by political behavior theory, STL leverages shared demographic variables with high predictive power in a polarized American context to transfer knowledge across surveys. Using a neural network pre-trained on the Cooperative Election Study (CES) 2020, freezing early layers to preserve learned structure, and fine-tuning top layers on the American National Election Studies (ANES) 2020, STL generates silicon responses CES 2022 and in held-out ANES 2020 data with accuracy rates of up to 93 percent. Results show that STL outperforms LLMs, especially on sensitive measures such as racial resentment. While LLMs silicon samples are costly and opaque, STL generates empirically grounded silicon responses with high individual-level accuracy, potentially helping to mitigate key challenges in social science and the polling industry.

Method: Constructed a fact-checking dataset from Reddit discussions, categorizing retrieved evidence into supporting, misleading, and unrelated types to create realistic test scenarios.

Result: All tested LLM-powered RAG systems performed worse than their zero-shot baselines when exposed to misleading retrievals, while human annotators consistently performed better, highlighting LLM susceptibility to noisy environments.

Conclusion: RAGuard is the first systematic benchmark for assessing RAG robustness against misleading evidence and should drive research toward more reliable real-world RAG systems.

Abstract: Retrieval-augmented generation (RAG) has shown impressive capabilities in mitigating hallucinations in large language models (LLMs). However, LLMs struggle to maintain consistent reasoning when exposed to misleading or conflicting evidence, especially in real-world domains such as politics, where information is polarized or selectively framed. Mainstream RAG benchmarks evaluate models under clean retrieval settings, where systems generate answers from gold-standard documents, or under synthetically perturbed settings, where documents are artificially injected with noise. These assumptions fail to reflect real-world conditions, often leading to an overestimation of RAG system performance. To address this gap, we introduce RAGuard, the first benchmark to evaluate the robustness of RAG systems against misleading retrievals. Unlike prior benchmarks that rely on synthetic noise, our fact-checking dataset captures naturally occurring misinformation by constructing its retrieval corpus from Reddit discussions. It categorizes retrieved evidence into three types: supporting, misleading, and unrelated, providing a realistic and challenging testbed for assessing how well RAG systems navigate different types of evidence. Our experiments reveal that, when exposed to potentially misleading retrievals, all tested LLM-powered RAG systems perform worse than their zero-shot baselines (i.e., no retrieval at all), while human annotators consistently perform better, highlighting LLMs’ susceptibility to noisy environments. To our knowledge, RAGuard is the first benchmark to systematically assess the robustness of the RAG against misleading evidence. We expect this benchmark to drive future research toward improving RAG systems beyond idealized datasets, making them more reliable for real-world applications. The dataset is available at https://huggingface.co/datasets/UCSC-IRKM/RAGuard.

Method: Introduced a behavioral game theory evaluation framework to disentangle reasoning capability from contextual effects. Tested 22 state-of-the-art LLMs with various prompting strategies and analyzed demographic feature impacts.

Result: GPT-o3-mini, GPT-o1, and DeepSeek-R1 dominated most games. Model scale alone doesn’t determine performance. CoT prompting only helps models at certain levels. Demographic biases were observed - GPT-4o performs better with female traits, Gemma favors heterosexual identities.

Conclusion: Need ethical standards and contextual alignment to balance improved reasoning with fairness, as LLMs show inherent biases in strategic decision-making that go beyond pure reasoning capability.

Abstract: Strategic decision-making involves interactive reasoning where agents adapt their choices in response to others, yet existing evaluations of large language models (LLMs) often emphasize Nash Equilibrium (NE) approximation, overlooking the mechanisms driving their strategic choices. To bridge this gap, we introduce an evaluation framework grounded in behavioral game theory, disentangling reasoning capability from contextual effects. Testing 22 state-of-the-art LLMs, we find that GPT-o3-mini, GPT-o1, and DeepSeek-R1 dominate most games yet also demonstrate that the model scale alone does not determine performance. In terms of prompting enhancement, Chain-of-Thought (CoT) prompting is not universally effective, as it increases strategic reasoning only for models at certain levels while providing limited gains elsewhere. Additionally, we investigate the impact of encoded demographic features on the models, observing that certain assignments impact the decision-making pattern. For instance, GPT-4o shows stronger strategic reasoning with female traits than males, while Gemma assigns higher reasoning levels to heterosexual identities compared to other sexual orientations, indicating inherent biases. These findings underscore the need for ethical standards and contextual alignment to balance improved reasoning with fairness.

Method: Refined suspension linkage-level modeling plus Damper-B-PINN framework that uses suspension dynamics as physical guidance and Bayesian inference to handle noise/uncertainty, with damper-characteristic physics conditioning module.

Result: Outperforms existing methods across various test conditions, especially extreme ones, using both CarSim simulation and real-world Formula Student race car data.

Conclusion: Damper-B-PINN enhances accuracy and robustness of dynamic wheel load estimation, improving reliability and safety of ADAS applications.

Abstract: Accurate state estimation is fundamental to intelligent vehicles. Wheel load, one of the most important chassis states, serves as an essential input for advanced driver assistance systems (ADAS) and exerts a direct influence on vehicle stability and safety. However, wheel load estimation remains challenging due to the complexity of chassis modeling and the susceptibility of nonlinear systems to noise. To address these issues, this paper first introduces a refined suspension linkage-level modeling approach that constructs a nonlinear instantaneous dynamic model by explicitly considering the complex geometric structure of the suspension. Building upon this, we propose a damper characteristics-based Bayesian physics-informed neural network (Damper-B-PINN) framework to estimate dynamic wheel load, which leverages the suspension dynamics as physical guidance of PINN while employing Bayesian inference to mitigate the effects of system noise and uncertainty. Moreover, a damper-characteristic physics conditioning (DPC) module is designed for embedding physical prior. The proposed Damper-B-PINN is evaluated using both high-fidelity simulation datasets generated by CarSim software and real-world datasets collected from a Formula Student race car. Experimental results demonstrate that our Damper-B-PINN consistently outperforms existing methods across various test conditions, particularly extreme ones. These findings highlight the potential of the proposed Damper-B-PINN framework to enhance the accuracy and robustness of dynamic wheel load estimation, thereby improving the reliability and safety of ADAS applications.

Method: Proposes a neuro-symbolic framework that: 1) learns symbolic representations from task demonstrations, 2) decomposes tasks into subtasks, 3) uses symbolic planning to generate abstract plans, and 4) learns neural skills to refine abstract plans into executable commands.

Result: Experimental results in three simulated robotic environments show increased data efficiency, improved generalization capabilities, and enhanced interpretability compared to baseline methods.

Conclusion: The neuro-symbolic approach successfully bridges the gap between learning individual skills and sequencing them for long, multi-step tasks, demonstrating practical advantages in robotic imitation learning.

Abstract: Imitation learning is a popular method for teaching robots new behaviors. However, most existing methods focus on teaching short, isolated skills rather than long, multi-step tasks. To bridge this gap, imitation learning algorithms must not only learn individual skills but also an abstract understanding of how to sequence these skills to perform extended tasks effectively. This paper addresses this challenge by proposing a neuro-symbolic imitation learning framework. Using task demonstrations, the system first learns a symbolic representation that abstracts the low-level state-action space. The learned representation decomposes a task into easier subtasks and allows the system to leverage symbolic planning to generate abstract plans. Subsequently, the system utilizes this task decomposition to learn a set of neural skills capable of refining abstract plans into actionable robot commands. Experimental results in three simulated robotic environments demonstrate that, compared to baselines, our neuro-symbolic approach increases data efficiency, improves generalization capabilities, and facilitates interpretability.

Method: Proposed RoR-Bench, a novel multi-modal benchmark for detecting LLM recitation behavior by testing simple reasoning problems with subtly shifted conditions, followed by empirical analysis.

Result: Existing cutting-edge LLMs exhibit extremely severe recitation behavior - changing one phrase in conditions causes 60% performance loss on elementary school-level arithmetic and reasoning problems.

Conclusion: Findings serve as a wake-up call to re-evaluate the true intelligence level of cutting-edge LLMs, suggesting their reasoning may be more about recitation than genuine understanding.

Abstract: The rapid escalation from elementary school-level to frontier problems of the difficulty for LLM benchmarks in recent years have weaved a miracle for researchers that we are only inches away from surpassing human intelligence. However, is the LLMs’ remarkable reasoning ability indeed comes from true intelligence by human standards, or are they simply reciting solutions witnessed during training at an Internet level? To study this problem, we propose RoR-Bench, a novel, multi-modal benchmark for detecting LLM’s recitation behavior when asked simple reasoning problems but with conditions subtly shifted, and conduct empirical analysis on our benchmark. Surprisingly, we found existing cutting-edge LLMs unanimously exhibits extremely severe recitation behavior; by changing one phrase in the condition, top models such as OpenAI-o1 and DeepSeek-R1 can suffer 60 percent performance loss on elementary school-level arithmetic and reasoning problems. Such findings are a wake-up call to the LLM community that compels us to re-evaluate the true intelligence level of cutting-edge LLMs.

Method: Extract “vectors of variable variations” from LLMs’ internal states and manipulate these vectors during inference to alter how social variables relate to decision-making in a Dictator Game setting.

Result: Manipulating these internal vectors can substantially change how social variables influence the model’s decision-making process.

Conclusion: This approach provides a principled way to study and regulate social concept encoding in transformer models, with implications for AI alignment, debiasing, and designing AI agents for social simulations.

Abstract: Large language models (LLMs) increasingly serve as human-like decision-making agents in social science and applied settings. These LLM-agents are typically assigned human-like characters and placed in real-life contexts. However, how these characters and contexts shape an LLM’s behavior remains underexplored. This study proposes and tests methods for probing, quantifying, and modifying an LLM’s internal representations in a Dictator Game – a classic behavioral experiment on fairness and prosocial behavior. We extract “vectors of variable variations” (e.g., “male” to “female”) from the LLM’s internal state. Manipulating these vectors during the model’s inference can substantially alter how those variables relate to the model’s decision-making. This approach offers a principled way to study and regulate how social concepts can be encoded and engineered within transformer-based models, with implications for alignment, debiasing, and designing AI agents for social simulations in both academic and commercial applications, strengthening sociological theory and measurement.

Method: Uses algorithmic information theory and Kolmogorov complexity to formalize explainability as approximation of complex models by simpler ones, quantifying both approximation error and explanation complexity.

Result: Established: (1) complexity gap theorem showing simpler explanations must differ from original models; (2) bounds on explanation complexity growing exponentially with input dimension but polynomially with error tolerance; (3) characterization of local vs global explainability gap showing local explanations can be significantly simpler.

Conclusion: No governance framework can simultaneously pursue unrestricted AI capabilities, human-interpretable explanations, and negligible error, highlighting important considerations for designing, evaluating, and overseeing explainable AI systems.

Abstract: This paper establishes a theoretical foundation for understanding the fundamental limits of AI explainability through algorithmic information theory. We formalize explainability as the approximation of complex models by simpler ones, quantifying both approximation error and explanation complexity using Kolmogorov complexity. Our key theoretical contributions include: (1) a complexity gap theorem proving that any explanation significantly simpler than the original model must differ from it on some inputs; (2) precise bounds showing that explanation complexity grows exponentially with input dimension but polynomially with error tolerance for Lipschitz functions; and (3) a characterization of the gap between local and global explainability, demonstrating that local explanations can be significantly simpler while maintaining accuracy in relevant regions. We further establish a regulatory impossibility theorem proving that no governance framework can simultaneously pursue unrestricted AI capabilities, human-interpretable explanations, and negligible error. These results highlight considerations likely to be relevant to the design, evaluation, and oversight of explainable AI systems.

Method: AIOS 1.0 implements a Model Context Protocol (MCP) server architecture to abstract computer states and actions, transforming computers into contextual environments that language models can natively comprehend. This decouples interface complexity from decision complexity.

Result: LiteCUA, a lightweight computer-use agent built on AIOS 1.0, achieves a 14.66% success rate on the OSWorld benchmark, outperforming several specialized agent frameworks despite its simple architecture.

Conclusion: Contextualizing computer environments for language models represents a promising direction for developing more capable computer-use agents and advancing toward AI that can interact effectively with digital systems.

Abstract: We present AIOS 1.0, a novel platform designed to advance computer-use agent (CUA) capabilities through environmental contextualization. While existing approaches primarily focus on building more powerful agent frameworks or enhancing agent models, we identify a fundamental limitation: the semantic disconnect between how language models understand the world and how computer interfaces are structured. AIOS 1.0 addresses this challenge by transforming computers into contextual environments that language models can natively comprehend, implementing a Model Context Protocol (MCP) server architecture to abstract computer states and actions. This approach effectively decouples interface complexity from decision complexity, enabling agents to reason more effectively about computing environments. To demonstrate our platform’s effectiveness, we introduce LiteCUA, a lightweight computer-use agent built on AIOS 1.0 that achieves a 14.66% success rate on the OSWorld benchmark, outperforming several specialized agent frameworks despite its simple architecture. Our results suggest that contextualizing computer environments for language models represents a promising direction for developing more capable computer-use agents and advancing toward AI that can interact with digital systems.

Method: Uses Composition-of-Principles framework where human users provide red-teaming principles to an AI agent, which automatically orchestrates strategies and generates jailbreak prompts in a unified, extensible workflow.

Result: CoP framework revealed unprecedented safety risks by discovering novel jailbreak prompts and improved the best-known single-turn attack success rate by up to 19.0 times against leading LLMs.

Conclusion: The agentic CoP framework provides an effective, automated approach to scale red-teaming processes and uncover critical safety vulnerabilities in LLMs that traditional methods miss.

Abstract: Recent advances in Large Language Models (LLMs) have spurred transformative applications in various domains, ranging from open-source to proprietary LLMs. However, jailbreak attacks, which aim to break safety alignment and user compliance by tricking the target LLMs into answering harmful and risky responses, are becoming an urgent concern. The practice of red-teaming for LLMs is to proactively explore potential risks and error-prone instances before the release of frontier AI technology. This paper proposes an agentic workflow to automate and scale the red-teaming process of LLMs through the Composition-of-Principles (CoP) framework, where human users provide a set of red-teaming principles as instructions to an AI agent to automatically orchestrate effective red-teaming strategies and generate jailbreak prompts. Distinct from existing red-teaming methods, our CoP framework provides a unified and extensible framework to encompass and orchestrate human-provided red-teaming principles to enable the automated discovery of new red-teaming strategies. When tested against leading LLMs, CoP reveals unprecedented safety risks by finding novel jailbreak prompts and improving the best-known single-turn attack success rate by up to 19.0 times.

Method: Proposed an informal but verifiable task formulation with two subtasks (bound estimation and relation prediction), created IneqMath dataset with expert-curated inequalities, and developed an LLM-as-judge evaluation framework with step-wise scrutiny.

Result: Evaluation of 29 leading LLMs showed less than 10% overall accuracy under step-wise scrutiny, with up to 65.5% drop from final answer equivalence accuracy. Scaling model size and test-time computation yielded limited gains.

Conclusion: There’s a critical gap between finding answers and constructing rigorous proofs for current LLMs. Promising directions include theorem-guided reasoning and self-refinement.

Abstract: Inequality proving, crucial across diverse scientific and mathematical fields, tests advanced reasoning skills such as discovering tight bounds and strategic theorem application. This makes it a distinct, demanding frontier for large language models (LLMs), offering insights beyond general mathematical problem-solving. Progress in this area is hampered by existing datasets that are often scarce, synthetic, or rigidly formal. We address this by proposing an informal yet verifiable task formulation, recasting inequality proving into two automatically checkable subtasks: bound estimation and relation prediction. Building on this, we release IneqMath, an expert-curated dataset of Olympiad-level inequalities, including a test set and training corpus enriched with step-wise solutions and theorem annotations. We also develop a novel LLM-as-judge evaluation framework, combining a final-answer judge with four step-wise judges designed to detect common reasoning flaws. A systematic evaluation of 29 leading LLMs on IneqMath reveals a surprising reality: even top models like o1 achieve less than 10% overall accuracy under step-wise scrutiny; this is a drop of up to 65.5% from their accuracy considering only final answer equivalence. This discrepancy exposes fragile deductive chains and a critical gap for current LLMs between merely finding an answer and constructing a rigorous proof. Scaling model size and increasing test-time computation yield limited gains in overall proof correctness. Instead, our findings highlight promising research directions such as theorem-guided reasoning and self-refinement. Code and data are available at https://ineqmath.github.io/.

Method: Developed SFE benchmark with 830 expert-verified VQA pairs across three question types (signal perception, attribute understanding, comparative reasoning) spanning 66 multimodal tasks in five scientific disciplines.

Result: State-of-the-art models GPT-o3 and InternVL-3 achieved only 34.08% and 26.52% respectively on SFE, showing significant gaps in scientific cognitive capabilities.

Conclusion: SFE reveals substantial room for improvement in MLLMs’ scientific reasoning abilities, and the benchmark aims to facilitate AI-enhanced scientific discovery advancements.

Abstract: Scientific discoveries increasingly rely on complex multimodal reasoning based on information-intensive scientific data and domain-specific expertise. Empowered by expert-level scientific benchmarks, scientific Multimodal Large Language Models (MLLMs) hold the potential to significantly enhance this discovery process in realistic workflows. However, current scientific benchmarks mostly focus on evaluating the knowledge understanding capabilities of MLLMs, leading to an inadequate assessment of their perception and reasoning abilities. To address this gap, we present the Scientists’ First Exam (SFE) benchmark, designed to evaluate the scientific cognitive capacities of MLLMs through three interconnected levels: scientific signal perception, scientific attribute understanding, scientific comparative reasoning. Specifically, SFE comprises 830 expert-verified VQA pairs across three question types, spanning 66 multimodal tasks across five high-value disciplines. Extensive experiments reveal that current state-of-the-art GPT-o3 and InternVL-3 achieve only 34.08% and 26.52% on SFE, highlighting significant room for MLLMs to improve in scientific realms. We hope the insights obtained in SFE will facilitate further developments in AI-enhanced scientific discoveries.

Method: Proposes sequential communication with two components: Next-Agent Prediction for selecting suitable agents at each step, and Next-Context Selection for agents to access relevant previous information.

Result: Achieves superior performance across multiple benchmarks while substantially reducing communication overhead compared to existing methods.

Conclusion: Sequential communication structure provides larger topology space and enables task-adaptive communication pipelines with role flexibility and global information flow.

Abstract: Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication. In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topology space for multi-agent communication. Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection (NCS), which enables each agent to selectively access relevant information from any previous step. Together, these components construct task-adaptive communication pipelines that support both role flexibility and global information flow. Extensive evaluations across multiple benchmarks demonstrate that our approach achieves superior performance while substantially reducing communication overhead.

Method: Conducted a targeted critical review of supervision literature outside AI, differentiated control (ex-ante, real-time, operational) from oversight (ex-post, policy/governance), and proposed a framework for aligning regulatory expectations with technical feasibility.

Result: Developed a framework for meaningful supervision, outlined documentation and integration methods for supervision in risk management, and created a maturity model for AI supervision based on Microsoft’s Responsible AI Maturity Model.

Conclusion: The paper provides clarity on supervision mechanisms, highlights their boundaries and limitations, and supports regulators, auditors, and practitioners in identifying when meaningful supervision is possible and where existing methods fall short.

Abstract: Oversight and control, which we collectively call supervision, are often discussed as ways to ensure that AI systems are accountable, reliable, and able to fulfill governance and management requirements. However, the requirements for “human oversight” risk codifying vague or inconsistent interpretations of key concepts like oversight and control. This ambiguous terminology could undermine efforts to design or evaluate systems that must operate under meaningful human supervision. This matters because the term is used by regulatory texts such as the EU AI Act. This paper undertakes a targeted critical review of literature on supervision outside of AI, along with a brief summary of past work on the topic related to AI. We next differentiate control as ex-ante or real-time and operational rather than policy or governance, and oversight as performed ex-post, or a policy and governance function. Control aims to prevent failures, while oversight focuses on detection, remediation, or incentives for future prevention. Building on this, we make three contributions. 1) We propose a framework to align regulatory expectations with what is technically and organizationally plausible, articulating the conditions under which each mechanism is possible, where they fall short, and what is required to make them meaningful in practice. 2) We outline how supervision methods should be documented and integrated into risk management, and drawing on the Microsoft Responsible AI Maturity Model, we outline a maturity model for AI supervision. 3) We explicitly highlight boundaries of these mechanisms, including where they apply, where they fail, and where it is clear that no existing methods suffice. This foregrounds the question of whether meaningful supervision is possible in a given deployment context, and can support regulators, auditors, and practitioners in identifying both present and future limitations.

Method: A two-stage pipeline: first training a Llama 3.1 8B student to imitate a Llama 3.3 70B teacher via supervised fine-tuning, followed by on-policy reinforcement learning. The approach uses bootstrapping on 1,370 configurations to estimate effective hyperparameters.

Result: Combining SFT with on-policy RL consistently outperforms either approach alone on both WorkArena and MiniWob++ benchmarks, requiring only 55% of the compute to match pure SFT’s peak performance on MiniWob++.

Conclusion: This strategy effectively pushes the compute-performance Pareto frontier and is the only approach that can close the gap with closed-source models, providing a compute-efficient solution for training LLM web agents.

