Method: Proposes OmniForcing framework with: 1) Asymmetric Block-Causal Alignment with zero-truncation Global Prefix to handle temporal asymmetry between modalities, 2) Audio Sink Token mechanism with Identity RoPE constraint to address audio token sparsity, 3) Joint Self-Forcing Distillation to correct cumulative cross-modal errors, and 4) modality-independent rolling KV-cache inference

Result: Achieves state-of-the-art streaming generation at ~25 FPS on a single GPU while maintaining multimodal synchronization and visual quality comparable to bidirectional teacher models

Conclusion: OmniForcing successfully enables real-time audio-visual generation by addressing key challenges in distilling bidirectional diffusion models into streaming autoregressive architectures

Abstract: Recent joint audio-visual diffusion models achieve remarkable generation quality but suffer from high latency due to their bidirectional attention dependencies, hindering real-time applications. We propose OmniForcing, the first framework to distill an offline, dual-stream bidirectional diffusion model into a high-fidelity streaming autoregressive generator. However, naively applying causal distillation to such dual-stream architectures triggers severe training instability, due to the extreme temporal asymmetry between modalities and the resulting token sparsity. We address the inherent information density gap by introducing an Asymmetric Block-Causal Alignment with a zero-truncation Global Prefix that prevents multi-modal synchronization drift. The gradient explosion caused by extreme audio token sparsity during the causal shift is further resolved through an Audio Sink Token mechanism equipped with an Identity RoPE constraint. Finally, a Joint Self-Forcing Distillation paradigm enables the model to dynamically self-correct cumulative cross-modal errors from exposure bias during long rollouts. Empowered by a modality-independent rolling KV-cache inference scheme, OmniForcing achieves state-of-the-art streaming generation at $\sim$25 FPS on a single GPU, maintaining multi-modal synchronization and visual quality on par with the bidirectional teacher.\textbf{Project Page:} \href{https://omniforcing.com}{https://omniforcing.com}

Method: 1) Conduct five diverse layer-wise analyses to identify paralinguistic vs. semantic understanding layers; 2) Propose Paralinguistic-Enhanced Fine-Tuning (PE-FT) with selective-layer fine-tuning and auxiliary dual-level classification head.

Result: PE-FT protocol efficiently resurfaces paralinguistic awareness, even surpassing all-layer fine-tuning performance. The method enables LALMs to better understand emotional and contextual cues in speech.

Conclusion: Paralinguistic awareness is crucial for human-like audio interaction. The proposed PE-FT protocol effectively enhances LALMs’ ability to understand both content and paralinguistic cues, advancing multimodal audio understanding.

Abstract: Large Audio Language Models (LALMs) have expanded the interaction with human to speech modality, which introduces great interactive potential, due to the paralinguistic cues implicitly indicating the user context. However, building on the current content-centred paradigm, LALMs usually neglect such paralinguistic cues and respond solely based on query content. In this work, to resurface the paralinguistic awareness in LALMs, we introduce five diverse layer-wise analyses to jointly identify paralinguistic layers and semantic understanding layers. Based on these insights, we propose a paralinguistic-enhanced fine-tuning (PE-FT) protocol accordingly to equip LALMs with paralinguistic-aware capabilities, including (1) selective-layer fine-tuning, and (2) an auxiliary dual-level classification head. Our experiments demonstrate that PE-FT protocol efficiently and effectively resurfaces the paralinguistic awareness, even surpassing the performance of the all-layer fine-tuning strategy.

Method: Comprehensive literature review approach with three main contributions: coverage across audio domains, unified taxonomy of ALM foundations, and establishment of research landscape.

Result: First systematic review of ALMs that helps researchers understand technology development and future trends while providing practical implementation references.

Conclusion: The review organizes ALM developments, establishes foundational taxonomy, and captures research landscape to advance the field and guide future work.

Abstract: Audio-Language Models (ALMs), trained on paired audio-text data, are designed to process, understand, and reason about audio-centric multimodal content. Unlike traditional supervised approaches that use predefined labels, ALMs leverage natural language supervision to better handle complex real-world audio scenes with multiple overlapping events. While demonstrating impressive zero-shot and task generalization capabilities, there is still a notable lack of systematic surveys that comprehensively organize and analyze developments. In this paper, we present the first systematic review of ALMs with three main contributions: (1) comprehensive coverage of ALM works across speech, music, and sound from a general audio perspective; (2) a unified taxonomy of ALM foundations, including model architectures and training objectives; (3) establishment of a research landscape capturing mutual promotion and constraints among different research aspects, aiding in summarizing evaluations, limitations, concerns and promising directions. Our review contributes to helping researchers understand the development of existing technologies and future trends, while also providing valuable references for implementation in practical applications.

Method: Develops a theoretical framework that analytically connects pre-trained LLM hyperparameters to throughput efficiency in speculative decoding systems. The theory enables prediction of throughput-optimal hyperparameters before model pre-training.

Result: The proposed theory allows for analytical determination of optimal hyperparameters for speculative decoding components, eliminating the need for expensive experimental optimization through LLM training.

Conclusion: The theoretical approach provides a cost-effective alternative to experimental optimization for speculative decoding systems, enabling efficient design of inference pipelines without extensive training overhead.

Abstract: Speculative decoding is a technique that uses multiple language models to accelerate infer- ence. Previous works have used an experi- mental approach to optimize the throughput of the inference pipeline, which involves LLM training and can be costly. This study of spec- ulative decoding proposes a theory that ana- lytically connects the key hyperparameters of pre-trained LLMs to the throughput efficiency of a downstream SD-based inference system. The theory allows the prediction of throughput- optimal hyperparameters for the components of an inference system before their pre-training.

Method: Proposes ARACH (Attention Reallocation via an Adaptive Context Hub), a plug-and-play inference-time mechanism that augments LLMs with an adaptive context hub to aggregate context and reallocate attention, operating without parameter updates.

Result: Extensive experiments across multiple language modeling tasks show consistent improvements with modest inference overhead. Attention analyses suggest ARACH mitigates the attention sink phenomenon.

Conclusion: Engineering a model’s internal computation offers a distinct inference-time strategy that differs fundamentally from both prompt-based test-time methods and training-based post-training approaches.

Abstract: Large language models (LLMs) achieve remarkable performance, yet further gains often require costly training. This has motivated growing interest in post-training techniques-especially training-free approaches that improve models at inference time without updating weights. Most training-free methods treat the model as a black box and improve outputs via input/output-level interventions, such as prompt design and test-time scaling through repeated sampling, reranking/verification, or search. In contrast, they rarely offer a plug-and-play mechanism to intervene in a model’s internal computation. We propose ARACH(Attention Reallocation via an Adaptive Context Hub), a training-free inference-time plug-in that augments LLMs with an adaptive context hub to aggregate context and reallocate attention. Extensive experiments across multiple language modeling tasks show consistent improvements with modest inference overhead and no parameter updates. Attention analyses further suggest that ARACH mitigates the attention sink phenomenon. These results indicate that engineering a model’s internal computation offers a distinct inference-time strategy, fundamentally different from both prompt-based test-time methods and training-based post-training approaches.

Method: DeReason partitions training data by reasoning intensity using LLM-based scoring into reasoning-intensive and non-reasoning-intensive subsets. Non-reasoning-intensive problems are allocated to SFT to build foundational domain knowledge, while difficult reasoning-intensive problems are reserved for RL to develop complex reasoning capabilities.

Result: The difficulty-based decoupling approach outperforms SFT-only, RL-only, and random-split baselines on general STEM and mathematical benchmarks, demonstrating that principled data allocation between SFT and RL stages yields better performance.

Conclusion: DeReason provides a systematic study of SFT-RL interplay for general reasoning and offers an effective post-training recipe that leverages complementary strengths of both approaches through difficulty-based data partitioning.

Abstract: Reinforcement learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for eliciting reasoning capabilities in large language models, particularly in mathematics and coding. While recent efforts have extended this paradigm to broader general scientific (STEM) domains, the complex interplay between supervised fine-tuning (SFT) and RL in these contexts remains underexplored. In this paper, we conduct controlled experiments revealing a critical challenge: for general STEM domains, RL applied directly to base models is highly sample-inefficient and is consistently surpassed by supervised fine-tuning (SFT) on moderate-quality responses. Yet sequential SFT followed by RL can further improve performance, suggesting that the two stages play complementary roles, and that how training data is allocated between them matters. Therefore, we propose DeReason, a difficulty-based data decoupling strategy for general reasoning. DeReason partitions training data by reasoning intensity estimated via LLM-based scoring into reasoning-intensive and non-reasoning-intensive subsets. It allocates broad-coverage, non-reasoning-intensive problems to SFT to establish foundational domain knowledge, and reserves a focused subset of difficult problems for RL to cultivate complex reasoning. We demonstrate that this principled decoupling yields better performance than randomly splitting the data for sequential SFT and RL. Extensive experiments on general STEM and mathematical benchmarks demonstrate that our decoupled curriculum training significantly outperforms SFT-only, RL-only, and random-split baselines. Our work provides a systematic study of the interplay between SFT and RL for general reasoning, offering a highly effective and generalized post-training recipe.

Method: Proposes MDER-DR framework: MDER indexing generates context-derived triple descriptions and integrates entity-level summaries; DR retrieval decomposes queries into resolvable triples and grounds them in KG via iterative reasoning. Forms LLM-driven QA pipeline robust to sparse/incomplete/complex relational data.

Result: Achieves substantial improvements over standard RAG baselines (up to 66%) on standard and domain-specific benchmarks while maintaining cross-lingual robustness.

Conclusion: The proposed domain-agnostic KG-based QA framework effectively addresses limitations of standard RAG over KGs by preserving contextual nuance and enabling robust multi-hop QA through novel indexing and retrieval mechanisms.

Abstract: Retrieval-Augmented Generation (RAG) over Knowledge Graphs (KGs) suffers from the fact that indexing approaches may lose important contextual nuance when text is reduced to triples, thereby degrading performance in downstream Question-Answering (QA) tasks, particularly for multi-hop QA, which requires composing answers from multiple entities, facts, or relations. We propose a domain-agnostic, KG-based QA framework that covers both the indexing and retrieval/inference phases. A new indexing approach called Map-Disambiguate-Enrich-Reduce (MDER) generates context-derived triple descriptions and subsequently integrates them with entity-level summaries, thus avoiding the need for explicit traversal of edges in the graph during the QA retrieval phase. Complementing this, we introduce Decompose-Resolve (DR), a retrieval mechanism that decomposes user queries into resolvable triples and grounds them in the KG via iterative reasoning. Together, MDER and DR form an LLM-driven QA pipeline that is robust to sparse, incomplete, and complex relational data. Experiments show that on standard and domain specific benchmarks, MDER-DR achieves substantial improvements over standard RAG baselines (up to 66%), while maintaining cross-lingual robustness. Our code is available at https://github.com/DataSciencePolimi/MDER-DR_RAG.

Method: Define iterative inference as Markovian generation chains, conduct rephrasing and translation experiments, and analyze through sentence-level Markov chain modeling with simulated data.

Result: Outputs either converge to small recurrent sets or produce novel sentences; iterative processes can increase or reduce diversity depending on temperature and initial inputs.

Conclusion: Iterative LLM inference dynamics reveal important patterns for multi-agent systems, with convergence/divergence behaviors influenced by model parameters and initial conditions.

Abstract: The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly processed by LLMs? In this paper, we define this iterative inference process as Markovian generation chains, where each step takes a specific prompt template and the previous output as input, without including any prior memory. In iterative rephrasing and round-trip translation experiments, the output either converges to a small recurrent set or continues to produce novel sentences over a finite horizon. Through sentence-level Markov chain modeling and analysis of simulated data, we show that iterative process can either increase or reduce sentence diversity depending on factors such as the temperature parameter and the initial input sentence. These results offer valuable insights into the dynamics of iterative LLM inference and their implications for multi-agent LLM systems.

Method: Employed multiple BERT and GPT-based language models to analyze works of two prominent Persian poets (Rumi and Parvin E’tesami), focusing on sentiment analysis and poetic meter examination.

Result: GPT4o can reliably analyze Persian poetry; Rumi’s poems express happier sentiments than Parvin E’tesami’s; Rumi’s poems show superiority in using meters to express wider sentiment variety.

Conclusion: LLMs can be effectively applied to computer-based semantic studies of poetry, reducing potential human biases in analysis, though the focus is on textual analysis rather than multimodal aspects.

Abstract: Recent advancements of the Artificial Intelligence (AI) have led to the development of large language models (LLMs) that are capable of understanding, analysing, and creating textual data. These language models open a significant opportunity in analyzing the literature and more specifically poetry. In the present work, we employ multiple Bidirectional encoder representations from transformers (BERT) and Generative Pre-trained Transformer (GPT) based language models to analyze the works of two prominent Persian poets: Jalal al-Din Muhammad Rumi (Rumi) and Parvin E’tesami. The main objective of this research is to investigate the capability of the modern language models in grasping complexities of the Persian poetry and explore potential correlations between the poems’ sentiment and their meters. Our findings in this study indicates that GPT4o language model can reliably be used in analysis of Persian poetry. Furthermore, the results of our sentiment analysis revealed that in general, Rumi’s poems express happier sentiments compared to Parvin E’tesami’s poems. Furthermore, comparing the utilization of poetic meters highlighted Rumi’s poems superiority in using meters to express a wider variety of sentiments. These findings are significant as they confirm that LLMs can be effectively applied in conducting computer-based semantic studies, where human interpretations are not required, and thereby significantly reducing potential biases in the analysis.

Method: Created ThReadMed-QA benchmark with 2,437 patient-physician conversation threads (8,204 QA pairs) from r/AskDocs. Evaluated 5 LLMs (GPT-5, GPT-4o, Claude Haiku, Gemini 2.5 Flash, Llama 3.3 70B) on 238 conversations using LLM-as-a-judge rubric. Introduced Conversational Consistency Score (CCS) and Error Propagation Rate (EPR) metrics.

Result: Even best model (GPT-5) achieved only 41.2% fully-correct responses. All models degraded significantly from turn 0 to turn 2 (p < 0.001), with wrong-answer rates roughly tripling by third turn. Strong initial performers showed steepest declines. CCS revealed high inconsistency, and EPR showed single wrong turn raises probability of subsequent wrong turn by 1.9-6.1x.

Conclusion: LLMs struggle with multi-turn medical conversations despite strong single-turn performance. There’s fundamental tension between single-turn capability and multi-turn reliability. New metrics (CCS, EPR) help quantify multi-turn failure modes in conversational AI.

Abstract: Medical question-answering benchmarks predominantly evaluate single-turn exchanges, failing to capture the iterative, clarification-seeking nature of real patient consultations. We introduce ThReadMed-QA, a benchmark of 2,437 fully-answered patient-physician conversation threads extracted from r/AskDocs, comprising 8,204 question-answer pairs across up to 9 turns. Unlike prior work relying on simulated dialogues, adversarial prompts, or exam-style questions, ThReadMed-QA captures authentic patient follow-up questions and verified physician responses, reflecting how patients naturally seek medical information online. We evaluate five state-of-the-art LLMs – GPT-5, GPT-4o, Claude Haiku, Gemini 2.5 Flash, and Llama 3.3 70B – on a stratified test split of 238 conversations (948 QA pairs) using a calibrated LLM-as-a-judge rubric grounded in physician ground truth. Even the strongest model, GPT-5, achieves only 41.2% fully-correct responses. All five models degrade significantly from turn 0 to turn 2 (p < 0.001), with wrong-answer rates roughly tripling by the third turn. We identify a fundamental tension between single-turn capability and multi-turn reliability: models with the strongest initial performance (GPT-5: 75.2; Claude Haiku: 72.3 out of 100) exhibit the steepest declines by turn 2 (dropping 16.2 and 25.0 points respectively), while weaker models plateau or marginally improve. We introduce two metrics to quantify multi-turn failure modes: Conversational Consistency Score (CCS) and Error Propagation Rate (EPR). CCS reveals that nearly one in three Claude Haiku conversations swings between a fully correct and a completely wrong response within the same thread. EPR shows that a single wrong turn raises the probability of a subsequent wrong turn by 1.9-6.1x across all models.

Method: Systematic evaluation of leading proprietary (Claude 3.5, GPT-4o, Gemini 1.5) and open-source (LLaMA 3.2, Gemma 2, Mistral, Nemotron 4) LLMs on TTC using three historical corpora (two English, one Portuguese), testing zero-shot and few-shot prompting, and fine-tuning settings.

Result: Proprietary models perform well, especially with few-shot prompting. Fine-tuning substantially improves open-source models but they still fail to match the performance delivered by proprietary LLMs.

Conclusion: LLMs show promise for Temporal Text Classification, with proprietary models currently outperforming open-source alternatives, suggesting room for improvement in open-source models for temporal language understanding tasks.

Abstract: Languages change over time. Computational models can be trained to recognize such changes enabling them to estimate the publication date of texts. Despite recent advancements in Large Language Models (LLMs), their performance on automatic dating of texts, also known as Temporal Text Classification (TTC), has not been explored. This study provides the first systematic evaluation of leading proprietary (Claude 3.5, GPT-4o, Gemini 1.5) and open-source (LLaMA 3.2, Gemma 2, Mistral, Nemotron 4) LLMs on TTC using three historical corpora, two in English and one in Portuguese. We test zero-shot and few-shot prompting, and fine-tuning settings. Our results indicate that proprietary models perform well, especially with few-shot prompting. They also indicate that fine-tuning substantially improves open-source models but that they still fail to match the performance delivered by proprietary LLMs.

Method: Use teacher-derived attribution maps as structured side signals to guide student models; extract attribution scores using Inseq library; inject scores into student transformer’s attention mechanism using four composition operators (addition, multiplication, averaging, replacement); evaluate across three language pairs and different attribution methods.

Result: Attention, Value Zeroing, and Layer Gradient × Activation methods consistently yield largest BLEU/chrF improvements; gradient-based methods show smaller, less consistent gains; attention-derived attributions better capture source-target alignment; Attributor transformer learns to reconstruct teacher’s attribution maps with accuracy correlating to downstream task utility.

Conclusion: Different attribution methods capture distinct signals; attention-based methods are most effective for seq2seq models; the Attributor transformer demonstrates that accurate attribution map reconstruction correlates with improved downstream task performance.

Abstract: The study of the attribution of input features to the output of neural network models is an active area of research. While numerous Explainable AI (XAI) techniques have been proposed to interpret these models, the systematic and automated evaluation of these methods in sequence-to-sequence (seq2seq) models is less explored. This paper introduces a new approach for evaluating explainability methods in transformer-based seq2seq models. We use teacher-derived attribution maps as a structured side signal to guide a student model, and quantify the utility of different attribution methods through the student’s ability to simulate targets. Using the Inseq library, we extract attribution scores over source-target sequence pairs and inject these scores into the attention mechanism of a student transformer model under four composition operators (addition, multiplication, averaging, and replacement). Across three language pairs (de-en, fr-en, ar-en) and attributions from Marian-MT and mBART models, Attention, Value Zeroing, and Layer Gradient $\times$ Activation consistently yield the largest gains in BLEU (and corresponding improvements in chrF) relative to baselines. In contrast, other gradient-based methods (Saliency, Integrated Gradients, DeepLIFT, Input $\times$ Gradient, GradientShap) lead to smaller and less consistent improvements. These results suggest that different attribution methods capture distinct signals and that attention-derived attributions better capture alignment between source and target representations in seq2seq models. Finally, we introduce an Attributor transformer that, given a source-target pair, learns to reconstruct the teacher’s attribution map. Our findings demonstrate that the more accurately the Attributor can reproduce attribution maps, the more useful an injection of those maps is for the downstream task. The source code can be found on GitHub.

Method: Developed a “stick-or-switch” evaluation framework to measure model conviction (defending correct diagnoses/safe abstentions) and flexibility (recognizing correct suggestions) across conversations. Evaluated 17 LLMs across three clinical datasets.

Result: Revealed the “conversation tax” - multi-turn interactions consistently degrade performance compared to single-shot baselines. Models frequently abandon initial correct diagnoses and safe abstentions to align with incorrect user suggestions. Several models exhibit “blind switching,” failing to distinguish between signal and incorrect suggestions.

Conclusion: Current LLMs struggle with maintaining diagnostic accuracy in multi-turn clinical conversations, showing concerning tendencies to defer to incorrect user suggestions rather than maintaining correct medical reasoning.

Abstract: Patients and clinicians are increasingly using chatbots powered by large language models (LLMs) for healthcare inquiries. While state-of-the-art LLMs exhibit high performance on static diagnostic reasoning benchmarks, their efficacy across multi-turn conversations, which better reflect real-world usage, has been understudied. In this paper, we evaluate 17 LLMs across three clinical datasets to investigate how partitioning the decision-space into multiple simpler turns of conversation influences their diagnostic reasoning. Specifically, we develop a “stick-or-switch” evaluation framework to measure model conviction (i.e., defending a correct diagnosis or safe abstention against incorrect suggestions) and flexibility (i.e., recognizing a correct suggestion when it is introduced) across conversations. Our experiments reveal the conversation tax, where multi-turn interactions consistently degrade performance when compared to single-shot baselines. Notably, models frequently abandon initial correct diagnoses and safe abstentions to align with incorrect user suggestions. Additionally, several models exhibit blind switching, failing to distinguish between signal and incorrect suggestions.

Method: Proposes particle filter models that explicitly represent structural hypotheses as finite particles, analyzes algorithmic consequences including amplification of garden-path effects, and demonstrates resampling produces digging-in effects.

Result: Shows particle filter models predict real-time digging-in effects where disambiguation difficulty increases with ambiguous region length, with magnitude scaling inversely with particle count.

Conclusion: Explicit structural representations in cognitive models are necessary to fully account for sentence processing difficulty, challenging pure surprisal-based approaches.

Abstract: Under surprisal theory, linguistic representations affect processing difficulty only through the bottleneck of surprisal. Our best estimates of surprisal come from large language models, which have no explicit representation of structural ambiguity. While LLM surprisal robustly predicts reading times across languages, it systematically underpredicts difficulty when structural expectations are violated – suggesting that representations of ambiguity are causally implicated in sentence processing. Particle filter models offer an alternative where structural hypotheses are explicitly represented as a finite set of particles. We prove several algorithmic consequences of particle filter models, including the amplification of garden-path effects. Most critically, we demonstrate that resampling, a common practice with these models, inherently produces real-time digging-in effects – where disambiguation difficulty increases with ambiguous region length. Digging-in magnitude scales inversely with particle count: fully parallel models predict no such effect.

Method: Created a dataset of 137 free-response problems adapted from standard materials science textbooks, covering topics like crystal structures, mechanical properties, diffusion, phase diagrams, and electronic properties. Provided expert-defined answer ranges to address ambiguity in reading numerical values from images. Evaluated several state-of-the-art multimodal LLMs including ChatGPT and GPT models via OpenAI APIs.

Result: Results show that although overall accuracy improves with model updates, current LLMs still struggle with genuine visual understanding and quantitative interpretation of materials science figures. Correct answers are often obtained by relying on memorized domain knowledge rather than by reading the provided images. The benchmark reveals weaknesses in visual reasoning, numerical precision, and significant-digit handling.

Conclusion: MaterialFigBench highlights persistent weaknesses in multimodal LLMs’ visual reasoning capabilities and provides a systematic, domain-specific foundation for advancing multimodal reasoning in materials science, guiding the development of future LLMs with stronger figure-based understanding.

Abstract: We present MaterialFigBench, a benchmark dataset designed to evaluate the ability of multimodal large language models (LLMs) to solve university-level materials science problems that require accurate interpretation of figures. Unlike existing benchmarks that primarily rely on textual representations, MaterialFigBench focuses on problems in which figures such as phase diagrams, stress-strain curves, Arrhenius plots, diffraction patterns, and microstructural schematics are indispensable for deriving correct answers. The dataset consists of 137 free-response problems adapted from standard materials science textbooks, covering a broad range of topics including crystal structures, mechanical properties, diffusion, phase diagrams, phase transformations, and electronic properties of materials. To address unavoidable ambiguity in reading numerical values from images, expert-defined answer ranges are provided where appropriate. We evaluate several state-of-the-art multimodal LLMs, including ChatGPT and GPT models accessed via OpenAI APIs, and analyze their performance across problem categories and model versions. The results reveal that, although overall accuracy improves with model updates, current LLMs still struggle with genuine visual understanding and quantitative interpretation of materials science figures. In many cases, correct answers are obtained by relying on memorized domain knowledge rather than by reading the provided images. MaterialFigBench highlights persistent weaknesses in visual reasoning, numerical precision, and significant-digit handling, while also identifying problem types where performance has improved. This benchmark provides a systematic and domain-specific foundation for advancing multimodal reasoning capabilities in materials science and for guiding the development of future LLMs with stronger figure-based understanding.

Method: BLooP (Bigram Lookahead Promotion) uses a hash table lookup at each decoding step to encourage LLMs to generate tokens that form bigrams from the source document, requiring no training, fine-tuning, or model modification.

Result: Improvements in ROUGE and BARTScore for multiple LLMs (Llama-3.1-8B-Instruct, Mistral-Nemo-Instruct-2407, Gemma-2-9b-it) on several datasets (CNN/DM, CCSum, Multi-News, SciTLDR). Human evaluation shows significant faithfulness improvements without reducing readability.

Conclusion: BLooP is an effective, simple training-free intervention that improves summarization faithfulness by leveraging source document bigrams during decoding.

Abstract: Abstractive summarization requires models to generate summaries that convey information in the source document. While large language models can generate summaries without fine-tuning, they often miss key details and include extraneous information. We propose BLooP (Bigram Lookahead Promotion), a simple training-free decoding intervention that encourages large language models (LLMs) to generate tokens that form bigrams from the source document. BLooP operates through a hash table lookup at each decoding step, requiring no training, fine-tuning, or model modification. We demonstrate improvements in ROUGE and BARTScore for Llama-3.1-8B-Instruct, Mistral-Nemo-Instruct-2407, and Gemma-2-9b-it on CNN/DM, CCSum, Multi-News, and SciTLDR. Human evaluation shows that BLooP significantly improves faithfulness without reducing readability. We make the code available at https://github.com/varuniyer/BLooP

Method: 1) Coarse-to-fine hybrid extraction combining statistical sampling and LLM semantic reasoning to identify and purify legal constituent elements; 2) LLM-assisted causal structure disambiguation using LLMs as constrained prior knowledge base for probabilistic evaluation and pruning of ambiguous causal directions; 3) Causal-aware judgment prediction model that constrains text attention intensity via generated causal graphs.

Result: Extensive experiments on LEVEN, QA, and CAIL datasets show the method significantly outperforms state-of-the-art baselines in both predictive accuracy and robustness, particularly in distinguishing confusing charges.

Conclusion: The proposed framework effectively addresses limitations of existing LJP methods by integrating LLM priors with causal discovery, improving both accuracy and robustness through better legal factor extraction and causal structure disambiguation.

Abstract: Mainstream methods for Legal Judgment Prediction (LJP) based on Pre-trained Language Models (PLMs) heavily rely on the statistical correlation between case facts and judgment results. This paradigm lacks explicit modeling of legal constituent elements and underlying causal logic, making models prone to learning spurious correlations and suffering from poor robustness. While introducing causal inference can mitigate this issue, existing causal LJP methods face two critical bottlenecks in real-world legal texts: inaccurate legal factor extraction with severe noise, and significant uncertainty in causal structure discovery due to Markov equivalence under sparse features. To address these challenges, we propose an enhanced causal inference framework that integrates Large Language Model (LLM) priors with statistical causal discovery. First, we design a coarse-to-fine hybrid extraction mechanism combining statistical sampling and LLM semantic reasoning to accurately identify and purify standard legal constituent elements. Second, to resolve structural uncertainty, we introduce an LLM-assisted causal structure disambiguation mechanism. By utilizing the LLM as a constrained prior knowledge base, we conduct probabilistic evaluation and pruning on ambiguous causal directions to generate legally compliant candidate causal graphs. Finally, a causal-aware judgment prediction model is constructed by explicitly constraining text attention intensity via the generated causal graphs. Extensive experiments on multiple benchmark datasets, including LEVEN , QA, and CAIL, demonstrate that our proposed method significantly outperforms state-of-the-art baselines in both predictive accuracy and robustness, particularly in distinguishing confusing charges.

Method: Proposes Tool-DC framework with two variants: 1) Training-free Tool-DC (TF) - plug-and-play approach using “Try-Check-Retry” paradigm; 2) Training-based Tool-DC (TB) - more inference-efficient version. Both reduce reasoning difficulty and leverage LLM self-reflection.

Result: Tool-DC (TF) achieves up to +25.10% average gains on BFCL and ACEBench benchmarks. Tool-DC (TB) enables Qwen2.5-7B to achieve comparable or better performance than proprietary LLMs like OpenAI o3 and Claude-Haiku-4.5.

Conclusion: Tool-DC effectively boosts LLM tool-calling performance for handling massive, noisy tools through divide-and-conquer approach and self-reflection capabilities.

Abstract: Tool-calling empowers Large Language Models (LLMs) to interact with external environments. However, current methods often struggle to handle massive and noisy candidate tools in long-context tool-calling tasks, limiting their real-world application. To this end, we propose Tool-DC, a Divide-and-Conquer framework for boosting tool-calling performance of LLMs. The core of Tool-DC is to reduce the reasoning difficulty and make full use of self-reflection ability of LLMs via a “Try-Check-Retry” paradigm. Specifically, Tool-DC involves two variants: 1) the training-free Tool-DC (TF), which is plug-and-play and flexible; 2) the training-based Tool-DC (TB), which is more inference-efficient. Extensive experiments show that both Tool-DC methods outperform their counterparts by a clear margin. Tool-DC (TF) brings up to +25.10% average gains against the baseline on BFCL and ACEBench benchmarks, while Tool-DC (TB) enables Qwen2.5-7B to achieve comparable or even better performance than proprietary LLMs, e.g., OpenAI o3 and Claude-Haiku-4.5.

Method: Trained on 70 languages with region-aware posttraining; includes a pretrained foundation model, globally balanced instruction-tuned variant, and three region-specialized models targeting Africa, South Asia, Europe, Asia-Pacific, and West Asia.

Result: Achieves state-of-the-art translation quality, strong multilingual understanding, and high-quality target-language generation with only 3.35B parameters, demonstrating an efficient scaling path for multilingual AI.

Conclusion: Tiny Aya presents an alternative, efficient scaling path for multilingual AI focused on balanced performance across languages and practical deployment, rather than just increasing model size.

Abstract: Tiny Aya redefines what a small multilingual language model can achieve. Trained on 70 languages and refined through region-aware posttraining, it delivers state-of-the-art in translation quality, strong multilingual understanding, and high-quality target-language generation, all with just 3.35B parameters. The release includes a pretrained foundation model, a globally balanced instruction-tuned variant, and three region-specialized models targeting languages from Africa, South Asia, Europe, Asia-Pacific, and West Asia. This report details the training strategy, data composition, and comprehensive evaluation framework behind Tiny Aya, and presents an alternative scaling path for multilingual AI: one centered on efficiency, balanced performance across languages, and practical deployment.

Method: Evaluated five model sizes from 360M to 8B across three architecture families (SmolLM2, Qwen2.5, Llama 3.1) under four retrieval conditions: no retrieval, BM25, dense retrieval using E5 large v2, and oracle retrieval. Introduced parametric knowledge split to separate questions models can answer alone from those requiring external knowledge.

Result: 1) Even with oracle retrieval, models ≤7B fail to extract correct answers 85-100% of the time on questions they cannot answer alone. 2) Adding retrieval context destroys 42-100% of answers models previously knew (distraction effect). 3) Dominant failure mode (2588 oracle failures analyzed) is irrelevant generation where models ignore provided context entirely.

Conclusion: For models below 7B parameters, the main limitation of RAG is context utilization rather than retrieval quality, and deploying RAG at this scale can lead to net negative trade-offs under standard evaluation conditions.

Abstract: Retrieval augmented generation RAG is widely deployed to improve factual accuracy in language models yet it remains unclear whether smaller models of size 7B parameters or less can effectively utilize retrieved information. To investigate this question we evaluate five model sizes from 360M to 8B across three architecture families SmolLM2 Qwen2.5 and Llama 3.1 under four retrieval conditions including no retrieval BM25 dense retrieval using E5 large v2 and oracle retrieval where the retrieved passage is guaranteed to contain the answer. We introduce a parametric knowledge split that separates questions a model can already answer from those that require external knowledge which allows us to isolate utilization failure from retrieval quality failure. We find three main results. First even with oracle retrieval models of size 7B or smaller fail to extract the correct answer 85 to 100 percent of the time on questions they cannot answer alone which indicates a fundamental utilization bottleneck. Second adding retrieval context destroys 42 to 100 percent of answers the model previously knew suggesting a distraction effect driven by the presence of context rather than its quality. Third an error analysis of 2588 oracle failures shows that the dominant failure mode is irrelevant generation where the model ignores the provided context entirely. These patterns hold across multiple prompt templates and retrieval methods. The results indicate that for models below 7B parameters the main limitation of RAG is context utilization rather than retrieval quality and that deploying RAG at this scale can lead to a net negative trade off under standard evaluation conditions.

Method: Central Supervisor dynamically decomposes user queries and delegates subtasks to modality-appropriate tools (object detection, OCR, speech transcription, etc.). Uses RouteLLM for text-only queries and SLM-assisted modality decomposition for non-text paths with adaptive routing strategies.

Result: Evaluated on 2,847 queries across 15 task categories: achieved 72% reduction in time-to-accurate-answer, 85% reduction in conversational rework, and 67% cost reduction compared to matched hierarchical baseline while maintaining accuracy parity.

Conclusion: Intelligent centralized orchestration fundamentally improves multimodal AI deployment economics by enabling efficient coordination of specialized tools across modalities.

Abstract: We present an agentic AI framework for autonomous multimodal query processing that coordinates specialized tools across text, image, audio, video, and document modalities. A central Supervisor dynamically decomposes user queries, delegates subtasks to modality-appropriate tools (e.g., object detection, OCR, speech transcription), and synthesizes results through adaptive routing strategies rather than predetermined decision trees. For text-only queries, the framework uses learned routing via RouteLLM, while non-text paths use SLM-assisted modality decomposition. Evaluated on 2,847 queries across 15 task categories, our framework achieves 72% reduction in time-to-accurate-answer, 85% reduction in conversational rework, and 67% cost reduction compared to the matched hierarchical baseline while maintaining accuracy parity. These results demonstrate that intelligent centralized orchestration fundamentally improves multimodal AI deployment economics.

Method: Proposes DapQ (decoding-aligned KV cache compression via position-aware pseudo queries), which uses position-aware pseudo queries to simulate output tokens. The key insight is that positional information is more critical than semantic content for constructing effective pseudo queries. This creates an observation window aligned with actual generation context for precise token eviction.

Result: Extensive evaluations across multiple benchmarks and LLMs show DapQ achieves superior performance, particularly under strict memory constraints (e.g., up to nearly lossless performance 99.5% on NIAH with 3% KV cache budgets).

Conclusion: DapQ provides an effective lightweight eviction framework for KV cache compression that aligns with actual generation context by using position-aware pseudo queries to approximate decoding-stage attention patterns.

Abstract: The Key-Value (KV) cache is crucial for efficient Large Language Models (LLMs) inference, but excessively long contexts drastically increase KV cache memory footprint. Existing KV cache compression methods typically rely on input-side attention patterns within a prompt observation window to estimate token importance during the prefill stage. They fail to preserve critical tokens for future generation since these assessments are not derived from the decoding process. Intuitively, an effective observation window should mirror the decoding-stage queries to accurately reflect which tokens the generation process will attend to. However, ground-truth decoding queries are inherently unavailable during inference. For constructing pseudo queries to approximate them, we find that positional information plays a more critical role than semantic content. Motivated by this insight, we propose decoding-aligned KV cache compression via position-aware pseudo queries (DapQ), a novel and lightweight eviction framework that leverages position-aware pseudo queries to simulate the output tokens, thereby establishing an effective observation window for importance assessment. It aligns closely with the actual generation context and enables precise token eviction. Extensive evaluations across multiple benchmarks and LLMs demonstrate that DapQ achieves superior performance, particularly under strict memory constraints (e.g., up to nearly lossless performance 99.5% on NIAH with 3% KV cache budgets).

Method: Hikari encodes READ/WRITE decisions into probabilistic WAIT tokens, uses Decoder Time Dilation to reduce autoregressive overhead and balance training, and employs supervised fine-tuning to recover from delays.

Result: Achieves new state-of-the-art BLEU scores for English-to-Japanese, German, and Russian translation in both low- and high-latency regimes, outperforming recent baselines.

Conclusion: Hikari demonstrates that policy-free, end-to-end simultaneous translation is feasible and achieves superior quality-latency trade-offs through integrated decision mechanisms and training techniques.

Abstract: Simultaneous machine translation (SiMT) has traditionally relied on offline machine translation models coupled with human-engineered heuristics or learned policies. We propose Hikari, a policy-free, fully end-to-end model that performs simultaneous speech-to-text translation and streaming transcription by encoding READ/WRITE decisions into a probabilistic WAIT token mechanism. We also introduce Decoder Time Dilation, a mechanism that reduces autoregressive overhead and ensures a balanced training distribution. Additionally, we present a supervised fine-tuning strategy that trains the model to recover from delays, significantly improving the quality-latency trade-off. Evaluated on English-to-Japanese, German, and Russian, Hikari achieves new state-of-the-art BLEU scores in both low- and high-latency regimes, outperforming recent baselines.

Method: Reconstructs tasks as influence diagrams where the LLM’s answer is the sole decision variable. Defines a utility function over conditional probability distributions within the diagram, and instructs the LLM to find the answer that maximizes expected utility, forcing explicit reasoning about each objective component.

Result: Validated on MovieLens 1M dataset across three frontier models (Claude Sonnet 4.6, GPT-5.4, and Gemini 2.5 Pro), showing consistent improvements in precision and Normalized Discounted Cumulative Gain (NDCG) over natural language baselines in multi-objective movie recommendation tasks.

Conclusion: Formal mathematical prompting via UtilityMax framework provides more precise optimization targets than natural language prompts, leading to better performance in multi-objective tasks by constraining LLMs to reason explicitly about each objective component.

Abstract: The success of a Large Language Model (LLM) task depends heavily on its prompt. Most use-cases specify prompts using natural language, which is inherently ambiguous when multiple objectives must be simultaneously satisfied. In this paper we introduce UtilityMax Prompting, a framework that specifies tasks using formal mathematical language. We reconstruct the task as an influence diagram in which the LLM’s answer is the sole decision variable. A utility function is defined over the conditional probability distributions within the diagram, and the LLM is instructed to find the answer that maximises expected utility. This constrains the LLM to reason explicitly about each component of the objective, directing its output toward a precise optimization target rather than a subjective natural language interpretation. We validate our approach on the MovieLens 1M dataset across three frontier models (Claude Sonnet 4.6, GPT-5.4, and Gemini 2.5 Pro), demonstrating consistent improvements in precision and Normalized Discounted Cumulative Gain (NDCG) over natural language baselines in a multi-objective movie recommendation task.

Method: Evaluated seven open-source LLMs on three tasks: (A) generation/extraction of pathology diagnosis text in predefined formats, (B) correction of typographical errors in Japanese pathology reports, and (C) subjective evaluation of model-generated explanatory text by medical professionals.

Result: Thinking models and medical-specialized models performed better in structured reporting tasks requiring reasoning and typo correction. Preferences for explanatory outputs varied substantially across raters. LLM utility differed by task, but open-source LLMs showed potential for limited clinical scenarios.

Conclusion: Open-source LLMs can be useful for assisting Japanese pathology report writing in limited but clinically relevant scenarios, though performance varies by task type and model specialization.

Abstract: The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored. We evaluated seven open-source LLMs from three perspectives: (A) generation and information extraction of pathology diagnosis text following predefined formats, (B) correction of typographical errors in Japanese pathology reports, and (C) subjective evaluation of model-generated explanatory text by pathologists and clinicians. Thinking models and medical-specialized models showed advantages in structured reporting tasks that required reasoning and in typo correction. In contrast, preferences for explanatory outputs varied substantially across raters. Although the utility of LLMs differed by task, our findings suggest that open-source LLMs can be useful for assisting Japanese pathology report writing in limited but clinically relevant scenarios.

Method: Proposes QChunker with a multi-agent debate framework: question outline generator, text segmenter, integrity reviewer, and knowledge completer. Uses document outlines for multi-path sampling and introduces ChunkScore for direct chunk quality evaluation.

Result: Constructed 45K high-quality dataset, transferred capability to small language models. ChunkScore effectively discriminates chunk quality. QChunker produces more logically coherent and information-rich chunks across four heterogeneous domains.

Conclusion: QChunker successfully addresses RAG’s fundamental constraints by improving text chunk quality through understanding-retrieval-augmentation paradigm, with ChunkScore providing efficient evaluation.

Abstract: The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained by the semantic integrity and information granularity of text chunks in its knowledge base. To address these challenges, this paper proposes QChunker, which restructures the RAG paradigm from retrieval-augmentation to understanding-retrieval-augmentation. Firstly, QChunker models the text chunking as a composite task of text segmentation and knowledge completion to ensure the logical coherence and integrity of text chunks. Drawing inspiration from Hal Gregersen’s “Questions Are the Answer” theory, we design a multi-agent debate framework comprising four specialized components: a question outline generator, text segmenter, integrity reviewer, and knowledge completer. This framework operates on the principle that questions serve as catalysts for profound insights. Through this pipeline, we successfully construct a high-quality dataset of 45K entries and transfer this capability to small language models. Additionally, to handle long evaluation chains and low efficiency in existing chunking evaluation methods, which overly rely on downstream QA tasks, we introduce a novel direct evaluation metric, ChunkScore. Both theoretical and experimental validations demonstrate that ChunkScore can directly and efficiently discriminate the quality of text chunks. Furthermore, during the text segmentation phase, we utilize document outlines for multi-path sampling to generate multiple candidate chunks and select the optimal solution employing ChunkScore. Extensive experimental results across four heterogeneous domains exhibit that QChunker effectively resolves aforementioned issues by providing RAG with more logically coherent and information-rich text chunks.