Abstract: LLM-based web agents have recently made significant progress, but much of it has occurred in closed-source systems, widening the gap with open-source alternatives. Progress has been held back by two key challenges: first, a narrow focus on single-step tasks that overlooks the complexity of multi-step web interactions; and second, the high compute costs required to post-train LLM-based web agents. To address this, we present the first statistically grounded study on compute allocation for LLM web-agent post-training. Our approach uses a two-stage pipeline, training a Llama 3.1 8B student to imitate a Llama 3.3 70B teacher via supervised fine-tuning (SFT), followed by on-policy reinforcement learning. We find this process highly sensitive to hyperparameter choices, making exhaustive sweeps impractical. To spare others from expensive trial-and-error, we sample 1,370 configurations and use bootstrapping to estimate effective hyperparameters. Our results show that combining SFT with on-policy RL consistently outperforms either approach alone on both WorkArena and MiniWob++. Further, this strategy requires only 55% of the compute to match the peak performance of pure SFT on MiniWob++, effectively pushing the compute-performance Pareto frontier, and is the only strategy that can close the gap with closed-source models.

Method: Used a Design-State Graph (DSG) representation bundling requirements, physical embodiments, and Python physics models. Compared a nine-role MAS that iteratively builds DSG against a two-agent Generator-Reflector loop. Conducted 60 experiments using Llama 3.3 70B and reasoning-distilled DeepSeek R1 70B across different configurations, temperatures, and seeds.

Result: Both systems maintained perfect JSON integrity and embodiment tagging. Requirement coverage remained minimal (<20%). Code compatibility peaked at 100% for 2AS but averaged below 50% for MAS. Only reasoning-distilled model reliably flagged workflow completion. MAS generated more granular DSGs (5-6 nodes) while 2AS mode-collapsed.

Conclusion: Structured multi-agent orchestration enhanced design detail, and reasoning-distilled LLM improved completion rates. However, low requirements coverage and fidelity gaps in coding persisted as challenges.

Abstract: Early-stage engineering design involves complex, iterative reasoning, yet existing large language model (LLM) workflows struggle to maintain task continuity and generate executable models. We evaluate whether a structured multi-agent system (MAS) can more effectively manage requirements extraction, functional decomposition, and simulator code generation than a simpler two-agent system (2AS). The target application is a solar-powered water filtration system as described in a cahier des charges. We introduce the Design-State Graph (DSG), a JSON-serializable representation that bundles requirements, physical embodiments, and Python-based physics models into graph nodes. A nine-role MAS iteratively builds and refines the DSG, while the 2AS collapses the process to a Generator-Reflector loop. Both systems run a total of 60 experiments (2 LLMs - Llama 3.3 70B vs reasoning-distilled DeepSeek R1 70B x 2 agent configurations x 3 temperatures x 5 seeds). We report a JSON validity, requirement coverage, embodiment presence, code compatibility, workflow completion, runtime, and graph size. Across all runs, both MAS and 2AS maintained perfect JSON integrity and embodiment tagging. Requirement coverage remained minimal (less than 20%). Code compatibility peaked at 100% under specific 2AS settings but averaged below 50% for MAS. Only the reasoning-distilled model reliably flagged workflow completion. Powered by DeepSeek R1 70B, the MAS generated more granular DSGs (average 5-6 nodes) whereas 2AS mode-collapsed. Structured multi-agent orchestration enhanced design detail. Reasoning-distilled LLM improved completion rates, yet low requirements and fidelity gaps in coding persisted.

Method: Extends NRPA with multiple policies exploring different solution space regions, maintains non-dominated fronts at each search level, and adapts policies based on diversity and isolation within Pareto fronts.

Result: Achieves competitive performance against state-of-the-art multi-objective algorithms in convergence and diversity, strongly outperforms evolutionary algorithms on constrained search spaces.

Conclusion: Pareto-NRPA successfully adapts NRPA to multi-objective optimization, demonstrating effectiveness on benchmark problems and representing the first multi-objective extension of NRPA.

Abstract: We introduce Pareto-NRPA, a new Monte-Carlo algorithm designed for multi-objective optimization problems over discrete search spaces. Extending the Nested Rollout Policy Adaptation (NRPA) algorithm originally formulated for single-objective problems, Pareto-NRPA generalizes the nested search and policy update mechanism to multi-objective optimization. The algorithm uses a set of policies to concurrently explore different regions of the solution space and maintains non-dominated fronts at each level of search. Policy adaptation is performed with respect to the diversity and isolation of sequences within the Pareto front. We benchmark Pareto-NRPA on two classes of problems: a novel bi-objective variant of the Traveling Salesman Problem with Time Windows problem (MO-TSPTW), and a neural architecture search task on well-known benchmarks. Results demonstrate that Pareto-NRPA achieves competitive performance against state-of-the-art multi-objective algorithms, both in terms of convergence and diversity of solutions. Particularly, Pareto-NRPA strongly outperforms state-of-the-art evolutionary multi-objective algorithms on constrained search spaces. To our knowledge, this work constitutes the first adaptation of NRPA to the multi-objective setting.

Method: Mantis is a foundation model trained entirely on mechanistic simulations, enabling out-of-the-box forecasting across diseases, regions, and outcomes without using real-world data during training.

Result: Mantis achieved lower mean absolute error than all models in CDC’s COVID-19 Forecast Hub on early pandemic forecasts and consistently ranked in top two models across all diseases tested, including respiratory, vector-borne, and waterborne pathogens.

Conclusion: Mantis captures fundamental contagion dynamics rather than memorizing disease-specific patterns, making it a practical foundation for general-purpose, accurate disease forecasting deployable where traditional models fail.

Abstract: Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for disease-specific data, bespoke training, and expert tuning. We introduce Mantis, a foundation model trained entirely on mechanistic simulations, which enables out-of-the-box forecasting across diseases, regions, and outcomes, even in settings with limited historical data. We evaluated Mantis against 48 forecasting models across six diseases with diverse transmission modes, assessing both point forecast accuracy (mean absolute error) and probabilistic performance (weighted interval score and coverage). Despite using no real-world data during training, Mantis achieved lower mean absolute error than all models in the CDC’s COVID-19 Forecast Hub when backtested on early pandemic forecasts. Across all other diseases tested, including respiratory, vector-borne, and waterborne pathogens, Mantis consistently ranked in the top two models across all evaluation metrics. Notably, Mantis generalized to diseases with transmission mechanisms not represented in its training data, demonstrating that it captures fundamental contagion dynamics rather than memorizing disease-specific patterns. These capabilities position Mantis as a practical foundation for disease forecasting: general-purpose, accurate, and deployable where traditional models fail.

Method: Created CausalARC testbed with tasks sampled from structural causal models, providing principled data augmentations including observational, interventional, and counterfactual feedback for few-shot in-context learning.

Result: Evaluated language models across four settings: abstract reasoning with test-time training, counterfactual reasoning, program synthesis, and causal discovery. Performance varied significantly across tasks and models.

Conclusion: There is substantial room for improvement in language model reasoning capabilities, particularly in handling causal reasoning tasks under data constraints and distribution shifts.

Abstract: On-the-fly reasoning often requires adaptation to novel problems under limited data and distribution shift. This work introduces CausalARC: an experimental testbed for AI reasoning in low-data and out-of-distribution regimes, modeled after the Abstraction and Reasoning Corpus (ARC). Each CausalARC reasoning task is sampled from a fully specified causal world model, formally expressed as a structural causal model. Principled data augmentations provide observational, interventional, and counterfactual feedback about the world model in the form of few-shot, in-context learning demonstrations. As a proof-of-concept, we illustrate the use of CausalARC for four language model evaluation settings: (1) abstract reasoning with test-time training, (2) counterfactual reasoning with in-context learning, (3) program synthesis, and (4) causal discovery with logical reasoning. Within- and between-model performance varied heavily across tasks, indicating room for significant improvement in language model reasoning.

Method: Organizes literature by reasoning topology (direct, linear chain, branch-structured) crossed with objectives (traditional analysis, explanation, causal inference, generation), using a compact tag set spanning decomposition, verification, tool use, and other aspects.

Result: Provides a comprehensive survey showing what each reasoning topology enables and where it breaks down, with curated datasets, benchmarks, and resources for study and deployment.

Conclusion: Future progress depends on benchmarks tying reasoning quality to utility and closed-loop testbeds that balance cost and risk, marking a shift toward reliability at scale with traceable evidence and credible outcomes.

Abstract: Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer. This survey defines the problem and organizes the literature by reasoning topology with three families: direct reasoning in one step, linear chain reasoning with explicit intermediates, and branch-structured reasoning that explores, revises, and aggregates. The topology is crossed with the main objectives of the field, including traditional time series analysis, explanation and understanding, causal inference and decision making, and time series generation, while a compact tag set spans these axes and captures decomposition and verification, ensembling, tool use, knowledge access, multimodality, agent loops, and LLM alignment regimes. Methods and systems are reviewed across domains, showing what each topology enables and where it breaks down in faithfulness or robustness, along with curated datasets, benchmarks, and resources that support study and deployment (https://github.com/blacksnail789521/Time-Series-Reasoning-Survey). Evaluation practices that keep evidence visible and temporally aligned are highlighted, and guidance is distilled on matching topology to uncertainty, grounding with observable artifacts, planning for shift and streaming, and treating cost and latency as design budgets. We emphasize that reasoning structures must balance capacity for grounding and self-correction against computational cost and reproducibility, while future progress will likely depend on benchmarks that tie reasoning quality to utility and on closed-loop testbeds that trade off cost and risk under shift-aware, streaming, and long-horizon settings. Taken together, these directions mark a shift from narrow accuracy toward reliability at scale, enabling systems that not only analyze but also understand, explain, and act on dynamic worlds with traceable evidence and credible outcomes.

Method: Proposes dynamical systems theory as a unifying framework, using differential calculus for modeling inference, learning, and control. Utilizes noise as a learning resource and differential genetic programming for discovering adaptive behaviors.

Result: The framework enables emergent neuromorphic intelligence where intelligent behavior arises from physical substrate dynamics, advancing both AI science and sustainability.

Conclusion: Dynamical systems theory provides the foundational framework needed to bridge diverse disciplines in neuromorphic computing, paving the way for sustainable and efficient AI systems that mimic brain-like intelligence.

Abstract: Neuromorphic computing seeks to replicate the remarkable efficiency, flexibility, and adaptability of the human brain in artificial systems. Unlike conventional digital approaches, which suffer from the Von Neumann bottleneck and depend on massive computational and energy resources, neuromorphic systems exploit brain-inspired principles of computation to achieve orders of magnitude greater energy efficiency. By drawing on insights from a wide range of disciplines – including artificial intelligence, physics, chemistry, biology, neuroscience, cognitive science and materials science – neuromorphic computing promises to deliver intelligent systems that are sustainable, transparent, and widely accessible. A central challenge, however, is to identify a unifying theoretical framework capable of bridging these diverse disciplines. We argue that dynamical systems theory provides such a foundation. Rooted in differential calculus, it offers a principled language for modeling inference, learning, and control in both natural and artificial substrates. Within this framework, noise can be harnessed as a resource for learning, while differential genetic programming enables the discovery of dynamical systems that implement adaptive behaviors. Embracing this perspective paves the way toward emergent neuromorphic intelligence, where intelligent behavior arises from the dynamics of physical substrates, advancing both the science and sustainability of AI.

Method: Uses functionally equivalent sampling to generate uncertainty measures through task-preserving input sampling that probes model consistency (equivalent samples) and sensitivity (complementary samples).

Result: Achieves 33.3% relative improvement for vision-LLMs and 29.6% for audio-LLMs in selective prediction performance (AUROC) for detecting mispredictions.

Conclusion: FESTA provides effective uncertainty estimation for multimodal LLMs using only input-output access, enabling better trust assessment and selective prediction.

Abstract: The accurate trust assessment of multimodal large language models (MLLMs) generated predictions, which can enable selective prediction and improve user confidence, is challenging due to the diverse multi-modal input paradigms. We propose Functionally Equivalent Sampling for Trust Assessment (FESTA), a multimodal input sampling technique for MLLMs, that generates an uncertainty measure based on the equivalent and complementary input samplings. The proposed task-preserving sampling approach for uncertainty quantification expands the input space to probe the consistency (through equivalent samples) and sensitivity (through complementary samples) of the model. FESTA uses only input-output access of the model (black-box), and does not require ground truth (unsupervised). The experiments are conducted with various off-the-shelf multi-modal LLMs, on both visual and audio reasoning tasks. The proposed FESTA uncertainty estimate achieves significant improvement (33.3% relative improvement for vision-LLMs and 29.6% relative improvement for audio-LLMs) in selective prediction performance, based on area-under-receiver-operating-characteristic curve (AUROC) metric in detecting mispredictions. The code implementation is open-sourced.

Method: Proposed theoretical framework and algorithmic task for evaluating outputs by novelty and utility degrees. Conducted empirical analysis of scaling behavior, model architecture effects, and identified the ideation-execution gap.

Result: Found optimal model depths and widths for creativity within fixed compute budgets. Discovered a fundamental novelty-utility tradeoff that explains the ideation-execution gap, where LLMs generate novel ideas but struggle with practical feasibility.

Conclusion: The persistent novelty-utility tradeoff casts doubt on LLMs’ long-term creative potential in current form. The framework provides foundation for understanding and improving AI creativity, bridging human-machine intelligence gaps.

Abstract: Artificial intelligence (AI) systems, and Large Language Models (LLMs) in particular, are increasingly employed for creative tasks like scientific idea generation, constituting a form of generalization from training data unaddressed by existing conceptual frameworks. Despite its similarities to compositional generalization (CG), combinatorial creativity (CC) is an open-ended ability. Instead of evaluating for accuracy or correctness against fixed targets, which would contradict the open-ended nature of CC, we propose a theoretical framework and algorithmic task for evaluating outputs by their degrees of novelty and utility. From here, we make several important empirical contributions: (1) We obtain the first insights into the scaling behavior of creativity for LLMs. (2) We discover that, for fixed compute budgets, there exist optimal model depths and widths for creative ability. (3) We find that the ideation-execution gap, whereby LLMs excel at generating novel scientific ideas but struggle to ensure their practical feasibility, may be explained by a more fundamental novelty-utility tradeoff characteristic of creativity algorithms in general. Importantly, this tradeoff remains persistent even at scale, casting doubt on the long-term creative potential of LLMs in their current form. Together, our conceptual framework and empirical findings provide a foundation for understanding and improving creativity in modern AI models, bridging the gap between human and machine intelligence.

Method: Extract benchmark signatures via stepwise forward selection with linear regressions across 32 LLMs and 88 benchmarks, using token perplexity from naturally authored corpora as predictive features.

Result: Found high performance overlaps but limited semantic overlaps; identified cross-functional overlaps across logic, math, language, instruction following, and world modeling, with coding as the least overlapping domain; benchmark signatures remained robust to format biases.

Conclusion: Benchmark signatures provide mechanistic insights into benchmark validity and LLM sensitivities, revealing the underlying landscape of interconnected LLM capabilities while being robust to format effects that confound performance-based analyses.

Abstract: We develop signatures of capacity familiarity to characterize large language model (LLM) benchmarks and their meaningful overlaps. Benchmark signatures probe the capacity required for benchmark performance. We formally define them as a set of salient tokens drawn from in-the-wild, naturally authored corpora, where LLM token perplexity, reflecting more or less pre-training exposure, becomes highly predictive of LLM benchmark performance. Through a large-scale meta-evaluation, we extract benchmark signatures via stepwise forward selection with linear regressions across 32 LLMs and 88 benchmarks spanning diverse knowledge, coding, logic, instruction following, math, language, reasoning, and world modeling. Our analysis situates signatures in relation to both the semantic similarity of benchmark questions and the correlation of model performance. While performance overlaps are universally high and semantic overlaps remain confined to a narrow mid-range, benchmark signatures prove highly informative in capturing variation, overlap, and divergence. We observe overlap in knowledge and reasoning subtasks, whereas multilingual and cultural benchmarks exhibit less similarity, even compared to cross-task overlap. Notably, performance-level results are strongly influenced by benchmark-orthogonal factors such as question format, highlighting limitations in LLM generalization, the conflation of performance with ability, and issues inherent in current mainstream benchmark agreement studies. Benchmark signatures, however, remain robust to such effects. Ultimately, we identify cross-functional overlaps across logic, math, language, instruction following, and world modeling, with coding emerging as the least overlapping domain. Together, these findings provide mechanistic insights into benchmark validity and LLM sensitivities, and sketch the underlying landscape of interconnected LLM capabilities.

Method: Conducted systematic analysis across diverse dimensions including evaluation protocols (likelihood-based vs generation-based), task categories, and model architectures, examining layer contributions through pruning experiments and distillation.

Result: Found that under likelihood-based metrics, only initial layers are critical and most layers can be pruned, but generation-based evaluation reveals indispensable roles for middle and deeper layers in reasoning and long-range coherence. Knowledge and retrieval are concentrated in shallow layers while reasoning accuracy depends on deeper layers.

Conclusion: Depth usage in LLMs is highly heterogeneous and context-dependent, requiring task-, metric-, and model-aware perspectives for both interpretation and compression of large models.

Abstract: Recent studies suggest that the deeper layers of Large Language Models (LLMs) contribute little to representation learning and can often be removed without significant performance loss. However, such claims are typically drawn from narrow evaluations and may overlook important aspects of model behavior. In this work, we present a systematic study of depth utilization across diverse dimensions, including evaluation protocols, task categories, and model architectures. Our analysis confirms that very deep layers are generally less effective than earlier ones, but their contributions vary substantially with the evaluation setting. Under likelihood-based metrics without generation, pruning most layers preserves performance, with only the initial few being critical. By contrast, generation-based evaluation uncovers indispensable roles for middle and deeper layers in enabling reasoning and maintaining long-range coherence. We further find that knowledge and retrieval are concentrated in shallow components, whereas reasoning accuracy relies heavily on deeper layers – yet can be reshaped through distillation. These results highlight that depth usage in LLMs is highly heterogeneous and context-dependent, underscoring the need for task-, metric-, and model-aware perspectives in both interpreting and compressing large models.

Method: Agent Spec provides a declarative language for defining AI agents and workflows independently of execution environments. It includes a standardized Evaluation harness to assess agent behavior across different runtimes (LangGraph, CrewAI, AutoGen, WayFlow) using three benchmarks (SimpleQA Verified, τ²-Bench, BIRD-SQL).

Result: The specification enables portability and interoperability across AI frameworks, benefiting agent developers with reusable components, framework developers with interchange format, researchers with reproducible results, and enterprises with faster deployment and scalability.

Conclusion: Agent Spec addresses fragmentation in AI agent development by providing a unified specification that promotes cross-framework compatibility and standardized evaluation, analogous to how HELM standardized LLM evaluation.

Abstract: Open Agent Specification (Agent Spec) is a declarative language for defining AI agents and workflows in a way that is compatible across different AI frameworks, promoting portability and interoperability within AI Agent frameworks. Agent Spec aims to resolve the challenges of fragmented agent development by providing a common unified specification that allows AI agents to be designed once and deployed across various frameworks, improving interoperability and reusability, while reducing redundant efforts. Additionally, Agent Spec facilitates development tools and portability, allowing AI agents to be defined independently of their execution environment and enabling teams to exchange solutions without implementation-specific limitations. Agent Spec benefits four key groups: (i) Agent developers, who gain a superset of reusable components and design patterns, enabling them to leverage a broader range of functionalities; (ii) Agent framework and tool developers, who can use Agent Spec as an interchange format and therefore benefit from cross-framework and tool support; (iii) Researchers, who can achieve reproducible results and comparability, facilitating more reliable and consistent outcomes; (iv) Enterprises, which see faster prototype-to-deployment, increased productivity, and greater scalability and maintainability for their AI agent solutions. This technical report provides an overview of the technical foundations of Agent Spec, including motivation, benefits, and future work. We also introduce a standardized Evaluation harness to assess agent behavior and agentic workflows across runtimes (LangGraph, CrewAI, AutoGen, and WayFlow), using three different benchmarks (SimpleQA Verified, $\tau^2$-Bench and BIRD-SQL) - analogous to how HELM and related harnesses standardized LLM evaluation - so that performance, robustness, and efficiency can be compared consistently across frameworks.

Method: Group sparsity penalties on attention mechanisms, information-theoretic anchor design, and dynamic rule injection to control localization degree without retraining.

Result: Mathematical proofs show attention concentrates on semantically relevant blocks with exponential bounds on entropy and pointer fidelity when sparsity thresholds are exceeded.

Conclusion: The framework provides practitioners with flexible control over model interpretability vs performance trade-offs through a tunable locality parameter.

Abstract: We present a novel framework for training large language models with continuously adjustable internal representations that span the full spectrum from localist (interpretable, rule-based) to distributed (generalizable, efficient) encodings. The key innovation is a locality dial, a tunable parameter that dynamically controls the degree of localization during both training and inference without requiring model retraining. This is achieved through group sparsity penalties on attention mechanisms, information-theoretic anchor design, and dynamic rule injection. We provide rigorous mathematical proofs establishing explicit threshold conditions under which attention provably concentrates on semantically relevant blocks, with exponential bounds on attention entropy and pointer fidelity. Specifically, we prove that when group sparsity penalties exceed certain threshold values, the model’s attention mechanisms concentrate on semantically relevant blocks, achieving low entropy and high fidelity with negligible error. This framework enables practitioners to continuously interpolate between interpretable and high-performance modes, supporting applications in regulated domains requiring both transparency and capability.

Method: Proposes a compact formal theory representing agents as fuzzy relation operators on inputs/outputs constrained by safety envelopes, weights reachable paths with continuation parameters to compute coverage generating functions, and provides geometric interpretation of search on induced graphs.

Result: Developed a workable language and operational tools to measure agents and their search spaces, with testable inferences validated through two instantiations, offering systematic formal description of iterative search constructed by LLMs.

Conclusion: The theory provides a systematic framework for formally describing and measuring LLM-assisted iterative search guided by domain priors, enabling better understanding and optimization of search processes in AI+Science applications.