Method: Proposes Multi-Task Reinforcement Learning for MLLM-as-a-Judge (MT-RL-Judge), a framework that jointly optimizes judge models across multiple tasks using reinforcement learning to leverage RL’s generalization capabilities.

Result: MT-RL-Judge outperforms strong baselines in both judgment consistency and correlation with human preferences, and exhibits robust generalization on out-of-distribution tasks.

Conclusion: The multi-task RL approach effectively addresses generalization limitations in MLLM judge models, creating more reliable and versatile evaluation systems for multimodal tasks.

Abstract: Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle to generalize to diverse contexts, which is a critical requirement for reliable evaluation. To address this limitation, we propose Multi-Task Reinforcement Learning for MLLM-as-a-Judge (MT-RL-Judge), a framework that jointly optimizes the judge model across multiple tasks, leveraging the generalization capabilities of RL. Experimental results against several strong baselines demonstrate that MT-RL-Judge outperforms strong baselines in both judgment consistency and correlation with human preferences. Furthermore, our approach exhibits robust generalization on out-of-distribution tasks, further validating its effectiveness.

Method: Qualitative analysis of interview data from 29 industry stakeholders using Chesterman’s framework of translation ethics and associated values as an analytical lens.

Result: Efficiency-oriented technological values aligned with ethics of service have become baseline expectations, while human value is repositioned through expertise, oversight, accountability, and contextual judgment. Adaptability emerges as a key mediating value linking human and technological domains.

Conclusion: Automation reshapes rather than replaces translation value, creating an interdependent configuration where technological efficiency enables human communicative work, with adaptability serving as a core professional requirement.

Abstract: This paper examines how value is constructed and negotiated in today’s increasingly automated language and translation industry. Drawing on interview data from twenty-nine industry stakeholders collected within the LT-LiDER project, the study analyses how human value, technological value, efficiency, and adaptability are articulated across different professional roles. Using Chesterman’s framework of translation ethics and associated values as an analytical lens, the paper shows that efficiency-oriented technological values aligned with the ethics of service have become baseline expectations in automated production environments, where speed, scalability, and deliverability dominate evaluation criteria. At the same time, human value is not displaced but repositioned, emerging primarily through expertise, oversight, accountability, and contextual judgment embedded within technology-mediated workflows. A central finding is the prominence of adaptability as a mediating value linking human and technological domains. Adaptability is constructed as a core professional requirement, reflecting expectations that translators continuously adjust their skills, roles, and identities in response to evolving tools and organisational demands. The paper argues that automation reshapes rather than replaces translation value, creating an interdependent configuration in which technological efficiency enables human communicative work.

Method: The authors propose evaluation on a SemCor-derived dataset that respects original corpus polysemy and frequency distributions. They assess pre-trained embeddings and clustering algorithms across different parts of speech, propose an LLM-based WSI method for English, and evaluate various data augmentation sources (LLM-generated, corpus, lexicon). They also test semi-supervised scenarios using Wiktionary for data augmentation with must-link constraints.

Result: Key findings: (1) No unsupervised method surpasses the strong “one cluster per lemma” heuristic baseline; (2) Results and best systems vary across parts of speech; (3) LLMs struggle with this task; (4) Data augmentation is beneficial; (5) Using Wiktionary helps and surpasses previous state-of-the-art by 3.3% on their test set.

Conclusion: WSI is not solved and requires better integration of lexicons with LLMs’ lexical semantics capabilities. The paper emphasizes the need for improved evaluation methodologies and better articulation between traditional lexical resources and modern language models.

Abstract: In the absence of sense-annotated data, word sense induction (WSI) is a compelling alternative to word sense disambiguation, particularly in low-resource or domain-specific settings. In this paper, we emphasize methodological problems in current WSI evaluation. We propose an evaluation on a SemCor-derived dataset, respecting the original corpus polysemy and frequency distributions. We assess pre-trained embeddings and clustering algorithms across parts of speech, and propose and evaluate an LLM-based WSI method for English. We evaluate data augmentation sources (LLM-generated, corpus and lexicon), and semi-supervised scenarios using Wiktionary for data augmentation, must-link constraints, number of clusters per lemma. We find that no unsupervised method (whether ours or previous) surpasses the strong “one cluster per lemma” heuristic (1cpl). We also show that (i) results and best systems may vary across POS, (ii) LLMs have troubles performing this task, (iii) data augmentation is beneficial and (iv) capitalizing on Wiktionary does help. It surpasses previous SOTA system on our test set by 3.3%. WSI is not solved, and calls for a better articulation of lexicons and LLMs’ lexical semantics capabilities.

Method: SemBench uses dictionary sense definitions and a sentence encoder to automatically generate synthetic benchmarks without needing curated example sentences. The framework is evaluated across three languages (English, Spanish, Basque) with various LLMs.

Result: SemBench rankings strongly correlate with standard WiC dataset rankings. Only a small number of examples is needed for stable, meaningful rankings. The framework works across languages with different resource levels.

Conclusion: SemBench provides a lightweight, adaptable, and data-efficient framework for cross-lingual evaluation of semantic understanding in LLMs, overcoming resource limitations of traditional benchmarks.

Abstract: Recent progress in Natural Language Processing (NLP) has been driven by the emergence of Large Language Models (LLMs), which exhibit remarkable generative and reasoning capabilities. However, despite their success, evaluating the true semantic understanding of these models remains a persistent challenge. Traditional benchmarks such as Word-in-Context (WiC) effectively probe this capability, but their creation is resource-intensive and often limited to high-resource languages. In this paper, we introduce SemBench, a framework for automatically generating synthetic benchmarks that assess the semantic competence of LLMs using only dictionary sense definitions and a sentence encoder. This approach eliminates the need for curated example sentences, making it both scalable and language-independent. We evaluate SemBench in three languages (English, Spanish, and Basque) spanning different levels of linguistic resources, and across a wide range of LLMs. Our results show that rankings derived from SemBench strongly correlate with those obtained from standard WiC datasets. Furthermore, our analysis demonstrates that only a small number of examples is required to achieve stable and meaningful rankings. Overall, SemBench provides a lightweight, adaptable, and data-efficient framework for cross-lingual evaluation of semantic understanding in LLMs.

Method: Created semi-synthetic dataset by generating English sentences based on linguistic patterns, translating to Hebrew using multiple MT engines, filtering via BLEU selection, manual evaluation by linguists, and introducing controlled translation errors for gender/number agreement challenges.

Result: Trained neural QE models (BERT, XLM-R) on the dataset and analyzed impact of dataset size, balance, and error distribution on model performance for sentence-level MT quality assessment.

Conclusion: Research advances QE models for under-resourced language pairs and morphology-rich languages, with future directions aimed at improving QE performance.

Abstract: Quality estimation (QE) plays a crucial role in machine translation (MT) workflows, as it serves to evaluate generated outputs that have no reference translations and to determine whether human post-editing or full retranslation is necessary. Yet, developing highly accurate, adaptable and reliable QE systems for under-resourced language pairs remains largely unsolved, due mainly to limited parallel corpora and to diverse language-dependent factors, such as with morphosyntactically complex languages. This study presents a semi-synthetic parallel dataset for English-to-Hebrew QE, generated by creating English sentences based on examples of usage that illustrate typical linguistic patterns, translating them to Hebrew using multiple MT engines, and filtering outputs via BLEU-based selection. Each translated segment was manually evaluated and scored by a linguist, and we also incorporated professionally translated English-Hebrew segments from our own resources, which were assigned the highest quality score. Controlled translation errors were introduced to address linguistic challenges, particularly regarding gender and number agreement, and we trained neural QE models, including BERT and XLM-R, on this dataset to assess sentence-level MT quality. Our findings highlight the impact of dataset size, distributed balance, and error distribution on model performance. We will describe the challenges, methodology and results of our experiments, and specify future directions aimed at improving QE performance. This research contributes to advancing QE models for under resourced language pairs, including morphology-rich languages.

Method: Used small GPT-2-style character-level transformers (3.5M-86M parameters) on synthetic math corpora with controlled mixtures of correct and incorrect rules. Tested random-error vs coherent incorrect rule systems, and natural-language-like synthetic worlds.

Result: Models strongly prefer correct completions in random-error settings (83.1% accuracy at balanced data, 67.0% even with only 10% correct data). Coherent incorrect rule systems eliminate preference (near-chance accuracy). Natural-language-like settings show weaker effect (57.7%). Embedding verification steps can restore correctness preference.

Conclusion: What appears as “truth bias” is largely a side effect of compression pressure and preference for internal consistency, rather than an intrinsic drive toward truth.

Abstract: Why do language models sometimes prefer correct statements even when trained on mixed-quality data? We introduce the Compression–Consistency Principle: next-token prediction favors hypotheses that allow shorter and more internally consistent descriptions of the training data. Truth bias emerges only when false alternatives are structurally harder to compress. We test this using small GPT-2-style character-level transformers (3.5M–86M parameters) on synthetic math corpora with controlled mixtures of correct and incorrect rules. In the random-error setting, models strongly prefer correct completions in paired evaluation: 83.1% accuracy at balanced data and 67.0% even when correct rules appear in only 10% of the corpus. Replacing random errors with a coherent but mathematically incorrect rule system largely eliminates the preference (near-chance accuracy). In a more natural-language-like synthetic world, the effect is weaker but still present (57.7%). Additional experiments show that embedding verification steps can restore preference for correctness even at small scale, while increasing the number of consistent rules produces a graded improvement in accuracy. Our results suggest that what appears as a “truth bias” is largely a side effect of compression pressure and preference for internal consistency, rather than an intrinsic drive toward truth. Full code and data are available at https://github.com/Rai220/compression-drives-truth.

Method: Proposed LegRAG framework with legal adaptive indexing (clause-boundary segmentation) and dual-path self-reflection mechanism; created Legal-DC benchmark dataset with 480 documents and 2,475 QA pairs.

Result: LegRAG outperforms state-of-the-art methods by 1.3% to 5.6% across key evaluation metrics; provides specialized benchmark and practical framework for Chinese legal RAG.

Conclusion: The research advances Chinese legal RAG systems through specialized benchmarks, practical frameworks, and empirical insights, with code and data publicly available.

Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising technology for legal document consultation, yet its application in Chinese legal scenarios faces two key limitations: existing benchmarks lack specialized support for joint retriever-generator evaluation, and mainstream RAG systems often fail to accommodate the structured nature of legal provisions. To address these gaps, this study advances two core contributions: First, we constructed the Legal-DC benchmark dataset, comprising 480 legal documents (covering areas such as market regulation and contract management) and 2,475 refined question-answer pairs, each annotated with clause-level references, filling the gap for specialized evaluation resources in Chinese legal RAG. Second, we propose the LegRAG framework, which integrates legal adaptive indexing (clause-boundary segmentation) with a dual-path self-reflection mechanism to ensure clause integrity while enhancing answer accuracy. Third, we introduce automated evaluation methods for large language models to meet the high-reliability demands of legal retrieval scenarios. LegRAG outperforms existing state-of-the-art methods by 1.3% to 5.6% across key evaluation metrics. This research provides a specialized benchmark, practical framework, and empirical insights to advance the development of Chinese legal RAG systems. Our code and data are available at https://github.com/legal-dc/Legal-DC.

Method: Implemented classification models for fake news detection and applied three XAI interpretability methods: SHAP, LIME, and Integrated Gradients. Compared their explanatory capabilities on neural network architectures.

Result: XAI enhanced model transparency while maintaining high detection accuracy. SHAP provided detailed local attributions, LIME offered simple intuitive explanations, and Integrated Gradients performed efficiently with convolutional models. Limitations included computational cost and sensitivity to parameterization.

Conclusion: Integrating XAI with NLP is an effective approach for improving the reliability and trustworthiness of fake news detection systems, with different methods offering complementary explanatory value.

Abstract: This article examines the application of Explainable Artificial Intelligence (XAI) in NLP based fake news detection and compares selected interpretability methods. The work outlines key aspects of disinformation, neural network architectures, and XAI techniques, with a focus on SHAP, LIME, and Integrated Gradients. In the experimental study, classification models were implemented and interpreted using these methods. The results show that XAI enhances model transparency and interpretability while maintaining high detection accuracy. Each method provides distinct explanatory value: SHAP offers detailed local attributions, LIME provides simple and intuitive explanations, and Integrated Gradients performs efficiently with convolutional models. The study also highlights limitations such as computational cost and sensitivity to parameterization. Overall, the findings demonstrate that integrating XAI with NLP is an effective approach to improving the reliability and trustworthiness of fake news detection systems.

Method: Comprehensive evaluation of various LLMs (open-source and closed-source) with different prompt types, output processing methods, few-shot example counts, and selection strategies across 15 challenging biomedical datasets.

Result: Achieved average PR AUC of >0.4 for zero-shot and nearly 0.5 for few-shot prompting, comparable to traditional classifiers like naïve Bayes (0.5), random forest (0.5-0.55), and fine-tuned transformers (0.5).

Conclusion: LLMs are viable biomedical text classifiers with performance approaching traditional methods; optimal setups include using output token probabilities for class probability prediction and careful prompt engineering.

Abstract: This work presents a systematic and in-depth investigation of the utility of large language models as text classifiers for biomedical article classification. The study uses several small and mid-size open source models, as well as selected closed source ones, and is more comprehensive than most prior work with respect to the scope of evaluated configurations: different types of prompts, output processing methods for generating both class and class probability predictions, as well as few-shot example counts and selection methods. The performance of the most successful configurations is compared to that of conventional classification algorithms. The obtained average PR AUC over 15 challenging datasets above 0.4 for zero-shot prompting and nearly 0.5 for few-shot prompting comes close to that of the naïve Bayes classifier (0.5), the random forest algorithm (0.5 with default settings or 0.55 with hyperparameter tuning) and fine-tuned transformer models (0.5). These results confirm the utility of large language models as text classifiers for non-trivial domains and provide practical recommendations of the most promising setups, including in particular using output token probabilities for class probability prediction.

Method: Created 12 models (1.3B parameters each) trained from scratch on ~100B tokens of temporally partitioned data with strict annual cutoffs (2013-2024), enhanced with instruction fine-tuning on both general and finance-specific datasets respecting temporal boundaries.

Result: Perplexity-based probing confirms each model’s knowledge is bounded by its cutoff year, while evaluation shows competitive performance with similar-scale models. Interactive web demo allows querying and comparing responses across different cutoff years.

Conclusion: DatedGPT provides a solution to lookahead bias in financial forecasting by creating temporally bounded models, enabling valid backtesting while maintaining competitive performance.

Abstract: In financial backtesting, large language models pretrained on internet-scale data risk introducing lookahead bias that undermines their forecasting validity, as they may have already seen the true outcome during training. To address this, we present DatedGPT, a family of twelve 1.3B-parameter language models, each trained from scratch on approximately 100 billion tokens of temporally partitioned data with strict annual cutoffs spanning 2013 to 2024. We further enhance each model with instruction fine-tuning on both general-domain and finance-specific datasets curated to respect the same temporal boundaries. Perplexity-based probing confirms that each model’s knowledge is effectively bounded by its data cutoff year, while evaluation on standard benchmarks shows competitive performance with existing models of similar scale. We provide an interactive web demo that allows users to query and compare responses from models across different cutoff years.

Method: Two-stage compression: 1) Structured hybrid pruning using NVIDIA Model Optimizer to reduce parameters by 33.4%, 2) Knowledge distillation using NVIDIA NeMo Framework for logit-based quality recovery, followed by alignment pipeline with SFT, DPO-P, and GRPO.

Result: Successfully recovered approximately 90% of baseline model’s performance while providing up to 50% inference speedup, creating a 7.35B parameter model from original 11.04B model.

Conclusion: The approach demonstrates an efficient pathway to create language models for less-represented languages, preserving original model quality while significantly reducing inference deployment costs through compression techniques.

Abstract: This report details the creation of Bielik-Minitron-7B, a compressed 7.35B parameter version of the Bielik-11B-v3.0 model, specifically optimized for European languages. By leveraging a two-stage compression methodology inspired by the NVIDIA Minitron approach, we combined structured hybrid pruning and knowledge distillation to reduce the model’s parameter count by 33.4%, from 11.04B to 7.35B. We utilized the NVIDIA Model Optimizer for structural pruning and the NVIDIA NeMo Framework for logit-based distillation for quality recovery. Following distillation, the model underwent a rigorous alignment pipeline consisting of Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO-P), and Reinforcement Learning (GRPO). Our final model successfully recovered approximately 90% of the baseline model’s performance while providing up to 50% inference speedup. This approach demonstrates an efficient pathway to create language models for less-represented languages, preserving the original model quality while reducing inference deployment costs.

Method: Proposes CoMMET, a Comprehensive Mental states and Moral Evaluation Task dataset inspired by the Theory of Mind Booklet Task, featuring multimodal inputs and multi-turn conversational testing.

Result: Comprehensive assessment of various LLMs reveals strengths and limitations in social cognitive capabilities, providing insights for future model improvement.

Conclusion: CoMMET offers a deeper understanding of LLMs’ social reasoning abilities and identifies directions for enhancing multimodal Theory of Mind capabilities in AI systems.

Abstract: Theory of Mind (ToM)-the ability to reason about the mental states of oneself and others-is a cornerstone of human social intelligence. As Large Language Models (LLMs) become ubiquitous in real-world applications, validating their capacity for this level of social reasoning is essential for effective and natural interactions. However, existing benchmarks for assessing ToM in LLMs are limited; most rely solely on text inputs and focus narrowly on belief-related tasks. In this paper, we propose a new multimodal benchmark dataset, CoMMET, a Comprehensive Mental states and Moral Evaluation Task inspired by the Theory of Mind Booklet Task. CoMMET expands the scope of evaluation by covering a broader range of mental states and introducing multi-turn testing. To the best of our knowledge, this is the first multimodal dataset to evaluate ToM in a multi-turn conversational setting. Through a comprehensive assessment of LLMs across different families and sizes, we analyze the strengths and limitations of current models and identify directions for future improvement. Our work offers a deeper understanding of the social cognitive capabilities of modern LLMs.

Method: Proposes using large language model (LLM) agents to generate realistic digital footprints. Starting from structured user profiles, the method creates diverse and plausible sequences of user events, producing corresponding digital artifacts like emails, messages, calendar entries, and reminders.

Result: Intrinsic evaluation shows the generated dataset is more diverse and realistic than existing baselines. Models fine-tuned on this synthetic data outperform those trained on other synthetic datasets when evaluated on real-world out-of-distribution tasks.

Conclusion: The LLM-based approach successfully addresses data scarcity in digital footprint research by generating high-quality synthetic data that improves model performance on real-world tasks.

Abstract: Digital footprints (records of individuals’ interactions with digital systems) are essential for studying behavior, developing personalized applications, and training machine learning models. However, research in this area is often hindered by the scarcity of diverse and accessible data. To address this limitation, we propose a novel method for synthesizing realistic digital footprints using large language model (LLM) agents. Starting from a structured user profile, our approach generates diverse and plausible sequences of user events, ultimately producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc. Intrinsic evaluation results demonstrate that the generated dataset is more diverse and realistic than existing baselines. Moreover, models fine-tuned on our synthetic data outperform those trained on other synthetic datasets when evaluated on real-world out-of-distribution tasks.

Method: CHiL(L)Grader incorporates calibrated confidence estimation using post-hoc temperature scaling, confidence-based selective prediction, and continual learning. It automates only high-confidence predictions while routing uncertain cases to human graders, and adapts to evolving rubrics and unseen questions.

Result: Across three short-answer grading datasets, CHiL(L)Grader automatically scores 35-65% of responses at expert-level quality (QWK >= 0.80). A QWK gap of 0.347 between accepted and rejected predictions confirms effective confidence-based routing. Each correction cycle strengthens the model’s grading capability.

Conclusion: Uncertainty quantification is key for reliable AI-assisted grading, and CHiL(L)Grader demonstrates that calibrated confidence estimation combined with human-in-the-loop workflows enables safe deployment of LLMs in educational assessment.

Abstract: Scaling educational assessment with large language models requires not just accuracy, but the ability to recognize when predictions are trustworthy. Instruction-tuned models tend to be overconfident, and their reliability deteriorates as curricula evolve, making fully autonomous deployment unsafe in high-stakes settings. We introduce CHiL(L)Grader, the first automated grading framework that incorporates calibrated confidence estimation into a human-in-the-loop workflow. Using post-hoc temperature scaling, confidence-based selective prediction, and continual learning, CHiL(L)Grader automates only high-confidence predictions while routing uncertain cases to human graders, and adapts to evolving rubrics and unseen questions. Across three short-answer grading datasets, CHiL(L)Grader automatically scores 35-65% of responses at expert-level quality (QWK >= 0.80). A QWK gap of 0.347 between accepted and rejected predictions confirms the effectiveness of the confidence-based routing. Each correction cycle strengthens the model’s grading capability as it learns from teacher feedback. These results show that uncertainty quantification is key for reliable AI-assisted grading.

Method: Introduces BTZSC benchmark with 22 public datasets spanning sentiment, topic, intent, and emotion classification. Systematically compares four model families (NLI cross-encoders, embedding models, rerankers, instruction-tuned LLMs) across 38 public and custom checkpoints.

Result: Modern rerankers (Qwen3-Reranker-8B) achieve SOTA with macro F1 = 0.72. Strong embedding models (GTE-large-en-v1.5) substantially close accuracy gap while offering best accuracy-latency tradeoff. Instruction-tuned LLMs (4-12B parameters) achieve competitive performance (macro F1 up to 0.67), excelling on topic classification. NLI cross-encoders plateau with backbone size increases.

Conclusion: BTZSC enables fair comparison of zero-shot text classification approaches. Rerankers set new SOTA, embedding models offer best practical tradeoffs, and scaling primarily benefits rerankers and LLMs over embedding models. The benchmark supports reproducible progress in zero-shot text understanding.

Abstract: Zero-shot text classification (ZSC) offers the promise of eliminating costly task-specific annotation by matching texts directly to human-readable label descriptions. While early approaches have predominantly relied on cross-encoder models fine-tuned for natural language inference (NLI), recent advances in text-embedding models, rerankers, and instruction-tuned large language models (LLMs) have challenged the dominance of NLI-based architectures. Yet, systematically comparing these diverse approaches remains difficult. Existing evaluations, such as MTEB, often incorporate labeled examples through supervised probes or fine-tuning, leaving genuine zero-shot capabilities underexplored. To address this, we introduce BTZSC, a comprehensive benchmark of 22 public datasets spanning sentiment, topic, intent, and emotion classification, capturing diverse domains, class cardinalities, and document lengths. Leveraging BTZSC, we conduct a systematic comparison across four major model families, NLI cross-encoders, embedding models, rerankers and instruction-tuned LLMs, encompassing 38 public and custom checkpoints. Our results show that: (i) modern rerankers, exemplified by Qwen3-Reranker-8B, set a new state-of-the-art with macro F1 = 0.72; (ii) strong embedding models such as GTE-large-en-v1.5 substantially close the accuracy gap while offering the best trade-off between accuracy and latency; (iii) instruction-tuned LLMs at 4–12B parameters achieve competitive performance (macro F1 up to 0.67), excelling particularly on topic classification but trailing specialized rerankers; (iv) NLI cross-encoders plateau even as backbone size increases; and (v) scaling primarily benefits rerankers and LLMs over embedding models. BTZSC and accompanying evaluation code are publicly released to support fair and reproducible progress in zero-shot text understanding.

Method: LabelPigeon framework uses XML tags to jointly perform translation and label projection. It employs a direct evaluation scheme for label projection and tests across 11 languages initially, then extends to 203 languages with varying annotation complexity.

Result: LabelPigeon outperforms baselines and actively improves translation quality in 11 languages. It shows consistent improvement across 203 languages attributed to additional fine-tuning. Achieves substantial gains in cross-lingual transfer across 27 languages and three downstream tasks, up to +39.9 F1 on NER.

Conclusion: XML-tagged label projection provides effective and efficient label transfer without compromising translation quality, demonstrating that joint translation and label projection can outperform separate approaches.

Abstract: Label projection is an effective technique for cross-lingual transfer, extending span-annotated datasets from a high-resource language to low-resource ones. Most approaches perform label projection as a separate step after machine translation, and prior work that combines the two reports degraded translation quality. We re-evaluate this claim with LabelPigeon, a novel framework that jointly performs translation and label projection via XML tags. We design a direct evaluation scheme for label projection, and find that LabelPigeon outperforms baselines and actively improves translation quality in 11 languages. We further assess translation quality across 203 languages and varying annotation complexity, finding consistent improvement attributed to additional fine-tuning. Finally, across 27 languages and three downstream tasks, we report substantial gains in cross-lingual transfer over comparable work, up to +39.9 F1 on NER. Overall, our results demonstrate that XML-tagged label projection provides effective and efficient label transfer without compromising translation quality.

Method: Operationalized translationese as segment-level translatedness scores from an automatic classifier. Translation task difficulty was conceptualized as source-text and cross-lingual transfer components, measured using information-theoretic metrics based on LLM surprisal, complemented by established syntactic and semantic features. Used a bidirectional English-German corpus with written and spoken subcorpora.

Result: Translationese can be partly explained by translation task difficulty, especially in English-to-German translation. Cross-lingual transfer difficulty contributed more than source-text complexity in most experiments. Information-theoretic indicators matched or outperformed traditional features in written mode but offered no advantage in spoken mode. Source-text syntactic complexity and translation-solution entropy were the strongest predictors across language pairs and modes.

Conclusion: Translationese reflects cognitive load inherent in translation tasks, with task difficulty being a significant predictor. Information-theoretic approaches show promise for written translation analysis, while traditional features remain relevant for spoken translation.

Abstract: Translations systematically diverge from texts originally produced in the target language, a phenomenon widely referred to as translationese. Translationese has been attributed to production tendencies (e.g. interference, simplification), socio-cultural variables, and language-pair effects, yet a unified explanatory account is still lacking. We propose that translationese reflects cognitive load inherent in the translation task itself. We test whether observable translationese can be predicted from quantifiable measures of translation task difficulty. Translationese is operationalised as a segment-level translatedness score produced by an automatic classifier. Translation task difficulty is conceptualised as comprising source-text and cross-lingual transfer components, operationalised mainly through information-theoretic metrics based on LLM surprisal, complemented by established syntactic and semantic alternatives. We use a bidirectional English-German corpus comprising written and spoken subcorpora. Results indicate that translationese can be partly explained by translation task difficulty, especially in English-to-German. For most experiments, cross-lingual transfer difficulty contributes more than source-text complexity. Information-theoretic indicators match or outperform traditional features in written mode, but offer no advantage in spoken mode. Source-text syntactic complexity and translation-solution entropy emerged as the strongest predictors of translationese across language pairs and modes.

Method: Extends LLM approach to sentence-level psycholinguistic features using fine-tuning. Compares fine-tuned models against zero-shot and few-shot prompting. Uses interpretable baseline predictors for comparison. Focuses on sentence memorability and reading times which involve contextual word relationships.

Result: Fine-tuned models provide estimates that correlate with human-derived norms and exceed predictive power of interpretable baseline predictors. This demonstrates LLMs contain useful information about sentence-level features. However, zero-shot and few-shot performance is very mixed, showing poor results.

Conclusion: LLMs contain valuable information about sentence-level psycholinguistic features that can be extracted through fine-tuning, but care is needed when using LLM-prompting as a proxy for human cognitive measures due to poor zero-shot/few-shot performance.

Abstract: Large Language Models (LLMs) have recently been shown to produce estimates of psycholinguistic norms, such as valence, arousal, or concreteness, for words and multiword expressions, that correlate with human judgments. These estimates are obtained by prompting an LLM, in zero-shot fashion, with a question similar to those used in human studies. Meanwhile, for other norms such as lexical decision time or age of acquisition, LLMs require supervised fine-tuning to obtain results that align with ground-truth values. In this paper, we extend this approach to the previously unstudied features of sentence memorability and reading times, which involve the relationship between multiple words in a sentence-level context. Our results show that via fine-tuning, models can provide estimates that correlate with human-derived norms and exceed the predictive power of interpretable baseline predictors, demonstrating that LLMs contain useful information about sentence-level features. At the same time, our results show very mixed zero-shot and few-shot performance, providing further evidence that care is needed when using LLM-prompting as a proxy for human cognitive measures.

Method: Developed three tasks in collaboration with professional sommeliers and native speakers: Wine Theory Question Answering (1,024 questions), Wine Feature Completion (1,000 examples), and Food-Wine Pairing (1,000 examples) across 8 languages.

Result: Top models achieve 97% on wine theory QA, but struggle with feature completion (65% peak) and food-wine pairing (MCC 0-0.39), showing sensory judgment remains challenging despite textual grounding.

Conclusion: SommBench provides a challenging benchmark for evaluating LLMs’ ability to emulate sensory expertise through text, revealing limitations in translating textual knowledge to sensory judgment tasks.

Abstract: With the rapid advances of large language models, it becomes increasingly important to systematically evaluate their multilingual and multicultural capabilities. Previous cultural evaluation benchmarks focus mainly on basic cultural knowledge that can be encoded in linguistic form. Here, we propose SommBench, a multilingual benchmark to assess sommelier expertise, a domain deeply grounded in the senses of smell and taste. While language models learn about sensory properties exclusively through textual descriptions, SommBench tests whether this textual grounding is sufficient to emulate expert-level sensory judgment. SommBench comprises three main tasks: Wine Theory Question Answering (WTQA), Wine Feature Completion (WFC), and Food-Wine Pairing (FWP). SommBench is available in multiple languages: English, Slovak, Swedish, Finnish, German, Danish, Italian, and Spanish. This helps separate a language model’s wine expertise from its language skills. The benchmark datasets were developed in close collaboration with a professional sommelier and native speakers of the respective languages, resulting in 1,024 wine theory question-answering questions, 1,000 wine feature-completion examples, and 1,000 food-wine pairing examples. We provide results for the most popular language models, including closed-weights models such as Gemini 2.5, and open-weights models, such as GPT-OSS and Qwen 3. Our results show that the most capable models perform well on wine theory question answering (up to 97% correct with a closed-weights model), yet feature completion (peaking at 65%) and food-wine pairing show (MCC ranging between 0 and 0.39) turn out to be more challenging. These results position SommBench as an interesting and challenging benchmark for evaluating the sommelier expertise of language models. The benchmark is publicly available at https://github.com/sommify/sommbench.

Method: Introduces Cross-Context Review (CCR) where review is conducted in a fresh session with no access to the production conversation history. Tested on 30 artifacts with 150 injected errors across four conditions: same-session Self-Review (SR), repeated Self-Review (SR2), context-aware Subagent Review (SA), and CCR.

Result: CCR achieved F1 score of 28.6%, outperforming SR (24.6%, p=0.008), SR2 (21.7%, p<0.001), and SA (23.8%, p=0.004). SR2 showed reviewing twice in same session didn’t beat reviewing once, confirming benefit comes from context separation rather than repetition.

Conclusion: Cross-Context Review provides a simple, model-agnostic method to improve LLM error detection by separating review context from production context, requiring only one extra session with no additional infrastructure.

Abstract: Large language models struggle to catch errors in their own outputs when the review happens in the same session that produced them. This paper introduces Cross-Context Review (CCR), a straightforward method where the review is conducted in a fresh session with no access to the production conversation history. We ran a controlled experiment: 30 artifacts (code, technical documents, presentation scripts) with 150 injected errors, tested under four review conditions – same-session Self-Review (SR), repeated Self-Review (SR2), context-aware Subagent Review (SA), and Cross-Context Review (CCR). Over 360 reviews, CCR reached an F1 of 28.6%, outperforming SR (24.6%, p=0.008, d=0.52), SR2 (21.7%, p<0.001, d=0.72), and SA (23.8%, p=0.004, d=0.57). The SR2 result matters most for interpretation: reviewing twice in the same session did not beat reviewing once (p=0.11), which rules out repetition as an explanation for CCR’s advantage. The benefit comes from context separation itself. CCR works with any model, needs no infrastructure, and costs only one extra session.

Method: Proposes LifeSim user simulator using Belief-Desire-Intention (BDI) model to generate coherent life trajectories and intention-driven behaviors. Creates LifeSim-Eval benchmark covering 8 life domains, 1,200 scenarios with multi-turn interactive evaluation of explicit/implicit intention completion, user profile recovery, and response quality.

Result: Experiments reveal current LLMs have significant limitations in handling implicit intentions and long-term user preference modeling, both in single-scenario and long-horizon settings.

Conclusion: LifeSim-Eval provides a comprehensive benchmark for evaluating personalized AI assistants that better reflects real-world complexity, highlighting current LLM limitations in cognitive modeling and long-term user understanding.

Abstract: The rapid advancement of large language models (LLMs) has accelerated progress toward universal AI assistants. However, existing benchmarks for personalized assistants remain misaligned with real-world user-assistant interactions, failing to capture the complexity of external contexts and users’ cognitive states. To bridge this gap, we propose LifeSim, a user simulator that models user cognition through the Belief-Desire-Intention (BDI) model within physical environments for coherent life trajectories generation, and simulates intention-driven user interactive behaviors. Based on LifeSim, we introduce LifeSim-Eval, a comprehensive benchmark for multi-scenario, long-horizon personalized assistance. LifeSim-Eval covers 8 life domains and 1,200 diverse scenarios, and adopts a multi-turn interactive method to assess models’ abilities to complete explicit and implicit intentions, recover user profiles, and produce high-quality responses. Under both single-scenario and long-horizon settings, our experiments reveal that current LLMs face significant limitations in handling implicit intention and long-term user preference modeling.

Method: Proposes QAQ framework that evaluates data quality from reverse direction: how well answers predict queries ($Q|A$). Defines Reverse Mutual Information (RMI) to quantify information gain about query conditioned on answer. Uses disagreement between strong and weak models to identify valid yet challenging samples.

Result: On WarriorCoder dataset, selecting just 25% of data using stratified RMI achieves comparable performance to full-data training, significantly outperforming existing data selection methods.

Conclusion: Highlights importance of bidirectional semantic coherence in synthetic data curation, offering scalable pathway to reduce computational costs without sacrificing model capability in code generation tasks.

Abstract: Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult to detect with current metrics. Existing data selection methods like Instruction-Following Difficulty (IFD) typically assess how hard a model generates an answer given a query ($A|Q$). However, this metric is ambiguous on noisy synthetic data, where low probability can distinguish between intrinsic task complexity and model-generated hallucinations. Here, we propose QAQ, a novel data selection framework that evaluates data quality from the reverse direction: how well can the answer predict the query ($Q|A$)? We define Reverse Mutual Information (RMI) to quantify the information gain about the query conditioned on the answer. Our analyses reveal that both extremes of RMI signal quality issues: low RMI indicates semantic misalignment, while excessively high RMI may contain defect patterns that LLMs easily recognize. Furthermore, we introduce a selection strategy based on the disagreement between strong and weak models to identify samples that are valid yet challenging. Experiments on the WarriorCoder dataset demonstrate that selecting just 25% of data using stratified RMI achieves comparable performance to full-data training, significantly outperforming existing data selection methods. Our approach highlights the importance of bidirectional semantic coherence in synthetic data curation, offering a scalable pathway to reduce computational costs without sacrificing model capability.

Method: Introduce MADQA benchmark with 2,250 human-authored questions grounded in 800 heterogeneous PDF documents, designed using Classical Test Theory for discriminative power. Develop novel evaluation protocol measuring accuracy-effort trade-off to assess agentic behavior.

Result: Best agents match human searchers in raw accuracy but succeed on different questions, relying on brute-force search to compensate for weak strategic planning. They fail to close the nearly 20% gap to oracle performance and persist in unproductive loops.

Conclusion: Current multimodal agents lack genuine strategic reasoning capabilities and rely on inefficient search methods. The released dataset and evaluation harness aim to facilitate transition from brute-force retrieval to calibrated, efficient reasoning.

Abstract: Multimodal agents offer a promising path to automating complex document-intensive workflows. Yet, a critical question remains: do these agents demonstrate genuine strategic reasoning, or merely stochastic trial-and-error search? To address this, we introduce MADQA, a benchmark of 2,250 human-authored questions grounded in 800 heterogeneous PDF documents. Guided by Classical Test Theory, we design it to maximize discriminative power across varying levels of agentic abilities. To evaluate agentic behaviour, we introduce a novel evaluation protocol measuring the accuracy-effort trade-off. Using this framework, we show that while the best agents can match human searchers in raw accuracy, they succeed on largely different questions and rely on brute-force search to compensate for weak strategic planning. They fail to close the nearly 20% gap to oracle performance, persisting in unproductive loops. We release the dataset and evaluation harness to help facilitate the transition from brute-force retrieval to calibrated, efficient reasoning.

Method: Two-stage training: 1) positional embedding adaptation, 2) full parameter continuous pre-training. Also creates compressed variants via knowledge distillation. Evaluated on 25 tasks including KLEJ benchmark, FinBench (financial tasks), and long-document understanding tasks.

Result: Model achieves best average performance among Polish and multilingual models, significantly outperforms competitive solutions in long-context tasks while maintaining comparable quality on short texts.

Conclusion: Successfully developed a high-quality Polish encoder model with extended context window (8192 tokens) that effectively handles long documents while maintaining performance on standard tasks.

Abstract: While decoder-only Large Language Models (LLMs) have recently dominated the NLP landscape, encoder-only architectures remain a cost-effective and parameter-efficient standard for discriminative tasks. However, classic encoders like BERT are limited by a short context window, which is insufficient for processing long documents. In this paper, we address this limitation for the Polish by introducing a high-quality Polish model capable of processing sequences of up to 8192 tokens. The model was developed by employing a two-stage training procedure that involves positional embedding adaptation and full parameter continuous pre-training. Furthermore, we propose compressed model variants trained via knowledge distillation. The models were evaluated on 25 tasks, including the KLEJ benchmark, a newly introduced financial task suite (FinBench), and other classification and regression tasks, specifically those requiring long-document understanding. The results demonstrate that our model achieves the best average performance among Polish and multilingual models, significantly outperforming competitive solutions in long-context tasks while maintaining comparable quality on short texts.

Method: Partition layers into Full layers (run indexers) and Shared layers (reuse nearest Full layer’s indices). Two approaches: training-free greedy search minimizing LM loss, and training-aware multi-layer distillation loss.

Result: On 30B DSA model: removes 75% of indexer computations with negligible quality degradation, achieving 1.82× prefill speedup and 1.48× decode speedup. Confirmed on production-scale GLM-5 model.

Conclusion: IndexCache effectively exploits cross-layer redundancy in sparse attention indexers, significantly improving efficiency while maintaining model quality.

Abstract: Long-context agentic workflows have emerged as a defining use case for large language models, making attention efficiency critical for both inference speed and serving cost. Sparse attention addresses this challenge effectively, and DeepSeek Sparse Attention (DSA) is a representative production-grade solution: a lightweight lightning indexer selects the top-k most relevant tokens per query, reducing core attention from $O(L^2)$ to $O(Lk)$. However, the indexer itself retains $O(L^2)$ complexity and must run independently at every layer, despite the fact that the resulting top-k selections are highly similar across consecutive layers. We present IndexCache, which exploits this cross-layer redundancy by partitioning layers into a small set of Full layers that run their own indexers and a majority of Shared layers that simply reuse the nearest Full layer’s top-k indices. We propose two complementary approaches to determine and optimize this configuration. Training-free IndexCache applies a greedy search algorithm that selects which layers to retain indexers by directly minimizing language modeling loss on a calibration set, requiring no weight updates. Training-aware IndexCache introduces a multi-layer distillation loss that trains each retained indexer against the averaged attention distributions of all layers it serves, enabling even simple interleaved patterns to match full-indexer accuracy. Experimental results on a 30B DSA model show that IndexCache can remove 75% of indexer computations with negligible quality degradation, achieving up to 1.82$\times$ prefill speedup and 1.48$\times$ decode speedup compared to standard DSA. These positive results are further confirmed by our preliminary experiments on the production-scale GLM-5 model (Figure 1).

Method: Frames HiSPA mitigation as binary classification at token level, exploits distinct patterns in Mamba’s block output embeddings (BOEs), and uses XGBoost classifier to identify malicious tokens with minimal computational overhead.

Result: Achieves 95.9% token-level F1 score and 99.3% document-level F1 score on malicious token detection, generalizes to unseen attack patterns (96.9% document-level F1 under leave-one-out cross-validation), and processes 1,032 tokens/sec with <4GB VRAM.

Conclusion: CLASP provides effective lightweight defense against HiSPAs for SSM-based and hybrid architectures, suitable for real-world deployment as front-line protection with minimal computational overhead.

Abstract: State space models (SSMs) like Mamba have gained significant traction as efficient alternatives to Transformers, achieving linear complexity while maintaining competitive performance. However, Hidden State Poisoning Attacks (HiSPAs), a recently discovered vulnerability that corrupts SSM memory through adversarial strings, pose a critical threat to these architectures and their hybrid variants. Framing the HiSPA mitigation task as a binary classification problem at the token level, we introduce the CLASP model to defend against this threat. CLASP exploits distinct patterns in Mamba’s block output embeddings (BOEs) and uses an XGBoost classifier to identify malicious tokens with minimal computational overhead. We consider a realistic scenario in which both SSMs and HiSPAs are likely to be used: an LLM screening résumés to identify the best candidates for a role. Evaluated on a corpus of 2,483 résumés totaling 9.5M tokens with controlled injections, CLASP achieves 95.9% token-level F1 score and 99.3% document-level F1 score on malicious tokens detection. Crucially, the model generalizes to unseen attack patterns: under leave-one-out cross-validation, performance remains high (96.9% document-level F1), while under clustered cross-validation with structurally novel triggers, it maintains useful detection capability (91.6% average document-level F1). Operating independently of any downstream model, CLASP processes 1,032 tokens per second with under 4GB VRAM consumption, potentially making it suitable for real-world deployment as a lightweight front-line defense for SSM-based and hybrid architectures. All code and detailed results are available at https://anonymous.4open.science/r/hispikes-91C0.

Method: The framework decomposes abstract research goals into target-domain questions, analyzes domain progress/challenges, reformulates challenges as domain-agnostic problems, retrieves analogous solutions from external disciplines, synthesizes insights back to target domain, and ranks source domains by interdisciplinary potential.

Result: Empirically improves average novelty by 21% and insightfulness by 16% while remaining grounded in the original research problem.

Conclusion: Idea-Catalyst provides a systematic approach to augment interdisciplinary reasoning rather than automate discovery, supporting creative breakthroughs through cross-domain insight synthesis.

Abstract: Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approaches to scientific discovery show promise for interdisciplinary research, but many prioritize rapidly designing experiments and solutions, bypassing the exploratory, collaborative reasoning processes that drive creative interdisciplinary breakthroughs. As a result, prior efforts largely prioritize automating scientific discovery rather than augmenting the reasoning processes that underlie scientific disruption. We present Idea-Catalyst, a novel framework that systematically identifies interdisciplinary insights to support creative reasoning in both humans and large language models. Starting from an abstract research goal, Idea-Catalyst is designed to assist the brainstorming stage, explicitly avoiding premature anchoring on specific solutions. The framework embodies key metacognitive features of interdisciplinary reasoning: (a) defining and assessing research goals, (b) awareness of a domain’s opportunities and unresolved challenges, and (c) strategic exploration of interdisciplinary ideas based on impact potential. Concretely, Idea-Catalyst decomposes an abstract goal (e.g., improving human-AI collaboration) into core target-domain research questions that guide the analysis of progress and open challenges within that domain. These challenges are reformulated as domain-agnostic conceptual problems, enabling retrieval from external disciplines (e.g., Psychology, Sociology) that address analogous issues. By synthesizing and recontextualizing insights from these domains back into the target domain, Idea-Catalyst ranks source domains by their interdisciplinary potential. Empirically, this targeted integration improves average novelty by 21% and insightfulness by 16%, while remaining grounded in the original research problem.

Method: Two-stage synthesize-and-reground framework: (1) Claim-Centric QA Synthesis generates faithful QA pairs on focused segments, (2) Document-Scale Regrounding programmatically re-embeds pairs into full-document tasks for realistic complexity

Result: Constructed SciMDR dataset with 300K QA pairs across 20K scientific papers, and SciMDR-Eval expert-annotated benchmark; models fine-tuned on SciMDR show significant improvements on scientific QA benchmarks requiring complex document-level reasoning

Conclusion: The synthesize-and-reground framework successfully addresses dataset construction challenges, enabling effective training of multimodal foundation models for scientific document comprehension

Abstract: Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faithfulness, and realism. To address this challenge, we introduce the synthesize-and-reground framework, a two-stage pipeline comprising: (1) Claim-Centric QA Synthesis, which generates faithful, isolated QA pairs and reasoning on focused segments, and (2) Document-Scale Regrounding, which programmatically re-embeds these pairs into full-document tasks to ensure realistic complexity. Using this framework, we construct SciMDR, a large-scale training dataset for cross-modal comprehension, comprising 300K QA pairs with explicit reasoning chains across 20K scientific papers. We further construct SciMDR-Eval, an expert-annotated benchmark to evaluate multimodal comprehension within full-length scientific workflows. Experiments demonstrate that models fine-tuned on SciMDR achieve significant improvements across multiple scientific QA benchmarks, particularly in those tasks requiring complex document-level reasoning.

Method: Modify loss function of pre-trained multimodal models by restricting top K softmax outputs during fine-tuning to improve adversarial robustness

Result: After fine-tuning, adversarial robustness of pre-trained models can be significantly improved against popular attacks

Conclusion: The approach effectively improves multimodal model robustness, with future research needed on output diversity, generalization, and robustness-performance trade-offs

Abstract: As Large Language Models make a breakthrough in natural language processing tasks (NLP), multimodal technique becomes extremely popular. However, it has been shown that multimodal NLP are vulnerable to adversarial attacks, where the outputs of a model can be dramatically changed by a perturbation to the input. While several defense techniques have been proposed both in computer vision and NLP models, the multimodal robustness of models have not been fully explored. In this paper, we study the adversarial robustness provided by modifying loss function of pre-trained multimodal models, by restricting top K softmax outputs. Based on the evaluation and scoring, our experiments show that after a fine-tuning, adversarial robustness of pre-trained models can be significantly improved, against popular attacks. Further research should be studying, such as output diversity, generalization and the robustness-performance trade-off of this kind of loss functions. Our code will be available after this paper is accepted

Method: Leverages advanced natural language processing including large language models and fuzzy matching techniques to automate the mapping process, with GDPR-compliant local deployment for data protection.

Result: Developed an open-source tool that can be deployed locally, maintaining high performance in converting informal medical terms to standardized concepts while ensuring data privacy.

Conclusion: Llettuce provides an improved solution for medical terminology standardization that addresses both technical challenges (semantic understanding) and practical concerns (data privacy, manual effort).

Abstract: This paper introduces Llettuce, an open-source tool designed to address the complexities of converting medical terms into OMOP standard concepts. Unlike existing solutions such as the Athena database search and Usagi, which struggle with semantic nuances and require substantial manual input, Llettuce leverages advanced natural language processing, including large language models and fuzzy matching, to automate and enhance the mapping process. Developed with a focus on GDPR compliance, Llettuce can be deployed locally, ensuring data protection while maintaining high performance in converting informal medical terms to standardised concepts.

Method: 1) Created ValiMath benchmark with 2147 human-verified math questions annotated with logical structure and domain coverage. 2) Developed MathQ-Verify pipeline that parses questions into atomic assumptions/conclusions and performs semantic consistency checks.

Result: MathQ-Verify achieves state-of-the-art performance, improving F1 score by up to 25 percentage points over baseline. Provides scalable solution for cleaning noisy mathematical datasets.

Conclusion: Systematic verification of mathematical question quality is crucial for reliable LLM training. MathQ-Verify offers effective pipeline for dataset curation and noise reduction.

Abstract: Large Language Models (LLMs) have recently achieved remarkable progress in mathematical reasoning. To enable such capabilities, many existing works distill strong reasoning models into long chains of thought or design algorithms to construct high-quality math question-answer (QA) data for training. However, these efforts primarily focus on generating correct reasoning paths and answers, while largely overlooking the correctness of the questions themselves. In this work, we present ValiMath, a benchmark consisting of 2147 human-verified mathematical questions covering a wide range of domains such as arithmetic, algebra, and geometry, which are synthesized and curated from the NuminaMath dataset. Each question is annotated with its logical structure, domain coverage, and question correctness, enabling fine-grained evaluation of question quality. ValiMath serves as a high-quality gold-standard test set for validating mathematical questions in LLM training corpora. Building upon this benchmark, we further propose MathQ-Verify, a pipeline that performs fine-grained parsing of mathematical questions into atomic assumptions and conclusions, and evaluates their semantic soundness through consistency checks. This pipeline achieves high precision in detecting flawed questions and provides a reliable foundation for cleaning noisy mathematical datasets. Experiments show that MathQ-Verify achieves state-of-the-art performance across multiple benchmarks, improving the F1 score by up to 25 percentage points over the direct verification baseline. MathQ-Verify offers a scalable and accurate solution for curating reliable mathematical datasets, reducing label noise and avoiding unnecessary computation on invalid questions. Our code and data are available at the repository https://github.com/OpenDCAI/MathQ-Verify.

Method: Semi-automated review using keyword filtering, LLM-based classification validated against expert labels, and topic clustering (HDBSCAN+BERTopic and LlooM) on 250,000 ACL and arXiv papers from 2022-2025.

Result: LLM-related papers increased 5x in ACL and 8x in arXiv; limitation research grew even faster, reaching over 30% of LLM papers by 2025. Reasoning is most studied limitation, followed by generalization, hallucination, bias, and security. ACL topics stable, arXiv shifted toward security, alignment, hallucinations, knowledge editing, and multimodality.

Conclusion: Provides quantitative view of LLM limitation research trends, showing rapid growth and evolving focus areas, with dataset and methodology released for community use.

Abstract: Large language model (LLM) research has grown rapidly, along with increasing concern about their limitations. In this survey, we conduct a data-driven, semi-automated review of research on limitations of LLMs (LLLMs) from 2022 to early 2025 using a bottom-up approach. From a corpus of 250,000 ACL and arXiv papers, we identify 14,648 relevant papers using keyword filtering, LLM-based classification, validated against expert labels, and topic clustering (via two approaches, HDBSCAN+BERTopic and LlooM). We find that the share of LLM-related papers increases over fivefold in ACL and nearly eightfold in arXiv between 2022 and 2025. Since 2022, LLLMs research grows even faster, reaching over 30% of LLM papers by 2025. Reasoning remains the most studied limitation, followed by generalization, hallucination, bias, and security. The distribution of topics in the ACL dataset stays relatively stable over time, while arXiv shifts toward security risks, alignment, hallucinations, knowledge editing, and multimodality. We offer a quantitative view of trends in LLLMs research and release a dataset of annotated abstracts and a validated methodology, available at: https://github.com/a-kostikova/LLLMs-Survey.

Method: The paper proposes developing physics-specialized AI agents through: 1) physics-specific training datasets, 2) reward signals that capture physical reasoning quality, 3) verification frameworks encoding fundamental principles, and 4) collaborative efforts between physics and AI communities to build specialized infrastructure.

Result: The paper presents a vision for physics-specialized AI agents that can seamlessly handle multimodal data, propose physically consistent hypotheses, and autonomously verify theoretical results, but does not report specific experimental results.

Conclusion: LLMs require domain-specialized training and tooling to be useful in real-world physics research. Realizing physics-specialized AI agents requires developing physics-specific training datasets, reward signals, and verification frameworks, calling for collaborative efforts between physics and AI communities.

Abstract: Large Language Models (LLMs) are rapidly advancing across diverse domains, yet their application in theoretical physics remains inadequate. While current models show competence in mathematical reasoning and code generation, we identify critical gaps in physical intuition, constraint satisfaction, and reliable reasoning that cannot be addressed through prompting alone. Physics demands approximation judgment, symmetry exploitation, and physical grounding that require AI agents specifically trained on physics reasoning patterns and equipped with physics-aware verification tools. We argue that LLM would require such domain-specialized training and tooling to be useful in real-world for physics research. We envision physics-specialized AI agents that seamlessly handle multimodal data, propose physically consistent hypotheses, and autonomously verify theoretical results. Realizing this vision requires developing physics-specific training datasets, reward signals that capture physical reasoning quality, and verification frameworks encoding fundamental principles. We call for collaborative efforts between physics and AI communities to build the specialized infrastructure necessary for AI-driven scientific discovery.

Method: Pipeline: 1) Transcribe audio with Whisper Large-v3, 2) Two-step GPT-4o correction (named entity refinement using official protocols, then semantic completeness evaluation), 3) Filtering based on Predicted BLEU score and GPT-4o evaluation scores.

Result: Final corpus contains 801 hours of audio, with 555 hours passing quality control. Achieves 6-point BLEU improvement over original sentence-level release, demonstrating effectiveness of ASR+LLM correction pipeline.

Conclusion: Combining robust ASR, LLM-based correction, and data-driven filtering creates high-quality speech corpora for low-resource, domain-specific applications like Swiss German parliamentary debates.

Abstract: This paper presents a new long-form release of the Swiss Parliaments Corpus, converting entire multi-hour Swiss German debate sessions (each aligned with the official session protocols) into high-quality speech-text pairs. Our pipeline starts by transcribing all session audio into Standard German using Whisper Large-v3 under high-compute settings. We then apply a two-step GPT-4o correction process: first, GPT-4o ingests the raw Whisper output alongside the official protocols to refine misrecognitions, mainly named entities. Second, a separate GPT-4o pass evaluates each refined segment for semantic completeness. We filter out any segments whose Predicted BLEU score (derived from Whisper’s average token log-probability) and GPT-4o evaluation score fall below a certain threshold. The final corpus contains 801 hours of audio, of which 555 hours pass our quality control. Compared to the original sentence-level SPC release, our long-form dataset achieves a 6-point BLEU improvement, demonstrating the power of combining robust ASR, LLM-based correction, and data-driven filtering for low-resource, domain-specific speech corpora.

Method: Develops a formal framework based on variance decomposition of model responses into three components: variability due to user intent, user articulation, and model uncertainty. Models that understand intent should attribute most output variance to intent differences rather than articulation style.

Result: Across five LLaMA and Gemma models, larger models typically assign greater share of variance to intent, indicating stronger comprehension, although gains are uneven and often modest with increasing model size.

Conclusion: Motivates moving beyond accuracy-only benchmarks toward semantic diagnostics that directly assess whether models understand what users intend, providing a framework for evaluating intent comprehension in LLMs.

Abstract: People judge interactions with large language models (LLMs) as successful when outputs match what they want, not what they type. Yet LLMs are trained to predict the next token solely from text input, not underlying intent. Because written language is an imperfect proxy for intent, and correlations between phrasing and desired outcomes can break down in training data, models that rely too heavily on surface cues may respond inconsistently to semantically equivalent prompts. This makes it essential to evaluate whether LLMs can reliably infer user intent-especially in high-stakes settings where robustness and generalization are critical. We introduce a formal framework for assessing intent comprehension in LLMs: whether a model demonstrates robust understanding of user intent by producing consistent outputs across semantically equivalent prompts while differentiating between prompts with distinct intents. Our evaluation approach is based on a variance decomposition of model responses into three components: variability due to user intent, user articulation, and model uncertainty. Models that understand what users want, and are not overly sensitive to textual cues, should attribute most output variance to intent differences, rather than articulation style. Applying this framework across diverse domains, we find that, within the five LLaMA and Gemma models we evaluate, larger models typically assign a greater share of variance to intent, indicating stronger comprehension of intent, although gains are uneven and often modest with increasing model size. These results motivate moving beyond accuracy-only benchmarks toward semantic diagnostics that directly assess whether models understand what users intend.

Method: Created cross-lingual functional benchmarks by translating existing functional benchmark templates from English to five additional languages (French, Spanish, Hindi, Arabic, Yoruba) spanning different resource availability levels.

Result: Significant performance drops between static and functional benchmarks (24%, 17%, 18% for CL-GSM Symbolic; 15-24% for CL-IFEval), with only minimal drops for M-MMLU (0.5-3%). Model robustness varies significantly across languages, with Arabic and English showing most consistent performance.

Conclusion: Static multilingual benchmarks don’t adequately reflect practical functional performance, and model robustness varies significantly across languages, highlighting the need for more realistic cross-lingual functional evaluations.

Abstract: Multi-lingual competence in large language models is often evaluated via static data benchmarks such as Belebele, M-MMLU and M-GSM. However, these evaluations often fail to provide an adequate understanding of the practical performance and robustness of models across multi-lingual settings. In response, we create multi-lingual functional benchmarks – Cross-Lingual Grade School Math Symbolic (CL-GSM Symbolic) and Cross-Lingual Instruction-Following Eval (CL-IFEval)– by translating existing functional benchmark templates from English to five additional languages that span the range of resources available for NLP: French, Spanish, Hindi, Arabic and Yoruba. Our results reveal that some static multi-lingual benchmarks capture functional performance much more closely than others (i.e. across models, there is a 24%, 17% and 18% decrease in performance between M-GSM and CL-GSM Symbolic in English, French and Spanish respectively; similarly there’s a 15 - 24% performance drop across languages between Belebele and CL-IFEval, and only a 0.5% to 3% performance drop between M-MMLU and CL-IFEval). Similarly, we find that model robustness across languages varies significantly, with certain languages (eg. Arabic, English) being the most consistently well performing across evaluation iterations.

Method: Introduced first multi-class annotated dataset for Roman Urdu hope speech (Generalized Hope, Realistic Hope, Unrealistic Hope, Not Hope), explored psychological foundations of hope, proposed custom attention-based transformer model optimized for Roman Urdu’s syntactic/semantic variability, evaluated with 5-fold cross-validation, and verified statistical significance with t-test.

Result: Proposed XLM-R model achieved best performance with cross-validation score of 0.78, outperforming baseline SVM (0.75) and BiLSTM (0.76) with gains of 4% and 2.63% respectively.

Conclusion: This study successfully addresses the gap in hope speech detection for low-resource code-mixed Roman Urdu, demonstrating the effectiveness of transformer models for informal language varieties and contributing to more inclusive NLP research.

Abstract: Hope is a positive emotional state involving the expectation of favorable future outcomes, while hope speech refers to communication that promotes optimism, resilience, and support, particularly in adverse contexts. Although hope speech detection has gained attention in Natural Language Processing (NLP), existing research mainly focuses on high-resource languages and standardized scripts, often overlooking informal and underrepresented forms such as Roman Urdu. To the best of our knowledge, this is the first study to address hope speech detection in code-mixed Roman Urdu by introducing a carefully annotated dataset, thereby filling a critical gap in inclusive NLP research for low-resource, informal language varieties. This study makes four key contributions: (1) it introduces the first multi-class annotated dataset for Roman Urdu hope speech, comprising Generalized Hope, Realistic Hope, Unrealistic Hope, and Not Hope categories; (2) it explores the psychological foundations of hope and analyzes its linguistic patterns in code-mixed Roman Urdu to inform dataset development; (3) it proposes a custom attention-based transformer model optimized for the syntactic and semantic variability of Roman Urdu, evaluated using 5-fold cross-validation; and (4) it verifies the statistical significance of performance gains using a t-test. The proposed model, XLM-R, achieves the best performance with a cross-validation score of 0.78, outperforming the baseline SVM (0.75) and BiLSTM (0.76), with gains of 4% and 2.63% respectively.

Method: Created the SOTA open-data Ettin suite with paired encoder-only and decoder-only models ranging from 17M to 1B parameters, trained on up to 2T tokens using identical recipes for both architectures.

Result: Encoder-only models excel at classification/retrieval tasks while decoders excel at generative tasks; adapting decoders to encoder tasks (or vice versa) through continued training is subpar compared to using the appropriate architecture.

Conclusion: Architecture choice should align with task type, as each excels in its domain; the Ettin suite provides comprehensive open-source artifacts for future research on language model architectures.

Abstract: The large language model (LLM) community focuses almost exclusively on decoder-only language models, since they are easier to use for text generation. However, a large subset of the community still uses encoder-only models for tasks such as classification or retrieval. Previous work has attempted to compare these architectures, but is forced to make comparisons with models that have different numbers of parameters, training techniques, and datasets. We introduce the SOTA open-data Ettin suite of models: paired encoder-only and decoder-only models ranging from 17 million parameters to 1 billion, trained on up to 2 trillion tokens. Using the same recipe for both encoder-only and decoder-only models produces SOTA recipes in both categories for their respective sizes, beating ModernBERT as an encoder and Llama 3.2 and SmolLM2 as decoders. Like previous work, we find that encoder-only models excel at classification and retrieval tasks while decoders excel at generative tasks. However, we show that adapting a decoder model to encoder tasks (and vice versa) through continued training is subpar compared to using only the reverse objective (i.e. a 400M encoder outperforms a 1B decoder on MNLI, and vice versa for generative tasks). We open-source all artifacts of this study including training data, training order segmented by checkpoint, and 200+ checkpoints to allow future work to analyze or extend all aspects of training.

Method: Proposes a benchmark with four practically relevant compositional tasks and introduces Learnable Calibration - an efficient method tailored for on-device applications with limited computational resources.

Result: The paper presents a benchmark for compositional multi-tasking and demonstrates that their Learnable Calibration method provides resource-efficient, high-performing solutions for on-device multi-tasking scenarios.

Conclusion: This work lays groundwork for advancing LLM capabilities in real-world multi-tasking scenarios, expanding applicability to complex, resource-constrained use cases through efficient adapter merging techniques.

Abstract: Adapter parameters provide a mechanism to modify the behavior of machine learning models and have gained significant popularity in the context of large language models (LLMs) and generative AI. These parameters can be merged to support multiple tasks via a process known as task merging. However, prior work on merging in LLMs, particularly in natural language processing, has been limited to scenarios where each test example addresses only a single task. In this paper, we focus on on-device settings and study the problem of text-based compositional multi-tasking, where each test example involves the simultaneous execution of multiple tasks. For instance, generating a translated summary of a long text requires solving both translation and summarization tasks concurrently. To facilitate research in this setting, we propose a benchmark comprising four practically relevant compositional tasks. We also present an efficient method (Learnable Calibration) tailored for on-device applications, where computational resources are limited, emphasizing the need for solutions that are both resource-efficient and high-performing. Our contributions lay the groundwork for advancing the capabilities of LLMs in real-world multi-tasking scenarios, expanding their applicability to complex, resource-constrained use cases.

Method: Three-stage framework: 1) Intent-Aware Retrieval module decomposes queries and retrieves information sources as MCP Servers, 2) DAG-based Task Planner models task dependencies as Directed Acyclic Graph for optimal parallel execution, 3) lightweight Distilled Agent Executor for efficient tool calling.

Result: TURA serves tens of millions of users, delivering robust real-time answers while meeting low-latency demands of large-scale industrial systems, bridging the gap between static RAG and dynamic information sources.

Conclusion: TURA is the first architecture to systematically bridge the gap between static RAG and dynamic information sources for world-class AI search products, combining RAG with agentic tool-use to handle both static content and real-time information.

Abstract: The advent of Large Language Models (LLMs) is transforming search engines into conversational AI search products, primarily using Retrieval-Augmented Generation (RAG) on web corpora. However, this paradigm has significant industrial limitations. Traditional RAG approaches struggle with real-time needs and structured queries that require accessing dynamically generated content like ticket availability or inventory. Limited to indexing static pages, search engines cannot perform the interactive queries needed for such time-sensitive data. Academic research has focused on optimizing RAG for static content, overlooking complex intents and the need for dynamic sources like databases and real-time APIs. To bridge this gap, we introduce TURA (Tool-Augmented Unified Retrieval Agent for AI Search), a novel three-stage framework that combines RAG with agentic tool-use to access both static content and dynamic, real-time information. TURA has three key components: an Intent-Aware Retrieval module to decompose queries and retrieve information sources encapsulated as Model Context Protocol (MCP) Servers, a DAG-based Task Planner that models task dependencies as a Directed Acyclic Graph (DAG) for optimal parallel execution, and a lightweight Distilled Agent Executor for efficient tool calling. TURA is the first architecture to systematically bridge the gap between static RAG and dynamic information sources for a world-class AI search product. Serving tens of millions of users, it leverages an agentic framework to deliver robust, real-time answers while meeting the low-latency demands of a large-scale industrial system.

Method: Proposes Violation-to-Resolution (V2R) dialogue type modeling norm violation progression; uses exemplar-based iterative refinement early in synthesis; constructs 10,800 multi-turn dialogues with turn-level annotations for norm adherence, intent, and emotion.

Result: Outperforms existing datasets in refinement quality, dialogue naturalness, and generalization; models trained on V2R-augmented data show improved pragmatic competence in ethically sensitive contexts.

Conclusion: Establishes new benchmark for culturally adaptive dialogue modeling with scalable methodology for norm-aware generation across linguistically and culturally diverse languages.

Abstract: Social norms govern culturally appropriate behavior in communication, enabling dialogue systems to produce responses that are not only coherent but also socially acceptable. We present NormGenesis, a multicultural framework for generating and annotating socially grounded dialogues across English, Chinese, and Korean. To model the dynamics of social interaction beyond static norm classification, we propose a novel dialogue type, Violation-to-Resolution (V2R), which models the progression of conversations following norm violations through recognition and socially appropriate repair. To improve pragmatic consistency in underrepresented languages, we implement an exemplar-based iterative refinement early in the dialogue synthesis process. This design introduces alignment with linguistic, emotional, and sociocultural expectations before full dialogue generation begins. Using this framework, we construct a dataset of 10,800 multi-turn dialogues annotated at the turn level for norm adherence, speaker intent, and emotional response. Human and LLM-based evaluations demonstrate that NormGenesis significantly outperforms existing datasets in refinement quality, dialogue naturalness, and generalization performance. We show that models trained on our V2R-augmented data exhibit improved pragmatic competence in ethically sensitive contexts. Our work establishes a new benchmark for culturally adaptive dialogue modeling and provides a scalable methodology for norm-aware generation across linguistically and culturally diverse languages.

Method: Propose Critique Reinforcement Learning (CRL) where models generate critiques for (question, solution) pairs, rewarded based on alignment of judgment labels. Create Critique-Coder trained on hybrid RL+CRL (20% CRL data substitution).

Result: Critique-Coder consistently outperforms RL-only baselines on all benchmarks. Critique-Coder-8B achieves >60% on LiveCodeBench (v5), beating DeepCoder-14B and GPT-o1. Also shows improved general reasoning on BBEH logic tasks.

Conclusion: CRL effectively complements standard RL for LLM reasoning, enhancing both coding and general reasoning abilities through critique training that transfers across tasks.

Abstract: Reinforcement Learning (RL) has emerged as a popular training paradigm, particularly when paired with reasoning models. While effective, it primarily focuses on generating responses and lacks mechanisms to explicitly foster critique or reflection. Several recent studies, like Critique-Fine-Tuning (CFT) and Critique-Guided-Distillation (CGD) have shown the benefits of explicitly teaching LLMs how to critique. Motivated by them, we propose Critique Reinforcement Learning (CRL), where the model is tasked with generating a critique for a given (question, solution) pair. The reward is determined solely by whether the final judgment label $c \in {\texttt{True}, \texttt{False}}$ of the generated critique aligns with the ground-truth judgment $c^*$. Building on this point, we introduce Critique-Coder, which is trained on a hybrid of RL and CRL by substituting 20% of the standard RL data with CRL data. We fine-tune multiple models (Critique-Coder) and evaluate them on different benchmarks to show their advantages over RL-only models. We show that Critique-Coder consistently outperforms RL-only baselines on all the evaluated benchmarks. Notably, our Critique-Coder-8B can reach over 60% on LiveCodeBench (v5), outperforming other reasoning models like DeepCoder-14B and GPT-o1. Beyond code generation, Critique-Coder also demonstrates enhanced general reasoning abilities, as evidenced by its better performance on logic reasoning tasks from the BBEH dataset. This indicates that the application of CRL on coding datasets enhances general reasoning and critique abilities, which are transferable across a broad range of tasks. Hence, we believe that CRL works as a great complement to standard RL for LLM reasoning.

Method: DiDi-Instruct uses a novel integral KL-divergence minimization framework to distill few-step students from pre-trained dLLMs. It introduces grouped reward normalization, intermediate-state matching, and reward-guided ancestral sampler to improve training stability, coverage, and inference quality.

Result: On OpenWebText, DiDi-Instruct achieves perplexity from 62.2 (8 NFEs) to 18.4 (128 NFEs), outperforming prior accelerated dLLMs and GPT-2 baseline. It provides up to 64× acceleration with only ~1% entropy loss and reduces training time by >20× compared to competing dLLM distillation methods.

Conclusion: DiDi-Instruct enables efficient and effective distillation for fast language generation, demonstrating robustness through ablation studies, model scaling, downstream tasks, and protein sequence generation.

Abstract: Fast and high-quality language generation is the holy grail that people pursue in the age of AI. In this work, we introduce Discrete Diffusion Divergence Instruct (DiDi-Instruct), a training-based method that initializes from a pre-trained diffusion large language model (dLLM) and distills a few-step student for fast generation. The model distilled with DiDi-Instruct matches or surpasses its dLLM teacher and the GPT-2 baseline while providing up to 64$\times$ acceleration. The theoretical foundation of DiDi-Instruct is a novel framework based on integral KL-divergence minimization, which leads to a practical training algorithm. We further introduce grouped reward normalization, intermediate-state matching, and the reward-guided ancestral sampler to improve training stability, model coverage, and inference quality. On the OpenWebText benchmark, DiDi-Instruct achieves perplexity ranging from 62.2 (8 NFEs) to 18.4 (128 NFEs), outperforming prior accelerated dLLMs and the GPT-2 baseline. These gains incur a negligible entropy loss (around $1$%) and reduce additional training wall-clock time by more than $20\times$ compared to competing dLLM distillation methods. We further validate the robustness and effectiveness of DiDi-Instruct through extensive ablation studies, model scaling, downstream task evaluations, and unconditional protein sequence generation. In conclusion, DiDi-Instruct enables efficient and effective distillation for language generation in the blink of an eye.

Method: Uses token attribution methods (GlobEnc and DecompX) to assign salience scores to tokens, ranks them, and retains only the top-k% tokens to create sparse frugalized prompts. Provides theoretical stability analysis.

Result: Strong empirical results across four NLP tasks show trade-off between retained tokens and performance. Reveals asymmetric performance patterns suggesting potential task contamination effects.

Conclusion: Contributes to understanding LLM behavior in performance-efficiency trade-offs and delineates boundaries between tasks tolerant of contextual sparsity vs. those requiring exhaustive context.

Abstract: Human communication heavily relies on laconism and inferential pragmatics, allowing listeners to successfully reconstruct rich meaning from sparse, telegraphic speech. In contrast, large language models (LLMs) owe much of their stellar performance to expansive input contexts, yet such verbosity inflates monetary costs, carbon footprint, and inference-time latency. This overhead manifests from the redundant low-utility tokens present in typical prompts, as only a fraction of tokens typically carries the majority of the semantic weight. Inspired by the aforementioned cognitive psycholinguistic processes, we address this inefficiency by introducing FrugalPrompt, a novel prompt compression framework for LLMs, which retains only the most semantically significant tokens. Leveraging two state-of-the-art token attribution methods, GlobEnc and DecompX, we assign salience scores to every token in an input sequence, rank them to retain the top-k% tokens, and obtain a sparse frugalized prompt. We establish the theoretical stability of our approach and provide strong empirical results across a suite of four NLP tasks to study the trade-off between the portion of retained tokens and performance. Experimental findings across retention settings reveal asymmetric performance patterns that suggest potential task contamination effects. We posit that our work contributes to a more nuanced understanding of LLM behavior in performance-efficiency trade-offs and delineates the boundary between tasks tolerant of contextual sparsity and those requiring exhaustive context.

Method: RECAP uses a feedback-driven loop where initial extraction attempts are evaluated by a secondary LLM that compares outputs against reference passages, identifies discrepancies, and generates minimal correction hints. These hints are fed back to guide subsequent generations. Includes jailbreaking module to overcome alignment-induced refusals

Result: On EchoTrace benchmark (30+ full books), RECAP achieved substantial gains over single-iteration approaches. With GPT-4.1, average ROUGE-L score for copyrighted text extraction improved from 0.38 to 0.47 (24% increase)

Conclusion: RECAP provides an effective approach for detecting memorized training data in LLMs through iterative feedback and jailbreaking techniques, offering a practical solution when direct training data inspection is not possible

Abstract: If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen? We believe the most compelling evidence arises when the model itself freely reproduces the target content. As such, we propose RECAP, an agentic pipeline designed to elicit and verify memorized training data from LLM outputs. At the heart of RECAP is a feedback-driven loop, where an initial extraction attempt is evaluated by a secondary language model, which compares the output against a reference passage and identifies discrepancies. These are then translated into minimal correction hints, which are fed back into the target model to guide subsequent generations. In addition, to address alignment-induced refusals, RECAP includes a jailbreaking module that detects and overcomes such barriers. We evaluate RECAP on EchoTrace, a new benchmark spanning over 30 full books, and the results show that RECAP leads to substantial gains over single-iteration approaches. For instance, with GPT-4.1, the average ROUGE-L score for the copyrighted text extraction improved from 0.38 to 0.47 - a nearly 24% increase.

Method: Develops a reference-free metric using three calculations: 1) compression ratio between original response and LLM abstractive summary, 2) compression ratio between original response and LLM extractive summary, and 3) word-removal compression where LLM removes non-essential words while preserving meaning.

Result: Experimental results show the metric effectively identifies redundancy in LLM outputs, providing a practical automated tool for evaluating response brevity without requiring ground truth human annotations.

Conclusion: The proposed metric offers a valuable automated solution for assessing LLM response conciseness, addressing the problem of verbose outputs while eliminating the need for human reference annotations.

Abstract: Large language models (LLMs) frequently generate responses that are lengthy and verbose, filled with redundant or unnecessary details. This diminishes clarity and user satisfaction, and it increases costs for model developers, especially with well-known proprietary models that charge based on the number of output tokens. In this paper, we introduce a novel reference-free metric for evaluating the conciseness of responses generated by LLMs. Our method quantifies non-essential content without relying on gold standard references and calculates the average of three calculations: i) a compression ratio between the original response and an LLM abstractive summary; ii) a compression ratio between the original response and an LLM extractive summary; and iii) wordremoval compression, where an LLM removes as many non-essential words as possible from the response while preserving its meaning, with the number of tokens removed indicating the conciseness score. Experimental results demonstrate that our proposed metric identifies redundancy in LLM outputs, offering a practical tool for automated evaluation of response brevity in conversational AI systems without the need for ground truth human annotations.

Method: Introduced SwissGov-RSD dataset with 224 multi-parallel documents in English-German, English-French, and English-Italian with token-level difference annotations. Evaluated various open-source and closed-source LLMs and encoder models across different fine-tuning settings.

Result: Current automatic approaches perform poorly compared to their performance on monolingual, sentence-level, and synthetic benchmarks, revealing a considerable gap for both LLMs and encoder models.

Conclusion: There’s a significant challenge in cross-lingual document-level semantic difference recognition that current models struggle with, highlighting the need for better approaches in this area.

Abstract: Recognizing semantic differences across documents, especially in different languages, is crucial for text generation evaluation and multilingual content alignment. However, as a standalone task it has received little attention. We address this by introducing SwissGov-RSD, the first naturalistic, document-level, cross-lingual dataset for semantic difference recognition. It encompasses a total of 224 multi-parallel documents in English-German, English-French, and English-Italian with token-level difference annotations by human annotators. We evaluate a variety of open-source and closed source large language models as well as encoder models across different fine-tuning settings on this new benchmark. Our results show that current automatic approaches perform poorly compared to their performance on monolingual, sentence-level, and synthetic benchmarks, revealing a considerable gap for both LLMs and encoder models. We make our code and datasets publicly available.

Method: Proposes Struct-SQL, a knowledge distillation framework that trains SLMs to emulate large LLMs using structured query execution plans as formal blueprints for reasoning. This replaces unstructured CoT traces with explicit, precise logical steps that better match the requirements of SQL generation.

Result: The SLM distilled with structured CoT achieves an absolute improvement of 8.1% over unstructured CoT distillation baselines. Error analysis shows significant reduction in syntactic errors, demonstrating the benefits of structured logical reasoning for reliable SQL generation.

Conclusion: Structured reasoning representations provide clearer, more reliable teaching signals for knowledge distillation in Text-to-SQL tasks. Using formal query execution plans as reasoning blueprints significantly improves SLM performance and reduces syntactic errors compared to unstructured approaches.

Abstract: Deploying accurate Text-to-SQL systems at the enterprise level faces a difficult trilemma involving cost, security and performance. Current solutions force enterprises to choose between expensive, proprietary Large Language Models (LLMs) and low-performing Small Language Models (SLMs). Efforts to improve SLMs often rely on distilling reasoning from large LLMs using unstructured Chain-of-Thought (CoT) traces, a process that remains inherently ambiguous. Instead, we hypothesize that a formal, structured reasoning representation provides a clearer, more reliable teaching signal, as the Text-to-SQL task requires explicit and precise logical steps. To evaluate this hypothesis, we propose Struct-SQL, a novel Knowledge Distillation (KD) framework that trains an SLM to emulate a powerful large LLM. Consequently, we adopt a query execution plan as a formal blueprint to derive this structured reasoning. Our SLM, distilled with structured CoT, achieves an absolute improvement of 8.1% over an unstructured CoT distillation baseline. A detailed error analysis reveals that a key factor in this gain is a marked reduction in syntactic errors. This demonstrates that teaching a model to reason using a structured logical blueprint is beneficial for reliable SQL generation in SLMs.

Method: Uses Wikipedia internal links and Wikidata entries for weak supervision to infer entity types, then employs multiple LLMs to verify and filter annotations, creating a novel pipeline for automated dataset creation.

Result: Created a corpus approximately 5 times larger than existing Luxembourgish NER datasets with broader and more balanced entity category coverage, providing a substantial new resource for multilingual NER research.

Conclusion: The judgeWEL dataset demonstrates an effective pipeline for creating NER resources for low-resource languages using weak supervision and LLM verification, addressing resource scarcity challenges.

Abstract: We present judgeWEL, a dataset for named entity recognition (NER) in Luxembourgish, automatically labelled and subsequently verified using large language models (LLM) in a novel pipeline. Building datasets for under-represented languages remains one of the major bottlenecks in natural language processing, where the scarcity of resources and linguistic particularities make large-scale annotation costly and potentially inconsistent. To address these challenges, we propose and evaluate a novel approach that leverages Wikipedia and Wikidata as structured sources of weak supervision. By exploiting internal links within Wikipedia articles, we infer entity types based on their corresponding Wikidata entries, thereby generating initial annotations with minimal human intervention. Because such links are not uniformly reliable, we mitigate noise by employing and comparing several LLMs to identify and retain only high-quality labelled sentences. The resulting corpus is approximately five times larger than the currently available Luxembourgish NER dataset and offers broader and more balanced coverage across entity categories, providing a substantial new resource for multilingual and low-resource NER research.

Method: The authors introduce Hidden State Poisoning Attacks (HiSPA) that use optimized short input phrases to irreversibly overwrite information in SSM hidden states. They create RoBench25 benchmark to evaluate model information retrieval under HiSPAs and test on both pure SSMs and hybrid SSM-Transformer models like Jamba.

Result: SSMs show significant vulnerability to HiSPAs, with even a recent 52B hybrid Jamba model collapsing on RoBench25 under optimized triggers. HiSPA triggers also weaken Jamba on Open-Prompt-Injections benchmark. Transformers remain unaffected. Interpretability analysis reveals patterns in Mamba’s hidden layers during attacks.

Conclusion: State space models have a critical vulnerability to hidden state poisoning attacks that doesn’t affect Transformers, revealing a fundamental security weakness in SSM architectures that needs addressing through mitigation systems.

Abstract: State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their adversarial robustness remains critically unexplored. This paper studies the phenomenon whereby specific short input phrases induce a partial amnesia effect in such models, by irreversibly overwriting information in their hidden states, referred to as a Hidden State Poisoning Attack (HiSPA). Our benchmark RoBench25 allows evaluating a model’s information retrieval capabilities when subject to HiSPAs, and confirms the vulnerability of SSMs against such attacks. Even a recent 52B hybrid SSM-Transformer model from the Jamba family collapses on RoBench25 under optimized HiSPA triggers, whereas pure Transformers do not. We also observe that HiSPA triggers significantly weaken the Jamba model on the popular Open-Prompt-Injections benchmark, unlike pure Transformers. Finally, our interpretability study reveals patterns in Mamba’s hidden layers during HiSPAs that could be used to build a HiSPA mitigation system. The full code and data to reproduce the experiments can be found at https://anonymous.4open.science/r/hispa_anonymous-5DB0.

Method: Proposes a unified lifecycle-based taxonomy organizing LLM research into six stages: Data Preparation, Model Preparation, Training, Alignment, Inference, and Evaluation. Provides systematic review of foundational theories and internal mechanisms.

Result: Creates a structured framework for understanding LLM theories across the entire lifecycle, analyzing core theoretical issues like data mixture justification, architectural representational limits, and alignment optimization dynamics.

Conclusion: Provides a roadmap for transitioning LLM development toward principled scientific discipline, identifying frontier challenges including synthetic data self-improvement limits, safety guarantee bounds, and mechanistic origins of emergent intelligence.

Abstract: The rapid emergence of Large Language Models (LLMs) has precipitated a profound paradigm shift in Artificial Intelligence, delivering monumental engineering successes that increasingly impact modern society. However, a critical paradox persists within the current field: despite the empirical efficacy, our theoretical understanding of LLMs remains disproportionately nascent, forcing these systems to be treated largely as ``black boxes’’. To address this theoretical fragmentation, this survey proposes a unified lifecycle-based taxonomy that organizes the research landscape into six distinct stages: Data Preparation, Model Preparation, Training, Alignment, Inference, and Evaluation. Within this framework, we provide a systematic review of the foundational theories and internal mechanisms driving LLM performance. Specifically, we analyze core theoretical issues such as the mathematical justification for data mixtures, the representational limits of various architectures, and the optimization dynamics of alignment algorithms. Moving beyond current best practices, we identify critical frontier challenges, including the theoretical limits of synthetic data self-improvement, the mathematical bounds of safety guarantees, and the mechanistic origins of emergent intelligence. By connecting empirical observations with rigorous scientific inquiry, this work provides a structured roadmap for transitioning LLM development from engineering heuristics toward a principled scientific discipline.

Method: Compare prompt-based generation with linear probes over the same internal LLM representations. Use layer-wise analysis to study where temporal information emerges, and examine how visual and multimodal inputs affect time series understanding.

Result: Zero-shot prompting performs near chance (F1 0.15-0.26), but linear probes achieve much better performance (F1 0.61-0.67), often matching or exceeding specialized time series models. Temporal information emerges in early transformer layers and is amplified by visual/multimodal inputs.

Conclusion: There’s a systematic mismatch between what LLMs internally represent and what prompt-based evaluation reveals. Current evaluations underestimate LLMs’ time series understanding, and multimodal inputs enhance temporal structure encoding.

Abstract: Prompt-based evaluations suggest that large language models (LLMs) perform poorly on time series classification, raising doubts about whether they encode meaningful temporal structure. We show that this conclusion reflects limitations of prompt-based generation rather than the model’s representational capacity by directly comparing prompt outputs with linear probes over the same internal representations. While zero-shot prompting performs near chance, linear probes improve average F1 from 0.15-0.26 to 0.61-0.67, often matching or exceeding specialized time series models. Layer-wise analyses further show that class-discriminative time series information emerges in early transformer layers and is amplified by visual and multimodal inputs. Together, these results demonstrate a systematic mismatch between what LLMs internally represent and what prompt-based evaluation reveals, leading current evaluations to underestimate their time series understanding.