Abstract: The generate-filter-refine (iterative paradigm) based on large language models (LLMs) has achieved progress in reasoning, programming, and program discovery in AI+Science. However, the effectiveness of search depends on where to search, namely, how to encode the domain prior into an operationally structured hypothesis space. To this end, this paper proposes a compact formal theory that describes and measures LLM-assisted iterative search guided by domain priors. We represent an agent as a fuzzy relation operator on inputs and outputs to capture feasible transitions; the agent is thereby constrained by a fixed safety envelope. To describe multi-step reasoning/search, we weight all reachable paths by a single continuation parameter and sum them to obtain a coverage generating function; this induces a measure of reachability difficulty; and it provides a geometric interpretation of search on the graph induced by the safety envelope. We further provide the simplest testable inferences and validate them via two instantiation. This theory offers a workable language and operational tools to measure agents and their search spaces, proposing a systematic formal description of iterative search constructed by LLMs.

Method: Developed a composite instability index (H-Risk) combining spectral margin, conditioning, temporal sensitivity, and innovation amplification. Applied this to linear-Gaussian simulations and extended to analyze LLMs through internal fragility measurements and critique prompts.

Result: Higher H-Risk predicts overconfident errors even under formal stability conditions. In LLMs, preliminary correlations found between internal fragility and miscalibration/hallucination. Lightweight critique prompts showed modest, mixed effects on calibration in small-scale tests.

Conclusion: The study establishes a structural bridge between Kantian self-limitation and feedback control, offering a principled diagnostic lens for reasoning system overconfidence and potential mitigation strategies.

Abstract: We reinterpret Kant’s Critique of Pure Reason as a theory of feedback stability, viewing reason as a regulator that keeps inference within the bounds of possible experience. We formalize this intuition via a composite instability index (H-Risk) combining spectral margin, conditioning, temporal sensitivity, and innovation amplification. In linear-Gaussian simulations, higher H-Risk predicts overconfident errors even under formal stability, revealing a gap between nominal and epistemic stability. Extending to large language models (LLMs), we observe preliminary correlations between internal fragility and miscalibration or hallucination (confabulation), and find that lightweight critique prompts may modestly improve or worsen calibration in small-scale tests. These results suggest a structural bridge between Kantian self-limitation and feedback control, offering a principled lens to diagnose and potentially mitigate overconfidence in reasoning systems.

Method: Proposes KG-Agent framework that builds a persistent State-Action Knowledge Graph from raw pixel interactions, uses graph topology for hybrid intrinsic rewards (state value + novelty), and links functionally similar GUI states to enable generalization.

Result: Significant improvements in exploration efficiency and strategic depth demonstrated in complex GUI environments (Civilization V and Slay the Spire) compared to state-of-the-art methods.

Conclusion: KG-Agent effectively addresses efficiency bottlenecks in API-free environments by structuring experience into knowledge graphs, enabling better generalization and long-horizon reasoning through hybrid reward mechanisms.

Abstract: Most existing software lacks accessible Application Programming Interfaces (APIs), requiring agents to operate solely through pixel-based Graphical User Interfaces (GUIs). In this API-free setting, large language model (LLM)-based agents face severe efficiency bottlenecks: limited to local visual experiences, they make myopic decisions and rely on inefficient trial-and-error, hindering both skill acquisition and long-term planning. To address these challenges, we propose KG-Agent, an experience-driven learning framework that structures an agent’s raw pixel-level interactions into a persistent State-Action Knowledge Graph (SA-KG). KG-Agent overcomes inefficient exploration by linking functionally similar but visually distinct GUI states, forming a rich neighborhood of experience that enables the agent to generalize from a diverse set of historical strategies. To support long-horizon reasoning, we design a hybrid intrinsic reward mechanism based on the graph topology, combining a state value reward for exploiting known high-value pathways with a novelty reward that encourages targeted exploration. This approach decouples strategic planning from pure discovery, allowing the agent to effectively value setup actions with delayed gratification. We evaluate KG-Agent in two complex, open-ended GUI-based decision-making environments (Civilization V and Slay the Spire), demonstrating significant improvements in exploration efficiency and strategic depth over the state-of-the-art methods.

Method: RELATE employs shared modality-specific encoders for categorical, numerical, textual, and temporal attributes, followed by a Perceiver-style cross-attention module that aggregates features into fixed-size, permutation-invariant node representations.

Result: On RelBench benchmark with ReLGNN and HGT, RELATE achieves performance within 3% of schema-specific encoders while reducing parameter counts by up to 5x.

Conclusion: RELATE enables varying schemas and multi-dataset pretraining for general-purpose GNNs, paving the way toward foundation models for relational graph data.

Abstract: Relational multi-table data is common in domains such as e-commerce, healthcare, and scientific research, and can be naturally represented as heterogeneous temporal graphs with multi-modal node attributes. Existing graph neural networks (GNNs) rely on schema-specific feature encoders, requiring separate modules for each node type and feature column, which hinders scalability and parameter sharing. We introduce RELATE (Relational Encoder for Latent Aggregation of Typed Entities), a schema-agnostic, plug-and-play feature encoder that can be used with any general purpose GNN. RELATE employs shared modality-specific encoders for categorical, numerical, textual, and temporal attributes, followed by a Perceiver-style cross-attention module that aggregates features into a fixed-size, permutation-invariant node representation. We evaluate RELATE on ReLGNN and HGT in the RelBench benchmark, where it achieves performance within 3% of schema-specific encoders while reducing parameter counts by up to 5x. This design supports varying schemas and enables multi-dataset pretraining for general-purpose GNNs, paving the way toward foundation models for relational graph data.

Method: Stores user-specific information in KV-Caches of both textual and visual tokens. Textual tokens from summarized metadata, visual tokens from distinct image patches. Retrieves related KV-Caches when answering questions to ensure accuracy.

Result: Achieves state-of-the-art results in both visual question answering and text-only tasks across multiple datasets, providing more accurate responses especially for fine-grained local details.

Conclusion: Jarvis presents a practical path toward personalized AI assistants by effectively leveraging user-specific information through KV-Cache retrieval, outperforming existing methods in accuracy.

Abstract: The rapid development of Vision-language models (VLMs) enables open-ended perception and reasoning. Recent works have started to investigate how to adapt general-purpose VLMs into personalized assistants. Even commercial models such as ChatGPT now support model personalization by incorporating user-specific information. However, existing methods either learn a set of concept tokens or train a VLM to utilize user-specific information. However, both pipelines struggle to generate accurate answers as personalized assistants. We introduce Jarvis, an innovative framework for a personalized AI assistant through personal KV-Cache retrieval, which stores user-specific information in the KV-Caches of both textual and visual tokens. The textual tokens are created by summarizing user information into metadata, while the visual tokens are produced by extracting distinct image patches from the user’s images. When answering a question, Jarvis first retrieves related KV-Caches from personal storage and uses them to ensure accuracy in responses. We also introduce a fine-grained benchmark built with the same distinct image patch mining pipeline, emphasizing accurate question answering based on fine-grained user-specific information. Jarvis is capable of providing more accurate responses, particularly when they depend on specific local details. Jarvis achieves state-of-the-art results in both visual question answering and text-only tasks across multiple datasets, indicating a practical path toward personalized AI assistants. The code and dataset will be released.

Method: Drawing on theoretical frameworks from Irving J. Good and Nick Bostrom, plus recent publications, the paper traces AI’s trajectory and explores the implications of AGI and superintelligence development.

Result: The analysis reveals that human extinction may result from machines’ exponentially growing cognitive power creating fundamentally alien intelligence that vastly exceeds humanity’s capabilities.

Conclusion: Existential risk from AI stems not from malicious intent but from uncontrollable, indifferent cognitive superiority that could render humanity obsolete.

Abstract: This article analyzes the existential risks artificial intelligence (AI) poses to humanity, tracing the trajectory from current AI to ultraintelligence. Drawing on Irving J. Good and Nick Bostrom’s theoretical work, plus recent publications (AI 2027; If Anyone Builds It, Everyone Dies), it explores AGI and superintelligence. Considering machines’ exponentially growing cognitive power and hypothetical IQs, it addresses the ethical and existential implications of an intelligence vastly exceeding humanity’s, fundamentally alien. Human extinction may result not from malice, but from uncontrollable, indifferent cognitive superiority.

Method: Proposed Mixed Density Diffuser (MDD) with tunable hyperparameters that control planning density throughout the temporal horizon, allowing non-uniform step skipping across different parts of the trajectory.

Result: MDD achieves new state-of-the-art performance across Maze2D, Franka Kitchen, and Antmaze D4RL task domains.

Conclusion: Adaptive density planning through tunable hyperparameters in diffusion planners significantly improves performance by optimizing step skipping across different temporal segments of trajectories.

Abstract: Recent studies demonstrate that diffusion planners benefit from sparse-step planning over single-step planning. Training models to skip steps in their trajectories helps capture long-term dependencies without additional or memory computational cost. However, predicting excessively sparse plans degrades performance. We hypothesize this temporal density threshold is non-uniform across a temporal horizon and that certain parts of a planned trajectory should be more densely planned. We propose Mixed Density Diffuser (MDD), a diffusion planner where the densities throughout the horizon are tunable hyperparameters. MDD achieves a new SOTA across the Maze2D, Franka Kitchen, and Antmaze D4RL task domains.

Method: OneCast decomposes time series into seasonal and trend components. Seasonal patterns are captured via lightweight projection with interpretable basis functions. Trend components are encoded into discrete tokens and inferred through masked discrete diffusion. Both outputs are combined for final forecasts.

Result: Extensive experiments across eight domains demonstrate that OneCast mostly outperforms state-of-the-art baselines in cross-domain time series forecasting.

Conclusion: The structured decomposition approach with specialized modeling for seasonal and trend components enables effective generalization across heterogeneous time series domains.

Abstract: Cross-domain time series forecasting is a valuable task in various web applications. Despite its rapid advancement, achieving effective generalization across heterogeneous time series data remains a significant challenge. Existing methods have made progress by extending single-domain models, yet often fall short when facing domain-specific trend shifts and inconsistent periodic patterns. We argue that a key limitation lies in treating temporal series as undifferentiated sequence, without explicitly decoupling their inherent structural components. To address this, we propose OneCast, a structured and modular forecasting framework that decomposes time series into seasonal and trend components, each modeled through tailored generative pathways. Specifically, the seasonal component is captured by a lightweight projection module that reconstructs periodic patterns via interpretable basis functions. In parallel, the trend component is encoded into discrete tokens at segment level via a semantic-aware tokenizer, and subsequently inferred through a masked discrete diffusion mechanism. The outputs from both branches are combined to produce a final forecast that captures seasonal patterns while tracking domain-specific trends. Extensive experiments across eight domains demonstrate that OneCast mostly outperforms state-of-the-art baselines.

Method: MCP-Flow uses an automated web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training, collecting data from 1166 servers and 11536 tools.

Result: The system produced 68733 high-quality instruction-function call pairs and 6439 trajectories, significantly exceeding prior work in scale and diversity, and demonstrated superior tool selection, function-call generation, and agentic task performance.

Conclusion: MCP-Flow provides a scalable foundation for advancing LLM agents’ proficiency in real-world MCP environments through automated large-scale data collection and training.

Abstract: Large Language Models (LLMs) increasingly rely on external tools to perform complex, realistic tasks, yet their ability to utilize the rapidly expanding Model Contextual Protocol (MCP) ecosystem remains limited. Existing MCP research covers few servers, depends on costly manual curation, and lacks training support, hindering progress toward real-world deployment. To overcome these limitations, we introduce MCP-Flow, an automated web-agent-driven pipeline for large-scale server discovery, data synthesis, and model training. MCP-Flow collects and filters data from 1166 servers and 11536 tools, producing 68733 high-quality instruction-function call pairs and 6439 trajectories, far exceeding prior work in scale and diversity. Extensive experiments demonstrate MCP-Flow’s effectiveness in driving superior MCP tool selection, function-call generation, and enhanced agentic task performance. MCP-Flow thus provides a scalable foundation for advancing LLM agents’ proficiency in real-world MCP environments. MCP-Flow is publicly available at \href{https://github.com/wwh0411/MCP-Flow}{https://github.com/wwh0411/MCP-Flow}.

Method: Multi-stage pipeline with parallel section generation, iterative refinement, real-time retrieval of recent publications, and multi-LLM evaluation framework for quality assessment.

Result: autosurvey2 consistently outperforms existing retrieval-based and automated baselines, achieving higher scores in structural coherence and topical relevance while maintaining strong citation fidelity.

Conclusion: autosurvey2 provides a scalable and reproducible solution for generating long-form academic surveys and contributes a foundation for future research on automated scholarly writing.

Abstract: The rapid growth of research literature, particularly in large language models (LLMs), has made producing comprehensive and current survey papers increasingly difficult. This paper introduces autosurvey2, a multi-stage pipeline that automates survey generation through retrieval-augmented synthesis and structured evaluation. The system integrates parallel section generation, iterative refinement, and real-time retrieval of recent publications to ensure both topical completeness and factual accuracy. Quality is assessed using a multi-LLM evaluation framework that measures coverage, structure, and relevance in alignment with expert review standards. Experimental results demonstrate that autosurvey2 consistently outperforms existing retrieval-based and automated baselines, achieving higher scores in structural coherence and topical relevance while maintaining strong citation fidelity. By combining retrieval, reasoning, and automated evaluation into a unified framework, autosurvey2 provides a scalable and reproducible solution for generating long-form academic surveys and contributes a solid foundation for future research on automated scholarly writing. All code and resources are available at https://github.com/annihi1ation/auto_research.

Method: Developed InnovatorBench with 20 research tasks across 6 categories, ResearchGym environment with rich action spaces, and a ReAct agent using frontier models like Claude-4, GPT-5, GLM-4.5, and Kimi-K2.

Result: Frontier models show promise in code-driven tasks but struggle with fragile algorithm tasks and long-horizon decision making, requiring over 11 hours to achieve best performance.

Conclusion: InnovatorBench demonstrates significant challenges in AI research automation and has potential to become the next generation code-based research benchmark.

Abstract: AI agents could accelerate scientific discovery by automating hypothesis formation, experiment design, coding, execution, and analysis, yet existing benchmarks probe narrow skills in simplified settings. To address this gap, we introduce InnovatorBench, a benchmark-platform pair for realistic, end-to-end assessment of agents performing Large Language Model (LLM) research. It comprises 20 tasks spanning Data Construction, Filtering, Augmentation, Loss Design, Reward Design, and Scaffold Construction, which require runnable artifacts and assessment of correctness, performance, output quality, and uncertainty. To support agent operation, we develop ResearchGym, a research environment offering rich action spaces, distributed and long-horizon execution, asynchronous monitoring, and snapshot saving. We also implement a lightweight ReAct agent that couples explicit reasoning with executable planning using frontier models such as Claude-4, GPT-5, GLM-4.5, and Kimi-K2. Our experiments demonstrate that while frontier models show promise in code-driven research tasks, they struggle with fragile algorithm-related tasks and long-horizon decision making, such as impatience, poor resource management, and overreliance on template-based reasoning. Furthermore, agents require over 11 hours to achieve their best performance on InnovatorBench, underscoring the benchmark’s difficulty and showing the potential of InnovatorBench to be the next generation of code-based research benchmark.

Method: Apollo allows human annotators to intervene only when agents drift from promising trajectories, providing lightweight guidance. It then applies supervision control to filter sub-optimal actions and prevent error propagation.

Result: When applied to train GLM-4.5 on InnovatorBench, Apollo achieved over 50% improvement over the untrained baseline and 28% improvement over a variant trained without human interaction.

Conclusion: Apollo demonstrates the critical role of human-in-the-loop sampling and provides a robust framework for handling long-horizon, domain-specialized tasks efficiently.

Abstract: Large Language Model (LLM) agents have recently shown strong potential in domains such as automated coding, deep research, and graphical user interface manipulation. However, training them to succeed on long-horizon, domain-specialized tasks remains challenging. Current methods primarily fall into two categories. The first relies on dense human annotations through behavior cloning, which is prohibitively expensive for long-horizon tasks that can take days or months. The second depends on outcome-driven sampling, which often collapses due to the rarity of valid positive trajectories on domain-specialized tasks. We introduce Apollo, a sampling framework that integrates asynchronous human guidance with action-level data filtering. Instead of requiring annotators to shadow every step, Apollo allows them to intervene only when the agent drifts from a promising trajectory, by providing prior knowledge, strategic advice, etc. This lightweight design makes it possible to sustain interactions for over 30 hours and produces valuable trajectories at a lower cost. Apollo then applies supervision control to filter out sub-optimal actions and prevent error propagation. Together, these components enable reliable and effective data collection in long-horizon environments. To demonstrate the effectiveness of Apollo, we evaluate it using InnovatorBench. Our experiments show that when applied to train the GLM-4.5 model on InnovatorBench, Apollo achieves more than a 50% improvement over the untrained baseline and a 28% improvement over a variant trained without human interaction. These results highlight the critical role of human-in-the-loop sampling and the robustness of Apollo’s design in handling long-horizon, domain-specialized tasks.

Method: Benchmarked DistilHuBERT and PaSST transformers against CNN-LSTM baseline using MFCC features on CREMA-D dataset. Conducted ablation study on PaSST variants with different classification heads (Linear, MLP, Attentive Pooling).

Result: DistilHuBERT achieved superior accuracy (70.64%) and F1 score (70.36%) with smallest model size. PaSST with MLP head performed best among its variants. Angry emotion was most accurately detected, while disgust was most challenging.

Conclusion: Lightweight transformers like DistilHuBERT offer compelling solutions for real-time speech emotion recognition on edge devices due to their high performance and small size.

Abstract: Emotion recognition from speech plays a vital role in the development of empathetic human-computer interaction systems. This paper presents a comparative analysis of lightweight transformer-based models, DistilHuBERT and PaSST, by classifying six core emotions from the CREMA-D dataset. We benchmark their performance against a traditional CNN-LSTM baseline model using MFCC features. DistilHuBERT demonstrates superior accuracy (70.64%) and F1 score (70.36%) while maintaining an exceptionally small model size (0.02 MB), outperforming both PaSST and the baseline. Furthermore, we conducted an ablation study on three variants of the PaSST, Linear, MLP, and Attentive Pooling heads, to understand the effect of classification head architecture on model performance. Our results indicate that PaSST with an MLP head yields the best performance among its variants but still falls short of DistilHuBERT. Among the emotion classes, angry is consistently the most accurately detected, while disgust remains the most challenging. These findings suggest that lightweight transformers like DistilHuBERT offer a compelling solution for real-time speech emotion recognition on edge devices. The code is available at: https://github.com/luckymaduabuchi/Emotion-detection-.

Method: Uses PINNs trained directly on governing equations of vocal-fold vibration and vocal-tract acoustics. Introduces differentiable approximation for vocal-fold collisions, treats vibration period as learnable parameter, and implements glottis-tract coupling as hard constraint.

Result: Successfully performed forward and inverse analyses, simultaneously estimating glottal flow rate, vocal-fold vibratory state, and subglottal pressure from speech signals. Same network architecture works for both analysis types.

Conclusion: The method inherits PINN advantages like mesh-free computation and natural nonlinearity handling, showing promise for wide applications in speech production analysis.

Abstract: The analysis of speech production based on physical models of the vocal folds and vocal tract is essential for studies on vocal-fold behavior and linguistic research. This paper proposes a speech production analysis method using physics-informed neural networks (PINNs). The networks are trained directly on the governing equations of vocal-fold vibration and vocal-tract acoustics. Vocal-fold collisions introduce nondifferentiability and vanishing gradients, challenging phenomena for PINNs. We demonstrate, however, that introducing a differentiable approximation function enables the analysis of vocal-fold vibrations within the PINN framework. The period of self-excited vocal-fold vibration is generally unknown. We show that by treating the period as a learnable network parameter, a periodic solution can be obtained. Furthermore, by implementing the coupling between glottal flow and vocal-tract acoustics as a hard constraint, glottis-tract interaction is achieved without additional loss terms. We confirmed the method’s validity through forward and inverse analyses, demonstrating that the glottal flow rate, vocal-fold vibratory state, and subglottal pressure can be simultaneously estimated from speech signals. Notably, the same network architecture can be applied to both forward and inverse analyses, highlighting the versatility of this approach. The proposed method inherits the advantages of PINNs, including mesh-free computation and the natural incorporation of nonlinearities, and thus holds promise for a wide range of applications.

Method: Proposes Hyperbolic Early-Exit networks (HypEE) with hierarchical training objective and novel entailment loss that enforces partial-ordering constraints in hyperbolic space.

Result: Significantly outperforms standard Euclidean EE baselines on multiple audio event detection tasks, especially at early exits, with improved efficiency and accuracy.

Conclusion: HypEE provides a principled geometric approach for early-exit networks that enhances both computational efficiency and prediction reliability through hyperbolic space learning.

Abstract: Deploying accurate event detection on resource-constrained devices is challenged by the trade-off between performance and computational cost. While Early-Exit (EE) networks offer a solution through adaptive computation, they often fail to enforce a coherent hierarchical structure, limiting the reliability of their early predictions. To address this, we propose Hyperbolic Early-Exit networks (HypEE), a novel framework that learns EE representations in the hyperbolic space. Our core contribution is a hierarchical training objective with a novel entailment loss, which enforces a partial-ordering constraint to ensure that deeper network layers geometrically refine the representations of shallower ones. Experiments on multiple audio event detection tasks and backbone architectures show that HypEE significantly outperforms standard Euclidean EE baselines, especially at the earliest, most computationally-critical exits. The learned geometry also provides a principled measure of uncertainty, enabling a novel triggering mechanism that makes the overall system both more efficient and more accurate than a conventional EE and standard backbone models without early-exits.

Method: Utilizes Large Audio Language Models to analyze audio generation outputs, retrieve difficult-to-generate concepts from an external database, and incorporate retrieved information into the generation process.