Method: Analyzed 397 human-LLM conversations about socio-political issues using linguistic and interactional feature analysis, mediation analyses to identify mechanisms, and moderation analyses to examine conditional effects by political efficacy.

Result: LLM explanatory richness partially supports confidence gains through fostering reflective insight, while knowledge gains operate entirely through cognitive engagement. Effects are highly conditional: confidence gains depend on how high-efficacy users handle uncertainty, and knowledge gains depend on high-efficacy users’ ability to leverage extended interactions.

Conclusion: Learning from LLMs is an interactional achievement rather than a uniform outcome of better explanations. Effective learning requires aligning LLM explanatory behavior with users’ engagement states in Human-AI interactive systems.

Abstract: Large language models (LLMs) are increasingly used as conversational partners for learning, yet the interactional dynamics supporting users’ learning and engagement are understudied. We analyze the linguistic and interactional features from both LLM and participant chats across 397 human-LLM conversations about socio-political issues to identify the mechanisms and conditions under which LLM explanations shape changes in political knowledge and confidence. Mediation analyses reveal that LLM explanatory richness partially supports confidence by fostering users’ reflective insight, whereas its effect on knowledge gain operates entirely through users’ cognitive engagement. Moderation analyses show that these effects are highly conditional and vary by political efficacy. Confidence gains depend on how high-efficacy users experience and resolve uncertainty. Knowledge gains depend on high-efficacy users’ ability to leverage extended interaction, with longer conversations benefiting primarily reflective users. In summary, we find that learning from LLMs is an interactional achievement, not a uniform outcome of better explanations. The findings underscore the importance of aligning LLM explanatory behavior with users’ engagement states to support effective learning in designing Human-AI interactive systems.

Method: Systematic comparison of proprietary and open-weight LLMs using naive document-level prompting setup, analyzing APE quality, contextual behavior, robustness to data poisoning attacks, and efficiency metrics.

Result: Proprietary LLMs achieve near human-level APE quality with simple one-shot prompting, regardless of document context. They show higher robustness to attacks than open-weight models but largely fail to exploit document-level context for contextual error correction. Standard automatic metrics don’t reliably reflect qualitative improvements.

Conclusion: While LLMs show promise for APE, proprietary models are impractical for real-world deployment due to cost/latency. The findings highlight the need for more efficient long-context modeling approaches for translation refinement, as current models don’t effectively leverage document context.

Abstract: Automatic post-editing (APE) aims to refine machine translations by correcting residual errors. Although recent large language models (LLMs) demonstrate strong translation capabilities, their effectiveness for APE–especially under document-level context–remains insufficiently understood. We present a systematic comparison of proprietary and open-weight LLMs under a naive document-level prompting setup, analyzing APE quality, contextual behavior, robustness, and efficiency. Our results show that proprietary LLMs achieve near human-level APE quality even with simple one-shot prompting, regardless of whether document context is provided. While these models exhibit higher robustness to data poisoning attacks than open-weight counterparts, this robustness also reveals a limitation: they largely fail to exploit document-level context for contextual error correction. Furthermore, standard automatic metrics do not reliably reflect these qualitative improvements, highlighting the continued necessity of human evaluation. Despite their strong performance, the substantial cost and latency overheads of proprietary LLMs render them impractical for real-world APE deployment. Overall, our findings elucidate both the promise and current limitations of LLM-based document-aware APE, and point toward the need for more efficient long-context modeling approaches for translation refinement.

Method: SYNTHAGENT uses a strong teacher model to create novel tasks and tool ecosystems, then rewrites them into intentionally underspecified instructions to force agents to query users. It includes an LLM-based user simulator for private information and mock tool system for stable responses. Task-level rubrics are constructed based on subgoals, user-agent interactions, and forbidden behaviors for rewards.

Result: Across 14 challenging datasets in math, search, and tool use, models trained on synthetic data achieve substantial gains, with small models outperforming larger baselines.

Conclusion: SYNTHAGENT effectively addresses bottlenecks in agentic training by synthesizing diverse tool-use data and simulating complete environments, enabling small LLMs to achieve better agentic capabilities.

Abstract: Small LLMs often struggle to match the agentic capabilities of large, costly models. While reinforcement learning can help, progress has been limited by two structural bottlenecks: existing open-source agentic training data are narrow in task variety and easily solved; real-world APIs lack diversity and are unstable for large-scale reinforcement learning rollout processes. We address these challenges with SYNTHAGENT, a framework that jointly synthesizes diverse tool-use training data and simulates complete environments. Specifically, a strong teacher model creates novel tasks and tool ecosystems, then rewrites them into intentionally underspecified instructions. This compels agents to actively query users for missing details. When handling synthetic tasks, an LLM-based user simulator provides user-private information, while a mock tool system delivers stable tool responses. For rewards, task-level rubrics are constructed based on required subgoals, user-agent interactions, and forbidden behaviors. Across 14 challenging datasets in math, search, and tool use, models trained on our synthetic data achieve substantial gains, with small models outperforming larger baselines.

Method: Uses two information-theoretic measures: Normalized Branching Factor (NBF, entropy-derived) and KL divergence between steered activations and targeted concepts in vocabulary space. Tests hypothesis that effective steering corresponds to structured entropy preservation and coherent KL alignment across decoding steps. Uses LLM-generated annotations as ground truth based on reliability study showing high inter-judge agreement between architecturally distinct LLMs.

Result: Mechanistic signals provide meaningful predictive power for identifying successful steering and estimating failure probability. Introduces stronger evaluation baseline for Contrastive Activation Addition (CAA) and Sparse Autoencoder-based steering methods.

Conclusion: Internal model signals (entropy and KL divergence) can effectively diagnose reliability of activation-based steering in LLMs, providing mechanistic understanding beyond black-box evaluation approaches.

Abstract: Activation-based steering enables Large Language Models (LLMs) to exhibit targeted behaviors by intervening on intermediate activations without retraining. Despite its widespread use, the mechanistic factors that govern when steering succeeds or fails remain poorly understood, as prior work has relied primarily on black-box outputs or LLM-based judges. In this study, we investigate whether the reliability of steering can be diagnosed using internal model signals. We focus on two information-theoretic measures: the entropy-derived Normalized Branching Factor (NBF), and the Kullback-Leibler (KL) divergence between steered activations and targeted concepts in the vocabulary space. We hypothesize that effective steering corresponds to structured entropy preservation and coherent KL alignment across decoding steps. Building on a reliability study demonstrating high inter-judge agreement between two architecturally distinct LLMs, we use LLM-generated annotations as ground truth and show that these mechanistic signals provide meaningful predictive power for identifying successful steering and estimating failure probability. We further introduce a stronger evaluation baseline for Contrastive Activation Addition (CAA) and Sparse Autoencoder-based steering, the two most widely adopted activation-steering methods.

Method: Developed three attack methods: 1) 3-layer MLP that improves reconstruction to 63.1% top-1 accuracy, 2) transformer-based sequence decoder that achieves 91.2% top-1 accuracy (94.8% top-10) on 32-token sequences from OpenWebText after training on 100M tokens. Evaluated practical leakage scenarios including distributed inference and side channels, and tested noise addition as a defense.

Result: Transformer-based decoder recovers 91.2% of tokens top-1 (94.8% top-10) on 32-token sequences. Adding noise reduces but does not eliminate reconstruction. Expert selections leak substantially more information than previously understood, comparable to the sensitivity of the underlying text.

Conclusion: Expert selections in MoE deployments should be treated as sensitive as the underlying text due to substantial information leakage. The findings connect MoE routing to embedding inversion literature and highlight security implications for distributed inference and side channel scenarios.

Abstract: We present a text-reconstruction attack on mixture-of-experts (MoE) language models that recovers tokens from expert selections alone. In MoE models, each token is routed to a subset of expert subnetworks; we show these routing decisions leak substantially more information than previously understood. Prior work using logistic regression achieves limited reconstruction; we show that a 3-layer MLP improves this to 63.1% top-1 accuracy, and that a transformer-based sequence decoder recovers 91.2% of tokens top-1 (94.8% top-10) on 32-token sequences from OpenWebText after training on 100M tokens. These results connect MoE routing to the broader literature on embedding inversion. We outline practical leakage scenarios (e.g., distributed inference and side channels) and show that adding noise reduces but does not eliminate reconstruction. Our findings suggest that expert selections in MoE deployments should be treated as sensitive as the underlying text.

Method: Proposes Model-Dowser that computes importance scores for each parameter using weight magnitudes, input activations, and output sensitivities. During fine-tuning, preserves high-importance parameters and updates only the remaining ones.

Result: Comprehensive experiments on LLaVA and NVILA show Model-Dowser effectively mitigates catastrophic forgetting, outperforms prior methods, and remains resource-efficient and scalable to multi-billion-parameter models.

Conclusion: Model-Dowser provides an effective, scalable solution for fine-tuning MLLMs while preserving pretrained generalization capabilities, addressing limitations of existing catastrophic forgetting mitigation methods.

Abstract: Fine-tuning Multimodal Large Language Models (MLLMs) on task-specific data is an effective way to improve performance on downstream applications. However, such adaptation often leads to a degradation in generalization on pretrained tasks, a phenomenon known as Catastrophic Forgetting. Existing methods that aim to mitigate this issue either become ineffective when fine-tuning deeper layers of the language decoder or scale poorly with increasing model size. To address these limitations, we propose Model-Dowser, a novel sparse fine-tuning approach for MLLMs. Model-Dowser measures a principled importance score for each model parameter with respect to pretrained generalization (prior to downstream adaptation) by jointly considering weight magnitudes, input activations, and output sensitivities. During fine-tuning, Model-Dowser selectively preserves high-importance parameters and updates the remaining. Comprehensive experiments on two representative MLLMs, LLaVA and NVILA, demonstrate that Model-Dowser effectively mitigates catastrophic forgetting and consistently outperforms prior methods, while remaining resource-efficient and scalable to multi-billion-parameter models.

Method: Collected data through a form with 6 open-ended questions plus PHQ-9 (depression) and GAD-7 (anxiety) screening questionnaires. Applied statistical analysis, Romanian LIWC text analysis, emotion detection, and topic modeling to analyze the corpus features.

Result: Created PsihoRo corpus with 205 respondents, making it the first open-source Romanian mental health dataset. Analysis revealed important linguistic and emotional features relevant to depression and anxiety in the Romanian population.

Conclusion: PsihoRo represents a crucial first step for mental health NLP research in Romanian, providing a valuable resource for understanding psychological constructs in this language context despite the relatively small sample size.

Abstract: Psychological corpora in NLP are collections of texts used to analyze human psychology, emotions, and mental health. These texts allow researchers to study psychological constructs, identify patterns related to mental health problems and analyze emotional language. However, collecting accurate mental health data from social media can be challenging due to the assumptions made by data collectors. A more effective approach involves gathering data through open-ended questions and then assessing participants’ mental health status using self-report screening surveys. This method was successfully employed for English, a language with a lot of psychological NLP resources. However, the same cannot be stated for Romanian, which currently has no open-source mental health corpus. To address this gap, we have collected the first open-source corpus focused on depression and anxiety in Romanian, by utilizing a form with 6 open-ended questions along with the standardized PHQ-9 and GAD-7 screening questionnaires. Although the PsihoRo corpus contains texts from only 205 respondents, it represents an important first step toward understanding and analyzing mental health issues within the Romanian population. We employ statistical analysis, text analysis using Romanian LIWC, emotion detection, and topic modeling to identify the most important features of this newly introduced resource for the NLP community. The data is publicly available at https://huggingface.co/datasets/Alegzandra/PsihoRo.

Method: Two complementary ideas: (1) truncated step-level sampling that generates trajectories sharing a common prefix but differing at the next step, isolating variation to single decision points; (2) dense, decomposed LLM-as-judge rewards that score each reasoning step, search query, and answer on ternary scale with separate quality dimensions.

Result: Theoretically proves truncated sampling reduces advantage estimate variance by up to factor T for T-step trajectories. Experiments on seven QA benchmarks show SLATE consistently outperforms both sparse-reward and process-reward baselines, with largest gains on harder multi-hop tasks and smaller models.

Conclusion: SLATE provides an effective framework for training LLMs to reason with search engines by addressing credit assignment through truncated sampling and dense decomposed rewards, yielding better policy gradients and improved performance.

Abstract: Training large language models to reason with search engines via reinforcement learning is hindered by a fundamental credit assignment problem: existing methods such as Search-R1 provide only a sparse outcome reward after an entire multi-step trajectory, making it infeasible to attribute success or failure to individual reasoning and retrieval decisions. Process-reward methods like StepSearch alleviate this by introducing step-level supervision, but rely on heuristic rewards such as TF-IDF overlap with gold documents, and still sample $k$ complete trajectories per example, retaining high gradient variance. We propose SLATE, a framework built on two complementary ideas: (1) truncated step-level sampling, which generates $k$ trajectories that share a common prefix and differ only at the next step, isolating variation to a single decision point; and (2) dense, decomposed LLM-as-judge rewards, which score each reasoning step, search query, and answer on a ternary scale with separate quality dimensions, providing richer supervision than binary outcome signals or undifferentiated step-level judgments. We theoretically prove that under the same dense reward structure, truncated sampling reduces the variance of advantage estimates by up to a factor of $T$ compared to full-trajectory sampling for $T$-step trajectories, yielding lower-variance and better-targeted policy gradients. Experiments on seven QA benchmarks confirm that SLATE consistently outperforms both sparse-reward and process-reward baselines, with the largest gains on harder multi-hop tasks and smaller models.

Method: Train models using reinforcement learning (RL) to produce profile-consistent answers through structured reasoning, inspired by recent advances in mathematical reasoning with RL.

Result: Reasoning enhances opinion modeling and is competitive with strong baselines across three political datasets (U.S., European, Swiss), but doesn’t fully remove bias.

Conclusion: Additional mechanisms beyond reasoning are needed to build faithful political digital twins using LLMs; the released method and datasets establish a baseline for future LLM opinion alignment research.

Abstract: Opinion modeling aims to capture individual or group political preferences, enabling applications such as digital democracies, where models could help shape fairer and more popular policies. Given their versatility, strong generalization capabilities, and demonstrated success across diverse text-to-text applications, large language models (LLMs) are natural candidates for this task. However, due to their statistical nature and limited causal understanding, they tend to produce biased opinions when prompted naively. In this work, we study whether reasoning can improve opinion alignment. Motivated by the recent advancement in mathematical reasoning enabled by reinforcement learning (RL), we train models to produce profile-consistent answers through structured reasoning. We evaluate our approach on three datasets covering U.S., European, and Swiss politics. Results indicate that reasoning enhances opinion modeling and is competitive with strong baselines, but does not fully remove bias, highlighting the need for additional mechanisms to build faithful political digital twins using LLMs. By releasing both our method and datasets, we establish a solid baseline to support future research on LLM opinion alignment.

Method: Comparative analysis using activation probing, early forced answering, and CoT monitoring across two large models (DeepSeek-R1 671B & GPT-OSS 120B) on tasks of varying difficulty (easy MMLU recall vs. difficult multihop GPQA-Diamond questions).

Result: Models’ final answers are decodable from activations far earlier in CoT than monitors can detect, especially for easy recall questions. Inflection points (backtracking, ‘aha’ moments) correlate with genuine belief shifts. Probe-guided early exit reduces tokens by 80% on MMLU and 30% on GPQA-Diamond with similar accuracy.

Conclusion: Attention probing serves as an efficient tool for detecting performative reasoning and enabling adaptive computation, revealing task-dependent differences in genuine vs. performative chain-of-thought reasoning.

Abstract: We provide evidence of performative chain-of-thought (CoT) in reasoning models, where a model becomes strongly confident in its final answer, but continues generating tokens without revealing its internal belief. Our analysis compares activation probing, early forced answering, and a CoT monitor across two large models (DeepSeek-R1 671B & GPT-OSS 120B) and find task difficulty-specific differences: The model’s final answer is decodable from activations far earlier in CoT than a monitor is able to say, especially for easy recall-based MMLU questions. We contrast this with genuine reasoning in difficult multihop GPQA-Diamond questions. Despite this, inflection points (e.g., backtracking, ‘aha’ moments) occur almost exclusively in responses where probes show large belief shifts, suggesting these behaviors track genuine uncertainty rather than learned “reasoning theater.” Finally, probe-guided early exit reduces tokens by up to 80% on MMLU and 30% on GPQA-Diamond with similar accuracy, positioning attention probing as an efficient tool for detecting performative reasoning and enabling adaptive computation.

Method: Developed perturbation-based framework testing LLM sensitivity across six quality axes (funding, timeline, competency, alignment, clarity, impact) using six EPSRC proposals. Compared three review architectures: single-pass review, section-by-section analysis, and ‘Council of Personas’ ensemble emulating expert panels.

Result: Section-level approach significantly outperformed alternatives in both detection rate and scoring reliability. Council method performed no better than baseline despite computational expense. Detection varied by perturbation type - alignment issues readily identified but clarity flaws largely missed. Human evaluation showed LLM feedback valid but skewed toward compliance checking over holistic assessment.

Conclusion: Current LLMs may provide supplementary value within EPSRC review but exhibit high variability and misaligned review priorities, with section-level analysis showing most promise among tested architectures.

Abstract: As AI-assisted grant proposals outpace manual review capacity in a kind of ``Malthusian trap’’ for the research ecosystem, this paper investigates the capabilities and limitations of LLM-based grant reviewing for high-stakes evaluation. Using six EPSRC proposals, we develop a perturbation-based framework probing LLM sensitivity across six quality axes: funding, timeline, competency, alignment, clarity, and impact. We compare three review architectures: single-pass review, section-by-section analysis, and a ‘Council of Personas’ ensemble emulating expert panels. The section-level approach significantly outperforms alternatives in both detection rate and scoring reliability, while the computationally expensive council method performs no better than baseline. Detection varies substantially by perturbation type, with alignment issues readily identified but clarity flaws largely missed by all systems. Human evaluation shows LLM feedback is largely valid but skewed toward compliance checking over holistic assessment. We conclude that current LLMs may provide supplementary value within EPSRC review but exhibit high variability and misaligned review priorities. We release our code and any non-protected data.

Method: Adaptation of ModernBERT encoder architecture to Arabic with two key innovations: 1) Transtokenized embedding initialization for Arabic language modeling, and 2) Native long-context modeling up to 8,192 tokens. The approach enables stable and effective long-context modeling for Arabic.

Result: Transtokenization yields dramatic improvements in masked language modeling performance compared to non-transtokenized initialization. AraModernBERT supports stable and effective long-context modeling with improved intrinsic language modeling performance at extended sequence lengths. Downstream evaluations on Arabic NLU tasks (inference, offensive language detection, question-question similarity, named entity recognition) confirm strong transfer to discriminative and sequence labeling settings.

Conclusion: The results highlight practical considerations for adapting modern encoder architectures to Arabic and other languages written in Arabic-derived scripts. Transtokenization is essential for Arabic language modeling, and the adapted architecture successfully handles long-context modeling up to 8,192 tokens.

Abstract: Encoder-only transformer models remain widely used for discriminative NLP tasks, yet recent architectural advances have largely focused on English. In this work, we present AraModernBERT, an adaptation of the ModernBERT encoder architecture to Arabic, and study the impact of transtokenized embedding initialization and native long-context modeling up to 8,192 tokens. We show that transtokenization is essential for Arabic language modeling, yielding dramatic improvements in masked language modeling performance compared to non-transtokenized initialization. We further demonstrate that AraModernBERT supports stable and effective long-context modeling, achieving improved intrinsic language modeling performance at extended sequence lengths. Downstream evaluations on Arabic natural language understanding tasks, including inference, offensive language detection, question-question similarity, and named entity recognition, confirm strong transfer to discriminative and sequence labeling settings. Our results highlight practical considerations for adapting modern encoder architectures to Arabic and other languages written in Arabic-derived scripts.

Method: Theoretical analysis of LLM behavior through autoregressive processes, examining how models infer transition probabilities between tokens across tasks using provided prompts. Analysis of error bounds and statistical properties of different prompt engineering techniques.

Result: LLMs can exactly infer transition probabilities between tokens across tasks using prompts. ICL enhances performance by reducing prompt ambiguity and facilitating posterior concentration on intended tasks. CoT prompting activates task decomposition capabilities, breaking complex problems into simpler sub-tasks mastered during pretraining.

Conclusion: The paper provides novel theoretical insights into LLM emergent behaviors, showing that prompt engineering techniques like ICL and CoT work through statistical mechanisms of ambiguity reduction and task decomposition, offering theoretical foundations for observed empirical successes.

Abstract: Large Language Models (LLMs) have demonstrated remarkable proficiency across diverse tasks, exhibiting emergent properties such as semantic prompt comprehension, In-Context Learning (ICL), and Chain-of-Thought (CoT) reasoning. Despite their empirical success, the theoretical mechanisms driving these phenomena remain poorly understood. This study dives into the foundations of these observations by addressing three critical questions: (1) How do LLMs accurately decode prompt semantics despite being trained solely on a next-token prediction objective? (2) Through what mechanism does ICL facilitate performance gains without explicit parameter updates? and (3) Why do intermediate reasoning steps in CoT prompting effectively unlock capabilities for complex, multi-step problems? Our results demonstrate that, through the autoregressive process, LLMs are capable of exactly inferring the transition probabilities between tokens across distinct tasks using provided prompts. We show that ICL enhances performance by reducing prompt ambiguity and facilitating posterior concentration on the intended task. Furthermore, we find that CoT prompting activates the model’s capacity for task decomposition, breaking complex problems into a sequence of simpler sub-tasks that the model has mastered during the pretraining phase. By comparing their individual error bounds, we provide novel theoretical insights into the statistical superiority of advanced prompt engineering techniques.

Method: Leveraged Wikipedia content, Wikidata knowledge graph structure, and social science expertise to create LatamQA dataset of 26k+ question/answer pairs from Wikipedia articles, transformed into multiple-choice questions in Spanish and Portuguese, then translated to English.

Result: Found (i) performance discrepancies between Latam countries, (ii) models perform better in their original language, and (iii) Iberian Spanish culture is better known than Latam culture by LLMs.

Conclusion: The LatamQA dataset enables quantification of LLM cultural biases in Latin American contexts, revealing significant disparities in model performance across different cultures and languages.

Abstract: Large Language Models (LLMs) exhibit inequalities with respect to various cultural contexts. Most prominent open-weights models are trained on Global North data and show prejudicial behavior towards other cultures. Moreover, there is a notable lack of resources to detect biases in non-English languages, especially from Latin America (Latam), a continent containing various cultures, even though they share a common cultural ground. We propose to leverage the content of Wikipedia, the structure of the Wikidata knowledge graph, and expert knowledge from social science in order to create a dataset of question/answer (Q/As) pairs, based on the different popular and social cultures of various Latin American countries. We create the LatamQA database of over 26k questions and associated answers extracted from 26k Wikipedia articles, and transformed into multiple-choice questions (MCQ) in Spanish and Portuguese, in turn translated to English. We use this MCQ to quantify the degree of knowledge of various LLMs and find out (i) a discrepancy in performances between the Latam countries, ones being easier than others for the majority of the models, (ii) that the models perform better in their original language, and (iii) that Iberian Spanish culture is better known than Latam one.

Method: Proposes SENS-ASR that reinforces acoustic information with semantic information extracted from past frame-embeddings using a context module. The module is trained via knowledge distillation from a sentence embedding language model fine-tuned on training dataset transcriptions.

Result: Experiments on standard datasets show SENS-ASR significantly improves Word Error Rate in small-chunk streaming scenarios compared to baseline streaming ASR systems.

Conclusion: Semantic information extracted from past context can effectively enhance streaming ASR performance, particularly in low-latency scenarios with limited future context.

Abstract: Many Automatic Speech Recognition (ASR) applications require streaming processing of the audio data. In streaming mode, ASR systems need to start transcribing the input stream before it is complete, i.e., the systems have to process a stream of inputs with a limited (or no) future context. Compared to offline mode, this reduction of the future context degrades the performance of Streaming-ASR systems, especially while working with low-latency constraint. In this work, we present SENS-ASR, an approach to enhance the transcription quality of Streaming-ASR by reinforcing the acoustic information with semantic information. This semantic information is extracted from the available past frame-embeddings by a context module. This module is trained using knowledge distillation from a sentence embedding Language Model fine-tuned on the training dataset transcriptions. Experiments on standard datasets show that SENS-ASR significantly improves the Word Error Rate on small-chunk streaming scenarios.

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Method: Proposes Robot-Conditioned Normalizing Flow (RC-NF) that decouples processing of task-aware robot and object states within normalizing flow, using only positive samples for unsupervised training and calculating anomaly scores via probability density function.

Result: Achieves SOTA performance on LIBERO-Anomaly-10 benchmark across all anomaly types, operates as plug-and-play module for VLA models with <100ms latency, enabling real-time rollback or replanning.

Conclusion: RC-NF significantly enhances robustness and adaptability of VLA-based robotic systems in dynamic environments through real-time anomaly detection and intervention capabilities.

Abstract: Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow (RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot’s state and the object’s motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the probability density function. We further present LIBERO-Anomaly-10, a benchmark comprising three categories of robotic anomalies for simulation evaluation. RC-NF achieves state-of-the-art performance across all anomaly types compared to previous methods in monitoring robotic tasks. Real-world experiments demonstrate that RC-NF operates as a plug-and-play module for VLA models (e.g., pi0), providing a real-time OOD signal that enables state-level rollback or task-level replanning when necessary, with a response latency under 100 ms. These results demonstrate that RC-NF noticeably enhances the robustness and adaptability of VLA-based robotic systems in dynamic environments.

Method: Proposes Geometry-Grounded Point Transformer (GGPT) with: 1) improved Structure-from-Motion pipeline for accurate camera poses and partial 3D point clouds, and 2) geometry-guided 3D point transformer that refines dense point maps under explicit partial-geometry supervision using optimized guidance encoding.

Result: GGPT substantially outperforms state-of-the-art feed-forward 3D reconstruction models in both in-domain and out-of-domain settings, producing geometrically consistent and spatially complete reconstructions that recover fine structures and fill gaps in textureless areas.

Conclusion: GGPT provides a principled mechanism for integrating geometric priors with dense feed-forward predictions, demonstrating strong generalization across architectures and datasets while addressing key limitations of current feed-forward approaches.

Abstract: Recent feed-forward networks have achieved remarkable progress in sparse-view 3D reconstruction by predicting dense point maps directly from RGB images. However, they often suffer from geometric inconsistencies and limited fine-grained accuracy due to the absence of explicit multi-view constraints. We introduce the Geometry-Grounded Point Transformer (GGPT), a framework that augments feed-forward reconstruction with reliable sparse geometric guidance. We first propose an improved Structure-from-Motion pipeline based on dense feature matching and lightweight geometric optimisation to efficiently estimate accurate camera poses and partial 3D point clouds from sparse input views. Building on this foundation, we propose a geometry-guided 3D point transformer that refines dense point maps under explicit partial-geometry supervision using an optimised guidance encoding. Extensive experiments demonstrate that our method provides a principled mechanism for integrating geometric priors with dense feed-forward predictions, producing reconstructions that are both geometrically consistent and spatially complete, recovering fine structures and filling gaps in textureless areas. Trained solely on ScanNet++ with VGGT predictions, GGPT generalises across architectures and datasets, substantially outperforming state-of-the-art feed-forward 3D reconstruction models in both in-domain and out-of-domain settings.

Method: Proposes TextBCS with stage-divided vision-language interaction that facilitates mutual information exchange between visual and text features at each down-sampling stage, leveraging text prompts to locate lesions in low contrast. Also uses evidential learning with variational Dirichlet distribution to quantify segmentation uncertainty for blurred boundaries.

Result: Extensive experiments show TextBCS outperforms other segmentation networks, achieving best breast tumor segmentation performance on publicly available datasets.

Conclusion: The proposed text-guided segmentation model with vision-language interaction and evidential learning effectively addresses challenges in breast tumor segmentation from MRI, demonstrating superior performance.

Abstract: Breast cancer is one of the most common causes of death among women worldwide, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods have limitations in accurately locating tumor contours due to the challenge of low contrast between cancer and normal areas and blurred boundaries. Leveraging text prompt information holds promise in ameliorating tumor segmentation effect by delineating segmentation regions. Inspired by this, we propose text-guided Breast Tumor Segmentation model (TextBCS) with stage-divided vision-language interaction and evidential learning. Specifically, the proposed stage-divided vision-language interaction facilitates information mutual between visual and text features at each stage of down-sampling, further exerting the advantages of text prompts to assist in locating lesion areas in low contrast scenarios. Moreover, the evidential learning is adopted to quantify the segmentation uncertainty of the model for blurred boundary. It utilizes the variational Dirichlet to characterize the distribution of the segmentation probabilities, addressing the segmentation uncertainties of the boundaries. Extensive experiments validate the superiority of our TextBCS over other segmentation networks, showcasing the best breast tumor segmentation performance on publicly available datasets.

Method: Three-stage approach: 1) Train trajectory adapter on multi-step video generator for precise control, 2) Distill generator into few-step version for acceleration, 3) Finetune adapter using hybrid diffusion+adversarial objectives to align with few-step generator. Also introduces FlashBench benchmark for evaluation.

Result: FlashMotion outperforms existing video distillation methods and previous multi-step models in both visual quality and trajectory consistency across two adapter architectures. The FlashBench benchmark enables comprehensive evaluation of long-sequence trajectory-controllable video generation.

Conclusion: FlashMotion successfully bridges the gap between trajectory control and efficient video generation, enabling high-quality, trajectory-accurate videos with significantly reduced computational overhead through few-step generation.

Abstract: Recent advances in trajectory-controllable video generation have achieved remarkable progress. Previous methods mainly use adapter-based architectures for precise motion control along predefined trajectories. However, all these methods rely on a multi-step denoising process, leading to substantial time redundancy and computational overhead. While existing video distillation methods successfully distill multi-step generators into few-step, directly applying these approaches to trajectory-controllable video generation results in noticeable degradation in both video quality and trajectory accuracy. To bridge this gap, we introduce FlashMotion, a novel training framework designed for few-step trajectory-controllable video generation. We first train a trajectory adapter on a multi-step video generator for precise trajectory control. Then, we distill the generator into a few-step version to accelerate video generation. Finally, we finetune the adapter using a hybrid strategy that combines diffusion and adversarial objectives, aligning it with the few-step generator to produce high-quality, trajectory-accurate videos. For evaluation, we introduce FlashBench, a benchmark for long-sequence trajectory-controllable video generation that measures both video quality and trajectory accuracy across varying numbers of foreground objects. Experiments on two adapter architectures show that FlashMotion surpasses existing video distillation methods and previous multi-step models in both visual quality and trajectory consistency.

Method: 1) Created UniCAC benchmark using automatic optical design to simulate diverse photographic cameras; 2) Introduced Optical Degradation Evaluator (ODE) framework for objective CAC task difficulty assessment and optical aberration quantification; 3) Conducted comprehensive experiments evaluating 24 image restoration and CAC algorithms; 4) Identified and analyzed three key performance factors: prior utilization, network architecture, and training strategy.

Result: The study provides systematic evaluation of CAC methods across diverse lenses, identifies three critical performance factors, and establishes a reliable framework for optical aberration quantification. The benchmark enables objective comparison and reveals insights into what makes CAC methods effective across different optical systems.

Conclusion: UniCAC benchmark and ODE framework provide foundational insights for computational aberration correction research, enabling better understanding of cross-lens generalization challenges. The identified key factors offer guidance for developing more universal CAC methods, and the resources (benchmarks, codes, Zemax files) support future investigations in this area.

Abstract: Prevalent Computational Aberration Correction (CAC) methods are typically tailored to specific optical systems, leading to poor generalization and labor-intensive re-training for new lenses. Developing CAC paradigms capable of generalizing across diverse photographic lenses offers a promising solution to these challenges. However, efforts to achieve such cross-lens universality within consumer photography are still in their early stages due to the lack of a comprehensive benchmark that encompasses a sufficiently wide range of optical aberrations. Furthermore, it remains unclear which specific factors influence existing CAC methods and how these factors affect their performance. In this paper, we present comprehensive experiments and evaluations involving 24 image restoration and CAC algorithms, utilizing our newly proposed UniCAC, a large-scale benchmark for photographic cameras constructed via automatic optical design. The Optical Degradation Evaluator (ODE) is introduced as a novel framework to objectively assess the difficulty of CAC tasks, offering credible quantification of optical aberrations and enabling reliable evaluation. Drawing on our comparative analysis, we identify three key factors – prior utilization, network architecture, and training strategy – that most significantly influence CAC performance, and further investigate their respective effects. We believe that our benchmark, dataset, and observations contribute foundational insights to related areas and lay the groundwork for future investigations. Benchmarks, codes, and Zemax files will be available at https://github.com/XiaolongQian/UniCAC.

Method: Proposes SimE framework using vision-language model with adapters specifically designed for IL tasks, discovering nonlinear relationship between adapter connections and IL capabilities.

Result: SimE outperforms traditional methods by 9.6% on TinyImageNet and other CLIP-based methods by 5.3% on CIFAR-100. Shows adapter connections between transformer blocks improve performance but within-block connections can degrade it.

Conclusion: SimE provides efficient incremental learning with vision-language models, with insights on adapter connection optimization and recommendations for using stronger CLIP models trained on larger datasets.

Abstract: Incremental Learning (IL) aims to learn new tasks while preserving previously acquired knowledge. Integrating the zero-shot learning capabilities of pre-trained vision-language models into IL methods has marked a significant advancement. However, these methods face three primary challenges: (1) the need for improved training efficiency; (2) reliance on a memory bank to store previous data; and (3) the necessity of a strong backbone to augment the model’s capabilities. In this paper, we propose SimE, a Simple and Efficient framework that employs a vision-language model with adapters designed specifically for the IL task. We report a remarkable phenomenon: there is a nonlinear correlation between the number of adaptive adapter connections and the model’s IL capabilities. While increasing adapter connections between transformer blocks improves model performance, adding more adaptive connections within transformer blocks during smaller incremental steps does not enhance, and may even degrade the model’s IL ability. Extensive experimental results show that SimE surpasses traditional methods by 9.6% on TinyImageNet and outperforms other CLIP-based methods by 5.3% on CIFAR-100. Furthermore, we conduct a systematic study to enhance the utilization of the zero-shot capabilities of CLIP. We suggest replacing SimE’s encoder with a CLIP model trained on larger datasets (e.g., LAION2B) and stronger architectures (e.g., ViT-L/14).

Method: Uses Polar-spiral Mamba (PsM) for omnidirectional voxel embedding in polar-spiral topology, Occupancy Cost Aggregation (OCA) for unifying geometric/semantic supervision, and Natural Modality Alignment (NMA) for pixel-voxel-text representation alignment.

Result: Achieves state-of-the-art performance on QuadOcc and Human360Occ benchmarks, exhibits remarkable cross-scene generalization and semantic scalability.

Conclusion: O3N paves the way toward universal 3D world modeling for embodied intelligence with its omnidirectional open-vocabulary approach.

Abstract: Understanding and reconstructing the 3D world through omnidirectional perception is an inevitable trend in the development of autonomous agents and embodied intelligence. However, existing 3D occupancy prediction methods are constrained by limited perspective inputs and predefined training distribution, making them difficult to apply to embodied agents that require comprehensive and safe perception of scenes in open world exploration. To address this, we present O3N, the first purely visual, end-to-end Omnidirectional Open-vocabulary Occupancy predictioN framework. O3N embeds omnidirectional voxels in a polar-spiral topology via the Polar-spiral Mamba (PsM) module, enabling continuous spatial representation and long-range context modeling across 360°. The Occupancy Cost Aggregation (OCA) module introduces a principled mechanism for unifying geometric and semantic supervision within the voxel space, ensuring consistency between the reconstructed geometry and the underlying semantic structure. Moreover, Natural Modality Alignment (NMA) establishes a gradient-free alignment pathway that harmonizes visual features, voxel embeddings, and text semantics, forming a consistent “pixel-voxel-text” representation triad. Extensive experiments on multiple models demonstrate that our method not only achieves state-of-the-art performance on QuadOcc and Human360Occ benchmarks but also exhibits remarkable cross-scene generalization and semantic scalability, paving the way toward universal 3D world modeling. The source code will be made publicly available at https://github.com/MengfeiD/O3N.

Method: Three-stage training: 1) Driving pre-training with decision adapter, 2) Open-loop VLM-E2E alignment, 3) Closed-loop alignment via hierarchical reinforcement learning in 3DGS environments.

Result: Achieves 19.3% F1 score improvement in dual-system consistency, 5.7% FDE reduction in open-loop, and 30.6% AF-CR reduction in closed-loop settings.

Conclusion: Senna-2 successfully aligns VLM decisions with E2E planning, enhancing driving safety and decision consistency through explicit dual-system alignment.

Abstract: Vision-language models (VLMs) enhance the planning capability of end-to-end (E2E) driving policy by leveraging high-level semantic reasoning. However, existing approaches often overlook the dual-system consistency between VLM’s high-level decision and E2E’s low-level planning. As a result, the generated trajectories may misalign with the intended driving decisions, leading to weakened top-down guidance and decision-following ability of the system. To address this issue, we propose Senna-2, an advanced VLM-E2E driving policy that explicitly aligns the two systems for consistent decision-making and planning. Our method follows a consistency-oriented three-stage training paradigm. In the first stage, we conduct driving pre-training to achieve preliminary decision-making and planning, with a decision adapter transmitting VLM decisions to E2E policy in the form of implicit embeddings. In the second stage, we align the VLM and the E2E policy in an open-loop setting. In the third stage, we perform closed-loop alignment via bottom-up Hierarchical Reinforcement Learning in 3DGS environments to reinforce the safety and efficiency. Extensive experiments demonstrate that Senna-2 achieves superior dual-system consistency (19.3% F1 score improvement) and significantly enhances driving safety in both open-loop (5.7% FDE reduction) and closed-loop settings (30.6% AF-CR reduction).

Method: FMVR disentangles visual representations into low- and high-frequency components using AvgPool and MaxPool. It modulates these frequencies with learnable parameters, using high-frequency as saliency filter and low-frequency as anti-saliency filter to preserve and restore visual semantics. Combined with Matryoshka Representation Learning for elastic token adjustment.

Result: FMVR-LLaVA reduces FLOPs of LLaVA-1.5-7B by 89% while maintaining almost 100% of original accuracy across 10 image-based and 4 video-based benchmarks.

Conclusion: FMVR provides an effective plug-and-play solution for visual token reduction in LMMs that preserves visual semantics, enabling significant computational savings without performance degradation.

Abstract: Large Multimodal Models (LMMs) struggle to adapt varying computational budgets due to numerous visual tokens. Previous methods attempted to reduce the number of visual tokens before or within LLMs. However, these strategies inevitably result in the loss of visual semantic. To address these issues, we introduce FMVR, a plug-and-play and extremely simple Frequency-Modulated Visual Restoration strategy to boost the reasoning ability of LMMs under visual token reduction. Specifically, FMVR disentangles the visual representation of fewer visual tokens into low- and high-frequency components through AvgPool and MaxPool. The derived frequencies are subsequently modulated using lightweight learnable parameters. The high-frequency from AvgPool acts as a saliency filter to enhance saliency visual semantics, while the low-frequency from MaxPool acts as an anti-saliency filter to strengthen weak visual semantics. It enables the preservation of visual semantics dominated by few visual tokens and the restoration of diluted visual semantics. Additionally, we inject FMVR into Matryoshka Representation Learning to learn coarse-to-fine visual token sets, thus enabling to elastically adjust the number of visual tokens during inference while maintaining comparable performance. Experiments across 10 image-based and 4 video-based bench marks demonstrate that FMVR-LLaVA reduce the FLOPs of LLaVA-1.5-7B by 89%, while maintaining almost 100% of the original accuracy. The code will be open.

Method: Proposes GOT-JEPA: model-predictive pretraining framework where teacher predictor generates pseudo-tracking models from clean current frame, student predictor learns to predict same models from corrupted frame. Also introduces OccuSolver for enhanced occlusion perception using point-centric tracking and iterative refinement of visibility states.

Result: Extensive evaluations on seven benchmarks show the method effectively enhances tracker generalization and robustness, improving performance in dynamic environments with occlusions and distractors.

Conclusion: The proposed approach addresses limitations in generalization and occlusion perception for object tracking, bridging gap between current trackers and human visual system capabilities through model-predictive pretraining and detailed occlusion modeling.

Abstract: The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity. In contrast, recent generic object trackers are often optimized for training targets, which limits robustness and generalization in unseen scenarios, and their occlusion reasoning remains coarse, lacking detailed modeling of occlusion patterns. To address these limitations in generalization and occlusion perception, we propose GOT-JEPA, a model-predictive pretraining framework that extends JEPA from predicting image features to predicting tracking models. Given identical historical information, a teacher predictor generates pseudo-tracking models from a clean current frame, and a student predictor learns to predict the same pseudo-tracking models from a corrupted version of the current frame. This design provides stable pseudo supervision and explicitly trains the predictor to produce reliable tracking models under occlusions, distractors, and other adverse observations, improving generalization to dynamic environments. Building on GOT-JEPA, we further propose OccuSolver to enhance occlusion perception for object tracking. OccuSolver adapts a point-centric point tracker for object-aware visibility estimation and detailed occlusion-pattern capture. Conditioned on object priors iteratively generated by the tracker, OccuSolver incrementally refines visibility states, strengthens occlusion handling, and produces higher-quality reference labels that progressively improve subsequent model predictions. Extensive evaluations on seven benchmarks show that our method effectively enhances tracker generalization and robustness.