Result: The method enhances LALMs’ ability to identify missing sound events and delivers improvements across different models, outperforming existing RAG-specialized approaches.

Conclusion: The proposed feedback-driven RAG approach effectively improves text-to-audio generation quality by leveraging external knowledge retrieval through Large Audio Language Models.

Abstract: We propose a general feedback-driven retrieval-augmented generation (RAG) approach that leverages Large Audio Language Models (LALMs) to address the missing or imperfect synthesis of specific sound events in text-to-audio (TTA) generation. Unlike previous RAG-based TTA methods that typically train specialized models from scratch, we utilize LALMs to analyze audio generation outputs, retrieve concepts that pre-trained models struggle to generate from an external database, and incorporate the retrieved information into the generation process. Experimental results show that our method not only enhances the ability of LALMs to identify missing sound events but also delivers improvements across different models, outperforming existing RAG-specialized approaches.

Method: The paper provides a comprehensive survey and analysis of current music AI research landscape, examining foundational representation models, explainability efforts, multimodal systems evolution, dataset limitations, model efficiency, and applied generative models across various applications.

Result: The survey identifies several promising research directions including: foundational models with explainability, multimodal systems, addressing dataset limitations, improving model efficiency, generative models with better evaluation and controllability, and copyright protection strategies for artists.

Conclusion: While not exhaustive, this survey illuminates promising research directions enabled by recent developments in music foundation models, highlighting opportunities in both technical foundations and applied domains while addressing important copyright implications.

Abstract: Parallel to rapid advancements in foundation model research, the past few years have witnessed a surge in music AI applications. As AI-generated and AI-augmented music become increasingly mainstream, many researchers in the music AI community may wonder: what research frontiers remain unexplored? This paper outlines several key areas within music AI research that present significant opportunities for further investigation. We begin by examining foundational representation models and highlight emerging efforts toward explainability and interpretability. We then discuss the evolution toward multimodal systems, provide an overview of the current landscape of music datasets and their limitations, and address the growing importance of model efficiency in both training and deployment. Next, we explore applied directions, focusing first on generative models. We review recent systems, their computational constraints, and persistent challenges related to evaluation and controllability. We then examine extensions of these generative approaches to multimodal settings and their integration into artists’ workflows, including applications in music editing, captioning, production, transcription, source separation, performance, discovery, and education. Finally, we explore copyright implications of generative music and propose strategies to safeguard artist rights. While not exhaustive, this survey aims to illuminate promising research directions enabled by recent developments in music foundation models.

Method: Speech-DRAME introduces three components: (1) Speech-DRAME-EvalBench with human-annotated bilingual data, (2) DRAME-Eval fine-tuned evaluation model, and (3) Speech-DRAME-RoleBench for comparing speech foundation models. It uses two complementary evaluation strategies: Archetype Evaluation (top-down) and Realism Evaluation (bottom-up).

Result: DRAME-Eval achieves significantly stronger agreement with human ratings compared to zero-shot ALLMs, with Pearson correlation improving from 0.480 to 0.629 in archetypes and from 0.390 to 0.625 in realism evaluation.

Conclusion: Speech-DRAME provides the first comprehensive, reproducible foundation for assessing spoken role-play by integrating transparent benchmark resources, modeling approaches, and system-level evaluation.

Abstract: Role-play has become a key testbed for generative models, expanding from text-only dialogue to multimodal interaction. Extending role-play to speech captures prosody, emotion, and delivery, but also poses new evaluation challenges. Current pipelines often use audio large language models (ALLMs) as zero-shot judges, which miss paralinguistic cues, collapse multiple aspects into coarse scores, and rely on synthetic speech references that fail to reflect real-world roles. We present Speech-DRAME, a unified framework that contributes at three levels: (i) Speech-DRAME-EvalBench, an evaluation benchmark with bilingual human-annotated data and protocols for training and testing speech evaluation models (SEMs), (ii) DRAME-Eval, a fine-tuned evaluation model, which substantially outperforms zero-shot and few-shot ALLMs, and (iii) Speech-DRAME-RoleBench, a speech role-play benchmark that leverages DRAME-Eval as an automatic judge to compare speech foundation models (SFMs). Speech-DRAME distinguishes between two complementary evaluation strategies: Archetype Evaluation, a top-down approach measuring adherence to broad role archetypes, and Realism Evaluation, a bottom-up approach grounded in real human speech that emphasizes nuanced role quality. Compared to zero-shot ALLM judges, DRAME-Eval achieves stronger agreement with human ratings (Pearson correlation from 0.480 to 0.629 in archetypes, and 0.390 to 0.625 in realism). By integrating transparent benchmark resources, modeling approaches, and system-level evaluation, Speech-DRAME provides the first comprehensive, reproducible foundation for assessing spoken role-play.

Method: Interactive system using a Yamaha Disklavier as shared physical interface for turn-taking collaboration - human performs, signals handover, and model generates coherent continuation performed acoustically on the piano.

Result: The model can maintain stylistic semantics and develop coherent phrasal ideas, demonstrating musically sophisticated dialogue capabilities.

Conclusion: Such embodied systems can engage in sophisticated musical dialogue and open a promising new path for human-AI co-creation.

Abstract: While generative models for music composition are increasingly capable, their adoption by musicians is hindered by text-prompting, an asynchronous workflow disconnected from the embodied, responsive nature of instrumental performance. To address this, we introduce Aria-Duet, an interactive system facilitating a real-time musical duet between a human pianist and Aria, a state-of-the-art generative model, using a Yamaha Disklavier as a shared physical interface. The framework enables a turn-taking collaboration: the user performs, signals a handover, and the model generates a coherent continuation performed acoustically on the piano. Beyond describing the technical architecture enabling this low-latency interaction, we analyze the system’s output from a musicological perspective, finding the model can maintain stylistic semantics and develop coherent phrasal ideas, demonstrating that such embodied systems can engage in musically sophisticated dialogue and open a promising new path for human-AI co-creation.

Method: Adapting a state-of-the-art neural audio codec (DAC) trained on a large-scale custom-synthesized dataset from diverse sources, using a multi-objective loss function combining time-domain, spectral, and signal-level fidelity metrics.

Result: The paper presents a proof-of-concept for high-fidelity, generative audio restoration using the adapted DAC model.

Conclusion: This work successfully demonstrates a proof-of-concept approach for music denoising using neural audio codecs, paving the way for high-fidelity audio restoration.

Abstract: Audio denoising is critical in signal processing, enhancing intelligibility and fidelity for applications like restoring musical recordings. This paper presents a proof-of-concept for adapting a state-of-the-art neural audio codec, the Descript Audio Codec (DAC), for music denoising. This work overcomes the limitations of traditional architectures like U-Nets by training the model on a large-scale, custom-synthesized dataset built from diverse sources. Training is guided by a multi objective loss function that combines time-domain, spectral, and signal-level fidelity metrics. Ultimately, this paper aims to present a PoC for high-fidelity, generative audio restoration.

Method: Framework combining dataset standardization, feature extraction via Variational Autoencoders (VAE), and classification via Temporal Convolutional Networks (TCN) to process lengthy continuous audio segments without manual threshold setting.

Result: TCN demonstrated robust classification with AUC scores exceeding 0.9 for sperm whale click train detection in 4-minute recordings, outperforming traditional expert handpicked features.

Conclusion: The proposed VAE-TCN framework effectively handles weakly labeled bioacoustics data and captures complex temporal patterns without requiring strong labeling or manual threshold setting.

Abstract: Bioacoustics data from Passive acoustic monitoring (PAM) poses a unique set of challenges for classification, particularly the limited availability of complete and reliable labels in datasets due to annotation uncertainty, biological complexity due the heterogeneity in duration of cetacean vocalizations, and masking of target sounds due to environmental and anthropogenic noise. This means that data is often weakly labelled, with annotations indicating presence/absence of species over several minutes. In order to effectively capture the complex temporal patterns and key features of lengthy continuous audio segments, we propose an interdisciplinary framework comprising dataset standardisation, feature extraction via Variational Autoencoders (VAE) and classification via Temporal Convolutional Networks (TCN). This approach eliminates the necessity for manual threshold setting or time-consuming strong labelling. To demonstrate the effectiveness of our approach, we use sperm whale (Physeter macrocephalus) click trains in 4-minute recordings as a case study, from a dataset comprising diverse sources and deployment conditions to maximise generalisability. The value of feature extraction via the VAE is demonstrated by comparing classification performance against the traditional and explainable approach of expert handpicking of features. The TCN demonstrated robust classification capabilities achieving AUC scores exceeding 0.9.

Method: Uses a masked generative model with prompt-guidance mechanism for in-context learning to accept reference speaker timbre, and a self-critic mechanism for iterative self-assessment and refinement during generation.

Result: Extensive experiments show AnyEnhance outperforms existing methods in both objective metrics and subjective listening tests across various enhancement tasks.

Conclusion: AnyEnhance provides an effective unified solution for voice enhancement that handles multiple tasks simultaneously with superior performance, enabled by prompt-guidance and self-critic mechanisms.

Abstract: We introduce AnyEnhance, a unified generative model for voice enhancement that processes both speech and singing voices. Based on a masked generative model, AnyEnhance is capable of handling both speech and singing voices, supporting a wide range of enhancement tasks including denoising, dereverberation, declipping, super-resolution, and target speaker extraction, all simultaneously and without fine-tuning. AnyEnhance introduces a prompt-guidance mechanism for in-context learning, which allows the model to natively accept a reference speaker’s timbre. In this way, it could boost enhancement performance when a reference audio is available and enable the target speaker extraction task without altering the underlying architecture. Moreover, we also introduce a self-critic mechanism into the generative process for masked generative models, yielding higher-quality outputs through iterative self-assessment and refinement. Extensive experiments on various enhancement tasks demonstrate AnyEnhance outperforms existing methods in terms of both objective metrics and subjective listening tests. Demo audios are publicly available at https://amphionspace.github.io/anyenhance. An open-source implementation is provided at https://github.com/viewfinder-annn/anyenhance-v1-ccf-aatc.

Method: Proposed Kolmogorov-Arnold Network (KAN) using learnable activation functions as an INR model, and FewSound - a hypernetwork-based architecture for enhancing INR parameter updates.

Result: KAN achieved lowest Log-Spectral Distance of 1.29 and highest Perceptual Evaluation of Speech Quality of 3.57 for 1.5s audio. FewSound outperformed state-of-the-art HyperSound with 33.3% improvement in MSE and 60.87% in SI-SNR.

Conclusion: KAN is a robust and adaptable audio representation with potential for scalability and integration into various hypernetwork frameworks, demonstrating superior performance in audio encoding tasks.

Abstract: Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for audio representation. KAN demonstrates superior perceptual performance over previous INRs, achieving the lowest Log-SpectralDistance of 1.29 and the highest Perceptual Evaluation of Speech Quality of 3.57 for 1.5 s audio. To extend KAN’s utility, we propose FewSound, a hypernetwork-based architecture that enhances INR parameter updates. FewSound outperforms the state-of-the-art HyperSound, with a 33.3% improvement in MSE and 60.87% in SI-SNR. These results show KAN as a robust and adaptable audio representation with the potential for scalability and integration into various hypernetwork frameworks. The source code can be accessed at https://github.com/gmum/fewsound.git.

Method: Real-world users input custom text prompts and compare outputs from two TTM systems, with preferences used to compile a leaderboard. Features include LLM-based routing for heterogeneous systems, collection of detailed preferences (listening data and natural language feedback), and rolling data release with privacy guarantees.

Result: Music Arena provides a standardized evaluation protocol, transparent data access policies, and music-specific features that address key challenges in the TTM ecosystem while demonstrating domain-specific adaptation of live evaluation.

Conclusion: Music Arena successfully fills gaps in TTM evaluation by offering scalable human preference assessment through an open platform with music-tailored features, renewable preference data, and increased transparency for the research community.

Abstract: We present Music Arena, an open platform for scalable human preference evaluation of text-to-music (TTM) models. Soliciting human preferences via listening studies is the gold standard for evaluation in TTM, but these studies are expensive to conduct and difficult to compare, as study protocols may differ across systems. Moreover, human preferences might help researchers align their TTM systems or improve automatic evaluation metrics, but an open and renewable source of preferences does not currently exist. We aim to fill these gaps by offering live evaluation for TTM. In Music Arena, real-world users input text prompts of their choosing and compare outputs from two TTM systems, and their preferences are used to compile a leaderboard. While Music Arena follows recent evaluation trends in other AI domains, we also design it with key features tailored to music: an LLM-based routing system to navigate the heterogeneous type signatures of TTM systems, and the collection of detailed preferences including listening data and natural language feedback. We also propose a rolling data release policy with user privacy guarantees, providing a renewable source of preference data and increasing platform transparency. Through its standardized evaluation protocol, transparent data access policies, and music-specific features, Music Arena not only addresses key challenges in the TTM ecosystem but also demonstrates how live evaluation can be thoughtfully adapted to unique characteristics of specific AI domains. Music Arena is available at: https://music-arena.org . Preference data is available at: https://huggingface.co/music-arena .

Method: Uses an enhanced Action Chunking Transformer to predict multi-step action sequences from human demonstrations, with a dynamically adjustable sliding horizon to balance responsiveness and precision.

Result: Achieves expert-level performance on commercial VR titles with limited training data, while maintaining real-time inference at 60 FPS on consumer-grade hardware.

Conclusion: VRScout provides a practical and scalable framework for automated VR game testing, with applications in quality assurance and safety auditing.

Abstract: Virtual Reality (VR) has rapidly become a mainstream platform for gaming and interactive experiences, yet ensuring the quality, safety, and appropriateness of VR content remains a pressing challenge. Traditional human-based quality assurance is labor-intensive and cannot scale with the industry’s rapid growth. While automated testing has been applied to traditional 2D and 3D games, extending it to VR introduces unique difficulties due to high-dimensional sensory inputs and strict real-time performance requirements. We present VRScout, a deep learning-based agent capable of autonomously navigating VR environments and interacting with virtual objects in a human-like and real-time manner. VRScout learns from human demonstrations using an enhanced Action Chunking Transformer that predicts multi-step action sequences. This enables our agent to capture higher-level strategies and generalize across diverse environments. To balance responsiveness and precision, we introduce a dynamically adjustable sliding horizon that adapts the agent’s temporal context at runtime. We evaluate VRScout on commercial VR titles and show that it achieves expert-level performance with only limited training data, while maintaining real-time inference at 60 FPS on consumer-grade hardware. These results position VRScout as a practical and scalable framework for automated VR game testing, with direct applications in both quality assurance and safety auditing.

Method: Used Sparse Autoencoders (SAEs) with an innovative contrasting prompt method to identify optimal steering features. Tested on Llama-3 8B using AI-Generated Prompts Dataset and Air Bench eu-dataset for feature selection.

Result: Achieved 18.9% improvement in safety performance while simultaneously increasing utility by 11.1%, demonstrating that targeted SAE steering can overcome traditional safety-utility tradeoffs.

Conclusion: Targeted SAE steering with principled feature selection methods provides an effective solution for improving LLM safety without sacrificing utility, representing a significant advancement over previous approaches.

Abstract: Large Language Model (LLM) deployment requires guiding the LLM to recognize and not answer unsafe prompts while complying with safe prompts. Previous methods for achieving this require adjusting model weights along with other expensive procedures. While recent advances in Sparse Autoencoders (SAEs) have enabled interpretable feature extraction from LLMs, existing approaches lack systematic feature selection methods and principled evaluation of safety-utility tradeoffs. We explored using different steering features and steering strengths using Sparse Auto Encoders (SAEs) to provide a solution. Using an accurate and innovative contrasting prompt method with the AI-Generated Prompts Dataset from teknium/OpenHermes-2p5-Mistral-7B and Air Bench eu-dataset to efficiently choose the best features in the model to steer, we tested this method on Llama-3 8B. We conclude that using this method, our approach achieves an 18.9% improvement in safety performance while simultaneously increasing utility by 11.1%, demonstrating that targeted SAE steering can overcome traditional safety-utility tradeoffs when optimal features are identified through principled selection methods.

Method: Proposed a test case generation framework that explores both immediate neighbors of unlearned content and broader areas of potential failures to probe where unlearned models reveal knowledge holes.

Result: Evaluation showed significant hidden costs: up to 98.7% of test cases yielded irrelevant or nonsensical responses from unlearned models, despite being answerable by the pretrained model.

Conclusion: Findings necessitate rethinking the conventional approach to evaluating knowledge preservation in unlearning, moving beyond standard, static benchmarks to detect unintended knowledge losses.

Abstract: Machine unlearning has emerged as a prevalent technical solution for selectively removing unwanted knowledge absorbed during pre-training, without requiring full retraining. While recent unlearning techniques can effectively remove undesirable content without severely compromising performance on standard benchmarks, we find that they may inadvertently create ``knowledge holes’’ – unintended losses of benign knowledge that standard benchmarks fail to capture. To probe where unlearned models reveal knowledge holes, we propose a test case generation framework that explores both immediate neighbors of unlearned content and broader areas of potential failures. Our evaluation demonstrates significant hidden costs of unlearning: up to 98.7% of the test cases yield irrelevant or nonsensical responses from unlearned models, despite being answerable by the pretrained model. These findings necessitate rethinking the conventional approach to evaluating knowledge preservation in unlearning, moving beyond standard, static benchmarks.

Method: SBR uses a routing mechanism to learn token complexity and ranking, then selectively passes fewer tokens in later layers based on this ranking to focus processing capacity on more complex regions.

Result: SBR reduces computational cost by approximately 50% in FLOPs, delivers up to 2x faster inference, and maintains accuracy while being compatible with various neural operators.

Conclusion: SBR effectively addresses the computational inefficiency in neural operators by adaptively allocating processing resources based on token complexity, making it suitable for large-scale engineering tasks.

Abstract: In recent years, Neural Operators(NO) have gradually emerged as a popular approach for solving Partial Differential Equations (PDEs). However, their application to large-scale engineering tasks suffers from significant computational overhead. And the fact that current models impose a uniform computational cost while physical fields exhibit vastly different complexities constitutes a fundamental mismatch, which is the root of this inefficiency. For instance, in turbulence flows, intricate vortex regions require deeper network processing compared to stable flows. To address this, we introduce a framework: Skip-Block Routing (SBR), a general framework designed for Transformer-based neural operators, capable of being integrated into their multi-layer architectures. First, SBR uses a routing mechanism to learn the complexity and ranking of tokens, which is then applied during inference. Then, in later layers, it decides how many tokens are passed forward based on this ranking. This way, the model focuses more processing capacity on the tokens that are more complex. Experiments demonstrate that SBR is a general framework that seamlessly integrates into various neural operators. Our method reduces computational cost by approximately 50% in terms of Floating Point Operations (FLOPs), while still delivering up to 2x faster inference without sacrificing accuracy.

Method: Design a NAS-based framework with a search space of efficient components that capture energy time series patterns, and a novel objective function that considers temporal generalization and exploration of the high-dimensional search space.

Result: An ensemble of lightweight architectures discovered with NAS outperforms state-of-the-art techniques like Transformers and pre-trained forecasting models in both efficiency and accuracy on energy production time series.

Conclusion: The NAS-based approach successfully automates model discovery for energy forecasting, achieving superior balance between computational efficiency and predictive performance compared to existing methods.

Abstract: The dynamic energy sector requires both predictive accuracy and runtime efficiency for short-term forecasting of energy generation under operational constraints, where timely and precise predictions are crucial. The manual configuration of complex methods, which can generate accurate global multi-step predictions without suffering from a computational bottleneck, represents a procedure with significant time requirements and high risk for human-made errors. A further intricacy arises from the temporal dynamics present in energy-related data. Additionally, the generalization to unseen data is imperative for continuously deploying forecasting techniques over time. To overcome these challenges, in this research, we design a neural architecture search (NAS)-based framework for the automated discovery of time series models that strike a balance between computational efficiency, predictive performance, and generalization power for the global, multi-step short-term forecasting of energy production time series. In particular, we introduce a search space consisting only of efficient components, which can capture distinctive patterns of energy time series. Furthermore, we formulate a novel objective function that accounts for performance generalization in temporal context and the maximal exploration of different regions of our high-dimensional search space. The results obtained on energy production time series show that an ensemble of lightweight architectures discovered with NAS outperforms state-of-the-art techniques, such as Transformers, as well as pre-trained forecasting models, in terms of both efficiency and accuracy.

Method: Proves existence of optimal reward threshold to separate winning/losing responses, enabling principled pseudo-labeling of unpaired data. Uses pseudo-labels to distill latent preferences from unlabeled data.

Result: SSPO with Llama3-8B-Instruct on just 1% of UltraFeedback consistently outperforms baselines trained on 10% of UltraFeedback, demonstrating remarkable data efficiency.

Conclusion: SSPO effectively reduces dependency on expensive labeled data while maintaining human alignment performance through semi-supervised learning approach.

Abstract: The field of preference optimization has made outstanding contributions to the alignment of language models with human preferences. Despite these advancements, recent methods still rely heavily on substantial paired (labeled) feedback data, leading to substantial resource expenditures. To address these challenges, we study the problem of Semi-Supervised Preference Optimization (SSPO) in which the idea is to learn from both a small number of pairwise preference labels and a large pool of unpaired samples simultaneously. Our key theoretical contribution proves the existence of an optimal reward threshold capable of separating winning and losing responses with high probability, which enables a principled pseudo-labeling of unpaired data. By leveraging these pseudo-labels, SSPO effectively distills latent preferences from large-scale unpaired data, thus maintaining human alignment while drastically reducing acquisition costs. Extensive experiments across datasets validate this remarkable data efficiency; for instance, SSPO trained with Llama3-8B-Instruct on just 1% of UltraFeedback consistently surpasses strong baselines trained on 10% of UltraFeedback.