Method: Introduces slot merging: a lightweight operation that merges overlapping slots using Soft-IoU scores between slot-attention maps. Selected slot pairs are combined via barycentric updates preserving gradient flow. The merging policy uses fixed thresholds inferred from overlap statistics without additional learnable modules.

Result: When integrated into DINOSAUR’s feature-reconstruction pipeline, the method improves object factorization and mask quality, surpassing other adaptive methods in object discovery and segmentation benchmarks.

Conclusion: Slot merging effectively addresses the overlapping slot problem in object-centric learning, providing better object discovery and segmentation without adding complexity to the model architecture.

Abstract: Slot-based object-centric learning represents an image as a set of latent slots with a decoder that combines them into an image or features. The decoder specifies how slots are combined into an output, but the slot set is typically fixed: the number of slots is chosen upfront and slots are only refined. This can lead to multiple slots competing for overlapping regions of the same entity rather than focusing on distinct regions. We introduce slot merging: a drop-in, lightweight operation on the slot set that merges overlapping slots during training. We quantify overlap with a Soft-IoU score between slot-attention maps and combine selected pairs via a barycentric update that preserves gradient flow. Merging follows a fixed policy, with the decision threshold inferred from overlap statistics, requiring no additional learnable modules. Integrated into the established feature-reconstruction pipeline of DINOSAUR, the proposed method improves object factorization and mask quality, surpassing other adaptive methods in object discovery and segmentation benchmarks.

Method: Groups LiDAR observations from same semantic objects across varying conditions to create radiometric fingerprints. Uses 312.4M beams from 4 campaigns with 5 sensors on A2D2 vehicle, associated with 6368 objects in CityGML 3.0 LOD3 model.

Result: Extracted radiometric fingerprints reveal intra-class patterns indicating class-dominant materials. Semantic model, method implementations, and 3DSensorDB geodatabase released as open source.

Conclusion: Radiometric fingerprints enable material inference in semantic 3D city models, expanding urban digital twin applications through structured material representation.

Abstract: Although semantic 3D city models are internationally available and becoming increasingly detailed, the incorporation of material information remains largely untapped. However, a structured representation of materials and their physical properties could substantially broaden the application spectrum and analytical capabilities for urban digital twins. At the same time, the growing number of repeated mobile laser scans of cities and their street spaces yields a wealth of observations influenced by the material characteristics of the corresponding surfaces. To leverage this information, we propose radiometric fingerprints of object surfaces by grouping LiDAR observations reflected from the same semantic object under varying distances, incident angles, environmental conditions, sensors, and scanning campaigns. Our study demonstrates how 312.4 million individual beams acquired across four campaigns using five LiDAR sensors on the Audi Autonomous Driving Dataset (A2D2) vehicle can be automatically associated with 6368 individual objects of the semantic 3D city model. The model comprises a comprehensive and semantic representation of four inner-city streets at Level of Detail (LOD) 3 with centimeter-level accuracy. It is based on the CityGML 3.0 standard and enables fine-grained sub-differentiation of objects. The extracted radiometric fingerprints for object surfaces reveal recurring intra-class patterns that indicate class-dominant materials. The semantic model, the method implementations, and the developed geodatabase solution 3DSensorDB are released under: https://github.com/tum-gis/sensordb

Method: Uses RGB images from a calibrated camera on a mixed reality head-mounted display to capture patient. An edge server infers patient-specific body-surface and skeleton model with spatial smoothing across views. Uses bony landmarks to estimate intercostal region and projects guidance back onto reconstructed body surface.

Result: Pilot experiments with healthy volunteers show consistent initial probe placement within anatomical variability acceptable for teleultrasound setup. Quantitative placement error measured across multiple transthoracic echocardiography scan planes.

Conclusion: The proposed framework enables automated patient registration and anatomy-informed initial probe placement guidance for teleultrasound, potentially improving accessibility and reducing reliance on expert assistance for probe positioning.

Abstract: Cardiac and lung ultrasound are technically demanding because operators must identify patient-specific intercostal acoustic windows and then navigate between standard views by adjusting probe position, rotation, and force across different imaging planes. These challenges are amplified in teleultrasound when a novice or robot faces the difficult task of first placing the probe on the patient without in-person expert assistance. We present a framework for automating Patient registration and anatomy-informed Initial Probe placement Guidance (PIPG) using only RGB images from a calibrated camera. The novice first captures the patient using the camera on a mixed reality (MR) head-mounted display (HMD). An edge server then infers a patient-specific body-surface and skeleton model, with spatial smoothing across multiple views. Using bony landmarks from the predicted skeleton, we estimate the intercostal region and project the guidance back onto the reconstructed body surface. To validate the framework, we overlaid the reconstructed body mesh and the virtual probe pose guidance across multiple transthoracic echocardiography scan planes in situ and measured the quantitative placement error. Pilot experiments with healthy volunteers suggest that the proposed probe placement prediction and MR guidance yield consistent initial placement within anatomical variability acceptable for teleultrasound setup

Method: Proposes InstantHDR with geometry-guided appearance modeling for multi-exposure fusion and a meta-network for generalizable scene-specific tone mapping. Also creates HDR-Pretrain dataset with 168 Blender-rendered scenes for pre-training.

Result: Achieves comparable synthesis performance to state-of-the-art optimization-based HDR methods with ~700× speed improvement in single-forward mode and ~20× improvement with post-optimization.

Conclusion: InstantHDR enables efficient HDR novel view synthesis from uncalibrated multi-exposure images, bridging the gap between optimization-based and feed-forward approaches for HDR scene reconstruction.

Abstract: High dynamic range (HDR) novel view synthesis (NVS) aims to reconstruct HDR scenes from multi-exposure low dynamic range (LDR) images. Existing HDR pipelines heavily rely on known camera poses, well-initialized dense point clouds, and time-consuming per-scene optimization. Current feed-forward alternatives overlook the HDR problem by assuming exposure-invariant appearance. To bridge this gap, we propose InstantHDR, a feed-forward network that reconstructs 3D HDR scenes from uncalibrated multi-exposure LDR collections in a single forward pass. Specifically, we design a geometry-guided appearance modeling for multi-exposure fusion, and a meta-network for generalizable scene-specific tone mapping. Due to the lack of HDR scene data, we build a pre-training dataset, called HDR-Pretrain, for generalizable feed-forward HDR models, featuring 168 Blender-rendered scenes, diverse lighting types, and multiple camera response functions. Comprehensive experiments show that our InstantHDR delivers comparable synthesis performance to the state-of-the-art optimization-based HDR methods while enjoying $\sim700\times$ and $\sim20\times$ reconstruction speed improvement with our single-forward and post-optimization settings. All code, models, and datasets will be released after the review process.

Method: Uses DINOv2 and WavLM foundation models for robust visual/audio representations. Introduces Hierarchical Granularity Alignment to dynamically align global facial semantics with local active patches. Employs Vision-Mamba architecture for temporal modeling with O(N) linear complexity to capture ultra-long-range dynamics. Includes asymmetric cross-attention for deep synchronization of audio cues with visual movements.

Result: Extensive experiments on Aff-Wild2 dataset show significant outperformance over existing baselines, achieving state-of-the-art performance. Framework secured top rankings in AU Detection track of 10th Affective Behavior Analysis in-the-wild Competition.

Conclusion: The proposed multimodal framework effectively addresses challenges in in-the-wild AU detection through hierarchical granularity alignment and state space models, demonstrating superior performance in handling extreme facial variations and complex audio-visual dependencies.

Abstract: Facial Action Unit (AU) detection in in-the-wild environments remains a formidable challenge due to severe spatial-temporal heterogeneity, unconstrained poses, and complex audio-visual dependencies. While recent multimodal approaches have made progress, they often rely on capacity-limited encoders and shallow fusion mechanisms that fail to capture fine-grained semantic shifts and ultra-long temporal contexts. To bridge this gap, we propose a novel multimodal framework driven by Hierarchical Granularity Alignment and State Space Models.Specifically, we leverage powerful foundation models, namely DINOv2 and WavLM, to extract robust and high-fidelity visual and audio representations, effectively replacing traditional feature extractors. To handle extreme facial variations, our Hierarchical Granularity Alignment module dynamically aligns global facial semantics with fine-grained local active patches. Furthermore, we overcome the receptive field limitations of conventional temporal convolutional networks by introducing a Vision-Mamba architecture. This approach enables temporal modeling with O(N) linear complexity, effectively capturing ultra-long-range dynamics without performance degradation. A novel asymmetric cross-attention mechanism is also introduced to deeply synchronize paralinguistic audio cues with subtle visual movements.Extensive experiments on the challenging Aff-Wild2 dataset demonstrate that our approach significantly outperforms existing baselines, achieving state-of-the-art performance. Notably, this framework secured top rankings in the AU Detection track of the 10th Affective Behavior Analysis in-the-wild Competition.

Method: Proposes UniCompress, a plug-in compression and decompression mechanism guided by learnable global meta tokens. The framework is lightweight and modular, enabling efficient integration into existing models without full retraining.

Result: Reduces image tokens by up to 4 times, achieves substantial gains in inference latency and training cost, with only minimal performance degradation on both image understanding and generation tasks.

Conclusion: Demonstrates the promise of token-efficient unified modeling for real-world multimodal applications, offering a practical solution to computational bottlenecks in unified vision-language models.

Abstract: Unified models aim to support both understanding and generation by encoding images into discrete tokens and processing them alongside text within a single autoregressive framework. This unified design offers architectural simplicity and cross-modal synergy, which facilitates shared parameterization, consistent training objectives, and seamless transfer between modalities. However, the large number of visual tokens required by such models introduces substantial computation and memory overhead, and this inefficiency directly hinders deployment in resource constrained scenarios such as embodied AI systems. In this work, we propose a unified token compression algorithm UniCompress that significantly reduces visual token count while preserving performance on both image understanding and generation tasks. Our method introduces a plug-in compression and decompression mechanism guided with learnable global meta tokens. The framework is lightweight and modular, enabling efficient integration into existing models without full retraining. Experimental results show that our approach reduces image tokens by up to 4 times, achieves substantial gains in inference latency and training cost, and incurs only minimal performance degradation, which demonstrates the promise of token-efficient unified modeling for real world multimodal applications.

Method: Designs a new alias-free UNet by carefully selecting state-of-the-art translation-equivariant layers to replace aliasing-prone components in traditional UNet architectures.

Result: The proposed equivariant architecture achieves competitive performance on image restoration tasks compared to non-equivariant baselines, with significantly improved measured translation equivariance. Ablation studies confirm each design change is crucial for empirical equivariance.

Conclusion: The alias-free UNet successfully addresses aliasing issues in traditional UNets, providing a more truly translation-equivariant architecture while maintaining competitive performance on image restoration tasks.

Abstract: The simplicity and effectiveness of the UNet architecture makes it ubiquitous in image restoration, image segmentation, and diffusion models. They are often assumed to be equivariant to translations, yet they traditionally consist of layers that are known to be prone to aliasing, which hinders their equivariance in practice. To overcome this limitation, we propose a new alias-free UNet designed from a careful selection of state-of-the-art translation-equivariant layers. We evaluate the proposed equivariant architecture against non-equivariant baselines on image restoration tasks and observe competitive performance with a significant increase in measured equivariance. Through extensive ablation studies, we also demonstrate that each change is crucial for its empirical equivariance. Our implementation is available at https://github.com/jscanvic/UNet-AF

Method: Proposes ReDiff with two key components: 1) reliability-guided sampling strategy to suppress unreliable responses during denoising, and 2) uncertainty-aware multi-candidate selection scheme to enhance final prediction reliability.

Result: Experiments on multi-center MRI datasets demonstrate improved structural fidelity and reduced artifacts compared with state-of-the-art methods.

Conclusion: The proposed reliability-aware diffusion framework addresses key limitations in MRI synthesis by improving both visual accuracy and spatial reliability, making synthesized images more clinically trustworthy.

Abstract: Low-field to high-field MRI synthesis has emerged as a cost-effective strategy to enhance image quality under hardware and acquisition constraints, particularly in scenarios where access to high-field scanners is limited or impractical. Despite recent progress in diffusion models, diffusion-based approaches often struggle to balance fine-detail recovery and structural fidelity. In particular, the uncontrolled generation of high-resolution details in structurally ambiguous regions may introduce anatomically inconsistent patterns, such as spurious edges or artificial texture variations. These artifacts can bias downstream quantitative analysis. For example, they may cause inaccurate tissue boundary delineation or erroneous volumetric estimation, ultimately reducing clinical trust in synthesized images. These limitations highlight the need for generative models that are not only visually accurate but also spatially reliable and anatomically consistent. To address this issue, we propose a reliability-aware diffusion framework (ReDiff) that improves synthesis robustness at both the sampling and post-generation stages. Specifically, we introduce a reliability-guided sampling strategy to suppress unreliable responses during the denoising process. We further develop an uncertainty-aware multi-candidate selection scheme to enhance the reliability of the final prediction. Experiments on multi-center MRI datasets demonstrate improved structural fidelity and reduced artifacts compared with state-of-the-art methods.

Method: Formulates imitation of force-exchanging human-human motion as multi-agent RL problem. Uses partner policies initialization from single-human motion-tracking controllers, dynamic reference retargeting to adapt assistant’s motion to recipient’s real-time pose, and contact-promoting rewards for physically meaningful support.

Result: First method capable of successfully tracking assistive interaction motions on established benchmarks, demonstrating benefits of multi-agent RL formulation for physically grounded and socially aware humanoid control.

Conclusion: Multi-agent RL approach enables humanoid robots to learn physically interactive assistive behaviors, representing significant advancement over previous contact-less motion tracking methods for humanoid robotics in caregiving applications.

Abstract: Humanoid robotics has strong potential to transform daily service and caregiving applications. Although recent advances in general motion tracking within physics engines (GMT) have enabled virtual characters and humanoid robots to reproduce a broad range of human motions, these behaviors are primarily limited to contact-less social interactions or isolated movements. Assistive scenarios, by contrast, require continuous awareness of a human partner and rapid adaptation to their evolving posture and dynamics. In this paper, we formulate the imitation of closely interacting, force-exchanging human-human motion sequences as a multi-agent reinforcement learning problem. We jointly train partner-aware policies for both the supporter (assistant) agent and the recipient agent in a physics simulator to track assistive motion references. To make this problem tractable, we introduce a partner policies initialization scheme that transfers priors from single-human motion-tracking controllers, greatly improving exploration. We further propose dynamic reference retargeting and contact-promoting reward, which adapt the assistant’s reference motion to the recipient’s real-time pose and encourage physically meaningful support. We show that AssistMimic is the first method capable of successfully tracking assistive interaction motions on established benchmarks, demonstrating the benefits of a multi-agent RL formulation for physically grounded and socially aware humanoid control.

Method: Proposes DriveXQA dataset with 102,505 QA pairs across 4 visual modalities, 5 sensor failure cases, and 5 weather conditions. Designs MVX-LLM with Dual Cross-Attention projector for efficient multimodal fusion

Result: DCA achieves improved performance under challenging conditions (GPTScore: 53.5 vs. 25.1 for baseline in foggy conditions). Dataset includes three QA types: global scene, allocentric, and ego-vehicle centric levels

Conclusion: The work addresses the gap in MLLMs for autonomous driving by providing a comprehensive multimodal dataset and efficient fusion architecture that performs well in adverse conditions

Abstract: Fusing sensors with complementary modalities is crucial for maintaining a stable and comprehensive understanding of abnormal driving scenes. However, Multimodal Large Language Models (MLLMs) are underexplored for leveraging multi-sensor information to understand adverse driving scenarios in autonomous vehicles. To address this gap, we propose the DriveXQA, a multimodal dataset for autonomous driving VQA. In addition to four visual modalities, five sensor failure cases, and five weather conditions, it includes $102,505$ QA pairs categorized into three types: global scene level, allocentric level, and ego-vehicle centric level. Since no existing MLLM framework adopts multiple complementary visual modalities as input, we design MVX-LLM, a token-efficient architecture with a Dual Cross-Attention (DCA) projector that fuses the modalities to alleviate information redundancy. Experiments demonstrate that our DCA achieves improved performance under challenging conditions such as foggy (GPTScore: $53.5$ vs. $25.1$ for the baseline). The established dataset and source code will be made publicly available.

Method: Augments a moving DFP system with a fixed, intrinsically calibrated global projector. Uses the global projector’s phase-derived pixel constraints with a PnP-style reprojection objective to estimate DFP system pose in a fixed reference frame without deterministic feature extraction.

Result: Achieves sub-millimeter pose accuracy with quantified uncertainty bounds, high repeatability under aggressive subsampling, robust operation on homogeneous surfaces and low-overlap views, and reduced error accumulation when correcting ICP-based trajectories.

Conclusion: Extends DFP toward accurate 3D mapping in quasi-static scenarios like inspection and metrology, though requires time-multiplexed acquisition for additional projector measurements.

Abstract: Digital fringe projection (DFP) enables micrometer-level 3D reconstruction, yet extending it to large-scale mapping remains challenging because six-degree-of-freedom pose estimation often cannot match the reconstruction’s precision. Conventional iterative closest point (ICP) registration becomes inefficient on multi-million-point clouds and typically relies on downsampling or feature-based selection, which can reduce local detail and degrade pose precision. Drift-correction methods improve long-term consistency but do not resolve sampling sensitivity in dense DFP point clouds.We propose a high-precision pose estimation method that augments a moving DFP system with a fixed, intrinsically calibrated global projector. Using the global projector’s phase-derived pixel constraints and a PnP-style reprojection objective, the method estimates the DFP system pose in a fixed reference frame without relying on deterministic feature extraction, and we experimentally demonstrate sampling invariance under coordinate-preserving subsampling. Experiments demonstrate sub-millimeter pose accuracy against a reference with quantified uncertainty bounds, high repeatability under aggressive subsampling, robust operation on homogeneous surfaces and low-overlap views, and reduced error accumulation when used to correct ICP-based trajectories. The method extends DFP toward accurate 3D mapping in quasi-static scenarios such as inspection and metrology, with the trade-off of time-multiplexed acquisition for the additional projector measurements.

Method: Proposes DeepHistoViT, a transformer-based framework using a customized Vision Transformer architecture with integrated attention mechanism designed to capture fine-grained cellular structures while improving interpretability through attention-based localization of diagnostically relevant regions.

Result: Achieved state-of-the-art performance across three histopathology datasets: 100% accuracy, precision, recall, F1-score, and ROC-AUC on lung and colon cancer datasets; 99.85% accuracy, 99.84% precision, 99.86% recall, 99.85% F1-score, and 99.99% ROC-AUC on acute lymphoblastic leukemia dataset.

Conclusion: Transformer-based architectures are effective for histopathological image analysis, and DeepHistoViT shows potential as an interpretable computer-assisted diagnostic tool to support pathologists in clinical decision-making.

Abstract: Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, manual histopathological examination is time-consuming, labour-intensive, and subject to inter-observer variability, creating a demand for reliable computer-assisted diagnostic tools. Recent advances in deep learning, particularly transformer-based architectures, have shown strong potential for modelling complex spatial dependencies in medical images. In this work, we propose DeepHistoViT, a transformer-based framework for automated classification of histopathological images. The model employs a customized Vision Transformer architecture with an integrated attention mechanism designed to capture fine-grained cellular structures while improving interpretability through attention-based localization of diagnostically relevant regions. The framework is evaluated on three publicly available histopathology datasets covering lung cancer, colon cancer, and acute lymphoblastic leukaemia. Experimental results demonstrate state-of-the-art performance across all datasets, with classification accuracy, precision, recall, F1-score, and ROC-AUC reaching 100 percent on the lung and colon cancer datasets, and 99.85 percent, 99.84 percent, 99.86 percent, 99.85 percent, and 99.99 percent respectively on the acute lymphoblastic leukaemia dataset. All performance metrics are reported with 95 percent confidence intervals. These results highlight the effectiveness of transformer-based architectures for histopathological image analysis and demonstrate the potential of DeepHistoViT as an interpretable computer-assisted diagnostic tool to support pathologists in clinical decision-making.

Method: Created DORI benchmark with 33,656 multiple-choice questions across 13,652 images from 14 sources, decomposing orientation into four dimensions evaluated at coarse (categorical) and granular (metric) levels with bounding-box isolation and standardized spatial reference frames.

Result: Evaluation of 24 state-of-the-art vision-language models shows poor performance: best models reach only 54.2% on coarse and 45.0% on granular judgments, with largest failures on compound rotations and inter-object reference frame shifts.

Conclusion: Object orientation understanding remains an unsolved challenge for multimodal systems, with models relying on categorical heuristics rather than geometric reasoning, revealing limitations hidden by existing benchmarks.

Abstract: Humans learn object orientation progressively, from recognizing which way an object faces, to mentally rotating it, to reasoning about orientations between objects. Current vision-language benchmarks largely conflate orientation with position and general scene understanding. We introduce Discriminative Orientation Reasoning Intelligence (DORI), a cognitively grounded hierarchical benchmark that makes object orientation the primary target. Inspired by stages of human orientation cognition, DORI decomposes orientation into four dimensions, each evaluated at coarse (categorical) and granular (metric) levels. Composed from 13,652 images across 14 sources, DORI provides 33,656 multiple-choice questions covering 67 object categories in real-world and synthetic settings. Its coarse-to-granular design isolates orientation from confounds such as object recognition difficulty, scene clutter, and linguistic ambiguity via bounding-box isolation, standardized spatial reference frames, and structured prompts. Evaluating 24 state-of-the-art vision-language models shows a clear pattern: models that perform well on general spatial benchmarks are near-random on object-centric orientation tasks. The best models reach only 54.2% on coarse and 45.0% on granular judgments, with largest failures on compound rotations and shifts in inter-object reference frames. Large coarse-to-granular gaps reveal reliance on categorical heuristics rather than geometric reasoning, a limitation hidden by existing benchmarks. These results identify orientation understanding as an unsolved challenge for multimodal systems, with implications for robotic manipulation, 3D scene reconstruction, and human-AI interaction.

Method: Comprehensive study integrating self-supervised backbones (I-JEPA, DINOv2, MAE) into planning frameworks. Evaluated under strict geographic splits on nuScenes (open-loop) and NAVSIM (closed-loop), comparing supervised vs. self-supervised pretraining for cross-city transfer.

Result: Self-supervised representation learning substantially reduces generalization gap. In open-loop: supervised backbone showed 9.77x L2 displacement and 19.43x collision ratio when transferring Boston→Singapore; self-supervised reduced to 1.20x and 0.75x. In closed-loop: self-supervised improved PDMS by up to 4% for all single-city training cities.

Conclusion: Representation learning strongly influences cross-city planning robustness, establishing zero-shot geographic transfer as a necessary test for evaluating end-to-end autonomous driving systems. Self-supervised pretraining improves generalization across cities with different road topologies and driving conventions.

Abstract: End-to-end autonomous driving models are typically trained on multi-city datasets using supervised ImageNet-pretrained backbones, yet their ability to generalize to unseen cities remains largely unexamined. When training and evaluation data are geographically mixed, models may implicitly rely on city-specific cues, masking failure modes that would occur under real domain shifts when generalizing to new locations. In this work we investigate zero-shot cross-city generalization in end-to-end trajectory planning and ask whether self-supervised visual representations improve transfer across cities. We conduct a comprehensive study by integrating self-supervised backbones (I-JEPA, DINOv2, and MAE) into planning frameworks. We evaluate performance under strict geographic splits on nuScenes in the open-loop setting and on NAVSIM in the closed-loop evaluation protocol. Our experiments reveal a substantial generalization gap when transferring models relying on traditional supervised backbones across cities with different road topologies and driving conventions, particularly when transferring from right-side to left-side driving environments. Self-supervised representation learning reduces this gap. In open-loop evaluation, a supervised backbone exhibits severe inflation when transferring from Boston to Singapore (L2 displacement ratio 9.77x, collision ratio 19.43x), whereas domain-specific self-supervised pretraining reduces this to 1.20x and 0.75x respectively. In closed-loop evaluation, self-supervised pretraining improves PDMS by up to 4 percent for all single-city training cities. These results show that representation learning strongly influences the robustness of cross-city planning and establish zero-shot geographic transfer as a necessary test for evaluating end-to-end autonomous driving systems.

Method: Proposes a data-centric paradigm using aligned (Caption, Trajectory, Video) triplets. Uses two collaborative agents: 1) VLM-based Planner that leverages spatial priors to obtain cinematic trajectories from text, and 2) Controller that renders trajectories into multi-shot video via camera adapter. Also constructs ShotVerse-Bench dataset with automated multi-shot camera calibration pipeline.

Result: ShotVerse effectively bridges unreliable textual control and manual plotting, achieving superior cinematic aesthetics and generating multi-shot videos that are both camera-accurate and cross-shot consistent.

Conclusion: The framework demonstrates that aligned triplets form an inherent joint distribution that can connect automated plotting and precise execution, offering a solution to the camera control bottleneck in cinematic video generation.

Abstract: Text-driven video generation has democratized film creation, but camera control in cinematic multi-shot scenarios remains a significant block. Implicit textual prompts lack precision, while explicit trajectory conditioning imposes prohibitive manual overhead and often triggers execution failures in current models. To overcome this bottleneck, we propose a data-centric paradigm shift, positing that aligned (Caption, Trajectory, Video) triplets form an inherent joint distribution that can connect automated plotting and precise execution. Guided by this insight, we present ShotVerse, a “Plan-then-Control” framework that decouples generation into two collaborative agents: a VLM (Vision-Language Model)-based Planner that leverages spatial priors to obtain cinematic, globally aligned trajectories from text, and a Controller that renders these trajectories into multi-shot video content via a camera adapter. Central to our approach is the construction of a data foundation: we design an automated multi-shot camera calibration pipeline aligns disjoint single-shot trajectories into a unified global coordinate system. This facilitates the curation of ShotVerse-Bench, a high-fidelity cinematic dataset with a three-track evaluation protocol that serves as the bedrock for our framework. Extensive experiments demonstrate that ShotVerse effectively bridges the gap between unreliable textual control and labor-intensive manual plotting, achieving superior cinematic aesthetics and generating multi-shot videos that are both camera-accurate and cross-shot consistent.

Method: Proposes R-MSD framework that explicitly models teacher sampling variance using task-adaptive teacher pools for robust supervision. Integrates quality-aware signal matching with adversarial distillation objective to filter teacher noise while maximizing knowledge transfer.

Result: Outperforms single sample distillation methods across comprehensive video understanding benchmarks. With 4B student model, achieves gains on VideoMME (+1.5%), Video-MMMU (+3.2%), and MathVerse (+3.6%). Original SFT+RL 4B baseline shows only marginal gains under same training budget.

Conclusion: R-MSD effectively addresses unreliable supervision in LVLM distillation by modeling teacher variance through multi-sample approach, demonstrating significant improvements over traditional methods in multimodal video understanding tasks.

Abstract: Traditional black-box distillation for Large Vision-Language Models (LVLMs) typically relies on a single teacher response per input, which often yields high-variance responses and format inconsistencies in multimodal or temporal scenarios. To mitigate this unreliable supervision, we propose R-MSD (Reliable Multi-Sample Distillation), a framework that explicitly models teacher sampling variance to enhance distillation stability. Rather than relying on a single teacher response, our approach leverages a task-adaptive teacher pool to provide robust supervision tailored to both closed-ended and open-ended reasoning. By integrating quality-aware signal matching with an adversarial distillation objective, our approach effectively filters teacher noise while maximizing knowledge transfer. Extensive evaluations across comprehensive video understanding benchmarks demonstrate that R-MSD consistently outperforms single sample distillation methods. We additionally include an original SFT+RL 4B baseline under the same training budget, which shows only marginal gains, while our method achieves significant improvements. With a 4B student model, our approach delivers gains on VideoMME (+1.5%), Video-MMMU (+3.2%), and MathVerse (+3.6%).

Method: 1) Role-specific queries that separate localization and captioning into independent components; 2) Contrastive alignment to enforce semantic consistency between corresponding outputs; 3) Temporal overlap suppression mechanism to penalize mutual overlaps and learn distinct event regions; 4) Lightweight module to capture core event concepts for richer captions.

Result: Demonstrated effectiveness on major DVC benchmarks YouCook2 and ActivityNet Captions, showing improved performance over existing methods.

Conclusion: Separating localization and captioning with role-specific queries, combined with contrastive alignment and temporal suppression, effectively addresses multi-task interference and redundancy in dense video captioning.

Abstract: Dense Video Captioning (DVC) is a challenging multimodal task that involves temporally localizing multiple events within a video and describing them with natural language. While query-based frameworks enable the simultaneous, end-to-end processing of localization and captioning, their reliance on shared queries often leads to significant multi-task interference between the two tasks, as well as temporal redundancy in localization. In this paper, we propose utilizing role-specific queries that separate localization and captioning into independent components, allowing each to exclusively learn its role. We then employ contrastive alignment to enforce semantic consistency between the corresponding outputs, ensuring coherent behavior across the separated queries. Furthermore, we design a novel suppression mechanism in which mutual temporal overlaps across queries are penalized to tackle temporal redundancy, supervising the model to learn distinct, non-overlapping event regions for more precise localization. Additionally, we introduce a lightweight module that captures core event concepts to further enhance semantic richness in captions through concept-level representations. We demonstrate the effectiveness of our method through extensive experiments on major DVC benchmarks YouCook2 and ActivityNet Captions.

Method: Exploits structural invariant that visual backbone is class-agnostic, allowing backbone computation to be shared between all classes (O(N) to O(1)). Combines with batched multi-class decoding, detection-only inference, and TensorRT FP16 deployment.

Result: Achieves 5.6x speedup at 3 classes, scaling to 25x at 80 classes. On COCO val2017: 55.8 AP at 15.8 FPS (4 classes, 1008x1008) on RTX 4080. For extreme latency: adapter distillation achieves 38.7 AP with 13.9 ms backbone.

Conclusion: DART enables real-time multi-class detection with SAM3 without modifying weights, surpassing purpose-built open-vocabulary detectors while being training-free.

Abstract: Recent advances in vision-language modeling have produced promptable detection and segmentation systems that accept arbitrary natural language queries at inference time. Among these, SAM3 achieves state-of-the-art accuracy by combining a ViT-H/14 backbone with cross-modal transformer decoding and learned object queries. However, SAM3 processes a single text prompt per forward pass. Detecting N categories requires N independent executions, each dominated by the 439M-parameter backbone. We present Detect Anything in Real Time (DART), a training-free framework that converts SAM3 into a real-time multi-class detector by exploiting a structural invariant: the visual backbone is class-agnostic, producing image features independent of the text prompt. This allows the backbone computation to be shared between all classes, reducing its cost from O(N) to O(1). Combined with batched multi-class decoding, detection-only inference, and TensorRT FP16 deployment, these optimizations yield 5.6x cumulative speedup at 3 classes, scaling to 25x at 80 classes, without modifying any model weight. On COCO val2017 (5,000 images, 80 classes), DART achieves 55.8 AP at 15.8 FPS (4 classes, 1008x1008) on a single RTX 4080, surpassing purpose-built open-vocabulary detectors trained on millions of box annotations. For extreme latency targets, adapter distillation with a frozen encoder-decoder achieves 38.7 AP with a 13.9 ms backbone. Code and models are available at https://github.com/mkturkcan/DART.

Method: Proposes STaRC framework that supervises frame-level saliency through a highlight detection module trained on binary labels from DVC ground truth. Uses saliency scores as unified temporal signal for retrieval via saliency-guided segmentation and caption generation through explicit Saliency Prompts injected into decoder.

Result: Achieves state-of-the-art performance on YouCook2 and ViTT benchmarks across most metrics, producing temporally coherent segments that align closely with actual event transitions.

Conclusion: STaRC overcomes limitations of existing methods by enforcing saliency-constrained segmentation, leading to more accurate retrieval and contextually grounded caption generation without needing additional annotation.

Abstract: Existing retrieval-augmented approaches for Dense Video Captioning (DVC) often fail to achieve accurate temporal segmentation aligned with true event boundaries, as they rely on heuristic strategies that overlook ground truth event boundaries. The proposed framework, \textbf{STaRC}, overcomes this limitation by supervising frame-level saliency through a highlight detection module. Note that the highlight detection module is trained on binary labels derived directly from DVC ground truth annotations without the need for additional annotation. We also propose to utilize the saliency scores as a unified temporal signal that drives retrieval via saliency-guided segmentation and informs caption generation through explicit Saliency Prompts injected into the decoder. By enforcing saliency-constrained segmentation, our method produces temporally coherent segments that align closely with actual event transitions, leading to more accurate retrieval and contextually grounded caption generation. We conduct comprehensive evaluations on the YouCook2 and ViTT benchmarks, where STaRC achieves state-of-the-art performance across most of the metrics. Our code is available at https://github.com/ermitaju1/STaRC

Method: Created INFACT benchmark with 9,800 QA instances with fine-grained taxonomies for faithfulness and factuality across real and synthetic videos. Evaluates models in four modes: Base (clean), Visual Degradation, Evidence Corruption, and Temporal Intervention. Measures reliability using Resist Rate (RR) and Temporal Sensitivity Score (TSS).

Result: Evaluation of 14 Video-LLMs shows higher Base-mode accuracy doesn’t guarantee reliability in induced modes. Evidence corruption reduces stability, temporal intervention causes largest degradation. Many open-source models show near-zero TSS on factuality, indicating temporal inertia on order-sensitive questions.

Conclusion: Video-LLMs need improved reliability beyond clean settings. INFACT provides comprehensive diagnostic benchmark for evaluating faithfulness and factuality hallucinations across diverse challenging conditions.

Abstract: Despite rapid progress, Video Large Language Models (Video-LLMs) remain unreliable due to hallucinations, which are outputs that contradict either video evidence (faithfulness) or verifiable world knowledge (factuality). Existing benchmarks provide limited coverage of factuality hallucinations and predominantly evaluate models only in clean settings. We introduce \textsc{INFACT}, a diagnostic benchmark comprising 9{,}800 QA instances with fine-grained taxonomies for faithfulness and factuality, spanning real and synthetic videos. \textsc{INFACT} evaluates models in four modes: Base (clean), Visual Degradation, Evidence Corruption, and Temporal Intervention for order-sensitive items. Reliability under induced modes is quantified using Resist Rate (RR) and Temporal Sensitivity Score (TSS). Experiments on 14 representative Video-LLMs reveal that higher Base-mode accuracy does not reliably translate to higher reliability in the induced modes, with evidence corruption reducing stability and temporal intervention yielding the largest degradation. Notably, many open-source baselines exhibit near-zero TSS on factuality, indicating pronounced temporal inertia on order-sensitive questions.

Method: 1) Semantic prompt feature enhancement using decoupled commonality and heterogeneity prompt pools to inject global context into local features; 2) Differentiable graph clustering solver that reframes edge sparsification as optimal transport to distill similarity matrices; 3) Cluster-level consistency guidance for model adaptation with dynamic decision boundary adjustment.

Result: SPEGC outperforms state-of-the-art CTTA methods on two medical image segmentation benchmarks, demonstrating superior adaptation to continuously changing domains without catastrophic forgetting.

Conclusion: The proposed SPEGC framework effectively addresses domain shift in medical image segmentation through semantic prompt enhancement and graph clustering, providing robust adaptation to changing domains while maintaining performance.

Abstract: In medical image segmentation tasks, the domain gap caused by the difference in data collection between training and testing data seriously hinders the deployment of pre-trained models in clinical practice. Continual Test-Time Adaptation (CTTA) aims to enable pre-trained models to adapt to continuously changing unlabeled domains, providing an effective approach to solving this problem. However, existing CTTA methods often rely on unreliable supervisory signals, igniting a self-reinforcing cycle of error accumulation that culminates in catastrophic performance degradation. To overcome these challenges, we propose a CTTA via Semantic-Prompt-Enhanced Graph Clustering (SPEGC) for medical image segmentation. First, we design a semantic prompt feature enhancement mechanism that utilizes decoupled commonality and heterogeneity prompt pools to inject global contextual information into local features, alleviating their susceptibility to noise interference under domain shift. Second, based on these enhanced features, we design a differentiable graph clustering solver. This solver reframes global edge sparsification as an optimal transport problem, allowing it to distill a raw similarity matrix into a refined and high-order structural representation in an end-to-end manner. Finally, this robust structural representation is used to guide model adaptation, ensuring predictions are consistent at a cluster-level and dynamically adjusting decision boundaries. Extensive experiments demonstrate that SPEGC outperforms other state-of-the-art CTTA methods on two medical image segmentation benchmarks. The source code is available at https://github.com/Jwei-Z/SPEGC-for-MIS.

Method: Uses sparse autoencoders (SAE) for high-resolution feature disentanglement, coupled neuron detection to identify non-sensitive features vulnerable to intervention, and analytical gradient orthogonalization that projects erasure vectors onto the null space of coupled neurons to decouple sensitive concepts from critical benign subspaces.

Result: Achieves high erasure precision, effectively removes harmful content while preserving generative manifold integrity, and significantly outperforms state-of-the-art baselines on safety benchmarks.

Conclusion: OrthoEraser provides an effective solution for safe concept erasure in T2I models by addressing the entanglement problem through orthogonalization, enabling precise removal of harmful content without damaging benign attributes.

Abstract: Text-to-image (T2I) models face significant safety risks from adversarial induction, yet current concept erasure methods often cause collateral damage to benign attributes when suppressing selected neurons entirely. This occurs because sensitive and benign semantics exhibit non-orthogonal superposition, sharing activation subspaces where their respective vectors are inherently entangled. To address this issue, we propose OrthoEraser, which leverages sparse autoencoders (SAE) to achieve high-resolution feature disentanglement and subsequently redefines erasure as an analytical orthogonalization projection that preserves the benign manifold’s invariance. OrthoEraser first employs SAE to decompose dense activations and segregate sensitive neurons. It then uses coupled neuron detection to identify non-sensitive features vulnerable to intervention. The key novelty lies in an analytical gradient orthogonalization strategy that projects erasure vectors onto the null space of the coupled neurons. This orthogonally decouples the sensitive concepts from the identified critical benign subspace, effectively preserving non-sensitive semantics. Experimental results on safety demonstrate that OrthoEraser achieves high erasure precision, effectively removing harmful content while preserving the integrity of the generative manifold, and significantly outperforming SOTA baselines. This paper contains results of unsafe models.

Method: Proposes ActiveFreq with two key components: 1) AcSelect module that autonomously prioritizes most informative mislabeled regions for refinement, and 2) FreqFormer segmentation backbone incorporating Fourier transform to map features from spatial to frequency domain for richer feature extraction.

Result: Achieves 3.74 NoC@90 on ISIC-2017 and 9.27 NoC@90 on OAI-ZIB datasets, with 23.5% and 12.8% improvements over previous best results. With just two clicks, reaches mIoU scores of 85.29% on ISIC-2017 and 75.76% on OAI-ZIB.

Conclusion: ActiveFreq demonstrates efficient and accurate interactive medical segmentation with reduced human intervention by integrating active learning with frequency domain analysis.

Abstract: Interactive segmentation is commonly used in medical image analysis to obtain precise, pixel-level labeling, typically involving iterative user input to correct mislabeled regions. However, existing approaches often fail to fully utilize user knowledge from interactive inputs and achieve comprehensive feature extraction. Specifically, these methods tend to treat all mislabeled regions equally, selecting them randomly for refinement without evaluating each region’s potential impact on segmentation quality. Additionally, most models rely solely on spatial domain features, overlooking frequency domain information that could enhance feature extraction and improve performance. To address these limitations, we propose ActiveFreq, a novel interactive segmentation framework that integrates active learning and frequency domain analysis to minimize human intervention while achieving high-quality labeling. ActiveFreq introduces AcSelect, an autonomous module that prioritizes the most informative mislabeled regions, ensuring maximum performance gain from each click. Moreover, we develop FreqFormer, a segmentation backbone incorporating a Fourier transform module to map features from the spatial to the frequency domain, enabling richer feature extraction. Evaluations on the ISIC-2017 and OAI-ZIB datasets demonstrate that ActiveFreq achieves high performance with reduced user interaction, achieving 3.74 NoC@90 on ISIC-2017 and 9.27 NoC@90 on OAI-ZIB, with 23.5% and 12.8% improvements over previous best results, respectively. Under minimal input conditions, such as two clicks, ActiveFreq reaches mIoU scores of 85.29% and 75.76% on ISIC-2017 and OAI-ZIB, highlighting its efficiency and accuracy in interactive medical segmentation.

Method: Gen-Fab is a conditional generative adversarial network (cGAN) based on Pix2Pix that takes design layouts (GDS format) as input and produces diverse high-resolution predictions resembling SEM images of fabricated devices. A latent noise vector is injected at the bottleneck to enable one-to-many mapping, capturing process variations at nanometer scale.

Result: Gen-Fab outperforms three baselines: deterministic U-Net (85.3% IoU), MC-Dropout U-Net (83.4% IoU), and ensemble of varied U-Nets (85.8% IoU), achieving highest IoU score of 89.8%. It also better aligns with real fabrication outcomes with lower KL divergence and Wasserstein distance, showing strong generalization to unseen geometries.

Conclusion: Gen-Fab provides an effective solution for predicting and modeling uncertainty in photonic fabrication outcomes, enabling accurate digital twins that capture the range of possible fabricated results from design layouts.

Abstract: Silicon photonic devices often exhibit fabrication-induced variations such as over-etching, underetching, and corner rounding, which can significantly alter device performance. These variations are non-uniform and are influenced by feature size and shape. Accurate digital twins are therefore needed to predict the range of possible fabricated outcomes for a given design. In this paper, we introduce Gen-Fab, a conditional generative adversarial network (cGAN) based on Pix2Pix to predict and model uncertainty in photonic fabrication outcomes. The proposed method takes a design layout (in GDS format) as input and produces diverse high-resolution predictions similar to scanning electron microscope (SEM) images of fabricated devices, capturing the range of process variations at the nanometer scale. To enable one-to-many mapping, we inject a latent noise vector at the model bottleneck. We compare Gen-Fab against three baselines: (1) a deterministic U-Net predictor, (2) an inference-time Monte Carlo Dropout U-Net, and (3) an ensemble of varied U-Nets. Evaluations on an out-of-distribution dataset of fabricated photonic test structures demonstrate that Gen-Fab outperforms all baselines in both accuracy and uncertainty modeling. An additional distribution shift analysis further confirms its strong generalization to unseen fabrication geometries. Gen-Fab achieves the highest intersection-over-union (IoU) score of 89.8%, outperforming the deterministic U-Net (85.3%), the MC-Dropout U-Net (83.4%), and varying U-Nets (85.8%). It also better aligns with the distribution of real fabrication outcomes, achieving lower Kullback-Leibler divergence and Wasserstein distance.

Method: Reformulates guidance as a local optimal control problem, yielding a closed-form Riemannian update that corrects off-manifold drift without retraining. Introduces Auto-MOG for dynamic energy-balancing guidance scheduling.

Result: MOG demonstrates superior fidelity and alignment compared to baselines with virtually no added computational overhead, effectively eliminating manual hyperparameter tuning through Auto-MOG.

Conclusion: MOG provides a geometry-aware solution to CFG’s limitations by keeping sampling trajectories on the data manifold, offering improved control for conditional diffusion models.

Abstract: Classifier-Free Guidance (CFG) serves as the de facto control mechanism for conditional diffusion, yet high guidance scales notoriously induce oversaturation, texture artifacts, and structural collapse. We attribute this failure to a geometric mismatch: standard CFG performs Euclidean extrapolation in ambient space, inadvertently driving sampling trajectories off the high-density data manifold. To resolve this, we present Manifold-Optimal Guidance (MOG), a framework that reformulates guidance as a local optimal control problem. MOG yields a closed-form, geometry-aware Riemannian update that corrects off-manifold drift without requiring retraining. Leveraging this perspective, we further introduce Auto-MOG, a dynamic energy-balancing schedule that adaptively calibrates guidance strength, effectively eliminating the need for manual hyperparameter tuning. Extensive validation demonstrates that MOG yields superior fidelity and alignment compared to baselines, with virtually no added computational overhead.

Method: Proposes FBCIR, a multi-modal focus interpretation method that identifies crucial visual and textual components for retrieval decisions. Also introduces a CIR data augmentation workflow that adds curated hard negatives to encourage balanced cross-modal reasoning.

Result: FBCIR reveals that focus imbalances are prevalent in existing CIR models, especially under hard negative settings. The proposed augmentation consistently improves performance in challenging cases while maintaining capabilities on standard benchmarks.

Conclusion: The interpretation method and data augmentation workflow provide a new perspective on CIR model diagnosis and robustness improvements, addressing focus imbalances in multi-modal reasoning.

Abstract: Composed image retrieval (CIR) requires multi-modal models to jointly reason over visual content and semantic modifications presented in text-image input pairs. While current CIR models achieve strong performance on common benchmark cases, their accuracies often degrades in more challenging scenarios where negative candidates are semantically aligned with the query image or text. In this paper, we attribute this degradation to focus imbalances, where models disproportionately attend to one modality while neglecting the other. To validate this claim, we propose FBCIR, a multi-modal focus interpretation method that identifies the most crucial visual and textual input components to a model’s retrieval decisions. Using FBCIR, we report that focus imbalances are prevalent in existing CIR models, especially under hard negative settings. Building on the analyses, we further propose a CIR data augmentation workflow that facilitates existing CIR datasets with curated hard negatives designed to encourage balanced cross-modal reasoning. Extensive experiments across multiple CIR models demonstrate that the proposed augmentation consistently improves performance in challenging cases, while maintaining their capabilities on standard benchmarks. Together, our interpretation method and data augmentation workflow provide a new perspective on CIR model diagnosis and robustness improvements.

Method: Proposes a unified UCOD framework with: 1) Multi-Cue Native Perception module integrating low-level texture cues with mid-level semantics for precise mask-object alignment; 2) Pseudo-Label Evolution Fusion using teacher-student interaction and depthwise separable convolution for semantic denoising; 3) Spectral Tensor Attention Fusion balancing semantic/structural information via compact spectral aggregation; 4) Local Pseudo-Label Refinement leveraging attention diversity for fine texture restoration and boundary enhancement.

Result: Extensive experiments on multiple UCOD datasets demonstrate state-of-the-art performance with superior detail perception, robust boundary alignment, and strong generalization under complex camouflage scenarios.

Conclusion: The proposed unified framework effectively addresses UCOD challenges by enhancing both pseudo-label reliability and feature fidelity through integrated multi-cue perception and refinement mechanisms.

Abstract: Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and their surroundings, as well as the reliance on noisy pseudo-labels that hinder fine-grained texture learning. While existing refinement strategies aim to alleviate label noise, they often overlook intrinsic perceptual cues, leading to boundary overflow and structural ambiguity. In contrast, learning without pseudo-label guidance yields coarse features with significant detail loss. To address these issues, we propose a unified UCOD framework that enhances both the reliability of pseudo-labels and the fidelity of features. Our approach introduces the Multi-Cue Native Perception module, which extracts intrinsic visual priors by integrating low-level texture cues with mid-level semantics, enabling precise alignment between masks and native object information. Additionally, Pseudo-Label Evolution Fusion intelligently refines labels through teacher-student interaction and utilizes depthwise separable convolution for efficient semantic denoising. It also incorporates Spectral Tensor Attention Fusion to effectively balance semantic and structural information through compact spectral aggregation across multi-layer attention maps. Finally, Local Pseudo-Label Refinement plays a pivotal role in local detail optimization by leveraging attention diversity to restore fine textures and enhance boundary fidelity. Extensive experiments on multiple UCOD datasets demonstrate that our method achieves state-of-the-art performance, characterized by superior detail perception, robust boundary alignment, and strong generalization under complex camouflage scenarios.

Method: MDS-VQA uses a model-informed data selection mechanism with two key components: 1) difficulty estimation via a failure predictor trained with ranking objective, and 2) diversity measurement using deep semantic video features. A greedy procedure balances difficulty and diversity under constrained labeling budgets.

Result: With only 5% selected subset per target domain, fine-tuned models improve mean SRCC from 0.651 to 0.722 and achieve top gMAD rank, demonstrating strong adaptation and generalization across multiple VQA datasets and models.

Conclusion: MDS-VQA effectively bridges the gap between model design and dataset curation by systematically identifying diverse, challenging samples that are particularly informative for active fine-tuning in video quality assessment.

Abstract: Learning-based video quality assessment (VQA) has advanced rapidly, yet progress is increasingly constrained by a disconnect between model design and dataset curation. Model-centric approaches often iterate on fixed benchmarks, while data-centric efforts collect new human labels without systematically targeting the weaknesses of existing VQA models. Here, we describe MDS-VQA, a model-informed data selection mechanism for curating unlabeled videos that are both difficult for the base VQA model and diverse in content. Difficulty is estimated by a failure predictor trained with a ranking objective, and diversity is measured using deep semantic video features, with a greedy procedure balancing the two under a constrained labeling budget. Experiments across multiple VQA datasets and models demonstrate that MDS-VQA identifies diverse, challenging samples that are particularly informative for active fine-tuning. With only a 5% selected subset per target domain, the fine-tuned model improves mean SRCC from 0.651 to 0.722 and achieves the top gMAD rank, indicating strong adaptation and generalization.

Method: 1) Depth-aware order-independent rendering scheme eliminates Gaussian depth sorting bottleneck; 2) Neural view-dependent enhancement strategy for accurate modeling of view-dependent effects; 3) First-order spherical harmonics distillation, neural vector quantization, and contribution-based pruning to compress the 3D Gaussian representation.

Result: Mobile-GS achieves real-time rendering and compact model size while preserving high visual quality, making it suitable for mobile applications. Extensive experiments demonstrate its effectiveness.

Conclusion: Mobile-GS successfully addresses the computational and storage challenges of 3DGS on mobile devices through innovative rendering optimization and compression techniques, enabling practical deployment on edge devices.

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for high-quality rendering across a wide range of applications.However, its high computational demands and large storage costs pose significant challenges for deployment on mobile devices. In this work, we propose a mobile-tailored real-time Gaussian Splatting method, dubbed Mobile-GS, enabling efficient inference of Gaussian Splatting on edge devices. Specifically, we first identify alpha blending as the primary computational bottleneck, since it relies on the time-consuming Gaussian depth sorting process. To solve this issue, we propose a depth-aware order-independent rendering scheme that eliminates the need for sorting, thereby substantially accelerating rendering. Although this order-independent rendering improves rendering speed, it may introduce transparency artifacts in regions with overlapping geometry due to the scarcity of rendering order. To address this problem, we propose a neural view-dependent enhancement strategy, enabling more accurate modeling of view-dependent effects conditioned on viewing direction, 3D Gaussian geometry, and appearance attributes. In this way, Mobile-GS can achieve both high-quality and real-time rendering. Furthermore, to facilitate deployment on memory-constrained mobile platforms, we also introduce first-order spherical harmonics distillation, a neural vector quantization technique, and a contribution-based pruning strategy to reduce the number of Gaussian primitives and compress the 3D Gaussian representation with the assistance of neural networks. Extensive experiments demonstrate that our proposed Mobile-GS achieves real-time rendering and compact model size while preserving high visual quality, making it well-suited for mobile applications.

Method: Integrates target risk levels with physically-grounded risk modeling to synthesize diverse dynamic trajectories as geometric anchors for diffusion-based video generation. Uses geometry-appearance alignment module and region-aware direct preference optimization (RA-DPO) with motion-aware masking to maintain spatial-temporal coherence and geometric fidelity.

Result: On nuScenes dataset, generates diverse long-tail scenarios with state-of-the-art visual quality, improving 3D detection mAP from 18.17 to 30.50 and reducing FID to 15.70.

Conclusion: Shifts world models from passive environment prediction to proactive, risk-controllable synthesis, providing a scalable toolchain for safety-oriented development of embodied intelligence.

Abstract: Generating safety-critical driving scenarios is crucial for evaluating and improving autonomous driving systems, but long-tail risky situations are rarely observed in real-world data and difficult to specify through manual scenario design. Existing generative approaches typically treat risk as an after-the-fact label and struggle to maintain geometric consistency in multi-view driving scenes. We present RiskMV-DPO, a general and systematic pipeline for physically-informed, risk-controllable multi-view scenario generation. By integrating target risk levels with physically-grounded risk modeling, we autonomously synthesize diverse and high-stakes dynamic trajectories that serve as explicit geometric anchors for a diffusion-based video generator. To ensure spatial-temporal coherence and geometric fidelity, we introduce a geometry-appearance alignment module and a region-aware direct preference optimization (RA-DPO) strategy with motion-aware masking to focus learning on localized dynamic regions.Experiments on the nuScenes dataset show that RiskMV-DPO can freely generate a wide spectrum of diverse long-tail scenarios while maintaining state-of-the-art visual quality, improving 3D detection mAP from 18.17 to 30.50 and reducing FID to 15.70. Our work shifts the role of world models from passive environment prediction to proactive, risk-controllable synthesis, providing a scalable toolchain for the safety-oriented development of embodied intelligence.

Method: Four-stage pipeline: 1) Hybrid Prior Construction fusing CLIP/GPT-3 textual knowledge with visual prototypes, 2) Support Set Augmentation generating intermediate samples, 3) Adaptive Distribution Rectification aligning test features, 4) Multi-Scale RBF Kernels ensemble.

Result: Achieves state-of-the-art one-shot adaptation with 65.83% average accuracy on 11 diverse benchmarks, significantly outperforming existing baselines like Tip-Adapter.

Conclusion: ReHARK demonstrates superior stability and accuracy for few-shot VLM adaptation through global regularization in RKHS, establishing new SOTA without training.

Abstract: The adaptation of large-scale Vision-Language Models (VLMs) like CLIP to downstream tasks with extremely limited data – specifically in the one-shot regime – is often hindered by a significant “Stability-Plasticity” dilemma. While efficient caching mechanisms have been introduced by training-free methods such as Tip-Adapter, these approaches often function as local Nadaraya-Watson estimators. Such estimators are characterized by inherent boundary bias and a lack of global structural regularization. In this paper, ReHARK (Refined Hybrid Adaptive RBF Kernels) is proposed as a synergistic training-free framework that reinterprets few-shot adaptation through global proximal regularization in a Reproducing Kernel Hilbert Space (RKHS). A multistage refinement pipeline is introduced, consisting of: (1) Hybrid Prior Construction, where zero-shot textual knowledge from CLIP and GPT-3 is fused with visual class prototypes to form a robust semantic-visual anchor; (2) Support Set Augmentation (Bridging), where intermediate samples are generated to smooth the transition between visual and textual modalities; (3) Adaptive Distribution Rectification, where test feature statistics are aligned with the augmented support set to mitigate domain shifts; and (4) Multi-Scale RBF Kernels, where an ensemble of kernels is employed to capture complex feature geometries across diverse scales. Superior stability and accuracy are demonstrated through extensive experiments on 11 diverse benchmarks. A new state-of-the-art for one-shot adaptation is established by ReHARK, which achieves an average accuracy of 65.83%, significantly outperforming existing baselines. Code is available at https://github.com/Jahid12012021/ReHARK.

Method: Uses a temporal Transformer to model motion dependencies within a short window of frames, confined to a sparse set of control nodes. Each node has decoupled canonical position and latent code for stable semantic anchoring. Enhanced by input masking strategy and two multi-frame losses for robustness.

Result: Achieves state-of-the-art reconstruction quality and real-time rendering speed, enabling high-fidelity reconstruction and interactive rendering of dynamic scenes.

Conclusion: Mango-GS provides an effective solution for reconstructing dynamic 3D scenes with photorealistic detail and strong temporal coherence through multi-frame optimization and node-guided motion modeling.

Abstract: Reconstructing dynamic 3D scenes with photorealistic detail and strong temporal coherence remains a significant challenge. Existing Gaussian splatting approaches for dynamic scene modeling often rely on per-frame optimization, which can overfit to instantaneous states instead of capturing underlying motion dynamics. To address this, we present Mango-GS, a multi-frame, node-guided framework for high-fidelity 4D reconstruction. Mango-GS leverages a temporal Transformer to model motion dependencies within a short window of frames, producing temporally consistent deformations. For efficiency, temporal modeling is confined to a sparse set of control nodes. Each node is represented by a decoupled canonical position and a latent code, providing a stable semantic anchor for motion propagation and preventing correspondence drift under large motion. Our framework is trained end-to-end, enhanced by an input masking strategy and two multi-frame losses to improve robustness. Extensive experiments demonstrate that Mango-GS achieves state-of-the-art reconstruction quality and real-time rendering speed, enabling high-fidelity reconstruction and interactive rendering of dynamic scenes.

Method: Introduces PCA-Enhanced Probabilistic U-Net that incorporates Principal Component Analysis for dimensionality reduction in posterior networks and uses inverse PCA to reconstruct critical information, enhancing latent space representational capacity.

Result: Achieves superior balance between segmentation accuracy and predictive variability compared to conventional generative models, while preserving ability to generate diverse segmentation hypotheses.

Conclusion: PEP U-Net advances generative modeling performance in medical image segmentation by mitigating redundancy and improving computational efficiency while maintaining uncertainty modeling capabilities.

Abstract: Ambiguous Medical Image Segmentation (AMIS) is significant to address the challenges of inherent uncertainties from image ambiguities, noise, and subjective annotations. Existing conditional variational autoencoder (cVAE)-based methods effectively capture uncertainty but face limitations including redundancy in high-dimensional latent spaces and limited expressiveness of single posterior networks. To overcome these issues, we introduce a novel PCA-Enhanced Probabilistic U-Net (\textbf{PEP U-Net}). Our method effectively incorporates Principal Component Analysis (PCA) for dimensionality reduction in the posterior network to mitigate redundancy and improve computational efficiency. Additionally, we further employ an inverse PCA operation to reconstruct critical information, enhancing the latent space’s representational capacity. Compared to conventional generative models, our method preserves the ability to generate diverse segmentation hypotheses while achieving a superior balance between segmentation accuracy and predictive variability, thereby advancing the performance of generative modeling in medical image segmentation.

Method: MANSION is a language-driven framework that generates realistic, navigable whole-building structures with vertical structural constraints. It creates diverse human-friendly scenes and includes MansionWorld dataset of 1,000+ buildings, plus a Task-Semantic Scene Editing Agent for environment customization via open-vocabulary commands.

Result: The framework successfully generates diverse building environments (hospitals, offices, etc.) and benchmarking shows state-of-the-art agents degrade sharply in these complex multi-floor settings, establishing MANSION as a critical testbed for spatial reasoning.

Conclusion: MANSION addresses the gap in building-scale embodied AI benchmarks and provides a framework for developing and evaluating cross-floor long-horizon tasks, revealing limitations of current agents in complex spatial reasoning scenarios.

Abstract: Real-world robotic tasks are long-horizon and often span multiple floors, demanding rich spatial reasoning. However, existing embodied benchmarks are largely confined to single-floor in-house environments, failing to reflect the complexity of real-world tasks. We introduce MANSION, the first language-driven framework for generating building-scale, multi-floor 3D environments. Being aware of vertical structural constraints, MANSION generates realistic, navigable whole-building structures with diverse, human-friendly scenes, enabling the development and evaluation of cross-floor long-horizon tasks. Building on this framework, we release MansionWorld, a dataset of over 1,000 diverse buildings ranging from hospitals to offices, alongside a Task-Semantic Scene Editing Agent that customizes these environments using open-vocabulary commands to meet specific user needs. Benchmarking reveals that state-of-the-art agents degrade sharply in our settings, establishing MANSION as a critical testbed for the next generation of spatial reasoning and planning.

Method: Proposes DIAE with Multimodal Aesthetic Perception (MAP) that converts ambiguous aesthetic instructions into explicit guidance using standardized aesthetic attributes and multimodal control signals. Introduces IIAEData dataset of imperfectly-paired images and a dual-branch supervision framework for weakly supervised training.

Result: DIAE outperforms baselines, achieving superior image aesthetic scores and content consistency scores in experiments.

Conclusion: The proposed DIAE framework effectively addresses image aesthetic enhancement challenges through multimodal perception and weakly supervised learning with imperfect data.

Abstract: Image aesthetic enhancement aims to perceive aesthetic deficiencies in images and perform corresponding editing operations, which is highly challenging and requires the model to possess creativity and aesthetic perception capabilities. Although recent advancements in image editing models have significantly enhanced their controllability and flexibility, they struggle with enhancing image aesthetic. The primary challenges are twofold: first, following editing instructions with aesthetic perception is difficult, and second, there is a scarcity of “perfectly-paired” images that have consistent content but distinct aesthetic qualities. In this paper, we propose Dual-supervised Image Aesthetic Enhancement (DIAE), a diffusion-based generative model with multimodal aesthetic perception. First, DIAE incorporates Multimodal Aesthetic Perception (MAP) to convert the ambiguous aesthetic instruction into explicit guidance by (i) employing detailed, standardized aesthetic instructions across multiple aesthetic attributes, and (ii) utilizing multimodal control signals derived from text-image pairs that maintain consistency within the same aesthetic attribute. Second, to mitigate the lack of “perfectly-paired” images, we collect “imperfectly-paired” dataset called IIAEData, consisting of images with varying aesthetic qualities while sharing identical semantics. To better leverage the weak matching characteristics of IIAEData during training, a dual-branch supervision framework is also introduced for weakly supervised image aesthetic enhancement. Experimental results demonstrate that DIAE outperforms the baselines and obtains superior image aesthetic scores and image content consistency scores.

Method: Uses 3,333 high-resolution geotagged images with 8,890 annotated building instances from the 2021 Midwest tornado outbreak. Compares CNN-based YOLO detectors against transformer-based RT-DETR models using a five-level damage classification framework based on IN-CORE damage states. Introduces soft ordinal classification targets and explicit ordinal-distance penalties for ordinal-aware supervision.

Result: YOLO models achieve highest detection accuracy (46.05% mAP@0.5) and throughput (66-276 FPS). RT-DETR models show stronger ordinal consistency (88.13% Ordinal Top-1 Accuracy, MAOE=0.65). RT-DETR with ordinal supervision achieves 44.70% mAP@0.5 (4.8pp improvement) and better ordinal metrics (91.15% Ordinal Top-1 Accuracy, MAOE=0.56).

Conclusion: Ordinal-aware supervision improves damage severity estimation when aligned with detector architecture, with CNN models excelling in detection accuracy/throughput and transformer models in ordinal consistency for severity grading.

Abstract: We present TornadoNet, a comprehensive benchmark for automated street-level building damage assessment evaluating how modern real-time object detection architectures and ordinal-aware supervision strategies perform under realistic post-disaster conditions. TornadoNet provides the first controlled benchmark demonstrating how architectural design and loss formulation jointly influence multi-level damage detection from street-view imagery, delivering methodological insights and deployable tools for disaster response. Using 3,333 high-resolution geotagged images and 8,890 annotated building instances from the 2021 Midwest tornado outbreak, we systematically compare CNN-based detectors from the YOLO family against transformer-based models (RT-DETR) for multi-level damage detection. Models are trained under standardized protocols using a five-level damage classification framework based on IN-CORE damage states, validated through expert cross-annotation. Baseline experiments reveal complementary architectural strengths. CNN-based YOLO models achieve highest detection accuracy and throughput, with larger variants reaching 46.05% mAP@0.5 at 66-276 FPS on A100 GPUs. Transformer-based RT-DETR models exhibit stronger ordinal consistency, achieving 88.13% Ordinal Top-1 Accuracy and MAOE of 0.65, indicating more reliable severity grading despite lower baseline mAP. To align supervision with the ordered nature of damage severity, we introduce soft ordinal classification targets and evaluate explicit ordinal-distance penalties. RT-DETR trained with calibrated ordinal supervision achieves 44.70% mAP@0.5, a 4.8 percentage-point improvement, with gains in ordinal metrics (91.15% Ordinal Top-1 Accuracy, MAOE = 0.56). These findings establish that ordinal-aware supervision improves damage severity estimation when aligned with detector architecture. Model & Data: https://github.com/crumeike/TornadoNet

Method: SVLL uses a three-stage framework: 1) decouples spatial grounding from temporal reasoning, 2) establishes robust visual dependency before introducing sequential action history, 3) introduces Bias-DPO - a novel alignment objective that maximizes likelihood on ground-truth actions while penalizing overconfident hallucinations.

Result: SVLL outperforms state-of-the-art open-source (Qwen2.5-VL-7B) and closed-source models (GPT-4o, Gemini-2.0-flash) on AI2-THOR benchmark in task success rate, while significantly reducing physical constraint violations. Also validated in real-world robotic deployments.

Conclusion: SVLL with Bias-DPO provides a robust framework for embodied task planning that ensures strict adherence to environmental affordances and suppresses physically impossible shortcuts through staged learning and improved alignment.

Abstract: Embodied task planning demands vision-language models to generate action sequences that are both visually grounded and causally coherent over time. However, existing training paradigms face a critical trade-off: joint end-to-end training often leads to premature temporal binding, while standard reinforcement learning methods suffer from optimization instability. To bridge this gap, we present Staged Vision-Language Learning (SVLL), a unified three-stage framework for robust, physically-grounded embodied planning. In the first two stages, SVLL decouples spatial grounding from temporal reasoning, establishing robust visual dependency before introducing sequential action history. In the final stage, we identify a key limitation of standard Direct Preference Optimization (DPO), its purely relative nature – optimizing only the preference gap between winning and losing trajectories while neglecting absolute likelihood constraints on optimal path, often yields unsafe or hallucinated behaviors. To address this, we further introduce Bias-DPO, a novel alignment objective that injects an inductive bias toward expert trajectories by explicitly maximizing likelihood on ground-truth actions while penalizing overconfident hallucinations. By anchoring the policy to the expert manifold and mitigating causal misalignment, SVLL, powered by Bias-DPO, ensures strict adherence to environmental affordances and effectively suppresses physically impossible shortcuts. Finally, extensive experiments on the interactive AI2-THOR benchmark and real-world robotic deployments demonstrate that SVLL outperforms both state-of-the-art open-source (e.g., Qwen2.5-VL-7B) and closed-source models (e.g., GPT-4o, Gemini-2.0-flash) in task success rate, while significantly reducing physical constraint violations.

Method: R4Det introduces three main components: 1) Panoramic Depth Fusion module that mutually reinforces absolute and relative depth estimation, 2) Deformable Gated Temporal Fusion module that operates without ego vehicle pose, and 3) Instance-Guided Dynamic Refinement module that extracts semantic prototypes from 2D instance guidance to handle cases with sparse radar data.

Result: R4Det achieves state-of-the-art 3D object detection results on the TJ4DRadSet and VoD datasets, demonstrating superior performance over existing radar-camera fusion methods.

Conclusion: R4Det effectively addresses key limitations in current radar-camera fusion approaches by improving depth estimation quality, enabling robust temporal fusion without pose dependency, and handling challenging cases with sparse radar data through instance-guided refinement.

Abstract: 4D radar-camera sensing configuration has gained increasing importance in autonomous driving. However, existing 3D object detection methods that fuse 4D Radar and camera data confront several challenges. First, their absolute depth estimation module is not robust and accurate enough, leading to inaccurate 3D localization. Second, the performance of their temporal fusion module will degrade dramatically or even fail when the ego vehicle’s pose is missing or inaccurate. Third, for some small objects, the sparse radar point clouds may completely fail to reflect from their surfaces. In such cases, detection must rely solely on visual unimodal priors. To address these limitations, we propose R4Det, which enhances depth estimation quality via the Panoramic Depth Fusion module, enabling mutual reinforcement between absolute and relative depth. For temporal fusion, we design a Deformable Gated Temporal Fusion module that does not rely on the ego vehicle’s pose. In addition, we built an Instance-Guided Dynamic Refinement module that extracts semantic prototypes from 2D instance guidance. Experiments show that R4Det achieves state-of-the-art 3D object detection results on the TJ4DRadSet and VoD datasets.

Method: 1) HTML-based automatic editing pipeline generating 330K training pairs across 15 languages; 2) Two benchmarks for evaluation; 3) Two-stage training: glyph-guided supervised fine-tuning for spatial/content priors, then multi-objective reinforcement learning for instruction adherence, text clarity, and background preservation.

Result: WeEdit outperforms previous open-source models by a clear margin across diverse text editing operations, demonstrating superior precision in text-centric image editing.

Conclusion: The systematic approach with specialized training, large-scale data, and comprehensive benchmarks effectively addresses the challenges of precise text-centric image editing.

Abstract: Instruction-based image editing aims to modify specific content within existing images according to user-provided instructions while preserving non-target regions. Beyond traditional object- and style-centric manipulation, text-centric image editing focuses on modifying, translating, or rearranging textual elements embedded within images. However, existing leading models often struggle to execute complex text editing precisely, frequently producing blurry or hallucinated characters. We attribute these failures primarily to the lack of specialized training paradigms tailored for text-centric editing, as well as the absence of large-scale datasets and standardized benchmarks necessary for a closed-loop training and evaluation system. To address these limitations, we present WeEdit, a systematic solution encompassing a scalable data construction pipeline, two benchmarks, and a tailored two-stage training strategy. Specifically, we propose a novel HTML-based automatic editing pipeline, which generates 330K training pairs covering diverse editing operations and 15 languages, accompanied by standardized bilingual and multilingual benchmarks for comprehensive evaluation. On the algorithmic side, we employ glyph-guided supervised fine-tuning to inject explicit spatial and content priors, followed by a multi-objective reinforcement learning stage to align generation with instruction adherence, text clarity, and background preservation. Extensive experiments demonstrate that WeEdit outperforms previous open-source models by a clear margin across diverse editing operations.

Method: Introduces LabanLite, a symbolic motion representation based on Labanotation that encodes atomic body-part actions as discrete symbols with textual templates. Uses LaMoGen framework where LLMs interpret motion patterns, relate them to text descriptions, and recombine symbols into executable plans.

Result: LaMoGen establishes new baselines for interpretability and controllability, outperforming prior methods on their Labanotation-based benchmark and two public datasets across symbolic, temporal, and harmony metrics.

Conclusion: Symbolic reasoning and agent-based design offer advantages for language-driven motion synthesis, enabling more interpretable, linguistically-grounded human motion generation.

Abstract: Human motion is highly expressive and naturally aligned with language, yet prevailing methods relying heavily on joint text-motion embeddings struggle to synthesize temporally accurate, detailed motions and often lack explainability. To address these limitations, we introduce LabanLite, a motion representation developed by adapting and extending the Labanotation system. Unlike black-box text-motion embeddings, LabanLite encodes each atomic body-part action (e.g., a single left-foot step) as a discrete Laban symbol paired with a textual template. This abstraction decomposes complex motions into interpretable symbol sequences and body-part instructions, establishing a symbolic link between high-level language and low-level motion trajectories. Building on LabanLite, we present LaMoGen, a Text-to-LabanLite-to-Motion Generation framework that enables large language models (LLMs) to compose motion sequences through symbolic reasoning. The LLM interprets motion patterns, relates them to textual descriptions, and recombines symbols into executable plans, producing motions that are both interpretable and linguistically grounded. To support rigorous evaluation, we introduce a Labanotation-based benchmark with structured description-motion pairs and three metrics that jointly measure text-motion alignment across symbolic, temporal, and harmony dimensions. Experiments demonstrate that LaMoGen establishes a new baseline for both interpretability and controllability, outperforming prior methods on our benchmark and two public datasets. These results highlight the advantages of symbolic reasoning and agent-based design for language-driven motion synthesis.

Method: Two-stage framework: 1) Motion Prior-Driven Initialization using 3D point tracks and motion bases for scene decomposition, 2) Geometric and Motion Constraints Refinement with learnable kinematic primitives (joint axis, pivot point, per-frame motion scalars).

Result: Achieves state-of-the-art performance on synthetic benchmarks and real-world casually captured monocular videos, significantly advancing digital twin creation under uncontrolled conditions.

Conclusion: Articulat3D enables scalable digital twin creation from monocular videos by combining motion priors with explicit geometric and kinematic constraints.

Abstract: Building high-fidelity digital twins of articulated objects from visual data remains a central challenge. Existing approaches depend on multi-view captures of the object in discrete, static states, which severely constrains their real-world scalability. In this paper, we introduce Articulat3D, a novel framework that constructs such digital twins from casually captured monocular videos by jointly enforcing explicit 3D geometric and motion constraints. We first propose Motion Prior-Driven Initialization, which leverages 3D point tracks to exploit the low-dimensional structure of articulated motion. By modeling scene dynamics with a compact set of motion bases, we facilitate soft decomposition of the scene into multiple rigidly-moving groups. Building on this initialization, we introduce Geometric and Motion Constraints Refinement, which enforces physically plausible articulation through learnable kinematic primitives parameterized by a joint axis, a pivot point, and per-frame motion scalars, yielding reconstructions that are both geometrically accurate and temporally coherent. Extensive experiments demonstrate that Articulat3D achieves state-of-the-art performance on synthetic benchmarks and real-world casually captured monocular videos, significantly advancing the feasibility of digital twin creation under uncontrolled real-world conditions. Our project page is at https://maxwell-zhao.github.io/Articulat3D.

Method: Proposes Dynamic Gradient Weighting (DyWeight), a lightweight learning-based solver with implicit coupling paradigm. Learns unconstrained time-varying parameters to adaptively aggregate historical gradients while intrinsically scaling effective step size through implicit time calibration.

Result: Achieves superior visual fidelity and stability with significantly fewer function evaluations across multiple datasets (CIFAR-10, FFHQ, AFHQv2, ImageNet64, LSUN-Bedroom) and models (Stable Diffusion, FLUX.1-dev).

Conclusion: DyWeight establishes new state-of-the-art among efficient diffusion solvers by better aligning numerical trajectory with model’s denoising dynamics under large integration steps.

Abstract: Diffusion Models (DMs) have achieved state-of-the-art generative performance across multiple modalities, yet their sampling process remains prohibitively slow due to the need for hundreds of function evaluations. Recent progress in multi-step ODE solvers has greatly improved efficiency by reusing historical gradients, but existing methods rely on handcrafted coefficients that fail to adapt to the non-stationary dynamics of diffusion sampling. To address this limitation, we propose Dynamic Gradient Weighting (DyWeight), a lightweight, learning-based multi-step solver that introduces a streamlined implicit coupling paradigm. By relaxing classical numerical constraints, DyWeight learns unconstrained time-varying parameters that adaptively aggregate historical gradients while intrinsically scaling the effective step size. This implicit time calibration accurately aligns the solver’s numerical trajectory with the model’s internal denoising dynamics under large integration steps, avoiding complex decoupled parameterizations and optimizations. Extensive experiments on CIFAR-10, FFHQ, AFHQv2, ImageNet64, LSUN-Bedroom, Stable Diffusion and FLUX.1-dev demonstrate that DyWeight achieves superior visual fidelity and stability with significantly fewer function evaluations, establishing a new state-of-the-art among efficient diffusion solvers. Code is available at https://github.com/Westlake-AGI-Lab/DyWeight

Method: Proposes SemiTooth framework with: 1) MS3Toothset dataset compilation with multi-source CBCT data and different-level annotations, 2) Multi-teacher multi-student architecture where distinct student networks learn from unlabeled data from different sources supervised by respective teachers, 3) Stricter Weighted-Confidence Constraint for multiple teachers to improve multi-source accuracy.

Result: Extensive experiments on MS3Toothset verify feasibility and superiority, achieving state-of-the-art performance in semi-supervised multi-source tooth segmentation scenario.

Conclusion: SemiTooth provides a generalizable semi-supervised framework that effectively addresses multi-source data challenges in clinical dental CBCT segmentation through innovative multi-teacher multi-student approach with confidence constraints.

Abstract: With the rapid advancement of artificial intelligence, intelligent dentistry for clinical diagnosis and treatment has become increasingly promising. As the primary clinical dentistry task, tooth structure segmentation for Cone-Beam Computed Tomography (CBCT) has made significant progress in recent years. However, challenges arise from the obtainment difficulty of full-annotated data, and the acquisition variability of multi-source data across different institutions, which have caused low-quality utilization, voxel-level inconsistency, and domain-specific disparity in CBCT slices. Thus, the rational and efficient utilization of multi-source and unlabeled data represents a pivotal problem. In this paper, we propose SemiTooth, a generalizable semi-supervised framework for multi-source tooth segmentation. Specifically, we first compile MS3Toothset, Multi-Source Semi-Supervised Tooth DataSet for clinical dental CBCT, which contains data from three sources with different-level annotations. Then, we design a multi-teacher and multi-student framework, i.e., SemiTooth, which promotes semi-supervised learning for multi-source data. SemiTooth employs distinct student networks that learn from unlabeled data with different sources, supervised by its respective teachers. Furthermore, a Stricter Weighted-Confidence Constraint is introduced for multiple teachers to improve the multi-source accuracy.Extensive experiments are conducted on MS3Toothset to verify the feasibility and superiority of the SemiTooth framework, which achieves SOTA performance on the semi-supervised and multi-source tooth segmentation scenario.

Method: 1) Multi-view prompts with unbalanced optimal transport for fine-grained patch-to-prompt correspondence while suppressing unreliable regions; 2) Bi-directional prompt design capturing complementary clean-oriented and noise-aware cues; 3) Alignment-guided selective refinement using optimal transport to correct only mislabeled samples while retaining reliable data.

Result: Experiments on synthetic and real-world noisy benchmarks show NA-MVP consistently outperforms state-of-the-art baselines, confirming its effectiveness for robust few-shot learning under noisy supervision.

Conclusion: NA-MVP enables robust few-shot learning in vision-language models by shifting from global matching to region-aware alignment that explicitly distinguishes clean cues from noisy ones through bi-directional multi-view prompt alignment.

Abstract: Vision-language models offer strong few-shot capability through prompt tuning but remain vulnerable to noisy labels, which can corrupt prompts and degrade cross-modal alignment. Existing approaches struggle because they often lack the ability to model fine-grained semantic cues and to adaptively separate clean from noisy signals. To address these challenges, we propose NA-MVP, a framework for Noise-Aware few-shot learning through bi-directional Multi-View Prompt alignment. NA-MVP is built upon a key conceptual shift: robust prompt learning requires moving from global matching to region-aware alignment that explicitly distinguishes clean cues from noisy ones. To realize this, NA-MVP employs (1) multi-view prompts combined with unbalanced optimal transport to achieve fine-grained patch-to-prompt correspondence while suppressing unreliable regions; (2) a bi-directional prompt design that captures complementary clean-oriented and noise-aware cues, enabling the model to focus on stable semantics; and (3) an alignment-guided selective refinement strategy that uses optimal transport to correct only mislabeled samples while retaining reliable data. Experiments on synthetic and real-world noisy benchmarks demonstrate that NA-MVP consistently outperforms state-of-the-art baselines, confirming its effectiveness in enabling robust few-shot learning under noisy supervision.

Method: Reformulates pseudo-label generation as Fused Gromov-Wasserstein problem jointly optimizing inter-feature similarity and intra-structural consistency. Uses 3D foundation model to define intra-structure in geometric space. Approximates computationally prohibitive FGW through anchor-based linearization. Uses soft-target loss blending guidance from probabilistic transport plan with network predictions.

Result: Achieves state-of-the-art performance on SPair-71k and AP-10k datasets, establishing new benchmark in semantic correspondence without explicit geometric annotations.

Conclusion: SoY successfully addresses geometric ambiguities in semantic correspondence by leveraging 3D foundation models and FGW optimization, enabling robust unsupervised learning without explicit geometric annotations.

Abstract: Semantic correspondence is essential for handling diverse in-the-wild images lacking explicit correspondence annotations. While recent 2D foundation models offer powerful features, adapting them for unsupervised learning via nearest-neighbor pseudo-labels has key limitations: it operates locally, ignoring structural relationships, and consequently its reliance on 2D appearance fails to resolve geometric ambiguities arising from symmetries or repetitive features. In this work, we address this by reformulating pseudo-label generation as a Fused Gromov-Wasserstein (FGW) problem, which jointly optimizes inter-feature similarity and intra-structural consistency. Our framework, Shape-of-You (SoY), leverages a 3D foundation model to define this intra-structure in the geometric space, resolving abovementioned ambiguity. However, since FGW is a computationally prohibitive quadratic problem, we approximate it through anchor-based linearization. The resulting probabilistic transport plan provides a structurally consistent but noisy supervisory signal. Thus, we introduce a soft-target loss dynamically blending guidance from this plan with network predictions to build a learning framework robust to this noise. SoY achieves state-of-the-art performance on SPair-71k and AP-10k datasets, establishing a new benchmark in semantic correspondence without explicit geometric annotations. Code is available at Shape-of-You.

Method: Two-stage hierarchical token pruning: 1) Inter-slice Anchor-based Filtering to eliminate slice-level temporal redundancy, 2) Dynamic Information Nucleus Selection using cumulative attention weights for adaptive token-level compression.

Result: Extensive experiments on three 3D medical benchmarks across diverse medical VLMs show massive token redundancy; MedPruner enables models like MedGemma to maintain or exceed original performance while retaining <5% of visual tokens.

Conclusion: MedPruner validates the necessity of dynamic token selection for practical clinical deployment of 3D medical VLMs, drastically reducing computational overhead while preserving performance.

Abstract: While specialized Medical Vision-Language Models (VLMs) have achieved remarkable success in interpreting 2D and 3D medical modalities, their deployment for 3D volumetric data remains constrained by significant computational inefficiencies. Current architectures typically suffer from massive anatomical redundancy due to the direct concatenation of consecutive 2D slices and lack the flexibility to handle heterogeneous information densities across different slices using fixed pruning ratios. To address these challenges, we propose MedPruner, a training-free and model-agnostic hierarchical token pruning framework specifically designed for efficient 3D medical image understanding. MedPruner introduces a two-stage mechanism: an Inter-slice Anchor-based Filtering module to eliminate slice-level temporal redundancy, followed by a Dynamic Information Nucleus Selection strategy that achieves adaptive token-level compression by quantifying cumulative attention weights. Extensive experiments on three 3D medical benchmarks and across three diverse medical VLMs reveal massive token redundancy in existing architectures. Notably, MedPruner enables models such as MedGemma to maintain or even exceed their original performance while retaining fewer than 5% of visual tokens, thereby drastically reducing computational overhead and validating the necessity of dynamic token selection for practical clinical deployment. Our code will be released.

Method: Built largest PET dataset (11041 scans, 59831 masks), developed SegAnyPET with 3D architecture and prompt engineering strategy for mask generation, enabling universal organ/lesion segmentation with human-in-the-loop workflow.

Result: Achieves strong zero-shot performance across multi-center, multi-tracer, multi-disease datasets, demonstrating general-purpose applicability to diverse segmentation tasks.

Conclusion: SegAnyPET represents a foundational model that can advance clinical applications of molecular imaging through universal PET segmentation capabilities.

Abstract: Positron emission tomography (PET) is a key nuclear medicine imaging modality that visualizes radiotracer distributions to quantify in vivo physiological and metabolic processes, playing an irreplaceable role in disease management. Despite its clinical importance, the development of deep learning models for quantitative PET image analysis remains severely limited, driven by both the inherent segmentation challenge from PET’s paucity of anatomical contrast and the high costs of data acquisition and annotation. To bridge this gap, we develop generalist foundational models for universal segmentation from 3D whole-body PET imaging. We first build the largest and most comprehensive PET dataset to date, comprising 11041 3D whole-body PET scans with 59831 segmentation masks for model development. Based on this dataset, we present SegAnyPET, an innovative foundational model with general-purpose applicability to diverse segmentation tasks. Built on a 3D architecture with a prompt engineering strategy for mask generation, SegAnyPET enables universal and scalable organ and lesion segmentation, supports efficient human correction with minimal effort, and enables a clinical human-in-the-loop workflow. Extensive evaluations on multi-center, multi-tracer, multi-disease datasets demonstrate that SegAnyPET achieves strong zero-shot performance across a wide range of segmentation tasks, highlighting its potential to advance the clinical applications of molecular imaging.