Method: Physics-Informed Neural Networks (PINNs) that embed physical laws into learning process, with systematic evaluation of accuracy, training efficiency, and generalization using classical ODE problems as testbeds.

Result: PINNs achieve superior results when loss function components (data loss, initial condition loss, residual loss) are properly balanced and hyperparameters are systematically tuned.

Conclusion: PINNs offer powerful approach for challenging ODE problems, with careful weighting of loss components and hyperparameter tuning being crucial for convergence to correct solutions.

Abstract: In this study, we present and validate the predictive capability of the Physics-Informed Neural Networks (PINNs) methodology for solving a variety of engineering and biological dynamical systems governed by ordinary differential equations (ODEs). While traditional numerical methods a re effective for many ODEs, they often struggle to achieve convergence in problems involving high stiffness, shocks, irregular domains, singular perturbations, high dimensions, or boundary discontinuities. Alternatively, PINNs offer a powerful approach for handling challenging numerical scenarios. In this study, classical ODE problems are employed as controlled testbeds to systematically evaluate the accuracy, training efficiency, and generalization capability under controlled conditions of the PINNs framework. Although not a universal solution, PINNs can achieve superior results by embedding physical laws directly into the learning process. We first analyze the existence and uniqueness properties of several benchmark problems and subsequently validate the PINNs methodology on these model systems. Our results demonstrate that for complex problems to converge to correct solutions, the loss function components data loss, initial condition loss, and residual loss must be appropriately balanced through careful weighting. We further establish that systematic tuning of hyperparameters, including network depth, layer width, activation functions, learning rate, optimization algorithms, w eight initialization schemes, and collocation point sampling, plays a crucial role in achieving accurate solutions. Additionally, embedding prior knowledge and imposing hard constraints on the network architecture, without loss the generality of the ODE system, significantly enhances the predictive capability of PINNs.

Method: ReLaX-Net employs layer-by-layer time-multiplexing to reuse parameters across multiple layers, requiring only the addition of fast switches to existing PNN hardware. This leverages the time-scale separation between fast dynamic elements and slowly trainable weight elements in PNNs.

Result: Numerical experiments on image classification and NLP tasks show ReLaX-Net improves computational performance with minor hardware modifications. It demonstrates favorable scaling, outperforming equivalent traditional RNNs/DNNs with the same number of parameters.

Conclusion: ReLaX-Net provides an effective approach to enhance PNN performance through parameter reuse and time-multiplexing, offering a path to bridge the performance gap between PNNs and digital neural networks while maintaining hardware efficiency.

Abstract: Physical Neural Networks (PNN) are promising platforms for next-generation computing systems. However, recent advances in digital neural network performance are largely driven by the rapid growth in the number of trainable parameters and, so far, demonstrated PNNs are lagging behind by several orders of magnitude in terms of scale. This mirrors size and performance constraints found in early digital neural networks. In that period, efficient reuse of parameters contributed to the development of parameter-efficient architectures such as convolutional neural networks. In this work, we numerically investigate hardware-friendly weight-tying for PNNs. Crucially, with many PNN systems, there is a time-scale separation between the fast dynamic active elements of the forward pass and the only slowly trainable elements implementing weights and biases. With this in mind,we propose the Reuse of Layers for eXpanding a Neural Network (ReLaX-Net) architecture, which employs a simple layer-by-layer time-multiplexing scheme to increase the effective network depth and efficiently use the number of parameters. We only require the addition of fast switches for existing PNNs. We validate ReLaX-Nets via numerical experiments on image classification and natural language processing tasks. Our results show that ReLaX-Net improves computational performance with only minor modifications to a conventional PNN. We observe a favorable scaling, where ReLaX-Nets exceed the performance of equivalent traditional RNNs or DNNs with the same number of parameters.

Method: Integrates Graph-BERT with a GRU layer to capture temporal evolution and modifies the algorithm to support directed edges for financial transaction analysis.

Result: Outperforms EvolveGCN before market shutdown and surpasses GCN after the event on the Elliptic Bitcoin dataset, with ablation study confirming GRU’s importance for temporal dynamics.

Conclusion: DynBERG effectively addresses the limitations of static graph models for financial fraud detection by incorporating temporal dynamics and directed edge support, demonstrating superior adaptability to market shifts.

Abstract: Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph Convolutional Networks (GCNs) are widely used for financial fraud detection, graph Transformer models such as Graph-BERT are gaining prominence due to their Transformer-based architecture, which mitigates issues such as over-smoothing. Graph-BERT is designed for static graphs and primarily evaluated on citation networks with undirected edges. However, financial transaction networks are inherently dynamic, with evolving structures and directed edges representing the flow of money. To address these challenges, we introduce DynBERG, a novel architecture that integrates Graph-BERT with a Gated Recurrent Unit (GRU) layer to capture temporal evolution over multiple time steps. Additionally, we modify the underlying algorithm to support directed edges, making DynBERG well-suited for dynamic financial transaction analysis. We evaluate our model on the Elliptic dataset, which includes Bitcoin transactions, including all transactions during a major cryptocurrency market event, the Dark Market Shutdown. By assessing DynBERG’s resilience before and after this event, we analyse its ability to adapt to significant market shifts that impact transaction behaviours. Our model is benchmarked against state-of-the-art dynamic graph classification approaches, such as EvolveGCN and GCN, demonstrating superior performance, outperforming EvolveGCN before the market shutdown and surpassing GCN after the event. Additionally, an ablation study highlights the critical role of incorporating a time-series deep learning component, showcasing the effectiveness of GRU in modelling the temporal dynamics of financial transactions.

Method: Integrates GNN for spatial reasoning, self-supervised pretraining for representation learning, and spatio-temporal adaptation mechanism for generalization across forecasting horizons.

Result: Achieves superior performance on ERA5 and MERRA-2 datasets compared to traditional NWP and recent deep learning methods; captures fine-grained meteorological patterns in Beijing and Shanghai.

Conclusion: Provides a scalable and label-efficient solution for future data-driven weather forecasting systems.

Abstract: Accurate and robust weather forecasting remains a fundamental challenge due to the inherent spatio-temporal complexity of atmospheric systems. In this paper, we propose a novel self-supervised learning framework that leverages spatio-temporal structures to improve multi-variable weather prediction. The model integrates a graph neural network (GNN) for spatial reasoning, a self-supervised pretraining scheme for representation learning, and a spatio-temporal adaptation mechanism to enhance generalization across varying forecasting horizons. Extensive experiments on both ERA5 and MERRA-2 reanalysis datasets demonstrate that our approach achieves superior performance compared to traditional numerical weather prediction (NWP) models and recent deep learning methods. Quantitative evaluations and visual analyses in Beijing and Shanghai confirm the model’s capability to capture fine-grained meteorological patterns. The proposed framework provides a scalable and label-efficient solution for future data-driven weather forecasting systems.

Method: Built on Modelica-based end-to-end models, LC-Opt provides a Gymnasium interface where RL agents optimize thermal controls like liquid supply temperature, flow rate, valve actuation, and cooling tower setpoints with dynamic workloads.

Result: The environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and supports additional components like heat recovery units.

Conclusion: LC-Opt democratizes access to detailed liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions through benchmarked RL approaches and interpretable control methods.

Abstract: Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab’s Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.

Method: Proposes FLoRA - fused forward-backward adapters (FFBA) that combine LoRA and parallel adapters. The adapters are fused into existing projection layers of the base model to minimize latency.

Result: Experimental results show FLoRA performs significantly better than LoRA in both accuracy and latency for similar parameter budgets.

Conclusion: FLoRA provides an effective parameter-efficient fine-tuning approach that outperforms popular methods like LoRA while maintaining computational efficiency.

Abstract: As the large language models (LLMs) grow in size each day, efficient training and fine-tuning has never been as important as nowadays. This resulted in the great interest in parameter efficient fine-tuning (PEFT), and effective methods including low-rank adapters (LoRA) has emerged. Although the various PEFT methods have been studied extensively in the recent years, the greater part of the subject remains unexplored with the huge degree of freedom. In this paper, we propose FLoRA, a family of fused forward-backward adapters (FFBA) for parameter-efficient fine-tuning of LLMs on downstream tasks. The FFBA combine ideas from the popular LoRA and parallel adapters to improve the overall fine-tuning accuracies. At the same time, latencies are minimized by fusing the forward and backward adapters into existing projection layers of the base model. Experimental results show that the proposed FFB adapters perform significantly better than the popularly used LoRA in both accuracy and latency for a similar parameter budget.

Method: Combines curated real-world datasets (AI workload traces, grid carbon intensity, electricity markets, weather across 20 regions) with physics-informed models of data center operations, providing a Gymnasium API with baseline controllers including reinforcement learning and rule-based strategies.

Result: Presents a challenging scheduling problem where a coordinating agent must dynamically reassign or defer tasks across configurable data center clusters to optimize multiple objectives including carbon emissions, energy costs, SLAs, and water use.

Conclusion: DCcluster-Opt accelerates the development and validation of next-generation sustainable computing solutions for geo-distributed data centers by offering a realistic, configurable, and accessible testbed for reproducible research.

Abstract: The increasing energy demands and carbon footprint of large-scale AI require intelligent workload management in globally distributed data centers. Yet progress is limited by the absence of benchmarks that realistically capture the interplay of time-varying environmental factors (grid carbon intensity, electricity prices, weather), detailed data center physics (CPUs, GPUs, memory, HVAC energy), and geo-distributed network dynamics (latency and transmission costs). To bridge this gap, we present DCcluster-Opt: an open-source, high-fidelity simulation benchmark for sustainable, geo-temporal task scheduling. DCcluster-Opt combines curated real-world datasets, including AI workload traces, grid carbon intensity, electricity markets, weather across 20 global regions, cloud transmission costs, and empirical network delay parameters with physics-informed models of data center operations, enabling rigorous and reproducible research in sustainable computing. It presents a challenging scheduling problem where a top-level coordinating agent must dynamically reassign or defer tasks that arrive with resource and service-level agreement requirements across a configurable cluster of data centers to optimize multiple objectives. The environment also models advanced components such as heat recovery. A modular reward system enables an explicit study of trade-offs among carbon emissions, energy costs, service level agreements, and water use. It provides a Gymnasium API with baseline controllers, including reinforcement learning and rule-based strategies, to support reproducible ML research and a fair comparison of diverse algorithms. By offering a realistic, configurable, and accessible testbed, DCcluster-Opt accelerates the development and validation of next-generation sustainable computing solutions for geo-distributed data centers.

Method: Identified DoRA’s success comes from increased singular value entropy, reformulated DoRA into efficient matrix form, and proposed two new methods: Pre-Diag (diagonal conditioning before LoRA) and SORA (parameter-efficient orthogonal rotation).

Result: Extensive experiments show proposed methods achieve superior performance and efficiency compared to both LoRA and DoRA on natural language understanding and generation tasks.

Conclusion: The unified framework enables systematic design of advanced PEFT methods, with Pre-Diag and SORA demonstrating state-of-the-art performance-efficiency trade-offs.

Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods are crucial for adapting large pre-trained models. Among these, LoRA is considered a foundational approach. Building on this, the influential DoRA method enhances performance by decomposing weight updates into magnitude and direction. However, its underlying mechanism remains unclear, and it introduces significant computational overhead. In this work, we first identify that DoRA’s success stems from its capacity to increase the singular value entropy of the weight update matrix, which promotes a more uniform update distribution akin to full fine-tuning. We then reformulate DoRA into a mathematically equivalent and more efficient matrix form, revealing it as a learnable weight conditioning method. Based on this insight, we propose a unified framework for designing advanced PEFT methods by exploring two orthogonal dimensions: the architectural placement and the transformation type of the conditioning matrix. Within this framework, we introduce two novel methods: (1) \textbf{Pre-Diag}, which applies a diagonal conditioning matrix before the LoRA update to efficiently calibrate the pre-trained weights, thereby enhancing performance while reducing training time; and (2) \textbf{S}kewed \textbf{O}rthogonal \textbf{R}otation \textbf{A}daptation (\textbf{SORA}), which employs a parameter-efficient orthogonal rotation to perform a more powerful, norm-preserving transformation of the feature space. Extensive experiments on natural language understanding and generation tasks demonstrate that our proposed methods achieve superior performance and efficiency compared to both LoRA and DoRA. The code is available at https://github.com/MaeChd/SORA.

Method: Applied FGA to MNIST and LSC datasets, evaluating how neural network architecture, training, and feature selection impact FGA’s effectiveness.

Result: FGA achieved higher precision than literature results, with feature selection significantly affecting recall but having negligible impact on precision.

Conclusion: FGA shows promise for industrial applications with improved precision, though recall is sensitive to model and feature selection choices.

Abstract: Understanding why neural networks make certain decisions is pivotal for their use in safety-critical applications. Feature-Guided Analysis (FGA) extracts slices of neural networks relevant to their tasks. Existing feature-guided approaches typically monitor the activation of the neural network neurons to extract the relevant rules. Preliminary results are encouraging and demonstrate the feasibility of this solution by assessing the precision and recall of Feature-Guided Analysis on two pilot case studies. However, the applicability in industrial contexts needs additional empirical evidence. To mitigate this need, this paper assesses the applicability of FGA on a benchmark made by the MNIST and LSC datasets. We assessed the effectiveness of FGA in computing rules that explain the behavior of the neural network. Our results show that FGA has a higher precision on our benchmark than the results from the literature. We also evaluated how the selection of the neural network architecture, training, and feature selection affect the effectiveness of FGA. Our results show that the selection significantly affects the recall of FGA, while it has a negligible impact on its precision.

Method: Developed Quadratic Direct Forecast (QDF) learning algorithm that uses an adaptively updated quadratic-form weighting matrix, where off-diagonal elements account for label autocorrelation and non-uniform diagonals match preferable weights for different forecasting tasks.

Result: QDF effectively improves performance of various forecast models, achieving state-of-the-art results in experiments.

Conclusion: The proposed quadratic-form weighted training objective successfully addresses limitations of traditional objectives and significantly enhances forecasting performance.

Abstract: The design of training objective is central to training time-series forecasting models. Existing training objectives such as mean squared error mostly treat each future step as an independent, equally weighted task, which we found leading to the following two issues: (1) overlook the label autocorrelation effect among future steps, leading to biased training objective; (2) fail to set heterogeneous task weights for different forecasting tasks corresponding to varying future steps, limiting the forecasting performance. To fill this gap, we propose a novel quadratic-form weighted training objective, addressing both of the issues simultaneously. Specifically, the off-diagonal elements of the weighting matrix account for the label autocorrelation effect, whereas the non-uniform diagonals are expected to match the most preferable weights of the forecasting tasks with varying future steps. To achieve this, we propose a Quadratic Direct Forecast (QDF) learning algorithm, which trains the forecast model using the adaptively updated quadratic-form weighting matrix. Experiments show that our QDF effectively improves performance of various forecast models, achieving state-of-the-art results. Code is available at https://anonymous.4open.science/r/QDF-8937.

Method: Introduces SpatialTraceGen framework with an automated Verifier that scalably ensures fidelity of multi-hop, multi-tool reasoning traces distilled from large teacher models.

Result: On CLEVR-Humans benchmark, the verifier-guided process improves average quality score by 17% and reduces quality variance by over 40%, creating a dataset of expert reasoning traces.

Conclusion: SpatialTraceGen provides structured step-by-step examples necessary for effective fine-tuning and sample-efficient offline reinforcement learning in spatial reasoning tasks.

Abstract: While Vision-Language Models (VLMs) excel in many areas, they struggle with complex spatial reasoning, which requires problem decomposition and strategic tool use. Fine-tuning smaller, more deployable models offers an efficient path to strong performance, but this is hampered by a major bottleneck: the absence of high-quality, step-by-step reasoning data. To address this data-efficiency gap, we introduce SpatialTraceGen, a framework to distill the reasoning processes of a large teacher model into a high-quality dataset of multi-hop, multi-tool reasoning traces. A key innovation is our automated Verifier, which scalably ensures the fidelity of each reasoning step, providing a cost-effective alternative to manual human annotation. On the CLEVR-Humans benchmark, this verifier-guided process improves the average quality score of traces by 17% while reducing quality variance by over 40%. SpatialTraceGen delivers a dataset of expert traces, providing the structured, step-by-step examples of tool use necessary for effective fine-tuning and sample-efficient offline reinforcement learning.

Method: Evaluated multiple FL algorithms in real deployment scenarios, comparing client-controlled and server-controlled workflows against centralized learning baseline, measuring accuracy, training time, communication overhead, and energy usage.

Result: The study provides empirical evaluation of FL effectiveness in real-world UAV thermal imaging scenarios, highlighting practical performance trade-offs between different FL approaches.

Conclusion: The findings serve as a valuable reference for understanding practical applications and limitations of FL methods in UAV-based image segmentation tasks.

Abstract: Federated Learning (FL) is an approach for training a shared Machine Learning (ML) model with distributed training data and multiple participants. FL allows bypassing limitations of the traditional Centralized Machine Learning CL if data cannot be shared or stored centrally due to privacy or technical restrictions – the participants train the model locally with their training data and do not need to share it among the other participants. This paper investigates the practical implementation and effectiveness of FL in a real-world scenario, specifically focusing on unmanned aerial vehicle (UAV)-based thermal images for common thermal feature detection in urban environments. The distributed nature of the data arises naturally and makes it suitable for FL applications, as images captured in two German cities are available. This application presents unique challenges due to non-identical distribution and feature characteristics of data captured at both locations. The study makes several key contributions by evaluating FL algorithms in real deployment scenarios rather than simulation. We compare several FL approaches with a centralized learning baseline across key performance metrics such as model accuracy, training time, communication overhead, and energy usage. This paper also explores various FL workflows, comparing client-controlled workflows and server-controlled workflows. The findings of this work serve as a valuable reference for understanding the practical application and limitations of the FL methods in segmentation tasks in UAV-based imaging.

Method: Divides each transformer layer into smaller modules, assigns importance scores to each module, and uses weighted random sampling to activate modules during optimization to reduce gradient variance.

Result: MISA achieves O(1/√K) convergence rate under non-convex stochastic conditions, reduces gradient variance compared to layer-wise sampling, and shows superior memory efficiency over baseline methods in experiments.

Conclusion: MISA effectively addresses memory limitations in LLM optimization through module-wise importance sampling, providing theoretical guarantees and practical improvements over existing methods.

Abstract: The substantial memory demands of pre-training and fine-tuning large language models (LLMs) require memory-efficient optimization algorithms. One promising approach is layer-wise optimization, which treats each transformer block as a single layer and optimizes it sequentially, while freezing the other layers to save optimizer states and activations. Although effective, these methods ignore the varying importance of the modules within each layer, leading to suboptimal performance. Moreover, layer-wise sampling provides only limited memory savings, as at least one full layer must remain active during optimization. To overcome these limitations, we propose Module-wise Importance SAmpling (MISA), a novel method that divides each layer into smaller modules and assigns importance scores to each module. MISA uses a weighted random sampling mechanism to activate modules, provably reducing gradient variance compared to layer-wise sampling. Additionally, we establish an (\mathcal{O}(1/\sqrt{K})) convergence rate under non-convex and stochastic conditions, where $K$ is the total number of block updates, and provide a detailed memory analysis showcasing MISA’s superiority over existing baseline methods. Experiments on diverse learning tasks validate the effectiveness of MISA. Source code is available at https://github.com/pkumelon/MISA.

Method: Train regression decision trees to map board states to neuron activations, extract decision paths where neurons are highly active, and convert them into human-readable logical forms.

Result: About half of layer 5 neurons (913 of 2,048) can be accurately described by compact, rule-based decision trees (R² > 0.7), with interventions showing 5-10x stronger degradation when ablating pattern-corresponding neurons.

Conclusion: The approach successfully identifies interpretable, rule-based computational patterns in neural networks and provides a tool for mapping game behaviors to implementing neurons, supporting future interpretability research.

Abstract: OthelloGPT, a transformer trained to predict valid moves in Othello, provides an ideal testbed for interpretability research. The model is complex enough to exhibit rich computational patterns, yet grounded in rule-based game logic that enables meaningful reverse-engineering. We present an automated approach based on decision trees to identify and interpret MLP neurons that encode rule-based game logic. Our method trains regression decision trees to map board states to neuron activations, then extracts decision paths where neurons are highly active to convert them into human-readable logical forms. These descriptions reveal highly interpretable patterns; for instance, neurons that specifically detect when diagonal moves become legal. Our findings suggest that roughly half of the neurons in layer 5 can be accurately described by compact, rule-based decision trees ($R^2 > 0.7$ for 913 of 2,048 neurons), while the remainder likely participate in more distributed or non-rule-based computations. We verify the causal relevance of patterns identified by our decision trees through targeted interventions. For a specific square, for specific game patterns, we ablate neurons corresponding to those patterns and find an approximately 5-10 fold stronger degradation in the model’s ability to predict legal moves along those patterns compared to control patterns. To facilitate future work, we provide a Python tool that maps rule-based game behaviors to their implementing neurons, serving as a resource for researchers to test whether their interpretability methods recover meaningful computational structures.

Method: EVINGCA expands rooted graphs via breadth-first search guided by continuously updated local distance and shape statistics, using spatial indexing for efficiency.

Result: EVINGCA achieves log-linear complexity in average case and shows competitive performance against baselines across synthetic, real-world, low-dimensional and high-dimensional datasets.

Conclusion: EVINGCA provides an effective alternative to existing clustering methods by treating cluster formation as an adaptive, evolving process with local statistical guidance.