Method: Training-free framework using Multi-Diffusion process in 3D latent space with adaptive weighting strategies (attention-entropy weighting and visibility weighting) for confidence-aware multi-view fusion, plus physics-aware optimization with collision and contact constraints.

Result: Significant improvements in reconstruction fidelity and layout plausibility on standard benchmarks and real-world multi-object scenes without additional training.

Conclusion: MV-SAM3D successfully addresses limitations of single-view 3D generation by enabling multi-view consistency and physical plausibility in layout-aware 3D scene generation.

Abstract: Recent unified 3D generation models have made remarkable progress in producing high-quality 3D assets from a single image. Notably, layout-aware approaches such as SAM3D can reconstruct multiple objects while preserving their spatial arrangement, opening the door to practical scene-level 3D generation. However, current methods are limited to single-view input and cannot leverage complementary multi-view observations, while independently estimated object poses often lead to physically implausible layouts such as interpenetration and floating artifacts. We present MV-SAM3D, a training-free framework that extends layout-aware 3D generation with multi-view consistency and physical plausibility. We formulate multi-view fusion as a Multi-Diffusion process in 3D latent space and propose two adaptive weighting strategies – attention-entropy weighting and visibility weighting – that enable confidence-aware fusion, ensuring each viewpoint contributes according to its local observation reliability. For multi-object composition, we introduce physics-aware optimization that injects collision and contact constraints both during and after generation, yielding physically plausible object arrangements. Experiments on standard benchmarks and real-world multi-object scenes demonstrate significant improvements in reconstruction fidelity and layout plausibility, all without any additional training. Code is available at https://github.com/devinli123/MV-SAM3D.

Method: Introduces discrete room-instance tokens to create a unified vocabulary bridging layouts and symbolic reasoning. Uses multimodal alignment and instruction tuning to enable the model to synthesize coherent, controllable layouts from text instructions.

Result: The framework achieves superior geometric validity and controllability while remaining efficient and locally deployable, demonstrating improved performance over existing approaches.

Conclusion: HouseMind successfully addresses the challenges of floor plan design by unifying understanding, generation, and editing in a single multimodal LLM framework with enhanced spatial reasoning capabilities.

Abstract: Architectural floor plan design demands joint reasoning over geometry, semantics, and spatial hierarchy, which remains a major challenge for current AI systems. Although recent diffusion and language models improve visual fidelity, they still struggle with coherent spatial reasoning and controllable generation. We present HouseMind, a multimodal large language model that unifies floor plan understanding, generation, and editing in one framework. We introduce discrete room-instance tokens to construct a unified vocabulary that bridges layouts and symbolic reasoning. With multimodal alignment and instruction tuning, the model synthesizes coherent, controllable layouts from text instructions. Experiments show how the framework achieves superior geometric validity and controllability while remaining efficient and locally deployable.

Method: Individual-aware Multimodal Depression-related Representation Learning Framework (IDRL) with two key components: 1) Disentangles multimodal representations into modality-common depression space, modality-specific depression space, and depression-unrelated space to enhance alignment while suppressing irrelevant information; 2) Individual-aware modality-fusion module (IAF) that dynamically adjusts weights of disentangled depression-related features based on predictive significance for adaptive cross-modal fusion per individual.

Result: Extensive experiments demonstrate that IDRL achieves superior and robust performance for multimodal depression detection compared to existing methods.

Conclusion: The proposed IDRL framework effectively addresses inter-modal inconsistency and individual differences in multimodal depression detection through representation disentanglement and adaptive fusion, leading to improved diagnostic performance.

Abstract: Depression is a severe mental disorder, and reliable identification plays a critical role in early intervention and treatment. Multimodal depression detection aims to improve diagnostic performance by jointly modeling complementary information from multiple modalities. Recently, numerous multimodal learning approaches have been proposed for depression analysis; however, these methods suffer from the following limitations: 1) inter-modal inconsistency and depression-unrelated interference, where depression-related cues may conflict across modalities while substantial irrelevant content obscures critical depressive signals, and 2) diverse individual depressive presentations, leading to individual differences in modality and cue importance that hinder reliable fusion. To address these issues, we propose Individual-aware Multimodal Depression-related Representation Learning Framework (IDRL) for robust depression diagnosis. Specifically, IDRL 1) disentangles multimodal representations into a modality-common depression space, a modality-specific depression space, and a depression-unrelated space to enhance modality alignment while suppressing irrelevant information, and 2) introduces an individual-aware modality-fusion module (IAF) that dynamically adjusts the weights of disentangled depression-related features based on their predictive significance, thereby achieving adaptive cross-modal fusion for different individuals. Extensive experiments demonstrate that IDRL achieves superior and robust performance for multimodal depression detection.

Method: Created FL-MedSegBench with 9 segmentation tasks across 10 imaging modalities (2D/3D) with realistic clinical heterogeneity. Systematically evaluated 8 generic FL and 5 personalized FL methods across multiple dimensions: segmentation accuracy, fairness, communication efficiency, convergence behavior, and generalization to unseen domains.

Result: Key findings: (1) Personalized FL methods (especially FedBN with client-specific batch normalization) outperform generic approaches; (2) No single method universally dominates; (3) Normalization-based personalization methods are robust to reduced communication frequency; (4) Methods like Ditto and FedRDN protect underperforming clients; (5) Generalization to unseen domains correlates with performance across participating clients.

Conclusion: FL-MedSegBench provides the first comprehensive benchmark for medical image segmentation in federated learning, offering empirically grounded guidelines for real-world clinical deployment and an open-source toolkit to accelerate reproducible research.

Abstract: Federated learning (FL) offers a privacy-preserving paradigm for collaborative medical image analysis without sharing raw data. However, the absence of standardized benchmarks for medical image segmentation hinders fair and comprehensive evaluation of FL methods. To address this gap, we introduce FL-MedSegBench, the first comprehensive benchmark for federated learning on medical image segmentation. Our benchmark encompasses nine segmentation tasks across ten imaging modalities, covering both 2D and 3D formats with realistic clinical heterogeneity. We systematically evaluate eight generic FL (gFL) and five personalized FL (pFL) methods across multiple dimensions: segmentation accuracy, fairness, communication efficiency, convergence behavior, and generalization to unseen domains. Extensive experiments reveal several key insights: (i) pFL methods, particularly those with client-specific batch normalization (\textit{e.g.}, FedBN), consistently outperform generic approaches; (ii) No single method universally dominates, with performance being dataset-dependent; (iii) Communication frequency analysis shows normalization-based personalization methods exhibit remarkable robustness to reduced communication frequency; (iv) Fairness evaluation identifies methods like Ditto and FedRDN that protect underperforming clients; (v) A method’s generalization to unseen domains is strongly tied to its ability to perform well across participating clients. We will release an open-source toolkit to foster reproducible research and accelerate clinically applicable FL solutions, providing empirically grounded guidelines for real-world clinical deployment. The source code is available at https://github.com/meiluzhu/FL-MedSegBench.

Method: Created OSCBench from instructional cooking data, organizing action-object interactions into regular, novel, and compositional scenarios. Evaluated six T2V models using both human studies and MLLM-based automatic evaluation.

Result: Despite strong performance on semantic and scene alignment, current T2V models consistently struggle with accurate and temporally consistent object state changes, especially in novel and compositional settings.

Conclusion: Object state change is a key bottleneck in text-to-video generation, and OSCBench serves as a diagnostic benchmark for advancing state-aware video generation models.

Abstract: Text-to-video (T2V) generation models have made rapid progress in producing visually high-quality and temporally coherent videos. However, existing benchmarks primarily focus on perceptual quality, text-video alignment, or physical plausibility, leaving a critical aspect of action understanding largely unexplored: object state change (OSC) explicitly specified in the text prompt. OSC refers to the transformation of an object’s state induced by an action, such as peeling a potato or slicing a lemon. In this paper, we introduce OSCBench, a benchmark specifically designed to assess OSC performance in T2V models. OSCBench is constructed from instructional cooking data and systematically organizes action-object interactions into regular, novel, and compositional scenarios to probe both in-distribution performance and generalization. We evaluate six representative open-source and proprietary T2V models using both human user study and multimodal large language model (MLLM)-based automatic evaluation. Our results show that, despite strong performance on semantic and scene alignment, current T2V models consistently struggle with accurate and temporally consistent object state changes, especially in novel and compositional settings. These findings position OSC as a key bottleneck in text-to-video generation and establish OSCBench as a diagnostic benchmark for advancing state-aware video generation models.

Method: Uses progressive input masking to observe attention patterns: backdoored images show abrupt attention shifts from trigger to benign content, causing embedding changes. Applies DBSCAN clustering on embedding sequences along masking trajectory to detect backdoors.

Result: Consistently outperforms existing defenses across diverse attack types, datasets, and model families. Works as plug-and-play with no retraining, compatible with CNNs, ViTs, CLIP, and LLaVA-1.5.

Conclusion: BackdoorIDS provides effective zero-shot detection for backdoor attacks in vision encoders, offering practical security for downstream applications using third-party models.

Abstract: Self-supervised and multimodal vision encoders learn strong visual representations that are widely adopted in downstream vision tasks and large vision-language models (LVLMs). However, downstream users often rely on third-party pretrained encoders with uncertain provenance, exposing them to backdoor attacks. In this work, we propose BackdoorIDS, a simple yet effective zero-shot, inference-time backdoor samples detection method for pretrained vision encoders. BackdoorIDS is motivated by two observations: Attention Hijacking and Restoration. Under progressive input masking, a backdoored image initially concentrates attention on malicious trigger features. Once the masking ratio exceeds the trigger’s robustness threshold, the trigger is deactivated, and attention rapidly shifts to benign content. This transition induces a pronounced change in the image embedding, whereas embeddings of clean images evolve more smoothly across masking progress. BackdoorIDS operationalizes this signal by extracting an embedding sequence along the masking trajectory and applying density-based clustering such as DBSCAN. An input is flagged as backdoored if its embedding sequence forms more than one cluster. Extensive experiments show that BackdoorIDS consistently outperforms existing defenses across diverse attack types, datasets, and model families. Notably, it is a plug-and-play approach that requires no retraining and operates fully zero-shot at inference time, making it compatible with a wide range of encoder architectures, including CNNs, ViTs, CLIP, and LLaVA-1.5.

Method: Proposes a memory-anchored streaming video reasoning framework with three-stage, multi-round chain-of-thought dataset, stage-matched training strategy, segment-level streaming causal mask, streaming positional encoding, and inference pipeline that overlaps watching and thinking with adaptive attention backend selection.

Result: Built on Qwen3-VL, improves single-round accuracy by 2.6% on StreamingBench and 3.79% on OVO-Bench; maintains performance in multi-round setting while reducing output tokens by 56%.

Conclusion: Think While Watching enables efficient streaming video reasoning with concurrent perception-generation, addressing memory decay and improving performance on both single-round and multi-round streaming video understanding tasks.

Abstract: Multimodal large language models (MLLMs) have shown strong performance on offline video understanding, but most are limited to offline inference or have weak online reasoning, making multi-turn interaction over continuously arriving video streams difficult. Existing streaming methods typically use an interleaved perception-generation paradigm, which prevents concurrent perception and generation and leads to early memory decay as streams grow, hurting long-range dependency modeling. We propose Think While Watching, a memory-anchored streaming video reasoning framework that preserves continuous segment-level memory during multi-turn interaction. We build a three-stage, multi-round chain-of-thought dataset and adopt a stage-matched training strategy, while enforcing strict causality through a segment-level streaming causal mask and streaming positional encoding. During inference, we introduce an efficient pipeline that overlaps watching and thinking and adaptively selects the best attention backend. Under both single-round and multi-round streaming input protocols, our method achieves strong results. Built on Qwen3-VL, it improves single-round accuracy by 2.6% on StreamingBench and by 3.79% on OVO-Bench. In the multi-round setting, it maintains performance while reducing output tokens by 56%. Code is available at: https://github.com/wl666hhh/Think_While_Watching/

Method: PROMO uses a Flow Matching DiT backbone with latent multi-modal conditional concatenation. It leverages conditioning efficiency and self-reference mechanisms to reduce inference overhead. The framework treats VTON as a structured image editing problem and uses paired VTON data as supervisory resource for training general-purpose editors.

Result: On standard benchmarks, PROMO surpasses both prior VTON methods and general image editing models in visual fidelity while delivering a competitive balance between quality and speed.

Conclusion: Flow-matching transformers with latent multi-modal conditioning and self-reference acceleration offer an effective and training-efficient solution for high-quality virtual try-on, demonstrating that the approach can transfer to broader image editing tasks.

Abstract: Virtual Try-on (VTON) has become a core capability for online retail, where realistic try-on results provide reliable fit guidance, reduce returns, and benefit both consumers and merchants. Diffusion-based VTON methods achieve photorealistic synthesis, yet often rely on intricate architectures such as auxiliary reference networks and suffer from slow sampling, making the trade-off between fidelity and efficiency a persistent challenge. We approach VTON as a structured image editing problem that demands strong conditional generation under three key requirements: subject preservation, faithful texture transfer, and seamless harmonization. Under this perspective, our training framework is generic and transfers to broader image editing tasks. Moreover, the paired data produced by VTON constitutes a rich supervisory resource for training general-purpose editors. We present PROMO, a promptable virtual try-on framework built upon a Flow Matching DiT backbone with latent multi-modal conditional concatenation. By leveraging conditioning efficiency and self-reference mechanisms, our approach substantially reduces inference overhead. On standard benchmarks, PROMO surpasses both prior VTON methods and general image editing models in visual fidelity while delivering a competitive balance between quality and speed. These results demonstrate that flow-matching transformers, coupled with latent multi-modal conditioning and self-reference acceleration, offer an effective and training-efficient solution for high-quality virtual try-on.

Method: UCAN unifies convolution and attention with window-based spatial attention and Hedgehog Attention for local texture and long-range dependencies, plus a distillation-based large-kernel module for high-frequency structure preservation, and uses cross-layer parameter sharing to reduce complexity.

Result: On Manga109 (4×), UCAN-L achieves 31.63 dB PSNR with only 48.4G MACs, surpassing recent lightweight models. On BSDS100, UCAN attains 27.79 dB, outperforming methods with significantly larger models.

Conclusion: UCAN achieves superior trade-off between accuracy, efficiency, and scalability for practical high-resolution image restoration, making it well-suited for deployment on resource-constrained devices.

Abstract: Hybrid CNN-Transformer architectures achieve strong results in image super-resolution, but scaling attention windows or convolution kernels significantly increases computational cost, limiting deployment on resource-constrained devices. We present UCAN, a lightweight network that unifies convolution and attention to expand the effective receptive field efficiently. UCAN combines window-based spatial attention with a Hedgehog Attention mechanism to model both local texture and long-range dependencies, and introduces a distillation-based large-kernel module to preserve high-frequency structure without heavy computation. In addition, we employ cross-layer parameter sharing to further reduce complexity. On Manga109 ($4\times$), UCAN-L achieves 31.63 dB PSNR with only 48.4G MACs, surpassing recent lightweight models. On BSDS100, UCAN attains 27.79 dB, outperforming methods with significantly larger models. Extensive experiments show that UCAN achieves a superior trade-off between accuracy, efficiency, and scalability, making it well-suited for practical high-resolution image restoration.

Method: Proposes PolyCrysDiff, a framework based on conditional latent diffusion for end-to-end generation of computable 3D polycrystalline microstructures. The method uses diffusion models to generate realistic grain morphologies, orientation distributions, and 3D spatial correlations while allowing control over grain attributes like size and sphericity.

Result: PolyCrysDiff achieves R² > 0.972 on grain attribute control and outperforms mainstream approaches like Markov random field (MRF)- and convolutional neural network (CNN)-based methods. Generated microstructures are validated through crystal plasticity finite element method (CPFEM) simulations, demonstrating physical validity and computability.

Conclusion: The framework enables systematic study of how grain-level microstructural characteristics affect mechanical properties, paving the way for accelerated, data-driven optimization and design of polycrystalline materials.

Abstract: The three-dimensional (3D) microstructures of polycrystalline materials exert a critical influence on their mechanical and physical properties. Realistic, controllable construction of these microstructures is a key step toward elucidating structure-property relationships, yet remains a formidable challenge. Herein, we propose PolyCrysDiff, a framework based on conditional latent diffusion that enables the end-to-end generation of computable 3D polycrystalline microstructures. Comprehensive qualitative and quantitative evaluations demonstrate that PolyCrysDiff faithfully reproduces target grain morphologies, orientation distributions, and 3D spatial correlations, while achieving an $R^2$ over 0.972 on grain attributes (e.g., size and sphericity) control, thereby outperforming mainstream approaches such as Markov random field (MRF)- and convolutional neural network (CNN)-based methods. The computability and physical validity of the generated microstructures are verified through a series of crystal plasticity finite element method (CPFEM) simulations. Leveraging PolyCrysDiff’s controllable generative capability, we systematically elucidate how grain-level microstructural characteristics affect the mechanical properties of polycrystalline materials. This development is expected to pave a key step toward accelerated, data-driven optimization and design of polycrystalline materials.

Method: Proposes Confidence-Driven Reinforcement Learning (CDRL) using original-noise image pairs and confidence-based rewards to enhance perceptual sensitivity and calibrate confidence. Also introduces Confidence-Aware Test-Time Scaling (CA-TTS) with Self-Consistency, Self-Reflection, and Visual Self-Check modules dynamically coordinated by confidence signals, using an Expert Model as Planner, Critic, and Voter.

Result: Achieves state-of-the-art results with consistent 8.8% gains across four benchmarks. Ablation studies confirm effectiveness of each module and demonstrate scaling superiority.

Conclusion: The proposed confidence calibration framework significantly improves MLLM reliability by addressing uncertainty awareness, with both training-time and test-time benefits for multimodal understanding.

Abstract: Recent advances in Multi-modal Large Language Models (MLLMs) have predominantly focused on enhancing visual perception to improve accuracy. However, a critical question remains unexplored: Do models know when they do not know? Through a probing experiment, we reveal a severe confidence miscalibration problem in MLLMs. To address this, we propose Confidence-Driven Reinforcement Learning (CDRL), which uses original-noise image pairs and a novel confidence-based reward to enhance perceptual sensitivity and robustly calibrate the model’s confidence. Beyond training benefits, calibrated confidence enables more effective test-time scaling as a free lunch. We further propose Confidence-Aware Test-Time Scaling (CA-TTS), which dynamically coordinates Self-Consistency, Self-Reflection, and Visual Self-Check modules guided by confidence signals. An Expert Model acts in multiple roles (e.g., Planner, Critic, Voter) to schedule these modules and provide external verification. Our integrated framework establishes new state-of-the-art results with consistent 8.8% gains across four benchmarks. More ablation studies demonstrate the effectiveness of each module and scaling superiority.

Method: Enhanced YOLO11 architecture with attention mechanisms: Squeeze-and-Excitation blocks replace convolutional blocks, redesigned backbone with attention, CARAFE upsampling, Simple Attention Modules for feature aggregation, and Parallel Hybrid Attention Mechanisms for channel/spatial/coordinate-wise attention.

Result: COTONET achieves mAP50 of 81.1% and mAP50-95 of 60.6%, outperforming standard YOLO baselines while using only 7.6M parameters and 27.8 GFLOPS, making it suitable for low-resource edge computing.

Conclusion: The attention-enhanced YOLO11 model provides robust cotton capsule detection across growth stages, enabling automated harvesting systems that can preserve cotton quality through delicate robotic manipulation.

Abstract: Cotton harvesting is a critical phase where cotton capsules are physically manipulated and can lead to fibre degradation. To maintain the highest quality, harvesting methods must emulate delicate manual grasping, to preserve cotton’s intrinsic properties. Automating this process requires systems capable of recognising cotton capsules across various phenological stages. To address this challenge, we propose COTONET, an enhanced custom YOLO11 model tailored with attention mechanisms to improve the detection of difficult instances. The architecture incorporates gradients in non-learnable operations to enhance shape and feature extraction. Key architectural modifications include: the replacement of convolutional blocks with Squeeze-and-Exitation blocks, a redesigned backbone integrating attention mechanisms, and the substitution of standard upsampling operations for Content Aware Reassembly of Features (CARAFE). Additionally, we integrate Simple Attention Modules (SimAM) for primary feature aggregation and Parallel Hybrid Attention Mechanisms (PHAM) for channel-wise, spatial-wise and coordinate-wise attention in the downward neck path. This configuration offers increased flexibility and robustness for interpreting the complexity of cotton crop growth. COTONET aligns with small-to-medium YOLO models utilizing 7.6M parameters and 27.8 GFLOPS, making it suitable for low-resource edge computing and mobile robotics. COTONET outperforms the standard YOLO baselines, achieving a mAP50 of 81.1% and a mAP50-95 of 60.6%.

Method: Dual-branch Vision Transformer with cross-resolution attention to fuse global meteorological data (25km) with local high-resolution PM2.5 data (1km). Introduces elevation-aware self-attention and wind-guided cross-attention to enforce physical consistency. Fully trainable and memory-efficient architecture.

Result: Generates complete 29-million-pixel European map in 1.8 seconds on a single GPU. Reduces RMSE by 4.7% at T+1 and 10.7% at T+3 compared to best single-scale baseline. Reduces bias in complex terrain by 36% in daily PM2.5 forecasting throughout Europe in 2022.

Conclusion: CRAN-PM demonstrates efficient, physically-consistent continent-scale environmental forecasting using Vision Transformers with cross-resolution attention and physics-guided attention mechanisms, enabling practical real-world environmental monitoring applications.

Abstract: Vision Transformers have achieved remarkable success in spatio-temporal prediction, but their scalability remains limited for ultra-high-resolution, continent-scale domains required in real-world environmental monitoring. A single European air-quality map at 1 km resolution comprises 29 million pixels, far beyond the limits of naive self-attention. We introduce CRAN-PM, a dual-branch Vision Transformer that leverages cross-resolution attention to efficiently fuse global meteorological data (25 km) with local high-resolution PM2.5 at the current time (1 km). Instead of including physically driven factors like temperature and topography as input, we further introduce elevation-aware self-attention and wind-guided cross-attention to force the network to learn physically consistent feature representations for PM2.5 forecasting. CRAN-PM is fully trainable and memory-efficient, generating the complete 29-million-pixel European map in 1.8 seconds on a single GPU. Evaluated on daily PM2.5 forecasting throughout Europe in 2022 (362 days, 2,971 European Environment Agency (EEA) stations), it reduces RMSE by 4.7% at T+1 and 10.7% at T+3 compared to the best single-scale baseline, while reducing bias in complex terrain by 36%.

Method: Proposes Endogenous Chain-of-Thought (EndoCoT) with two components: 1) iterative thought guidance module that refines latent thought states through MLLM reasoning, and 2) terminal thought grounding module that aligns final state with ground-truth answers to maintain textual supervision.

Result: Achieves 92.1% average accuracy across diverse benchmarks (Maze, TSP, VSP, Sudoku), outperforming strongest baseline by 8.3 percentage points.

Conclusion: EndoCoT enables MLLMs to provide meticulously reasoned guidance that diffusion models can execute progressively, solving complex tasks in a step-by-step manner through activated reasoning potential.

Abstract: Recently, Multimodal Large Language Models (MLLMs) have been widely integrated into diffusion frameworks primarily as text encoders to tackle complex tasks such as spatial reasoning. However, this paradigm suffers from two critical limitations: (i) MLLMs text encoder exhibits insufficient reasoning depth. Single-step encoding fails to activate the Chain-of-Thought process, which is essential for MLLMs to provide accurate guidance for complex tasks. (ii) The guidance remains invariant during the decoding process. Invariant guidance during decoding prevents DiT from progressively decomposing complex instructions into actionable denoising steps, even with correct MLLM encodings. To this end, we propose Endogenous Chain-of-Thought (EndoCoT), a novel framework that first activates MLLMs’ reasoning potential by iteratively refining latent thought states through an iterative thought guidance module, and then bridges these states to the DiT’s denoising process. Second, a terminal thought grounding module is applied to ensure the reasoning trajectory remains grounded in textual supervision by aligning the final state with ground-truth answers. With these two components, the MLLM text encoder delivers meticulously reasoned guidance, enabling the DiT to execute it progressively and ultimately solve complex tasks in a step-by-step manner. Extensive evaluations across diverse benchmarks (e.g., Maze, TSP, VSP, and Sudoku) achieve an average accuracy of 92.1%, outperforming the strongest baseline by 8.3 percentage points.

Method: Created VTEdit-Bench with 24,220 test image pairs across five VTON tasks of increasing complexity. Developed VTEdit-QA, a reference-aware VLM-based evaluator that assesses model consistency, cloth consistency, and overall image quality. Evaluated eight universal editing models and seven specialized VTON models.

Result: Top universal editors are competitive on conventional VTON tasks and generalize more stably to harder scenarios, but struggle with complex reference configurations, especially multi-cloth conditioning.

Conclusion: Universal multi-reference image editing models show promise for flexible VTON systems but need improvement for complex scenarios. The benchmark enables systematic evaluation and comparison between universal and specialized approaches.

Abstract: As virtual try-on (VTON) continues to advance, a growing number of real-world scenarios have emerged, pushing beyond the ability of the existing specialized VTON models. Meanwhile, universal multi-reference image editing models have progressed rapidly and exhibit strong generalization in visual editing, suggesting a promising route toward more flexible VTON systems. However, despite their strong capabilities, the strengths and limitations of universal editors for VTON remain insufficiently explored due to the lack of systematic evaluation benchmarks. To address this gap, we introduce VTEdit-Bench, a comprehensive benchmark designed to evaluate universal multi-reference image editing models across various realistic VTON scenarios. VTEdit-Bench contains 24,220 test image pairs spanning five representative VTON tasks with progressively increasing complexity, enabling systematic analysis of robustness and generalization. We further propose VTEdit-QA, a reference-aware VLM-based evaluator that assesses VTON performance from three key aspects: model consistency, cloth consistency, and overall image quality. Through this framework, we systematically evaluate eight universal editing models and compare them with seven specialized VTON models. Results show that top universal editors are competitive on conventional tasks and generalize more stably to harder scenarios, but remain challenged by complex reference configurations, particularly multi-cloth conditioning.

Method: Proposes Neighbor Forcing, a diffusion-step-consistent AR formulation that propagates temporally adjacent frames as latent neighbors under the same noise condition, and introduces structured ConvKV memory that compresses causal attention keys/values into fixed-length representations.

Result: Enables hour-scale real-time human animation with 20 FPS streaming inference on just two H100/H200 GPUs, achieving state-of-the-art performance in lip-sync accuracy, animation quality, and emotional expressiveness with lowest inference cost.

Conclusion: The proposed approach significantly improves training convergence, hour-scale generation quality, and inference efficiency compared to existing AR diffusion methods for real-time human animation.

Abstract: Autoregressive (AR) diffusion models offer a promising framework for sequential generation tasks such as video synthesis by combining diffusion modeling with causal inference. Although they support streaming generation, existing AR diffusion methods struggle to scale efficiently. In this paper, we identify two key challenges in hour-scale real-time human animation. First, most forcing strategies propagate sample-level representations with mismatched diffusion states, causing inconsistent learning signals and unstable convergence. Second, historical representations grow unbounded and lack structure, preventing effective reuse of cached states and severely limiting inference efficiency. To address these challenges, we propose Neighbor Forcing, a diffusion-step-consistent AR formulation that propagates temporally adjacent frames as latent neighbors under the same noise condition. This design provides a distribution-aligned and stable learning signal while preserving drifting throughout the AR chain. Building upon this, we introduce a structured ConvKV memory mechanism that compresses the keys and values in causal attention into a fixed-length representation, enabling constant-memory inference and truly infinite video generation without relying on short-term motion-frame memory. Extensive experiments demonstrate that our approach significantly improves training convergence, hour-scale generation quality, and inference efficiency compared to existing AR diffusion methods. Numerically, LiveAct enables hour-scale real-time human animation and supports 20 FPS real-time streaming inference on as few as two NVIDIA H100 or H200 GPUs. Quantitative results demonstrate that our method attains state-of-the-art performance in lip-sync accuracy, human animation quality, and emotional expressiveness, with the lowest inference cost.

Method: Proposes a framework using sparse 3D hand joints as control signals with an efficient control module that resolves occlusion ambiguities while preserving 3D information. Uses occlusion-aware feature extraction penalizing unreliable signals from hidden joints, 3D-based weighting for dynamically occluded target joints, and direct injection of 3D geometric embeddings into latent space. Includes automated annotation pipeline for training data and cross-embodiment benchmark construction.

Result: Extensive experiments show the approach significantly outperforms state-of-the-art baselines, generating high-fidelity egocentric videos with realistic interactions and exhibiting exceptional cross-embodiment generalization to robotic hands.

Conclusion: The proposed framework successfully addresses limitations of existing methods by using 3D hand joints as embodiment-agnostic control signals, resolving occlusion issues, and enabling robust cross-embodiment video generation for both human and robotic hands.

Abstract: Motion-controllable video generation is crucial for egocentric applications in virtual reality and embodied AI. However, existing methods often struggle to achieve 3D-consistent fine-grained hand articulation. By adopting on 2D trajectories or implicit poses, they collapse 3D geometry into spatially ambiguous signals or over rely on human-centric priors. Under severe egocentric occlusions, this causes motion inconsistencies and hallucinated artifacts, as well as preventing cross-embodiment generalization to robotic hands. To address these limitations, we propose a novel framework that generates egocentric videos from a single reference frame, leveraging sparse 3D hand joints as embodiment-agnostic control signals with clear semantic and geometric structures. We introduce an efficient control module that resolves occlusion ambiguities while fully preserving 3D information. Specifically, it extracts occlusion-aware features from the source reference frame by penalizing unreliable visual signals from hidden joints, and employs a 3D-based weighting mechanism to robustly handle dynamically occluded target joints during motion propagation. Concurrently, the module directly injects 3D geometric embeddings into the latent space to strictly enforce structural consistency. To facilitate robust training and evaluation, we develop an automated annotation pipeline that yields over one million high-quality egocentric video clips paired with precise hand trajectories. Additionally, we register humanoid kinematic and camera data to construct a cross-embodiment benchmark. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art baselines, generating high-fidelity egocentric videos with realistic interactions and exhibiting exceptional cross-embodiment generalization to robotic hands.

Method: HELAM uses: (1) hierarchy-specific class tokens within Vision Transformers to capture nuanced label interactions, (2) graph convolutional networks to explicitly encode hierarchical structure and generate hierarchy-aware embeddings, and (3) a self-supervised branch to effectively leverage unlabeled imagery.

Result: HELAM achieves state-of-the-art performance on four remote sensing image datasets (UCM, AID, DFC-15, MLRSNet), consistently outperforming strong baselines in both supervised and semi-supervised settings, with particular strength in low-label scenarios.

Conclusion: HELAM effectively addresses limitations in hierarchical multi-label classification for remote sensing by combining explicit hierarchy modeling with self-supervised learning, demonstrating strong performance especially when labeled data is limited.

Abstract: Hierarchical multi-label classification (HMLC) is essential for modeling complex label dependencies in remote sensing. Existing methods, however, struggle with multi-path hierarchies where instances belong to multiple branches, and they rarely exploit unlabeled data. We introduce HELM (\textit{Hierarchical and Explicit Label Modeling}), a novel framework that overcomes these limitations. HELM: (i) uses hierarchy-specific class tokens within a Vision Transformer to capture nuanced label interactions; (ii) employs graph convolutional networks to explicitly encode the hierarchical structure and generate hierarchy-aware embeddings; and (iii) integrates a self-supervised branch to effectively leverage unlabeled imagery. We perform a comprehensive evaluation on four remote sensing image (RSI) datasets (UCM, AID, DFC-15, MLRSNet). HELM achieves state-of-the-art performance, consistently outperforming strong baselines in both supervised and semi-supervised settings, demonstrating particular strength in low-label scenarios.

Method: Combines projected residual-stream decomposition, zero-shot Concept Activation Vectors, and bias-augmented TextSpan analysis to locate bias at individual attention heads in CLIP ViT-L-14 encoder on FACET benchmark professions.

Result: Identified four terminal-layer heads whose ablation reduces gender bias (Cramer’s V: 0.381→0.362) while improving accuracy (+0.42%). Single final-layer head contributes most reduction. Age bias ablation produces weaker effects, suggesting more diffuse encoding.

Conclusion: Head-level bias localization is feasible for discriminative vision encoders, with localizability varying across protected attributes (gender more localizable than age). Provides mechanistic understanding of bias in vision transformers.

Abstract: Standard fairness audits of foundation models quantify that a model is biased, but not where inside the network the bias resides. We propose a mechanistic fairness audit that combines projected residual-stream decomposition, zero-shot Concept Activation Vectors, and bias-augmented TextSpan analysis to locate demographic bias at the level of individual attention heads in vision transformers. As a feasibility case study, we apply this pipeline to the CLIP ViT-L-14 encoder on 42 profession classes of the FACET benchmark, auditing both gender and age bias. For gender, the pipeline identifies four terminal-layer heads whose ablation reduces global bias (Cramer’s V: 0.381 -> 0.362) while marginally improving accuracy (+0.42%); a layer-matched random control confirms that this effect is specific to the identified heads. A single head in the final layer contributes to the majority of the reduction in the most stereotyped classes, and class-level analysis shows that corrected predictions shift toward the correct occupation. For age, the same pipeline identifies candidate heads, but ablation produces weaker and less consistent effects, suggesting that age bias is encoded more diffusely than gender bias in this model. These results provide preliminary evidence that head-level bias localisation is feasible for discriminative vision encoders and that the degree of localisability may vary across protected attributes. keywords: Bias . CLIP . Mechanistic Interpretability . Vision Transformer . Fairness

Method: HyperExpress uses hyperbolic space learning for hierarchical concept disentanglement and concept-wise optimization to maintain inter-concept relationships while ensuring composability

Result: Method demonstrates outstanding performance in extracting compositionally interpretable intrinsic concepts from single images

Conclusion: Proposed CI-ICE task and HyperExpress method successfully address compositional concept extraction from single images using diffusion models

Abstract: Unsupervised Concept Extraction aims to extract concepts from a single image; however, existing methods suffer from the inability to extract composable intrinsic concepts. To address this, this paper introduces a new task called Compositional and Interpretable Intrinsic Concept Extraction (CI-ICE). The CI-ICE task aims to leverage diffusion-based text-to-image models to extract composable object-level and attribute-level concepts from a single image, such that the original concept can be reconstructed through the combination of these concepts. To achieve this goal, we propose a method called HyperExpress, which addresses the CI-ICE task through two core aspects. Specifically, first, we propose a concept learning approach that leverages the inherent hierarchical modeling capability of hyperbolic space to achieve accurate concept disentanglement while preserving the hierarchical structure and relational dependencies among concepts; second, we introduce a concept-wise optimization method that maps the concept embedding space to maintain complex inter-concept relationships while ensuring concept composability. Our method demonstrates outstanding performance in extracting compositionally interpretable intrinsic concepts from a single image.

Method: Pairs aerial images with rendered OpenStreetMap tiles, leverages VLM’s OCR and chart comprehension to generate captions enriched by OSM metadata, then fine-tunes on resulting corpus with satellite imagery alone.

Result: Achieves state-of-the-art results on 10 benchmarks across image-text-to-text tasks when mixed with real data, outperforming 9 baselines while being substantially cheaper to train.

Conclusion: Given a strong foundation model, alignment with crowd-sourced geographic data is a practical and scalable path for remote sensing domain adaptation without manual labeling or external teachers.

Abstract: Vision-Language Models (VLMs) adapted to remote sensing rely heavily on domain-specific image-text supervision, yet high-quality annotations for satellite and aerial imagery remain scarce and expensive to produce. Prevailing pseudo-labeling pipelines address this gap by distilling knowledge from large frontier models, but this dependence on large teachers is costly, limits scalability, and caps achievable performance at the ceiling of the teacher. We propose OSMDA: a self-contained domain adaptation framework that eliminates this dependency. Our key insight is that a capable base VLM can serve as its own annotation engine: by pairing aerial images with rendered OpenStreetMap (OSM) tiles, we leverage optical character recognition and chart comprehension capabilities of the model to generate captions enriched by OSM’s vast auxiliary metadata. The model is then fine-tuned on the resulting corpus with satellite imagery alone, yielding OSMDA-VLM, a domain-adapted VLM that requires no manual labeling and no stronger external model. We conduct exhaustive evaluations spanning 10 benchmarks across image-text-to-text tasks and comparing against 9 competitive baselines. When equally mixed with real data, our method achieves state-of-the-art results, while being substantially cheaper to train than teacher-dependent alternatives. These results suggest that, given a strong foundation model, alignment with crowd-sourced geographic data is a practical and scalable path towards remote sensing domain adaptation. Dataset and model weights will be made publicly available.

Method: Collaborative explicit-implicit reconstruction with implicit SDF network and explicit handler points; physical properties disentangling module; dual-diffuse-albedo network; spatial-aware editing module with cross-view propagation-based 3D segmentation.

Result: Achieves more realistic and fine-grained editing results than SOTA methods while requiring less editing time on both real and synthetic datasets.

Conclusion: CEI-3D enables realistic and fine-grained 3D editing through collaborative explicit-implicit representation with disentangled properties and spatial-aware editing.

Abstract: Existing 3D editing methods often produce unrealistic and unrefined results due to the deeply integrated nature of their reconstruction networks. To address the challenge, this paper introduces CEI-3D, an editing-oriented reconstruction pipeline designed to facilitate realistic and fine-grained editing. Specifically, we propose a collaborative explicit-implicit reconstruction approach, which represents the target object using an implicit SDF network and a differentially sampled, locally controllable set of handler points. The implicit network provides a smooth and continuous geometry prior, while the explicit handler points offer localized control, enabling mutual guidance between the global 3D structure and user-specified local editing regions. To independently control each attribute of the handler points, we design a physical properties disentangling module to decouple the color of the handler points into separate physical properties. We also propose a dual-diffuse-albedo network in this module to process the edited and non-edited regions through separate branches, thereby preventing undesired interference from editing operations. Building on the reconstructed collaborative explicit-implicit representation with disentangled properties, we introduce a spatial-aware editing module that enables part-wise adjustment of relevant handler points. This module employs a cross-view propagation-based 3D segmentation strategy, which helps users to edit the specified physical attributes of a target part efficiently. Extensive experiments on both real and synthetic datasets demonstrate that our approach achieves more realistic and fine-grained editing results than the state-of-the-art (SOTA) methods while requiring less editing time. Our code is available on https://github.com/shiyue001/CEI-3D.

Method: Developed RICE-NET, a multimodal 3D deep learning model that integrates longitudinal MRI data (conventional T1-weighted MRI) with radiotherapy dose distributions for automated lesion classification. Used a cohort of 92 patients and conducted extensive ablation experiments to quantify contributions of each timepoint and modality.

Result: Achieved an F1 score of 0.92 on an independent test set. Ablation experiments showed that reliable classification largely depends on the radiation map. Occlusion-based interpretability analyses confirmed the model’s focus on clinically relevant regions.

Conclusion: Multimodal deep learning integrating MRI data with radiotherapy dose distributions can enhance diagnostic accuracy and support clinical decision-making in neuro-oncology for differentiating tumor recurrence from radiation-induced enhancements.

Abstract: The differentiation between tumor recurrence and radiation-induced contrast enhancements in post-treatment glioblastoma patients remains a major clinical challenge. Existing approaches rely on clinically sparsely available diffusion MRI or do not consider radiation maps, which are gaining increasing interest in the tumor board for this differentiation. We introduce RICE-NET, a multimodal 3D deep learning model that integrates longitudinal MRI data with radiotherapy dose distributions for automated lesion classification using conventional T1-weighted MRI data. Using a cohort of 92 patients, the model achieved an F1 score of 0.92 on an independent test set. During extensive ablation experiments, we quantified the contribution of each timepoint and modality and showed that reliable classification largely depends on the radiation map. Occlusion-based interpretability analyses further confirmed the model’s focus on clinically relevant regions. These findings highlight the potential of multimodal deep learning to enhance diagnostic accuracy and support clinical decision-making in neuro-oncology.

Method: FutureCAD uses LLMs fine-tuned with SFT and RL for CAD generation capabilities, combined with BRepGround transformer for grounding natural language queries to B-Rep primitives. The framework generates executable CadQuery scripts and introduces text-based query mechanism for geometric selection.

Result: The method achieves state-of-the-art CAD generation performance, demonstrating effective bridging of parametric and B-Rep approaches through the novel framework.

Conclusion: FutureCAD successfully bridges the gap between parametric CAD modeling and B-Rep synthesis, enabling more sophisticated AI-driven CAD generation for complex industrial product design through its LLM-based approach with B-Rep grounding.

Abstract: The field of Computer-Aided Design (CAD) generation has made significant progress in recent years. Existing methods typically fall into two separate categorie: parametric CAD modeling and direct boundary representation (B-Rep) synthesis. In modern feature-based CAD systems, parametric modeling and B-Rep are inherently intertwined, as advanced parametric operations (e.g., fillet and chamfer) require explicit selection of B-Rep geometric primitives, and the B-Rep itself is derived from parametric operations. Consequently, this paradigm gap remains a critical factor limiting AI-driven CAD modeling for complex industrial product design. This paper present FutureCAD, a novel text-to-CAD framework that leverages large language models (LLMs) and a B-Rep grounding transformer (BRepGround) for high-fidelity CAD generation. Our method generates executable CadQuery scripts, and introduces a text-based query mechanism that enables the LLM to specify geometric selections via natural language, which BRepGround then grounds to the target primitives. To train our framework, we construct a new dataset comprising real-world CAD models. For the LLM, we apply supervised fine-tuning (SFT) to establish fundamental CAD generation capabilities, followed by reinforcement learning (RL) to improve generalization. Experiments show that FutureCAD achieves state-of-the-art CAD generation performance.

Method: Systematic analysis of 96 peer-reviewed articles (2017-2026) examining GAN-based approaches. Categorizes GAN architectures into six classes: Vanilla GANs, Multi-Scale GANs, Conditional GANs, Attention-Enhanced GANs, Style-based GANs, and Hybrid Architecture GANs.