Abstract: Clustering algorithms often rely on restrictive assumptions: K-Means and Gaussian Mixtures presuppose convex, Gaussian-like clusters, while DBSCAN and HDBSCAN capture non-convexity but can be highly sensitive. I introduce EVINGCA (Evolving Variance-Informed Nonparametric Graph Construction Algorithm), a density-variance based clustering algorithm that treats cluster formation as an adaptive, evolving process on a nearest-neighbor graph. EVINGCA expands rooted graphs via breadth-first search, guided by continuously updated local distance and shape statistics, replacing fixed density thresholds with local statistical feedback. With spatial indexing, EVINGCA features log-linear complexity in the average case and exhibits competitive performance against baselines across a variety of synthetic, real-world, low-d, and high-d datasets.

Method: Used EEG recordings during natural speech perception, compared embeddings from wav2vec2 (sound to language) and CLIP (words to images) models, evaluated alignment with brain activity using ridge regression and contrastive decoding, and tested three layer combination strategies: individual layers, progressive concatenation, and progressive summation.

Result: The findings suggest that combining multimodal, layer-aware representations may improve our ability to decode how the brain understands language as experience rather than just sound.

Conclusion: Multimodal, layer-aware model representations can better capture the brain’s hierarchical processing of language, moving beyond acoustic processing to include rich semantic and experiential associations.

Abstract: When we hear the word “house”, we don’t just process sound, we imagine walls, doors, memories. The brain builds meaning through layers, moving from raw acoustics to rich, multimodal associations. Inspired by this, we build on recent work from Meta that aligned EEG signals with averaged wav2vec2 speech embeddings, and ask a deeper question: which layers of pre-trained models best reflect this layered processing in the brain? We compare embeddings from two models: wav2vec2, which encodes sound into language, and CLIP, which maps words to images. Using EEG recorded during natural speech perception, we evaluate how these embeddings align with brain activity using ridge regression and contrastive decoding. We test three strategies: individual layers, progressive concatenation, and progressive summation. The findings suggest that combining multimodal, layer-aware representations may bring us closer to decoding how the brain understands language, not just as sound, but as experience.

Method: TR-GRPO extends GRPO by assigning token-level weights positively correlated with predicted probability, downweighting low-probability tokens and emphasizing high-probability ones.

Result: TR-GRPO consistently outperforms GRPO across RLVR tasks including logic, math, and agentic reasoning.

Conclusion: Regulating token contributions during RL training is crucial, and TR-GRPO establishes itself as a robust framework for enhancing LLM reasoning.

Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful approach for strengthening the reasoning capabilities of large language models (LLMs). Among existing algorithms, Group Relative Policy Optimization (GRPO) has demonstrated strong performance, yet it suffers from a critical issue: low-probability tokens disproportionately dominate gradient updates due to their inherently large gradient magnitudes. This imbalance leads to unstable training and suppresses the contribution of high-probability tokens that are more reliable for learning. In this work, we introduce Token-Regulated Group Relative Policy Optimization (TR-GRPO), a simple yet effective extension of GRPO that assigns token-level weights positively correlated with the model’s predicted probability. By downweighting low-probability tokens and emphasizing high-probability ones, TR-GRPO mitigates gradient over-amplification while preserving informative learning signals. Extensive experiments demonstrate that TR-GRPO consistently outperforms GRPO across RLVR tasks, including logic, math, and agentic reasoning, highlighting the importance of regulating token contributions during RL training and establishing TR-GRPO as a robust framework for enhancing LLM reasoning.

Method: The method performs latent domain clustering on image features and fuses domain-specific text features based on similarity between input images and discovered latent domains, enabling adaptive knowledge transfer.

Result: Experiments on four benchmarks show consistent improvements over VLM-based baselines, demonstrating the effectiveness of the approach for domain generalization without domain labels.

Conclusion: The proposed strategy provides new insights into improving robustness under domain shift and enables effective domain generalization without requiring explicit domain labels.

Abstract: The objective of domain generalization (DG) is to enable models to be robust against domain shift. DG is crucial for deploying vision-language models (VLMs) in real-world applications, yet most existing methods rely on domain labels that may not be available and often ambiguous. We instead study the DG setting where models must generalize well without access to explicit domain labels. Our key idea is to represent an unseen target domain as a combination of latent domains automatically discovered from training data, enabling the model to adaptively transfer knowledge across domains. To realize this, we perform latent domain clustering on image features and fuse domain-specific text features based on the similarity between the input image and each latent domain. Experiments on four benchmarks show that this strategy yields consistent gains over VLM-based baselines and provides new insights into improving robustness under domain shift.

Method: Comparative study between Bayesian Optimization (BoTorch Ax and qEHVI) and fine-tuned LLMs/BERT models using Parameter-Efficient Fine-Tuning (PEFT), framing the problem as regression with custom output heads.

Result: BoTorch qEHVI achieved perfect convergence (GD=0.0), while the best LLM (WizardMath-7B) achieved GD=1.21, significantly outperforming traditional BoTorch Ax baseline (GD=15.03).

Conclusion: Specialized BO frameworks remain performance leaders for guaranteed convergence, but fine-tuned LLMs are validated as promising, computationally fast alternatives for multi-objective optimization tasks.

Abstract: This paper investigates the performance of Large Language Models (LLMs) as generative optimizers for solving constrained multi-objective regression tasks, specifically within the challenging domain of inverse design (property-to-structure mapping). This problem, critical to materials informatics, demands finding complex, feasible input vectors that lie on the Pareto optimal front. While LLMs have demonstrated universal effectiveness across generative and reasoning tasks, their utility in constrained, continuous, high-dimensional numerical spaces tasks they weren’t explicitly architected for remains an open research question. We conducted a rigorous comparative study between established Bayesian Optimization (BO) frameworks and a suite of fine-tuned LLMs and BERT models. For BO, we benchmarked the foundational BoTorch Ax implementation against the state-of-the-art q-Expected Hypervolume Improvement (qEHVI, BoTorchM). The generative approach involved fine-tuning models via Parameter-Efficient Fine-Tuning (PEFT), framing the challenge as a regression problem with a custom output head. Our results show that BoTorch qEHVI achieved perfect convergence (GD=0.0), setting the performance ceiling. Crucially, the best-performing LLM (WizardMath-7B) achieved a Generational Distance (GD) of 1.21, significantly outperforming the traditional BoTorch Ax baseline (GD=15.03). We conclude that specialized BO frameworks remain the performance leader for guaranteed convergence, but fine-tuned LLMs are validated as a promising, computationally fast alternative, contributing essential comparative metrics to the field of AI-driven optimization. The findings have direct industrial applications in optimizing formulation design for resins, polymers, and paints, where multi-objective trade-offs between mechanical, rheological, and chemical properties are critical to innovation and production efficiency.

Method: Employ inference strategies during execution time that utilize specific time and compute budgets to explore multiple attempts before outputting final solutions in complex multi-agent RL problems.

Result: Achieved up to 126% improvement (45% average) over previous state-of-the-art across 17 tasks, using only a couple seconds of extra wall-clock time. Demonstrated promising compute scaling properties through over 60k experiments.

Conclusion: Inference phase strategies at execution time are key to breaking performance ceilings in complex multi-agent RL problems, offering substantial improvements with minimal computational overhead.

Abstract: Reinforcement learning (RL) systems have countless applications, from energy-grid management to protein design. However, such real-world scenarios are often extremely difficult, combinatorial in nature, and require complex coordination between multiple agents. This level of complexity can cause even state-of-the-art RL systems, trained until convergence, to hit a performance ceiling which they are unable to break out of with zero-shot inference. Meanwhile, many digital or simulation-based applications allow for an inference phase that utilises a specific time and compute budget to explore multiple attempts before outputting a final solution. In this work, we show that such an inference phase employed at execution time, and the choice of a corresponding inference strategy, are key to breaking the performance ceiling observed in complex multi-agent RL problems. Our main result is striking: we can obtain up to a 126% and, on average, a 45% improvement over the previous state-of-the-art across 17 tasks, using only a couple seconds of extra wall-clock time during execution. We also demonstrate promising compute scaling properties, supported by over 60k experiments, making it the largest study on inference strategies for complex RL to date. Our experimental data and code are available at https://sites.google.com/view/inference-strategies-rl.

Method: SynBrain uses two components: BrainVAE for probabilistic neural representation learning with visual semantic constraints, and a Semantic-to-Neural Mapper that projects visual semantics into neural response manifold for fMRI synthesis.

Result: SynBrain outperforms state-of-the-art methods in subject-specific visual-to-fMRI encoding, adapts efficiently to new subjects with few-shot data, and synthesizes high-quality fMRI signals that improve fMRI-to-image decoding performance.

Conclusion: SynBrain successfully models the one-to-many visual-to-neural mapping, reveals functional consistency across trials and subjects, and captures interpretable patterns shaped by biological neural variability.

Abstract: Deciphering how visual stimuli are transformed into cortical responses is a fundamental challenge in computational neuroscience. This visual-to-neural mapping is inherently a one-to-many relationship, as identical visual inputs reliably evoke variable hemodynamic responses across trials, contexts, and subjects. However, existing deterministic methods struggle to simultaneously model this biological variability while capturing the underlying functional consistency that encodes stimulus information. To address these limitations, we propose SynBrain, a generative framework that simulates the transformation from visual semantics to neural responses in a probabilistic and biologically interpretable manner. SynBrain introduces two key components: (i) BrainVAE models neural representations as continuous probability distributions via probabilistic learning while maintaining functional consistency through visual semantic constraints; (ii) A Semantic-to-Neural Mapper acts as a semantic transmission pathway, projecting visual semantics into the neural response manifold to facilitate high-fidelity fMRI synthesis. Experimental results demonstrate that SynBrain surpasses state-of-the-art methods in subject-specific visual-to-fMRI encoding performance. Furthermore, SynBrain adapts efficiently to new subjects with few-shot data and synthesizes high-quality fMRI signals that are effective in improving data-limited fMRI-to-image decoding performance. Beyond that, SynBrain reveals functional consistency across trials and subjects, with synthesized signals capturing interpretable patterns shaped by biological neural variability. Our code is available at https://github.com/MichaelMaiii/SynBrain.

Method: Transform integers into wavelet-domain representations, extract multi-scale statistical features, and cluster using k-means algorithm without label supervision.

Result: Achieved 69.67% classification accuracy in distinguishing odd vs even numbers, revealing meaningful structural differences in the feature space.

Conclusion: Classical signal-processing techniques can uncover latent structure in discrete symbolic domains, providing insights for repurposing feature engineering and clustering in unconventional ML problems.

Abstract: This paper explores a deliberately over-engineered approach to the classical problem of parity detection – determining whether a number is odd or even – by combining wavelet-based feature extraction with unsupervised clustering. Instead of relying on modular arithmetic, integers are transformed into wavelet-domain representations, from which multi-scale statistical features are extracted and clustered using the k-means algorithm. The resulting feature space reveals meaningful structural differences between odd and even numbers, achieving a classification accuracy of approximately 69.67% without any label supervision. These results suggest that classical signal-processing techniques, originally designed for continuous data, can uncover latent structure even in purely discrete symbolic domains. Beyond parity detection, the study provides an illustrative perspective on how feature engineering and clustering may be repurposed for unconventional machine learning problems, potentially bridging symbolic reasoning and feature-based learning.

Method: Frame visual recognition as program synthesis, training a VLM to decompile raster images into executable programs composed of geometric primitives (Bézier curves) in the mathematical domain.

Result: The model outperforms strong zero-shot baselines including GPT-4o and demonstrates zero-shot generalization from modern Chinese characters to ancient Oracle Bone Script reconstruction.

Conclusion: The model acquires an abstract, transferable geometric grammar that enables structured visual understanding beyond pixel-level pattern recognition.

Abstract: While Vision-language Models (VLMs) have demonstrated strong semantic capabilities, their ability to interpret the underlying geometric structure of visual information is less explored. Pictographic characters, which combine visual form with symbolic structure, provide an ideal test case for this capability. We formulate this visual recognition challenge in the mathematical domain, where each character is represented by an executable program of geometric primitives. This is framed as a program synthesis task, training a VLM to decompile raster images into programs composed of B'ezier curves. Our model, acting as a “visual decompiler”, demonstrates performance superior to strong zero-shot baselines, including GPT-4o. The most significant finding is that when trained solely on modern Chinese characters, the model is able to reconstruct ancient Oracle Bone Script in a zero-shot context. This generalization provides strong evidence that the model acquires an abstract and transferable geometric grammar, moving beyond pixel-level pattern recognition to a more structured form of visual understanding.

Method: Introduces a unified architecture of standardized engines for data splitting, execution, preprocessing, training, inprocessing, postprocessing, evaluation, and reporting. Each engine encapsulates one methodological task with lightweight interfaces, enabling transparent, auditable workflows.

Result: Provides a framework that structures fairness methods as interchangeable engines, allowing researchers to integrate, compare, and evaluate interventions across the modeling pipeline while generalizing to explainability, robustness, and compliance metrics.

Conclusion: While motivated by algorithmic fairness, flowengineR ultimately provides a general infrastructure for any workflow context where reproducibility, transparency, and extensibility are essential, enabling distributed responsibilities and independent development.

Abstract: flowengineR is an R package designed to provide a modular and extensible framework for building reproducible algorithmic workflows for general-purpose machine learning pipelines. It is motivated by the rapidly evolving field of algorithmic fairness where new metrics, mitigation strategies, and machine learning methods continuously emerge. A central challenge in fairness, but also far beyond, is that existing toolkits either focus narrowly on single interventions or treat reproducibility and extensibility as secondary considerations rather than core design principles. flowengineR addresses this by introducing a unified architecture of standardized engines for data splitting, execution, preprocessing, training, inprocessing, postprocessing, evaluation, and reporting. Each engine encapsulates one methodological task yet communicates via a lightweight interface, ensuring workflows remain transparent, auditable, and easily extensible. Although implemented in R, flowengineR builds on ideas from workflow languages (CWL, YAWL), graph-oriented visual programming languages (KNIME), and R frameworks (BatchJobs, batchtools). Its emphasis, however, is less on orchestrating engines for resilient parallel execution but rather on the straightforward setup and management of distinct engines as data structures. This orthogonalization enables distributed responsibilities, independent development, and streamlined integration. In fairness context, by structuring fairness methods as interchangeable engines, flowengineR lets researchers integrate, compare, and evaluate interventions across the modeling pipeline. At the same time, the architecture generalizes to explainability, robustness, and compliance metrics without core modifications. While motivated by fairness, it ultimately provides a general infrastructure for any workflow context where reproducibility, transparency, and extensibility are essential.

Method: The model uses fixed-point iteration with a versatile spectral modulation filter that provides flexible-pass filtering, selectively attenuating high-frequency components while preserving low-frequency structural information in graph signals.

Result: Extensive experiments on benchmark graph datasets demonstrate the model’s effectiveness and resilience against adversarial attacks.

Conclusion: Fix-GCN offers a flexible and efficient framework for preserving essential graph information while mitigating adversarial manipulation through iterative node representation updates.

Abstract: Adversarial attacks present a significant risk to the integrity and performance of graph neural networks, particularly in tasks where graph structure and node features are vulnerable to manipulation. In this paper, we present a novel model, called fixed-point iterative graph convolutional network (Fix-GCN), which achieves robustness against adversarial perturbations by effectively capturing higher-order node neighborhood information in the graph without additional memory or computational complexity. Specifically, we introduce a versatile spectral modulation filter and derive the feature propagation rule of our model using fixed-point iteration. Unlike traditional defense mechanisms that rely on additional design elements to counteract attacks, the proposed graph filter provides a flexible-pass filtering approach, allowing it to selectively attenuate high-frequency components while preserving low-frequency structural information in the graph signal. By iteratively updating node representations, our model offers a flexible and efficient framework for preserving essential graph information while mitigating the impact of adversarial manipulation. We demonstrate the effectiveness of the proposed model through extensive experiments on various benchmark graph datasets, showcasing its resilience against adversarial attacks.

Method: Used ordinal regression techniques for level prediction, built a dedicated dataset for level estimation, developed a human-inspired benchmark model, and created a specialized evaluation procedure based on domain knowledge.

Result: The research provides an overview and evaluation of state-of-the-art ordinal regression methods for monster level prediction, along with a benchmark model and evaluation framework for comparison.

Conclusion: The study establishes a foundation for automated monster level prediction in RPGs, offering machine learning alternatives to manual methods and providing tools for meaningful performance comparisons in this domain.

Abstract: In recent years, the pen and paper RPG market has experienced significant growth. As a result, companies are increasingly exploring the integration of AI technologies to enhance player experience and gain a competitive edge. One of the key challenges faced by publishers is designing new opponents and estimating their challenge level. Currently, there are no automated methods for determining a monster’s level; the only approaches used are based on manual testing and expert evaluation. Although these manual methods can provide reasonably accurate estimates, they are time-consuming and resource-intensive. Level prediction can be approached using ordinal regression techniques. This thesis presents an overview and evaluation of state-of-the-art methods for this task. It also details the construction of a dedicated dataset for level estimation. Furthermore, a human-inspired model was developed to serve as a benchmark, allowing comparison between machine learning algorithms and the approach typically employed by pen and paper RPG publishers. In addition, a specialized evaluation procedure, grounded in domain knowledge, was designed to assess model performance and facilitate meaningful comparisons.

Method: MaGNet introduces three key innovations: (1) MAGE block with bidirectional Mamba and adaptive gating for temporal modeling, (2) Feature-wise and Stock-wise 2D Spatiotemporal Attention modules for feature fusion and cross-stock dependencies, (3) dual hypergraph framework with Temporal-Causal Hypergraph and Global Probabilistic Hypergraph for multi-scale relational learning.

Result: Extensive experiments on six major stock indices demonstrate MaGNet outperforms state-of-the-art methods in both superior predictive performance and exceptional investment returns with robust risk management capabilities.

Conclusion: MaGNet effectively addresses the limitations of existing stock prediction methods by integrating advanced temporal modeling with sophisticated relational reasoning, achieving state-of-the-art performance in stock trend prediction.

Abstract: Stock trend prediction is crucial for profitable trading strategies and portfolio management yet remains challenging due to market volatility, complex temporal dynamics and multifaceted inter-stock relationships. Existing methods struggle to effectively capture temporal dependencies and dynamic inter-stock interactions, often neglecting cross-sectional market influences, relying on static correlations, employing uniform treatments of nodes and edges, and conflating diverse relationships. This work introduces MaGNet, a novel Mamba dual-hyperGraph Network for stock prediction, integrating three key innovations: (1) a MAGE block, which leverages bidirectional Mamba with adaptive gating mechanisms for contextual temporal modeling and integrates a sparse Mixture-of-Experts layer to enable dynamic adaptation to diverse market conditions, alongside multi-head attention for capturing global dependencies; (2) Feature-wise and Stock-wise 2D Spatiotemporal Attention modules enable precise fusion of multivariate features and cross-stock dependencies, effectively enhancing informativeness while preserving intrinsic data structures, bridging temporal modeling with relational reasoning; and (3) a dual hypergraph framework consisting of the Temporal-Causal Hypergraph (TCH) that captures fine-grained causal dependencies with temporal constraints, and Global Probabilistic Hypergraph (GPH) that models market-wide patterns through soft hyperedge assignments and Jensen-Shannon Divergence weighting mechanism, jointly disentangling localized temporal influences from instantaneous global structures for multi-scale relational learning. Extensive experiments on six major stock indices demonstrate MaGNet outperforms state-of-the-art methods in both superior predictive performance and exceptional investment returns with robust risk management capabilities. Codes available at: https://github.com/PeilinTime/MaGNet.

Method: Formalize as probabilistic graph optimization, derive empirical insights from pilot experiments, and propose Agent-REINFORCE framework that uses LLM agents to map REINFORCE pipeline to sampling-feedback-update with textual gradients.

Result: Agent-REINFORCE outperforms traditional and LLM-based baselines in sample efficiency and search performance, effectively identifying optimal graphs under joint objectives of accuracy and inference latency.

Conclusion: The proposed approach successfully addresses the combinatorial search challenge in Test-Time Scaling by leveraging LLM agents and probabilistic graph optimization to find task-specific optimal multi-LLM collaboration architectures.

Abstract: Test-Time Scaling (TTS) improves large language models (LLMs) by allocating additional computation during inference, typically through parallel, sequential, or hybrid scaling. However, prior studies often assume fixed collaboration architectures (e.g., topologies) and single-model usage, overlooking that optimal architectures and model combinations can vary across tasks. Therefore, we study the novel problem of searching for compute-optimal model combinations and architectures in TTS under a fixed budget. We formalize it as a multi-LLM collaboration graph, where nodes encode roles and LLM model assignments, and edges capture information flow. This problem is challenging because (i) the combinatorial search space is prohibitively large, and (ii) task-specific requirements demand tailored designs. To address these, we reformulate the problem as probabilistic graph optimization and, through pilot experiments, derive three empirical insights into TTS collaboration graphs. Guided by these insights, we propose Agent-REINFORCE, an LLM-agent-augmented framework that mirrors the REINFORCE pipeline by mapping sampling-gradient-update to sampling-feedback-update, where feedback serves as a textual gradient to update the probabilistic graph and efficiently search for optimal multi-LLM collaboration graphs. Experiments show that Agent-REINFORCE outperforms both traditional and LLM-based baselines in sample efficiency and search performance, and effectively identifies optimal graphs under joint objectives of accuracy and inference latency.

Method: Proposes domain-specific parameter-efficient fine-tuning with intra- and inter-domain disentanglement objectives to prevent embedding shifts, deviation-free knowledge preservation to maintain stable decision boundaries, and domain-aware distribution discrimination for graphs with unobservable domains.

Result: Extensive experiments show GraphKeeper achieves state-of-the-art results with 6.5%-16.6% improvement over runner-up methods with negligible forgetting, and can be seamlessly integrated with various graph foundation models.

Conclusion: GraphKeeper effectively addresses catastrophic forgetting in graph domain-incremental learning scenarios and demonstrates broad applicative potential with graph foundation models.

Abstract: Graph incremental learning (GIL), which continuously updates graph models by sequential knowledge acquisition, has garnered significant interest recently. However, existing GIL approaches focus on task-incremental and class-incremental scenarios within a single domain. Graph domain-incremental learning (Domain-IL), aiming at updating models across multiple graph domains, has become critical with the development of graph foundation models (GFMs), but remains unexplored in the literature. In this paper, we propose Graph Domain-Incremental Learning via Knowledge Dientanglement and Preservation (GraphKeeper), to address catastrophic forgetting in Domain-IL scenario from the perspectives of embedding shifts and decision boundary deviations. Specifically, to prevent embedding shifts and confusion across incremental graph domains, we first propose the domain-specific parameter-efficient fine-tuning together with intra- and inter-domain disentanglement objectives. Consequently, to maintain a stable decision boundary, we introduce deviation-free knowledge preservation to continuously fit incremental domains. Additionally, for graphs with unobservable domains, we perform domain-aware distribution discrimination to obtain precise embeddings. Extensive experiments demonstrate the proposed GraphKeeper achieves state-of-the-art results with 6.5%~16.6% improvement over the runner-up with negligible forgetting. Moreover, we show GraphKeeper can be seamlessly integrated with various representative GFMs, highlighting its broad applicative potential.

Method: Unsupervised conditional-labeled GAN framework that uses same damage-level measurements as inputs, forces conditional convergence to different damage states, compares convergence scores, and employs SVM classifier and PCA for assessment.

Result: The approach accurately captures damage over healthy measurements, creates digital twin measurements at different damage states, and provides pattern recognition and data generation capabilities for structural health monitoring.

Conclusion: The framework provides a powerful tool for vibration-based system-level monitoring and scalable infrastructure resilience, overcoming limitations of current methods by not requiring prior health state information.

Abstract: The optimization-based damage detection and damage state digital twinning capabilities are examined here of a novel conditional-labeled generative adversarial network methodology. The framework outperforms current approaches for fault anomaly detection as no prior information is required for the health state of the system: a topic of high significance for real-world applications. Specifically, current artificial intelligence-based digital twinning approaches suffer from the uncertainty related to obtaining poor predictions when a low number of measurements is available, physics knowledge is missing, or when the damage state is unknown. To this end, an unsupervised framework is examined and validated rigorously on the benchmark structural health monitoring measurements of Z24 Bridge: a post-tensioned concrete highway bridge in Switzerland. In implementing the approach, firstly, different same damage-level measurements are used as inputs, while the model is forced to converge conditionally to two different damage states. Secondly, the process is repeated for a different group of measurements. Finally, the convergence scores are compared to identify which one belongs to a different damage state. The process for both healthy-to-healthy and damage-to-healthy input data creates, simultaneously, measurements for digital twinning purposes at different damage states, capable of pattern recognition and machine learning data generation. Further to this process, a support vector machine classifier and a principal component analysis procedure is developed to assess the generated and real measurements of each damage category, serving as a secondary new dynamics learning indicator in damage scenarios. Importantly, the approach is shown to capture accurately damage over healthy measurements, providing a powerful tool for vibration-based system-level monitoring and scalable infrastructure resilience.

Method: Examined GRU, LSTM, and CNN neural networks compared to physics-based residual Kalman filter (RKF) on three cases: simulated structure with shaker excitation, California building under seismic base excitation, and IASC-ASCE benchmark problem for impact/instant loading.

Result: Methods outperform each other on different loading scenarios. RKF outperforms neural networks in physically parametrized identifiable cases.

Conclusion: Different methods have varying performance depending on loading scenarios, with RKF showing superior performance in physically identifiable cases while neural networks perform well in other scenarios.

Abstract: The dynamic structural load identification capabilities of the gated recurrent unit, long short-term memory, and convolutional neural networks are examined herein. The examination is on realistic small dataset training conditions and on a comparative view to the physics-based residual Kalman filter (RKF). The dynamic load identification suffers from the uncertainty related to obtaining poor predictions when in civil engineering applications only a low number of tests are performed or are available, or when the structural model is unidentifiable. In considering the methods, first, a simulated structure is investigated under a shaker excitation at the top floor. Second, a building in California is investigated under seismic base excitation, which results in loading for all degrees of freedom. Finally, the International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring benchmark problem is examined for impact and instant loading conditions. Importantly, the methods are shown to outperform each other on different loading scenarios, while the RKF is shown to outperform the networks in physically parametrized identifiable cases.

Method: Uses Virtualized Module for isolating PEFT modifications and supports multiple adapters, plus optimized computation flow with merged fine-tuning/inference paths and efficient kernel design.

Result: Achieves up to 3.0× throughput of state-of-the-art co-serving system on inference tasks and 46.4× higher SLO attainment than PEFT on unified fine-tuning/inference tasks.

Conclusion: Loquetier provides a practical solution for unified LoRA fine-tuning and serving, demonstrating superior performance and flexibility across various task settings.

Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning (PEFT) technique for adapting large language models (LLMs) to downstream tasks. While prior work has explored strategies for integrating LLM training and serving, there still remains a gap in unifying fine-tuning and inference for LoRA-based models. We present Loquetier, a virtualized multi-LoRA framework that seamlessly integrates LoRA fine-tuning and serving within a single runtime. Loquetier introduces two key components: (1) a Virtualized Module that isolates PEFT-based modifications and supports multiple adapters on a shared base model, and (2) an optimized computation flow with a kernel design that merges fine-tuning and inference paths in forward propagation, enabling efficient batching and minimizing kernel invocation overhead. Extensive experiments across three task settings show that Loquetier consistently outperforms existing baselines in both performance and flexibility, achieving up to $3.0\times$ the throughput of the state-of-the-art co-serving system on inference-only tasks and $46.4\times$ higher SLO attainment than PEFT on unified fine-tuning and inference tasks. The implementation of Loquetier is publicly available at https://github.com/NJUDeepEngine/Loquetier.

Method: Three-component approach: Neural ODE learns system dynamics, Transformer generates symbolic candidate invariants, and symbolic-numeric verifier validates candidates.

Result: Framework significantly outperforms baselines on canonical physical systems and demonstrates robustness for discovering mathematical principles from imperfect data.

Conclusion: The decoupled learn-then-search approach is effective for automated discovery of conserved quantities from noisy observational data.

Abstract: The discovery of conservation laws is a cornerstone of scientific progress. However, identifying these invariants from observational data remains a significant challenge. We propose a hybrid framework to automate the discovery of conserved quantities from noisy trajectory data. Our approach integrates three components: (1) a Neural Ordinary Differential Equation (Neural ODE) that learns a continuous model of the system’s dynamics, (2) a Transformer that generates symbolic candidate invariants conditioned on the learned vector field, and (3) a symbolic-numeric verifier that provides a strong numerical certificate for the validity of these candidates. We test our framework on canonical physical systems and show that it significantly outperforms baselines that operate directly on trajectory data. This work demonstrates the robustness of a decoupled learn-then-search approach for discovering mathematical principles from imperfect data.

Method: Uses DPPO (Deliberate Practice Policy Optimization) framework with a metaloop (RL-Refine-Diagnose-SFT loop) for deliberate practice, trained on 1000+ A800 GPUs with high-quality dataset distilled from 4+ billion tokens.

Result: Achieves 20.3% performance uplift from base model, outperforms 100B-level open-source counterparts by 10.6%, and matches leading proprietary systems on embodied benchmarks.

Conclusion: Pelican-VL 1.0 successfully demonstrates the effectiveness of the DPPO framework and large-scale training for creating high-performance embodied AI systems.

Abstract: This report presents Pelican-VL 1.0, a new family of open-source embodied brain models with parameter scales ranging from 7 billion to 72 billion. Our explicit mission is clearly stated as: To embed powerful intelligence into various embodiments. Pelican-VL 1.0 is currently the largest-scale open-source embodied multimodal brain model. Its core advantage lies in the in-depth integration of data power and intelligent adaptive learning mechanisms. Specifically, metaloop distilled a high-quality dataset from a raw dataset containing 4+ billion tokens. Pelican-VL 1.0 is trained on a large-scale cluster of 1000+ A800 GPUs, consuming over 50k+ A800 GPU-hours per checkpoint. This translates to a 20.3% performance uplift from its base model and outperforms 100B-level open-source counterparts by 10.6%, placing it on par with leading proprietary systems on well-known embodied benchmarks. We establish a novel framework, DPPO (Deliberate Practice Policy Optimization), inspired by human metacognition to train Pelican-VL 1.0. We operationalize this as a metaloop that teaches the AI to practice deliberately, which is a RL-Refine-Diagnose-SFT loop.

Method: Uses discrete orthogonal Meixner polynomials with learnable shape parameters (beta and c), combined with a novel stabilization technique using Laplacian scaling and per-basis LayerNorm to overcome numerical instability.

Result: MeixnerNet achieves competitive-to-superior performance against ChebyNet at optimal K=2 setting (winning 2/3 benchmarks) and shows exceptional robustness to polynomial degree K variations, while ChebyNet collapses in performance.

Conclusion: MeixnerNet provides a more theoretically grounded approach for spectral GNNs using discrete orthogonal polynomials, offering both competitive performance and superior robustness to hyperparameter variations compared to continuous-domain polynomial filters.

Abstract: Spectral Graph Neural Networks (GNNs) have achieved state-of-the-art results by defining graph convolutions in the spectral domain. A common approach, popularized by ChebyNet, is to use polynomial filters based on continuous orthogonal polynomials (e.g., Chebyshev). This creates a theoretical disconnect, as these continuous-domain filters are applied to inherently discrete graph structures. We hypothesize this mismatch can lead to suboptimal performance and fragility to hyperparameter settings. In this paper, we introduce MeixnerNet, a novel spectral GNN architecture that employs discrete orthogonal polynomials – specifically, the Meixner polynomials $M_k(x; \beta, c)$. Our model makes the two key shape parameters of the polynomial, beta and c, learnable, allowing the filter to adapt its polynomial basis to the specific spectral properties of a given graph. We overcome the significant numerical instability of these polynomials by introducing a novel stabilization technique that combines Laplacian scaling with per-basis LayerNorm. We demonstrate experimentally that MeixnerNet achieves competitive-to-superior performance against the strong ChebyNet baseline at the optimal K = 2 setting (winning on 2 out of 3 benchmarks). More critically, we show that MeixnerNet is exceptionally robust to variations in the polynomial degree K, a hyperparameter to which ChebyNet proves to be highly fragile, collapsing in performance where MeixnerNet remains stable.

Method: Used event and tracking data from 2023 J1 League with 189 features including player positions, velocities, and spatial configurations, employing XGBoost classifier.

Result: High predictive accuracy with AUC of 0.982 and Brier score of 0.015; SHAP analysis revealed offensive player speed, defensive gaps, and spatial distributions as key factors; moderate positive correlation with shots and crosses conceded.

Conclusion: Line Breaks are closely linked to scoring opportunity creation and provide a quantitative framework for understanding football tactical dynamics.

Abstract: In football, attacking teams attempt to break through the opponent’s defensive line to create scoring opportunities. This action, known as a Line Break, is a critical indicator of offensive effectiveness and tactical performance, yet previous studies have mainly focused on shots or goal opportunities rather than on how teams break the defensive line. In this study, we develop a machine learning model to predict Line Breaks using event and tracking data from the 2023 J1 League season. The model incorporates 189 features, including player positions, velocities, and spatial configurations, and employs an XGBoost classifier to estimate the probability of Line Breaks. The proposed model achieved high predictive accuracy, with an AUC of 0.982 and a Brier score of 0.015. Furthermore, SHAP analysis revealed that factors such as offensive player speed, gaps in the defensive line, and offensive players’ spatial distributions significantly contribute to the occurrence of Line Breaks. Finally, we found a moderate positive correlation between the predicted probability of being Line-Broken and the number of shots and crosses conceded at the team level. These results suggest that Line Breaks are closely linked to the creation of scoring opportunities and provide a quantitative framework for understanding tactical dynamics in football.

Method: Uses cross-fluctuations (centered-moment statistics from statistical physics) to detect sharp transitions in sampling dynamics. For variance-preserving SDEs, derives closed-form expressions for cross-fluctuations that are efficiently computable for reverse trajectories.

Result: Demonstrates that detecting transitions directly boosts sampling efficiency, accelerates class-conditional and rare-class generation, and improves zero-shot image classification and style transfer without expensive grid search or retraining.

Conclusion: The framework bridges discrete Markov chain theory, phase analysis, and modern generative modeling, unifying classical coupling and mixing concepts with continuous dynamics in stochastic SDEs and non-Markovian samplers.

Abstract: We analyse how the sampling dynamics of distributions evolve in score-based diffusion models using cross-fluctuations, a centered-moment statistic from statistical physics. Specifically, we show that starting from an unbiased isotropic normal distribution, samples undergo sharp, discrete transitions, eventually forming distinct events of a desired distribution while progressively revealing finer structure. As this process is reversible, these transitions also occur in reverse, where intermediate states progressively merge, tracing a path back to the initial distribution. We demonstrate that these transitions can be detected as discontinuities in $n^{\text{th}}$-order cross-fluctuations. For variance-preserving SDEs, we derive a closed-form for these cross-fluctuations that is efficiently computable for the reverse trajectory. We find that detecting these transitions directly boosts sampling efficiency, accelerates class-conditional and rare-class generation, and improves two zero-shot tasks–image classification and style transfer–without expensive grid search or retraining. We also show that this viewpoint unifies classical coupling and mixing from finite Markov chains with continuous dynamics while extending to stochastic SDEs and non Markovian samplers. Our framework therefore bridges discrete Markov chain theory, phase analysis, and modern generative modeling.

Method: Proposes a dynamic multi-expert gating framework that uses internal model signals (meta-features like stability and uncertainty) to select the best trajectory predictor per sample, formulated as a pairwise-ranking problem over internal model signals without requiring post-hoc calibration.

Result: On nuPlan-mini dataset (1,287 samples), achieves FDE of 2.567m (9.5% reduction over GameFormer’s 2.835m), realizing 57.8% of oracle performance bound. In open-loop simulations, reduces FDE on left-turn scenarios by ~10% after trajectory horizon alignment.

Conclusion: Adaptive hybrid systems enhance trajectory reliability in safety-critical autonomous driving, providing a practical pathway beyond static single-model paradigms with consistent improvements across both offline validation and open-loop evaluation.

Abstract: Recent deep trajectory predictors (e.g., Jiang et al., 2023; Zhou et al., 2022) have achieved strong average accuracy but remain unreliable in complex long-tail driving scenarios. These limitations reveal the weakness of the prevailing “one-model-fits-all” paradigm, particularly in safety-critical urban contexts where simpler physics-based models can occasionally outperform advanced networks (Kalman, 1960). To bridge this gap, we propose a dynamic multi-expert gating framework that adaptively selects the most reliable trajectory predictor among a physics-informed LSTM, a Transformer, and a fine-tuned GameFormer on a per-sample basis. Our method leverages internal model signals (meta-features) such as stability and uncertainty (Gal and Ghahramani, 2016), which we demonstrate to be substantially more informative than geometric scene descriptors. To the best of our knowledge, this is the first work to formulate trajectory expert selection as a pairwise-ranking problem over internal model signals (Burges et al., 2005), directly optimizing decision quality without requiring post-hoc calibration. Evaluated on the nuPlan-mini dataset (Caesar et al., 2021) with 1,287 samples, our LLM-enhanced tri-expert gate achieves a Final Displacement Error (FDE) of 2.567 m, representing a 9.5 percent reduction over GameFormer (2.835 m), and realizes 57.8 percent of the oracle performance bound. In open-loop simulations, after trajectory horizon alignment, the same configuration reduces FDE on left-turn scenarios by approximately 10 percent, demonstrating consistent improvements across both offline validation and open-loop evaluation. These results indicate that adaptive hybrid systems enhance trajectory reliability in safety-critical autonomous driving, providing a practical pathway beyond static single-model paradigms.

Method: Integrated system for CCL signal acquisition, comprehensive preprocessing methods (standardization, LDS, random cropping, LSR, time scaling, multiple sampling), and AlexNet-based neural network models evaluated through systematic experimentation.

Result: F1 scores improved from 0.937 and 0.952 to 1.0 for both benchmark models. Standardization, LDS, and random cropping are fundamental requirements, while LSR, time scaling, and multiple sampling enhance generalization.

Conclusion: The proposed data augmentation methods effectively address gaps in training casing collar recognition models in CCL data-limited environments, with validation confirming practical applicability.

Abstract: Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network-based CCL signal recognition has achieved significant progress in collar identification, preprocessing methods for such applications remain underdeveloped. Moreover, the limited availability of real well data poses substantial challenges for training neural network models that require extensive datasets. This paper presents a system integrated into downhole tools for CCL signal acquisition to facilitate dataset construction. We propose comprehensive preprocessing methods for data augmentation and evaluate their effectiveness using our AlexNet-based neural network models. Through systematic experimentation across various configuration combinations, we analyze the contribution of each augmentation method. Results demonstrate that standardization, label distribution smoothing (LDS), and random cropping are fundamental requirements for model training, while label smoothing regularization (LSR), time scaling, and multiple sampling significantly enhance model generalization capability. The F1 scores of our two benchmark models trained with the proposed augmentation methods maximumly improve from 0.937 and 0.952 to 1.0 and 1.0, respectively. Performance validation on real CCL waveforms confirms the effectiveness and practical applicability of our approach. This work addresses the gaps in data augmentation methodologies for training casing collar recognition models in CCL data-limited environments.

Method: Comparative analysis of three adaptation techniques: Supervised Finetuning (SFT), LoRA (Parameter-Efficient Fine-Tuning), and In-Context Learning (ICL) in data-scarce scenarios.

Result: LoRA provides most effective balance - successfully instills new skills with minimal impact on base model’s general knowledge. SFT excels at skill acquisition but highly susceptible to catastrophic forgetting. ICL effective for factual knowledge but struggles with complex skills.

Conclusion: LoRA offers optimal adaptation strategy, highlighting critical distinction between skill acquisition vs knowledge integration, and trade-offs between task-specific performance and preservation of general capabilities.

Abstract: The remarkable capabilities of Large Language Models (LLMs) often need to be tailored for specific applications, requiring the integration of new knowledge or the acquisition of new skills. While full fine-tuning is a powerful adaptation method, it is computationally expensive and can lead to a degradation of general reasoning abilities, a phenomenon known as catastrophic forgetting. A range of alternative techniques exists, each with its own trade-offs. In-Context Learning (ICL) is fast but limited by context length, while Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) offer a middle ground by minimizing parameter changes. However, the challenge of catastrophic forgetting persists, raising questions about the best adaptation strategy for a given task. This paper presents a comparative analysis of Supervised Finetuning (SFT), LoRA, and ICL in data-scarce scenarios. We find that LoRA provides the most effective balance, successfully instilling new skills with minimal impact on the base model’s general knowledge. In contrast, while SFT excels at skill acquisition, it is highly susceptible to catastrophic forgetting. ICL is effective for incorporating factual knowledge but struggles with complex skills. Our findings offer a practical framework for selecting an LLM adaptation strategy. We highlight the critical distinction between skill acquisition and knowledge integration, clarify the trade-offs between task-specific performance and the preservation of general capabilities.

Method: Integrates probabilistic feature sampling (importance-aware sampling) with hyperparameter tuning via Simulated Annealing, focusing on meaningful features and dynamic parameter optimization.

Result: Demonstrates consistent accuracy improvements and provides meaningful insights into feature relevance across credit risk, IoT anomaly detection, medical diagnostics, and biological data analysis.

Conclusion: The combination of importance-aware sampling and metaheuristic optimization effectively enhances Random Forest performance and generalization capabilities.

Abstract: This paper introduces a novel framework for enhancing Random Forest classifiers by integrating probabilistic feature sampling and hyperparameter tuning via Simulated Annealing. The proposed framework exhibits substantial advancements in predictive accuracy and generalization, adeptly tackling the multifaceted challenges of robust classification across diverse domains, including credit risk evaluation, anomaly detection in IoT ecosystems, early-stage medical diagnostics, and high-dimensional biological data analysis. To overcome the limitations of conventional Random Forests, we present an approach that places stronger emphasis on capturing the most relevant signals from data while enabling adaptive hyperparameter configuration. The model is guided towards features that contribute more meaningfully to classification and optimizing this with dynamic parameter tuning. The results demonstrate consistent accuracy improvements and meaningful insights into feature relevance, showcasing the efficacy of combining importance aware sampling and metaheuristic optimization.

Method: Used an interpretable machine-learning framework combining SHAP and ALE to analyze vegetation-climate interactions across 138 Indian cities. Analyzed Heat Index (HI) at 1km resolution across different climate zones and urban densities.

Result: Cooling strengthens with EVI >= 0.4 and LAI >= 0.05, but reverses to warming when EVI >= 0.5, LAI >= 0.2, and fPAR >= 0.5. In humid dense cores, warming occurs earlier at fPAR >= 0.25 due to vegetation elevating humidity faster than removing heat.

Conclusion: The study establishes climatic limits for vegetation-driven cooling and provides quantitative thresholds for climate-specific greening strategies to create equitable, heat-resilient cities.

Abstract: Efforts to green cities for cooling are succeeding unevenly because the same vegetation that cools surfaces can also intensify how hot the air feels. Previous studies have identified humid heat as a growing urban hazard, yet how physiologically active vegetation governs this trade-off between cooling and moisture accumulation remains poorly understood, leaving mitigation policy and design largely unguided. Here we quantify how vegetation structure and function influence the Heat Index (HI), a combined measure of temperature and humidity in 138 Indian cities spanning tropical savanna, semi-arid steppe, and humid subtropical climates, and across dense urban cores and semi-urban rings. Using an extreme-aware, one kilometre reconstruction of HI and an interpretable machine-learning framework that integrates SHapley Additive Explanations (SHAP) and Accumulated Local Effects (ALE), we isolate vegetation-climate interactions. Cooling generally strengthens for EVI >= 0.4 and LAI >= 0.05, but joint-high regimes begin to reverse toward warming when EVI >= 0.5, LAI >= 0.2, and fPAR >= 0.5,with an earlier onset for fPAR >= 0.25 in humid, dense cores. In such environments, highly physiologically active vegetation elevates near-surface humidity faster than it removes heat, reversing its cooling effect and amplifying perceived heat stress. These findings establish the climatic limits of vegetation-driven cooling and provide quantitative thresholds for climate-specific greening strategies that promote equitable and heat-resilient cities.

Method: Proposes HeraldLight with dual LLMs: Herald Module extracts context and forecasts queue lengths, LLM-Agent makes fine-grained traffic control decisions, and LLM-Critic refines outputs using score-based fine-tuning.

Result: Outperforms state-of-the-art baselines with 20.03% reduction in average travel time across all scenarios and 10.74% reduction in average queue length on Jinan and Hangzhou scenarios.

Conclusion: HeraldLight effectively addresses limitations of both LLM-based and RL methods in traffic signal control, demonstrating superior performance through its dual LLM architecture with error correction mechanisms.

Abstract: Leveraging large language models (LLMs) in traffic signal control (TSC) improves optimization efficiency and interpretability compared to traditional reinforcement learning (RL) methods. However, existing LLM-based approaches are limited by fixed time signal durations and are prone to hallucination errors, while RL methods lack robustness in signal timing decisions and suffer from poor generalization. To address these challenges, this paper proposes HeraldLight, a dual LLMs architecture enhanced by Herald guided prompts. The Herald Module extracts contextual information and forecasts queue lengths for each traffic phase based on real-time conditions. The first LLM, LLM-Agent, uses these forecasts to make fine grained traffic signal control, while the second LLM, LLM-Critic, refines LLM-Agent’s outputs, correcting errors and hallucinations. These refined outputs are used for score-based fine-tuning to improve accuracy and robustness. Simulation experiments using CityFlow on real world datasets covering 224 intersections in Jinan (12), Hangzhou (16), and New York (196) demonstrate that HeraldLight outperforms state of the art baselines, achieving a 20.03% reduction in average travel time across all scenarios and a 10.74% reduction in average queue length on the Jinan and Hangzhou scenarios. The source code is available on GitHub: https://github.com/BUPT-ANTlab/HeraldLight.

Method: Integrates deep learning (CNN for feature extraction, linear programming for statistical features) with intelligent particle swarm optimization, using fuzzy association rule scheduling and deep reinforcement learning for model optimization, and neural networks for dynamic change fitting.

Result: Simulations show reduced resource consumption, enhanced spatial planning, improved real-time decision adjustment, distribution path optimization, and robust intelligent control in dynamic environments.

Conclusion: The hybrid deep learning and intelligent particle swarm optimization approach effectively improves supply chain decision-making efficiency and adaptive control capabilities.

Abstract: To improve decision-making and planning efficiency in back-end centralized redundant supply chains, this paper proposes a decision model integrating deep learning with intelligent particle swarm optimization. A distributed node deployment model and optimal planning path are constructed for the supply chain network. Deep learning such as convolutional neural networks extracts features from historical data, and linear programming captures high-order statistical features. The model is optimized using fuzzy association rule scheduling and deep reinforcement learning, while neural networks fit dynamic changes. A hybrid mechanism of “deep learning feature extraction - intelligent particle swarm optimization” guides global optimization and selects optimal decisions for adaptive control. Simulations show reduced resource consumption, enhanced spatial planning, and in dynamic environments improved real-time decision adjustment, distribution path optimization, and robust intelligent control.

Method: Used Sparse Autoencoders (SAEs) to identify latents in Gemma-2 models that correlate with Black individuals, then tested steering via these latents to control model outputs.

Result: Found SAE latents activate on both reasonable inputs (e.g., “African American”) and problematic words (e.g., “incarceration”). Steering increased risk assignments like predicting patients becoming “belligerent”.

Conclusion: SAEs are useful for identifying problematic demographic reliance in clinical LLMs, but bias mitigation via SAE steering offers only marginal improvements for complex clinical tasks.

Abstract: LLMs are increasingly being used in healthcare. This promises to free physicians from drudgery, enabling better care to be delivered at scale. But the use of LLMs in this space also brings risks; for example, such models may worsen existing biases. How can we spot when LLMs are (spuriously) relying on patient race to inform predictions? In this work we assess the degree to which Sparse Autoencoders (SAEs) can reveal (and control) associations the model has made between race and stigmatizing concepts. We first identify SAE latents in Gemma-2 models which appear to correlate with Black individuals. We find that this latent activates on reasonable input sequences (e.g., “African American”) but also problematic words like “incarceration”. We then show that we can use this latent to steer models to generate outputs about Black patients, and further that this can induce problematic associations in model outputs as a result. For example, activating the Black latent increases the risk assigned to the probability that a patient will become “belligerent”. We evaluate the degree to which such steering via latents might be useful for mitigating bias. We find that this offers improvements in simple settings, but is less successful for more realistic and complex clinical tasks. Overall, our results suggest that: SAEs may offer a useful tool in clinical applications of LLMs to identify problematic reliance on demographics but mitigating bias via SAE steering appears to be of marginal utility for realistic tasks.

Method: PDE-SHARP employs three stages: (1) Analysis: mathematical chain-of-thought analysis including PDE classification, solution type detection, and stability analysis; (2) Genesis: solver generation based on mathematical insights; (3) Synthesis: collaborative selection-hybridization tournaments where LLM judges iteratively refine implementations through flexible performance feedback.

Result: PDE-SHARP requires fewer than 13 solver evaluations on average compared to 30+ for baseline methods, improves accuracy uniformly across tested PDEs by 4× on average, and demonstrates robust performance across different LLM architectures.

Conclusion: PDE-SHARP successfully reduces computational costs while achieving superior solver accuracy through its three-stage framework that leverages LLM inference to replace expensive scientific computations.

Abstract: Current LLM-driven approaches using test-time computing to generate PDE solvers execute a large number of solver samples to identify high-accuracy solvers. These paradigms are especially costly for complex PDEs requiring substantial computational resources for numerical evaluation. We introduce PDE-SHARP, a framework to reduce computational costs by replacing expensive scientific computation by cheaper LLM inference that achieves superior solver accuracy with 60-75% fewer computational evaluations. PDE-SHARP employs three stages: (1) Analysis: mathematical chain-of-thought analysis including PDE classification, solution type detection, and stability analysis; (2) Genesis: solver generation based on mathematical insights from the previous stage; and (3) Synthesis: collaborative selection-hybridization tournaments in which LLM judges iteratively refine implementations through flexible performance feedback. To generate high-quality solvers, PDE-SHARP requires fewer than 13 solver evaluations on average compared to 30+ for baseline methods, improving accuracy uniformly across tested PDEs by $4\times$ on average, and demonstrates robust performance across LLM architectures, from general-purpose to specialized reasoning models.

Method: Proposed EL-MIA framework for auditing entity-level membership risks, constructed benchmark dataset, systematically compared existing MIA techniques and two new methods, analyzed relationship with model scale, training epochs, and surface factors.

Result: Existing MIA methods are limited for entity-level membership inference of sensitive attributes, but susceptibility can be outlined with relatively straightforward methods.

Conclusion: Highlights the need for stronger adversaries to stress test the threat model for entity-level membership inference in LLMs.

Abstract: Membership inference attacks (MIA) aim to infer whether a particular data point is part of the training dataset of a model. In this paper, we propose a new task in the context of LLM privacy: entity-level discovery of membership risk focused on sensitive information (PII, credit card numbers, etc). Existing methods for MIA can detect the presence of entire prompts or documents in the LLM training data, but they fail to capture risks at a finer granularity. We propose the ``EL-MIA’’ framework for auditing entity-level membership risks in LLMs. We construct a benchmark dataset for the evaluation of MIA methods on this task. Using this benchmark, we conduct a systematic comparison of existing MIA techniques as well as two newly proposed methods. We provide a comprehensive analysis of the results, trying to explain the relation of the entity level MIA susceptability with the model scale, training epochs, and other surface level factors. Our findings reveal that existing MIA methods are limited when it comes to entity-level membership inference of the sensitive attributes, while this susceptibility can be outlined with relatively straightforward methods, highlighting the need for stronger adversaries to stress test the provided threat model.

Method: Uses pretrained non-autoregressive generative LLMs (Diffusion LLMs) as surrogates for prompt search, enabling direct conditional generation of prompts through parallelizable sampling instead of per-instance discrete optimization.

Result: Generated prompts are low-perplexity, diverse jailbreaks that show strong transferability to various black-box target models including robustly trained and proprietary LLMs.

Conclusion: The framework enables efficient adversarial prompting and opens new directions for red teaming, automated prompt optimization, and utilization of emerging Flow- and Diffusion-based LLMs.

Abstract: We introduce a novel framework that transforms the resource-intensive (adversarial) prompt optimization problem into an \emph{efficient, amortized inference task}. Our core insight is that pretrained, non-autoregressive generative LLMs, such as Diffusion LLMs, which model the joint distribution over prompt-response pairs, can serve as powerful surrogates for prompt search. This approach enables the direct conditional generation of prompts, effectively replacing costly, per-instance discrete optimization with a small number of parallelizable samples. We provide a probabilistic analysis demonstrating that under mild fidelity assumptions, only a few conditional samples are required to recover high-reward (harmful) prompts. Empirically, we find that the generated prompts are low-perplexity, diverse jailbreaks that exhibit strong transferability to a wide range of black-box target models, including robustly trained and proprietary LLMs. Beyond adversarial prompting, our framework opens new directions for red teaming, automated prompt optimization, and leveraging emerging Flow- and Diffusion-based LLMs.

Method: Systematic comparison of diffusion model applications using iterative denoising framework adapted to distinct molecular representations, chemical spaces, and design objectives for small molecules and therapeutic peptides.

Result: Diffusion models excel at structure-based design for small molecules (generating pocket-fitting ligands) and functional sequence generation for peptides, but face distinct challenges in each domain.

Conclusion: Full potential of diffusion models will be unlocked by bridging modality-specific gaps and integrating them into automated Design-Build-Test-Learn platforms for targeted therapeutic creation.

Abstract: Diffusion models have emerged as a leading framework in generative modeling, showing significant potential to accelerate and transform the traditionally slow and costly process of drug discovery. This review provides a systematic comparison of their application in designing two principal therapeutic modalities: small molecules and therapeutic peptides. We analyze how a unified framework of iterative denoising is adapted to the distinct molecular representations, chemical spaces, and design objectives of each modality. For small molecules, these models excel at structure-based design, generating novel, pocket-fitting ligands with desired physicochemical properties, yet face the critical hurdle of ensuring chemical synthesizability. Conversely, for therapeutic peptides, the focus shifts to generating functional sequences and designing de novo structures, where the primary challenges are achieving biological stability against proteolysis, ensuring proper folding, and minimizing immunogenicity. Despite these distinct challenges, both domains face shared hurdles: the need for more accurate scoring functions, the scarcity of high-quality experimental data, and the crucial requirement for experimental validation. We conclude that the full potential of diffusion models will be unlocked by bridging these modality-specific gaps and integrating them into automated, closed-loop Design-Build-Test-Learn (DBTL) platforms, thereby shifting the paradigm from chemical exploration to the targeted creation of novel therapeutics.

Method: Iterative fine-tuning strategy using multiple reward signals, generalizing state-of-the-art RL-based methods with theoretical analysis.

Result: Experimental results show effectiveness across diverse domains including text, biological sequence, and small molecule generation.

Conclusion: Multi-reward RL fine-tuning provides superior performance compared to state-of-the-art baselines in applications requiring multiple evaluation criteria.

Abstract: Fine-tuning foundation models has emerged as a powerful approach for generating objects with specific desired properties. Reinforcement learning (RL) provides an effective framework for this purpose, enabling models to generate outputs that maximize a given reward function. However, in many applications such as text generation and drug discovery, it can be suboptimal to optimize using a single reward signal, as multiple evaluation criteria are often necessary. This paper proposes a novel reinforcement learning-based method for fine-tuning foundation models using multiple reward signals. By employing an iterative fine-tuning strategy across these rewards, our approach generalizes state-of-the-art RL-based methods. We further provide a theoretical analysis that offers insights into the performance of multi-reward RL fine-tuning. Experimental results across diverse domains including text, biological sequence, and small molecule generation, demonstrate the effectiveness of the proposed algorithm compared to state-of-the-art baselines.

Method: Used ensemble learning of three state-of-the-art deep transfer learning networks, combined with XAI techniques to explain the basis of predictions and ensure reliability.

Result: The proposed model achieves high accuracy in melanoma detection while providing interpretable explanations for its predictions through XAI techniques.

Conclusion: Combining ensemble deep learning with XAI techniques can overcome the explainability limitations of traditional DL models, making AI-based melanoma detection more reliable and trustworthy for healthcare applications.

Abstract: Melanoma is one of the most aggressive and deadliest skin cancers, leading to mortality if not detected and treated in the early stages. Artificial intelligence techniques have recently been developed to help dermatologists in the early detection of melanoma, and systems based on deep learning (DL) have been able to detect these lesions with high accuracy. However, the entire community must overcome the explainability limit to get the maximum benefit from DL for diagnostics in the healthcare domain. Because of the black box operation’s shortcomings in DL models’ decisions, there is a lack of reliability and trust in the outcomes. However, Explainable Artificial Intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This paper proposes a machine learning model using ensemble learning of three state-of-the-art deep transfer Learning networks, along with an approach to ensure the reliability of the predictions by utilizing XAI techniques to explain the basis of the predictions.

Method: Proving a lower bound that matches the previously known upper bound from Kale (2014) to establish the tight minimax optimal regret rate.

Result: The minimax optimal expected regret is proven to be Θ(√(TK log(N/K))), where K is number of arms, N is number of experts, and T is time horizon.

Conclusion: The paper completes the theoretical understanding of this bandit problem by establishing the exact minimax optimal regret bound through matching upper and lower bounds.

Abstract: We determine the minimax optimal expected regret in the classic non-stochastic multi-armed bandit with expert advice problem, by proving a lower bound that matches the upper bound of Kale (2014). The two bounds determine the minimax optimal expected regret to be $\Theta\left( \sqrt{T K \log (N/K) } \right)$, where $K$ is the number of arms, $N$ is the number of experts, and $T$ is the time horizon.

Method: The authors propose X-TRAJ (xLSTM-based vehicle trajectory prediction framework) and its physics-aware variant X-TRACK, which explicitly integrates vehicle motion kinematics into the model learning process through physical constraints.

Result: Comprehensive evaluation on highD and NGSIM datasets demonstrates that X-TRACK outperforms state-of-the-art baselines in vehicle trajectory prediction.

Conclusion: The proposed xLSTM-based frameworks, particularly the physics-aware X-TRACK variant, effectively generate realistic and feasible vehicle trajectories by incorporating motion kinematics, achieving superior performance over existing methods.

Abstract: Recent advancements in Recurrent Neural Network (RNN) architectures, particularly the Extended Long Short Term Memory (xLSTM), have addressed the limitations of traditional Long Short Term Memory (LSTM) networks by introducing exponential gating and enhanced memory structures. These improvements make xLSTM suitable for time-series prediction tasks as they exhibit the ability to model long-term temporal dependencies better than LSTMs. Despite their potential, these xLSTM-based models remain largely unexplored in the context of vehicle trajectory prediction. Therefore, this paper introduces a novel xLSTM-based vehicle trajectory prediction framework, X-TRAJ, and its physics-aware variant, X-TRACK (eXtended LSTM for TRAjectory prediction Constraint by Kinematics), which explicitly integrates vehicle motion kinematics into the model learning process. By introducing physical constraints, the proposed model generates realistic and feasible trajectories. A comprehensive evaluation on the highD and NGSIM datasets demonstrates that X-TRACK outperforms state-of-the-art baselines.

Method: Domain-informed RL agents trained using Proximal Policy Optimization across diverse initial conditions and flow regimes, with domain knowledge incorporated via reward functions that encourage Bénard cell merging as a desirable macroscopic property.

Result: Domain-informed RL agents reduced convective heat transport by up to 33% in laminar regimes and 10% in chaotic regimes, significantly outperforming conventional controllers. They produced steady flows, faster training convergence, and generalization across flow regimes without retraining.

Conclusion: Domain-informed priors greatly enhance RL-based control robustness for chaotic flows, making real-world deployment more feasible by improving generalization and sample efficiency.

Abstract: Chaotic convective flows arise in many real-world systems, such as microfluidic devices and chemical reactors. Stabilizing these flows is highly desirable but remains challenging, particularly in chaotic regimes where conventional control methods often fail. Reinforcement Learning (RL) has shown promise for control in laminar flow settings, but its ability to generalize and remain robust under chaotic and turbulent dynamics is not well explored, despite being critical for real-world deployment. In this work, we improve the practical feasibility of RL-based control of such flows focusing on Rayleigh-B'enard Convection (RBC), a canonical model for convective heat transport. To enhance generalization and sample efficiency, we introduce domain-informed RL agents that are trained using Proximal Policy Optimization across diverse initial conditions and flow regimes. We incorporate domain knowledge in the reward function via a term that encourages B'enard cell merging, as an example of a desirable macroscopic property. In laminar flow regimes, the domain-informed RL agents reduce convective heat transport by up to 33%, and in chaotic flow regimes, they still achieve a 10% reduction, which is significantly better than the conventional controllers used in practice. We compare the domain-informed to uninformed agents: Our results show that the domain-informed reward design results in steady flows, faster convergence during training, and generalization across flow regimes without retraining. Our work demonstrates that elegant domain-informed priors can greatly enhance the robustness of RL-based control of chaotic flows, bringing real-world deployment closer.

Method: Analyzed multiple open-weight models on MMLU benchmark, tracking calibration evolution across network layers and identifying a low-dimensional calibration direction in the residual stream.

Result: Discovered a distinct confidence correction phase in upper/later layers where model confidence is actively recalibrated after decision certainty is reached. Perturbation of the identified calibration direction significantly improves ECE and MCE metrics without harming accuracy.

Conclusion: Calibration is a distributed phenomenon shaped throughout the network forward pass, not just in the final projection, revealing how confidence-regulating mechanisms operate within LLMs.

Abstract: Large Language Models (LLMs) have demonstrated inherent calibration capabilities, where predicted probabilities align well with correctness, despite prior findings that deep neural networks are often overconfident. Recent studies have linked this behavior to specific components in the final layer, such as entropy neurons and the unembedding matrix null space. In this work, we provide a complementary perspective by investigating how calibration evolves throughout the network depth. Analyzing multiple open-weight models on the MMLU benchmark, we uncover a distinct confidence correction phase in the upper/later layers, where model confidence is actively recalibrated after decision certainty has been reached. Furthermore, we identify a low-dimensional calibration direction in the residual stream whose perturbation significantly improves calibration metrics (ECE and MCE) without harming accuracy. Our findings suggest that calibration is a distributed phenomenon, shaped throughout the network forward pass, not just in its final projection, providing new insights into how confidence-regulating mechanisms operate within LLMs.

Method: Implemented 8 UQ techniques on models for AF classification and blood pressure regression tasks using PPG data, with comprehensive evaluation procedure including local calibration and adaptivity assessment.

Result: Found complex reliability patterns across techniques - optimal method depends on uncertainty expression, evaluation metric, and reliability scale. Local calibration provides practical insights not captured by global metrics.

Conclusion: UQ evaluation criteria should align with practical use cases, prioritizing small-scale reliability for limited patient measurements while maintaining predictive performance.

Abstract: In principle, deep learning models trained on medical time-series, including wearable photoplethysmography (PPG) sensor data, can provide a means to continuously monitor physiological parameters outside of clinical settings. However, there is considerable risk of poor performance when deployed in practical measurement scenarios leading to negative patient outcomes. Reliable uncertainties accompanying predictions can provide guidance to clinicians in their interpretation of the trustworthiness of model outputs. It is therefore of interest to compare the effectiveness of different approaches. Here we implement an unprecedented set of eight uncertainty quantification (UQ) techniques to models trained on two clinically relevant prediction tasks: Atrial Fibrillation (AF) detection (classification), and two variants of blood pressure regression. We formulate a comprehensive evaluation procedure to enable a rigorous comparison of these approaches. We observe a complex picture of uncertainty reliability across the different techniques, where the most optimal for a given task depends on the chosen expression of uncertainty, evaluation metric, and scale of reliability assessed. We find that assessing local calibration and adaptivity provides practically relevant insights about model behaviour that otherwise cannot be acquired using more commonly implemented global reliability metrics. We emphasise that criteria for evaluating UQ techniques should cater to the model’s practical use case, where the use of a small number of measurements per patient places a premium on achieving small-scale reliability for the chosen expression of uncertainty, while preserving as much predictive performance as possible.

Method: Hybrid generation framework with model-based covariate synthesis monitored via distance-to-closest-record filtering, combined with separately learned propensity and outcome models to ensure (W, A, Y) triplets retain causal structure. Includes synthetic pairing strategy to mitigate positivity violations.

Result: The framework enables generation of synthetic data that preserves underlying causal structure and supports robust causal analysis, with evaluation protocol leveraging unlimited synthetic samples to benchmark traditional estimators (IPTW, AIPW, substitution) under complex covariate distributions.

Conclusion: This work lays the groundwork for LLM-powered data pipelines that support robust causal analysis by addressing the critical gap in preserving causal parameters in synthetic data generation.