Result: Substantial progress in porosity accuracy (within 1% of original samples), permeability prediction (up to 79% reduction in mean relative errors), and reconstruction volumes (from 64³ to 2,200³ voxels). Identifies persistent challenges in computational efficiency, memory constraints, and structural continuity in 2D-to-3D transformations.

Conclusion: GAN-based approaches have revolutionized porous material reconstruction, with systematic analysis providing a framework for selecting appropriate architectures based on application requirements, though challenges remain in efficiency and scale.

Abstract: Digital reconstruction of porous materials has become increasingly critical for applications ranging from geological reservoir characterization to tissue engineering and electrochemical device design. While traditional methods such as micro-computed tomography and statistical reconstruction approaches have established foundations in this field, the emergence of deep learning techniques, particularly Generative Adversarial Networks (GANs), has revolutionized porous media reconstruction capabilities. This review systematically analyzes 96 peer-reviewed articles published from 2017 to early 2026, examining the evolution and applications of GAN-based approaches for porous material image reconstruction. We categorize GAN architectures into six distinct classes, namely Vanilla GANs, Multi-Scale GANs, Conditional GANs, Attention-Enhanced GANs, Style-based GANs, and Hybrid Architecture GANs. Our analysis reveals substantial progress including improvements in porosity accuracy (within 1% of original samples), permeability prediction (up to 79% reduction in mean relative errors), and achievable reconstruction volumes (from initial $64^3$ to current $2{,}200^3$ voxels). Despite these advances, persistent challenges remain in computational efficiency, memory constraints for large-scale reconstruction, and maintaining structural continuity in 2D-to-3D transformations. This systematic analysis provides a comprehensive framework for selecting appropriate GAN architectures based on specific application requirements.

Method: Introduces a new evaluation framework that decouples MLLM capabilities and creates the ZeroSense Benchmark with low semantic correlation testing samples to eliminate contextual dependencies and ensure evaluation purely reflects VTC quality.

Result: Extensive experiments across multiple datasets show that VTC quality and downstream task accuracy diverge significantly, demonstrating the necessity of the proposed decoupled evaluation framework.

Conclusion: The paper establishes that current evaluation protocols are inadequate for assessing visual-text compression quality and proposes a new framework that provides more accurate measurement by separating compression quality from model inference capabilities.

Abstract: Recent visual-text compression (VTC) methods, typified by DeepSeek-OCR, report impressive high token compression ratios for long-context modeling tasks by leveraging text-to-image rendering. However, existing evaluation protocols heavily rely on downstream task performance. Such evaluation metrics fail to accurately measure text preservation due to the strong inherent linguistic priors of Multimodal Large Language Models (MLLMs). In this work, we introduce a new evaluation framework that decouples MLLMs’ capabilities to faithfully assess VTC quality. Within this framework, we further introduce the ZeroSense Benchmark to ensure low semantic correlation of testing samples. By eliminating contextual dependencies, our benchmark guarantees that the evaluation results are purely reflective of VTC quality, unaffected by the semantic inference capabilities of downstream models. Extensive experiments across multiple datasets demonstrate that VTC quality and downstream task accuracy diverge significantly, highlighting the necessity of our decoupled evaluation framework.

Method: Derain-Agent equips base deraining models with: 1) a Planning Network that intelligently schedules optimal sequences of restoration tools for each instance, and 2) a Strength Modulation mechanism that applies tools with spatially adaptive intensity for region-specific correction.

Result: The method demonstrates strong generalization, consistently boosting performance of state-of-the-art deraining models on both synthetic and real-world benchmarks.

Conclusion: Derain-Agent provides an effective framework for transitioning deraining from static to dynamic, agent-based restoration, enabling precise correction of residual errors without prohibitive iterative search costs.

Abstract: While deep learning has advanced single-image deraining, existing models suffer from a fundamental limitation: they employ a static inference paradigm that fails to adapt to the complex, coupled degradations (e.g., noise artifacts, blur, and color deviation) of real-world rain. Consequently, restored images often exhibit residual artifacts and inconsistent perceptual quality. In this work, we present Derain-Agent, a plug-and-play refinement framework that transitions deraining from static processing to dynamic, agent-based restoration. Derain-Agent equips a base deraining model with two core capabilities: 1) a Planning Network that intelligently schedules an optimal sequence of restoration tools for each instance, and 2) a Strength Modulation mechanism that applies these tools with spatially adaptive intensity. This design enables precise, region-specific correction of residual errors without the prohibitive cost of iterative search. Our method demonstrates strong generalization, consistently boosting the performance of state-of-the-art deraining models on both synthetic and real-world benchmarks.

Method: Characterize RS single-view geometry for observed world points/lines, derive which motion/scene parameters are recoverable, systematically develop minimal reconstruction problems, implement proof-of-concept solvers.

Result: Evaluation shows feasibility of approach with practical limitations; demonstrates what can be reconstructed from single RS images using geometric constraints.

Conclusion: Provides theoretical foundation for RS SfM by characterizing single-view RS geometry, enabling systematic reconstruction from minimal data, though practical challenges remain.

Abstract: Rolling-shutter (RS) cameras are ubiquitous, but RS SfM (structure-from-motion) has not been fully solved yet. This work suggests an approach to remedy this: We characterize RS single-view geometry of observed world points or lines. Exploiting this geometry, we describe which motion and scene parameters can be recovered from a single RS image and systematically derive minimal reconstruction problems. We evaluate several representative cases with proof-of-concept solvers, highlighting both feasibility and practical limitations.

Method: Frame-based paradigm with independent frame generation; uses explicit 3D anchors and implicit spatial memory for multi-view spatial consistency; progressive three-stage training pipeline transforms pretrained image diffusion model into controllable frame model and finally into real-time generator through few-step distillation.

Result: Achieves strong multi-view consistency while supporting interactive exploration on consumer-grade GPUs; provides efficient alternative to traditional video-based world models for real-time world simulation.

Conclusion: InSpatio-WorldFM offers a practical solution for real-time spatial intelligence with low latency, making it suitable for applications requiring interactive world simulation and exploration.

Abstract: We present InSpatio-WorldFM, an open-source real-time frame model for spatial intelligence. Unlike video-based world models that rely on sequential frame generation and incur substantial latency due to window-level processing, InSpatio-WorldFM adopts a frame-based paradigm that generates each frame independently, enabling low-latency real-time spatial inference. By enforcing multi-view spatial consistency through explicit 3D anchors and implicit spatial memory, the model preserves global scene geometry while maintaining fine-grained visual details across viewpoint changes. We further introduce a progressive three-stage training pipeline that transforms a pretrained image diffusion model into a controllable frame model and finally into a real-time generator through few-step distillation. Experimental results show that InSpatio-WorldFM achieves strong multi-view consistency while supporting interactive exploration on consumer-grade GPUs, providing an efficient alternative to traditional video-based world models for real-time world simulation.

Method: Combines dense CNN architecture with region of interest prompt encoding, Efficient Channel Attention, and knowledge distillation from SAM2 and SAM3 models. Optimized for edge and in-sensor execution with INT8 quantization.

Result: Achieves 65.45% mIoU on COCO and 64.01% on LVIS, outperforming existing SAM-based and edge-oriented baselines. INT8 quantized model enables 11.82 ms latency on Sony IMX500 vision sensor with negligible accuracy degradation.

Conclusion: Demonstrates that high-quality, spatially flexible promptable segmentation is feasible directly at sensor level, with knowledge distillation from large SAM models providing significant performance improvements over supervised training.

Abstract: Real-time, on-device segmentation is critical for latency-sensitive and privacy-aware applications such as smart glasses and Internet-of-Things devices. We introduce PicoSAM3, a lightweight promptable visual segmentation model optimized for edge and in-sensor execution, including deployment on the Sony IMX500 vision sensor. PicoSAM3 has 1.3 M parameters and combines a dense CNN architecture with region of interest prompt encoding, Efficient Channel Attention, and knowledge distillation from SAM2 and SAM3. On COCO and LVIS, PicoSAM3 achieves 65.45% and 64.01% mIoU, respectively, outperforming existing SAM-based and edge-oriented baselines at similar or lower complexity. The INT8 quantized model preserves accuracy with negligible degradation while enabling real-time in-sensor inference at 11.82 ms latency on the IMX500, fully complying with its memory and operator constraints. Ablation studies show that distillation from large SAM models yields up to +14.5% mIoU improvement over supervised training and demonstrate that high-quality, spatially flexible promptable segmentation is feasible directly at the sensor level.

Method: Evaluated both classical and deep learning based image registration techniques, specifically testing NeMAR (trained under 6 different configurations) and MURF on off-road forestry data to assess their suitability.

Result: NeMAR showed partial success but had GAN loss instability issues affecting geometric consistency. MURF demonstrated promising large-scale feature alignment during shared information extraction but struggled with fine details in dense vegetation.

Conclusion: While preliminary, the study shows current techniques need further refinement for robust, multi-scale registration in off-road forest applications, highlighting specific challenges with geometric consistency and fine detail preservation.

Abstract: RGB-NIR image registration plays an important role in sensor-fusion, image enhancement and off-road autonomy. In this work, we evaluate both classical and Deep Learning (DL) based image registration techniques to access their suitability for off-road forestry applications. NeMAR, trained under 6 different configurations, demonstrates partial success however, its GAN loss instability suggests challenges in preserving geometric consistency. MURF, when tested on off-road forestry data shows promising large scale feature alignment during shared information extraction but struggles with fine details in dense vegetation. Even though this is just a preliminary evaluation, our study necessitates further refinements for robust, multi-scale registration for off-road forest applications.

Method: Integrates planetary reflectance models into Gaussian splatting framework to create physics-based neural scene representations for autonomous reconstruction and photometric characterization of asteroid surfaces

Result: Validated on real NASA Dawn mission imagery, showing superior rendering performance and surface reconstruction accuracy compared to traditional spherical harmonic parameterization

Conclusion: Physics-based Gaussian splatting with reflectance models improves small-body surface reconstruction and characterization for space missions

Abstract: Image-based surface reconstruction and characterization are crucial for missions to small celestial bodies (e.g., asteroids), as it informs mission planning, navigation, and scientific analysis. Recent advances in Gaussian splatting enable high-fidelity neural scene representations but typically rely on a spherical harmonic intensity parameterization that is strictly appearance-based and does not explicitly model material properties or light-surface interactions. We introduce AstroSplat, a physics-based Gaussian splatting framework that integrates planetary reflectance models to improve the autonomous reconstruction and photometric characterization of small-body surfaces from in-situ imagery. The proposed framework is validated on real imagery taken by NASA’s Dawn mission, where we demonstrate superior rendering performance and surface reconstruction accuracy compared to the typical spherical harmonic parameterization.

Method: Uses CLIP (frozen) for visual encoding, Wav2Vec 2.0 (frozen) for audio representation, Temporal Convolutional Network for temporal dependencies, bi-directional cross-attention fusion module for visual-audio interaction, and text-guided contrastive objective with CLIP text features.

Result: Achieves improved performance over unimodal modeling on ABAW 10th EXPR benchmark, demonstrating effectiveness of combining temporal visual modeling, audio representation learning, and cross-modal fusion.

Conclusion: Proposed framework provides strong multimodal baseline for robust emotion recognition in unconstrained real-world environments by effectively leveraging complementary information from vision and audio modalities.

Abstract: Emotion recognition in in-the-wild video data remains a challenging problem due to large variations in facial appearance, head pose, illumination, background noise, and the inherently dynamic nature of human affect. Relying on a single modality, such as facial expressions or speech, is often insufficient to capture these complex emotional cues. To address this issue, we propose a multimodal emotion recognition framework for the Expression (EXPR) Recognition task in the 10th Affective Behavior Analysis in-the-wild (ABAW) Challenge. Our approach leverages large-scale pre-trained models, namely CLIP for visual encoding and Wav2Vec 2.0 for audio representation learning, as frozen backbone networks. To model temporal dependencies in facial expression sequences, we employ a Temporal Convolutional Network (TCN) over fixed-length video windows. In addition, we introduce a bi-directional cross-attention fusion module, in which visual and audio features interact symmetrically to enhance cross-modal contextualization and capture complementary emotional information. A lightweight classification head is then used for final emotion prediction. We further incorporate a text-guided contrastive objective based on CLIP text features to encourage semantically aligned visual representations. Experimental results on the ABAW 10th EXPR benchmark show that the proposed framework provides a strong multimodal baseline and achieves improved performance over unimodal modeling. These results demonstrate the effectiveness of combining temporal visual modeling, audio representation learning, and cross-modal fusion for robust emotion recognition in unconstrained real-world environments.

Method: 1) Created HomeSafe-Bench via hybrid pipeline combining physical simulation with advanced video generation, featuring 438 diverse cases across six functional areas with fine-grained annotations. 2) Proposed HD-Guard: hierarchical streaming architecture with FastBrain (lightweight, high-frequency screening) and SlowBrain (asynchronous, large-scale multimodal reasoning) for real-time safety monitoring.

Result: HD-Guard achieves superior trade-off between latency and performance for unsafe action detection. Analysis identifies critical bottlenecks in current VLM-based safety detection systems.

Conclusion: HomeSafe-Bench provides a challenging benchmark for evaluating VLMs on household safety, while HD-Guard demonstrates effective hierarchical architecture for real-time safety monitoring in embodied agents.

Abstract: The rapid evolution of embodied agents has accelerated the deployment of household robots in real-world environments. However, unlike structured industrial settings, household spaces introduce unpredictable safety risks, where system limitations such as perception latency and lack of common sense knowledge can lead to dangerous errors. Current safety evaluations, often restricted to static images, text, or general hazards, fail to adequately benchmark dynamic unsafe action detection in these specific contexts. To bridge this gap, we introduce \textbf{HomeSafe-Bench}, a challenging benchmark designed to evaluate Vision-Language Models (VLMs) on unsafe action detection in household scenarios. HomeSafe-Bench is contrusted via a hybrid pipeline combining physical simulation with advanced video generation and features 438 diverse cases across six functional areas with fine-grained multidimensional annotations. Beyond benchmarking, we propose \textbf{Hierarchical Dual-Brain Guard for Household Safety (HD-Guard)}, a hierarchical streaming architecture for real-time safety monitoring. HD-Guard coordinates a lightweight FastBrain for continuous high-frequency screening with an asynchronous large-scale SlowBrain for deep multimodal reasoning, effectively balancing inference efficiency with detection accuracy. Evaluations demonstrate that HD-Guard achieves a superior trade-off between latency and performance, while our analysis identifies critical bottlenecks in current VLM-based safety detection.

Method: Learns training-time drifting field that attracts predicted actions toward expert demonstration modes while repelling from other generated samples. Uses sigmoid-scheduled loss transition from coarse distribution learning to mode-sharpening refinement, and multi-scale field aggregation for varying spatial granularities.

Result: Achieves state-of-the-art performance on three simulation benchmarks (Adroit, Meta-World, RoboTwin) and real-world robotic manipulation tasks while requiring 10× fewer function evaluations than diffusion-based alternatives.

Conclusion: Ada3Drift enables high-fidelity single-step generation from 3D point cloud observations, making real-time multimodal robotic control feasible by shifting computational burden to training time.

Abstract: Diffusion-based visuomotor policies effectively capture multimodal action distributions through iterative denoising, but their high inference latency limits real-time robotic control. Recent flow matching and consistency-based methods achieve single-step generation, yet sacrifice the ability to preserve distinct action modes, collapsing multimodal behaviors into averaged, often physically infeasible trajectories. We observe that the compute budget asymmetry in robotics (offline training vs.\ real-time inference) naturally motivates recovering this multimodal fidelity by shifting iterative refinement from inference time to training time. Building on this insight, we propose Ada3Drift, which learns a training-time drifting field that attracts predicted actions toward expert demonstration modes while repelling them from other generated samples, enabling high-fidelity single-step generation (1 NFE) from 3D point cloud observations. To handle the few-shot robotic regime, Ada3Drift further introduces a sigmoid-scheduled loss transition from coarse distribution learning to mode-sharpening refinement, and multi-scale field aggregation that captures action modes at varying spatial granularities. Experiments on three simulation benchmarks (Adroit, Meta-World, and RoboTwin) and real-world robotic manipulation tasks demonstrate that Ada3Drift achieves state-of-the-art performance while requiring $10\times$ fewer function evaluations than diffusion-based alternatives.

Method: Proposes CrossEarth-SAR, a physics-guided sparse mixture-of-experts architecture incorporating physical descriptors. Uses CrossEarth-SAR-200K dataset (unified public/private SAR imagery) for large-scale pre-training. Introduces benchmark suite with 22 sub-benchmarks across 8 domain gaps.

Result: Achieves state-of-the-art results on 20 benchmarks, surpassing previous methods by over 10% mIoU on some benchmarks under multi-gap transfer. Establishes first unified standard for domain generalization semantic segmentation on SAR imagery.

Conclusion: CrossEarth-SAR demonstrates effectiveness of physics-guided foundation models for SAR cross-domain semantic segmentation, providing comprehensive benchmark and dataset for future research.

Abstract: Synthetic Aperture Radar (SAR) enables global, all-weather earth observation. However, owing to diverse imaging mechanisms, domain shifts across sensors and regions severely hinder its semantic generalization. To address this, we present CrossEarth-SAR, the first billion-scale SAR vision foundation model built upon a novel physics-guided sparse mixture-of-experts (MoE) architecture incorporating physical descriptors, explicitly designed for cross-domain semantic segmentation. To facilitate large-scale pre-training, we develop CrossEarth-SAR-200K, a weakly and fully supervised dataset that unifies public and private SAR imagery. We also introduce a benchmark suite comprising 22 sub-benchmarks across 8 distinct domain gaps, establishing the first unified standard for domain generalization semantic segmentation on SAR imagery. Extensive experiments demonstrate that CrossEarth-SAR achieves state-of-the-art results on 20 benchmarks, surpassing previous methods by over 10% mIoU on some benchmarks under multi-gap transfer. All code, benchmark and datasets will be publicly available.

Method: Extends 2D alignment to 3D photogrammetric space using a transformer-based architecture that achieves 3D awareness and aggregates global information across all views. Uses camera poses to guide image warping for global alignment in 3D space and employs multi-feature joint optimization for seam computation. Created a large-scale real-world dataset for training and evaluation.

Result: Extensive experiments show the method significantly outperforms existing alternatives in alignment accuracy and perceptual quality.

Conclusion: The 3D-aware approach with transformer architecture and camera pose guidance effectively addresses limitations of traditional panorama stitching methods, achieving superior results through global geometric consistency.

Abstract: Prior panorama stitching approaches heavily rely on pairwise feature correspondences and are unable to leverage geometric consistency across multiple views. This leads to severe distortion and misalignment, especially in challenging scenes with weak textures, large parallax, and repetitive patterns. Given that multi-view geometric correspondences can be directly constructed in 3D space, making them more accurate and globally consistent, we extend the 2D alignment task to the 3D photogrammetric space. We adopt a novel transformer-based architecture to achieve 3D awareness and aggregate global information across all views. It directly utilizes camera poses to guide image warping for global alignment in 3D space and employs a multi-feature joint optimization strategy to compute the seams. Additionally, to establish an evaluation benchmark and train our network, we constructed a large-scale dataset of real-world scenes. Extensive experiments show that our method significantly outperforms existing alternatives in alignment accuracy and perceptual quality.

Method: Developed Nyxus as a novel feature extraction library from ground up for scalable out-of-core processing, covering multiple biomedical domains (radiomics, cellular analysis), with rigorous testing against established standards, and packaged for various user needs (Python package, CLI, Napari plugin, OCI container).

Result: Created a comprehensive feature extraction library that enables programmatic tuning of feature sets for optimal computational efficiency, supports both CPU and GPU processing, and provides multiple access methods for different user skill levels and workflow requirements.

Conclusion: Nyxus addresses critical computational challenges in big biomedical image data processing by providing a scalable, comprehensive feature extraction solution that bridges domain-specific needs while enabling novel machine learning and deep learning applications.

Abstract: Modern imaging instruments can produce terabytes to petabytes of data for a single experiment. The biggest barrier to processing big image datasets has been computational, where image analysis algorithms often lack the efficiency needed to process such large datasets or make tradeoffs in robustness and accuracy. Deep learning algorithms have vastly improved the accuracy of the first step in an analysis workflow (region segmentation), but the expansion of domain specific feature extraction libraries across scientific disciplines has made it difficult to compare the performance and accuracy of extracted features. To address these needs, we developed a novel feature extraction library called Nyxus. Nyxus is designed from the ground up for scalable out-of-core feature extraction for 2D and 3D image data and rigorously tested against established standards. The comprehensive feature set of Nyxus covers multiple biomedical domains including radiomics and cellular analysis, and is designed for computational scalability across CPUs and GPUs. Nyxus has been packaged to be accessible to users of various skill sets and needs: as a Python package for code developers, a command line tool, as a Napari plugin for low to no-code users or users that want to visualize results, and as an Open Container Initiative (OCI) compliant container that can be used in cloud or super-computing workflows aimed at processing large data sets. Further, Nyxus enables a new methodological approach to feature extraction allowing for programmatic tuning of many features sets for optimal computational efficiency or coverage for use in novel machine learning and deep learning applications.

Method: Combines single pixel imaging (SPI) using microLED-on-CMOS digital light projector for ultrafast pattern generation with low-complexity machine learning models (extreme learning machine and backpropagation-trained deep neural network). Uses spatiotemporal transformation of information without image reconstruction.

Result: Demonstrated image classification at multi-kHz frame rates on MNIST digits benchmark. Both ELM and DNN models show good performance with low computational overhead comparable to image generation time. SPI-based ELM shows potential for efficient anomaly detection in ultrafast imaging.

Conclusion: SPI combined with low-complexity ML enables ultrafast image classification without reconstruction, suitable for real-time applications like autonomous vehicles. The approach offers efficient anomaly detection capabilities.

Abstract: Pattern recognition and image classification are essential tasks in machine vision. Autonomous vehicles, for example, require being able to collect the complex information contained in a changing environment and classify it in real time. Here, we experimentally demonstrate image classification at multi-kHz frame rates combining the technique of single pixel imaging (SPI) with a low complexity machine learning model. The use of a microLED-on-CMOS digital light projector for SPI enables ultrafast pattern generation for sub-ms image encoding. We investigate the classification accuracy of our experimental system against the broadly accepted benchmarking task of the MNIST digits classification. We compare the classification performance of two machine learning models: An extreme learning machine (ELM) and a backpropagation trained deep neural network. The complexity of both models is kept low so the overhead added to the inference time is comparable to the image generation time. Crucially, our single pixel image classification approach is based on a spatiotemporal transformation of the information, entirely bypassing the need for image reconstruction. By exploring the performance of our SPI based ELM as binary classifier we demonstrate its potential for efficient anomaly detection in ultrafast imaging scenarios.

Method: Proposes Semantic Geometry Preservation for Continual Learning (SeGP-CL) with: 1) Dual-targeted projected gradient descent (DPGD) to construct adversarial anchors identifying drift-prone regions, 2) Anchor-guided cross-modal geometry distillation (ACGD) to preserve cross-modal structure, 3) Lightweight text semantic-geometry regularization (TSGR) to stabilize textual reference frames, and 4) Anchor-induced raw-space drift estimation for transferring old visual prototypes with dual-path inference.

Result: Extensive experiments on five continual learning benchmarks show SeGP-CL consistently improves stability and forward transfer, achieving state-of-the-art performance while better preserving semantic geometry of VLMs compared to existing methods.

Conclusion: SeGP-CL effectively mitigates catastrophic forgetting in continual learning of VLMs by explicitly preserving cross-modal semantic geometry through adversarial anchor construction and geometry distillation, maintaining better semantic structure across learning stages.

Abstract: Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without explicitly preserving the cross-modal semantic geometry inherited from pretraining and previous stages, allowing new-task supervision to induce geometric distortion. We observe that the most pronounced drift tends to concentrate in vulnerable neighborhoods near the old-new semantic interface, where shared visual patterns are easily re-explained by new textual semantics. To address this under an exemplar-free constraint, we propose Semantic Geometry Preservation for Continual Learning (SeGP-CL). SeGP-CL first probes the drift-prone region by constructing a compact set of adversarial anchors with dual-targeted projected gradient descent (DPGD), which drives selected new-task seeds toward old-class semantics while remaining faithful in raw visual space. During training, we preserve cross-modal structure by anchor-guided cross-modal geometry distillation (ACGD), and stabilize the textual reference frame across tasks via a lightweight text semantic-geometry regularization (TSGR). After training, we estimate anchor-induced raw-space drift to transfer old visual prototypes and perform dual-path inference by fusing cross-modal and visual cues. Extensive experiments on five continual learning benchmarks demonstrate that SeGP-CL consistently improves stability and forward transfer, achieving state-of-the-art performance while better preserving semantic geometry of VLMs.

Method: Proposes using h-transform to constrain diffusion sampling processes under desired conditions. Modifies transition probabilities at each timestep by adding a drift function to steer generation toward ideal fine samples. Introduces noise-level-aware schedule to handle approximation errors by gradually de-weighting the guidance term as errors increase.

Result: Extensive experiments across diverse image and video generation tasks demonstrate effectiveness and generalization of the method.

Conclusion: The proposed training-free method enables coarse-guided visual generation without requiring knowledge of forward transformation operators, achieving both guidance adherence and high-quality synthesis through noise-aware scheduling.

Abstract: Coarse-guided visual generation, which synthesizes fine visual samples from degraded or low-fidelity coarse references, is essential for various real-world applications. While training-based approaches are effective, they are inherently limited by high training costs and restricted generalization due to paired data collection. Accordingly, recent training-free works propose to leverage pretrained diffusion models and incorporate guidance during the sampling process. However, these training-free methods either require knowing the forward (fine-to-coarse) transformation operator, e.g., bicubic downsampling, or are difficult to balance between guidance and synthetic quality. To address these challenges, we propose a novel guided method by using the h-transform, a tool that can constrain stochastic processes (e.g., sampling process) under desired conditions. Specifically, we modify the transition probability at each sampling timestep by adding to the original differential equation with a drift function, which approximately steers the generation toward the ideal fine sample. To address unavoidable approximation errors, we introduce a noise-level-aware schedule that gradually de-weights the term as the error increases, ensuring both guidance adherence and high-quality synthesis. Extensive experiments across diverse image and video generation tasks demonstrate the effectiveness and generalization of our method.

Method: Combines training of oriented planar primitives (explicit representation) with neural rendering (implicit representation) to handle temporally and pose-consistent geometry while capturing fine-grained appearance details.

Result: NBAvatar achieves up to 30% LPIPS reduction under high-resolution megapixel rendering compared to Gaussian-based avatar methods, while improving PSNR and SSIM. It also achieves higher structural similarity than state-of-the-art hand-face interaction method InteractAvatar.

Conclusion: The hybrid explicit-implicit representation approach effectively handles complex non-rigid deformations in avatar rendering, particularly for hand-face interactions, and outperforms existing methods in rendering quality.

Abstract: We present NBAvatar - a method for realistic rendering of head avatars handling non-rigid deformations caused by hand-face interaction. We introduce a novel representation for animated avatars by combining the training of oriented planar primitives with neural rendering. Such a combination of explicit and implicit representations enables NBAvatar to handle temporally and pose-consistent geometry, along with fine-grained appearance details provided by the neural rendering technique. In our experiments, we demonstrate that NBAvatar implicitly learns color transformations caused by face-hand interactions and surpasses existing approaches in terms of novel-view and novel-pose rendering quality. Specifically, NBAvatar achieves up to 30% LPIPS reduction under high-resolution megapixel rendering compared to Gaussian-based avatar methods, while also improving PSNR and SSIM, and achieves higher structural similarity compared to the state-of-the-art hand-face interaction method InteractAvatar.

Method: Two-stage optimization framework: 1) Robust camera tracking by extending single-camera visual SLAM to multi-camera setting using spatiotemporal connection graph with intra-camera temporal continuity and inter-camera spatial overlap, plus wide-baseline initialization; 2) Dense depth refinement by optimizing dense inter- and intra-camera consistency using wide-baseline optical flow

Result: Method significantly outperforms state-of-the-art feed-forward models on both synthetic and real-world benchmarks while requiring less memory; introduces MultiCamRobolab dataset with ground-truth poses from motion capture system

Conclusion: Proposed framework enables practical dense dynamic scene reconstruction from multiple freely moving cameras, overcoming limitations of prior approaches through robust tracking and optimization techniques

Abstract: We address the challenging problem of dense dynamic scene reconstruction and camera pose estimation from multiple freely moving cameras – a setting that arises naturally when multiple observers capture a shared event. Prior approaches either handle only single-camera input or require rigidly mounted, pre-calibrated camera rigs, limiting their practical applicability. We propose a two-stage optimization framework that decouples the task into robust camera tracking and dense depth refinement. In the first stage, we extend single-camera visual SLAM to the multi-camera setting by constructing a spatiotemporal connection graph that exploits both intra-camera temporal continuity and inter-camera spatial overlap, enabling consistent scale and robust tracking. To ensure robustness under limited overlap, we introduce a wide-baseline initialization strategy using feed-forward reconstruction models. In the second stage, we refine depth and camera poses by optimizing dense inter- and intra-camera consistency using wide-baseline optical flow. Additionally, we introduce MultiCamRobolab, a new real-world dataset with ground-truth poses from a motion capture system. Finally, we demonstrate that our method significantly outperforms state-of-the-art feed-forward models on both synthetic and real-world benchmarks, while requiring less memory.

Method: Organizes alternative operators into five families: (1) decomposition-based operators, (2) adaptive weighted operators, (3) basis-adaptive operators, (4) integral and kernel operators, and (5) attention-based operators. Provides formal definitions, structural property analysis, and comparative analysis across dimensions like linearity, locality, equivariance, and computational cost.

Result: A comprehensive taxonomy and comparative framework for understanding convolution alternatives, with analysis of each family’s suitability for image-to-image and image-to-label tasks.

Conclusion: The paper provides a systematic organization of convolution alternatives, outlines their relative strengths and weaknesses, and identifies open challenges and future research directions in this area.

Abstract: The convolution operator is the fundamental building block of modern convolutional neural networks (CNNs), owing to its simplicity, translational equivariance, and efficient implementation. However, its structure as a fixed, linear, locally-averaging operator limits its ability to capture structured signal properties such as low-rank decompositions, adaptive basis representations, and non-uniform spatial dependencies. This paper presents a systematic taxonomy of operators that extend or replace the standard convolution in learning-based image processing pipelines. We organise the landscape of alternative operators into five families: (i) decomposition-based operators, which separate structural and noise components through singular value or tensor decompositions; (ii) adaptive weighted operators, which modulate kernel contributions as a function of spatial position or signal content; (iii) basis-adaptive operators, which optimise the analysis bases together with the network weights; (iv) integral and kernel operators, which generalise the convolution to position-dependent and non-linear kernels; and (v) attention-based operators, which relax the locality assumption entirely. For each family, we provide a formal definition, a discussion of its structural properties with respect to the convolution, and a critical analysis of the tasks for which the operator is most appropriate. We further provide a comparative analysis of all families across relevant dimensions – linearity, locality, equivariance, computational cost, and suitability for image-to-image and image-to-label tasks – and outline the open challenges and future directions of this research area.

Method: LoV3D uses a stepped pipeline that: 1) reads longitudinal T1-weighted brain MRI, 2) produces region-level anatomical assessment, 3) conducts longitudinal comparison with prior scans, and 4) outputs three-class diagnosis with synthesized summary. It employs a clinically-weighted Verifier that scores outputs against normative references from standardized volume metrics, enabling Direct Preference Optimization without human annotations.

Result: Achieves 93.7% three-class diagnostic accuracy (+34.8% over baseline), 97.2% two-class accuracy (+4% over SOTA), and 82.6% region-level anatomical classification accuracy (+33.1% over VLM baselines). Zero-shot transfer yields 95.4% on MIRIAD and 82.9% on AIBL datasets.

Conclusion: LoV3D provides an effective framework for training 3D vision-language models on medical imaging data, significantly improving diagnostic accuracy while reducing hallucinations through grounding techniques. The method shows strong generalizability across different sites, scanners, and populations.

Abstract: Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such as Alzheimer’s disease assessment. However, current deep-learning tools fragment this process: classifiers reduce a scan to a label, volumetric pipelines produce uninterpreted measurements, and vision-language models (VLMs) may generate fluent but potentially hallucinated conclusions. We present LoV3D, a pipeline for training 3D vision-language models, which reads longitudinal T1-weighted brain MRI, produces a region-level anatomical assessment, conducts longitudinal comparison with the prior scan, and finally outputs a three-class diagnosis (Cognitively Normal, Mild Cognitive Impairment, or Dementia) along with a synthesized diagnostic summary. The stepped pipeline grounds the final diagnosis by enforcing label consistency, longitudinal coherence, and biological plausibility, thereby reducing the risks of hallucinations. The training process introduces a clinically-weighted Verifier that scores candidate outputs automatically against normative references derived from standardized volume metrics, driving Direct Preference Optimization without a single human annotation. On a subject-level held-out ADNI test set (479 scans, 258 subjects), LoV3D achieves 93.7% three-class diagnostic accuracy (+34.8% over the no-grounding baseline), 97.2% on two-class diagnosis accuracy (+4% over the SOTA) and 82.6% region-level anatomical classification accuracy (+33.1% over VLM baselines). Zero-shot transfer yields 95.4% on MIRIAD (100% Dementia recall) and 82.9% three-class accuracy on AIBL, confirming high generalizability across sites, scanners, and populations. Code is available at https://github.com/Anonymous-TEVC/LoV-3D.

Method: Node-RF combines Neural Ordinary Differential Equations (NODEs) with dynamic Neural Radiance Fields (NeRFs). It learns an implicit scene state that evolves over time via an ODE solver, propagating feature embeddings through differential calculus. A NeRF-based renderer interprets these embeddings to synthesize arbitrary views for long-range extrapolation.

Result: The method demonstrates the ability to characterize abstract system behavior without explicit models and identify critical points for future predictions. It achieves long-range extrapolation beyond observed trajectories at constant memory cost.

Conclusion: Node-RF provides a continuous-time, spatiotemporal representation that generalizes beyond observed trajectories, enabling better prediction of scene dynamics from visual observations through the integration of Neural ODEs and dynamic NeRFs.

Abstract: Predicting scene dynamics from visual observations is challenging. Existing methods capture dynamics only within observed boundaries failing to extrapolate far beyond the training sequence. Node-RF (Neural ODE-based NeRF) overcomes this limitation by integrating Neural Ordinary Differential Equations (NODEs) with dynamic Neural Radiance Fields (NeRFs), enabling a continuous-time, spatiotemporal representation that generalizes beyond observed trajectories at constant memory cost. From visual input, Node-RF learns an implicit scene state that evolves over time via an ODE solver, propagating feature embeddings via differential calculus. A NeRF-based renderer interprets calculated embeddings to synthesize arbitrary views for long-range extrapolation. Training on multiple motion sequences with shared dynamics allows for generalization to unseen conditions. Our experiments demonstrate that Node-RF can characterize abstract system behavior without explicit model to identify critical points for future predictions.

Method: EvoTok uses a residual evolution process within a shared latent space. It encodes images into cascaded residual tokens via residual vector quantization, creating an evolution trajectory where earlier stages capture low-level details and deeper stages transition to high-level semantic representations.

Result: Achieves strong reconstruction quality (0.43 rFID on ImageNet-1K at 256x256) despite training on only 13M images. When integrated with LLMs, shows promising performance on 7/9 visual understanding benchmarks and remarkable results on image generation benchmarks (GenEval, GenAI-Bench).

Conclusion: Modeling visual representations as an evolving trajectory provides an effective and principled solution for unifying visual understanding and generation in multimodal LLMs.

Abstract: The development of unified multimodal large language models (MLLMs) is fundamentally challenged by the granularity gap between visual understanding and generation: understanding requires high-level semantic abstractions, while image generation demands fine-grained pixel-level representations. Existing approaches usually enforce the two supervision on the same set of representation or decouple these two supervision on separate feature spaces, leading to interference and inconsistency, respectively. In this work, we propose EvoTok, a unified image tokenizer that reconciles these requirements through a residual evolution process within a shared latent space. Instead of maintaining separate token spaces for pixels and semantics, EvoTok encodes an image into a cascaded sequence of residual tokens via residual vector quantization. This residual sequence forms an evolution trajectory where earlier stages capture low-level details and deeper stages progressively transition toward high-level semantic representations. Despite being trained on a relatively modest dataset of 13M images, far smaller than the billion-scale datasets used by many previous unified tokenizers, EvoTok achieves a strong reconstruction quality of 0.43 rFID on ImageNet-1K at 256x256 resolution. When integrated with a large language model, EvoTok shows promising performance across 7 out of 9 visual understanding benchmarks, and remarkable results on image generation benchmarks such as GenEval and GenAI-Bench. These results demonstrate that modeling visual representations as an evolving trajectory provides an effective and principled solution for unifying visual understanding and generation.

Method: First curates realistic interaction data using multimodal large language models, then creates a full text-to-3D pipeline that achieves orders-of-magnitude improvements in interaction fidelity.

Result: Surpasses baselines by 4-15x in text consistency and 3-7x in 3D model quality, with strong generalization to diverse categories and interaction types while maintaining high-quality 3D generation.

Conclusion: Hoi3DGen enables precise generation of textured 3D human-object interactions from text descriptions, addressing key limitations of existing approaches through multimodal LLM-powered data curation.

Abstract: Modeling and generating 3D human-object interactions from text is crucial for applications in AR, XR, and gaming. Existing approaches often rely on score distillation from text-to-image models, but their results suffer from the Janus problem and do not follow text prompts faithfully due to the scarcity of high-quality interaction data. We introduce Hoi3DGen, a framework that generates high-quality textured meshes of human-object interaction that follow the input interaction descriptions precisely. We first curate realistic and high-quality interaction data leveraging multimodal large language models, and then create a full text-to-3D pipeline, which achieves orders-of-magnitude improvements in interaction fidelity. Our method surpasses baselines by 4-15x in text consistency and 3-7x in 3D model quality, exhibiting strong generalization to diverse categories and interaction types, while maintaining high-quality 3D generation.

Method: HATS (Hardness-Aware Trajectory Synthesis) defines hardness as the degree of semantic ambiguity associated with an action and uses two complementary modules: (1) hardness-driven exploration that guides data collection toward ambiguous yet informative interactions, and (2) alignment-guided refinement that iteratively validates and repairs instruction-execution alignment. The modules operate in a closed loop where exploration supplies refinement with challenging trajectories, while refinement feedback updates the hardness signal to guide future exploration.

Result: Extensive experiments show that agents trained with HATS consistently outperform state-of-the-art baselines across benchmark GUI environments.

Conclusion: The HATS framework effectively addresses the semantic ambiguity problem in GUI agent training by focusing on challenging, ambiguous actions through a closed-loop exploration-refinement process, leading to more robust and generalizable agents.

Abstract: Graphical user interface (GUI) agents powered by large vision-language models (VLMs) have shown remarkable potential in automating digital tasks, highlighting the need for high-quality trajectory data to support effective agent training. Yet existing trajectory synthesis pipelines often yield agents that fail to generalize beyond simple interactions. We identify this limitation as stemming from the neglect of semantically ambiguous actions, whose meanings are context-dependent, sequentially dependent, or visually ambiguous. Such actions are crucial for real-world robustness but are under-represented and poorly processed in current datasets, leading to semantic misalignment between task instructions and execution. To address these issues, we propose HATS, a Hardness-Aware Trajectory Synthesis framework designed to mitigate the impact of semantic ambiguity. We define hardness as the degree of semantic ambiguity associated with an action and develop two complementary modules: (1) hardness-driven exploration, which guides data collection toward ambiguous yet informative interactions, and (2) alignment-guided refinement, which iteratively validates and repairs instruction-execution alignment. The two modules operate in a closed loop: exploration supplies refinement with challenging trajectories, while refinement feedback updates the hardness signal to guide future exploration. Extensive experiments show that agents trained with HATS consistently outperform state-of-the-art baselines across benchmark GUI environments.

Method: Introduces EgoIntent benchmark with 3,014 steps spanning 15 diverse indoor/outdoor daily-life scenarios. Each clip is truncated immediately before key outcomes occur, preventing future-frame leakage. Evaluates models on three dimensions: local intent (What), global intent (Why), and next-step plan (Next).

Result: Evaluated 15 MLLMs (both closed-source and open-source). Best-performing model achieved only 33.31 average score across three intent dimensions, showing step-level intent understanding in egocentric videos remains highly challenging.

Conclusion: Step-level intent understanding in egocentric videos is an underexplored and challenging problem that requires further investigation, with current MLLMs performing poorly on the EgoIntent benchmark despite their general video reasoning capabilities.

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable video reasoning capabilities across diverse tasks. However, their ability to understand human intent at a fine-grained level in egocentric videos remains largely unexplored. Existing benchmarks focus primarily on episode-level intent reasoning, overlooking the finer granularity of step-level intent understanding. Yet applications such as intelligent assistants, robotic imitation learning, and augmented reality guidance require understanding not only what a person is doing at each step, but also why and what comes next, in order to provide timely and context-aware support. To this end, we introduce EgoIntent, a step-level intent understanding benchmark for egocentric videos. It comprises 3,014 steps spanning 15 diverse indoor and outdoor daily-life scenarios, and evaluates models on three complementary dimensions: local intent (What), global intent (Why), and next-step plan (Next). Crucially, each clip is truncated immediately before the key outcome of the queried step (e.g., contact or grasp) occurs and contains no frames from subsequent steps, preventing future-frame leakage and enabling a clean evaluation of anticipatory step understanding and next-step planning. We evaluate 15 MLLMs, including both state-of-the-art closed-source and open-source models. Even the best-performing model achieves an average score of only 33.31 across the three intent dimensions, underscoring that step-level intent understanding in egocentric videos remains a highly challenging problem that calls for further investigation.

Method: Uses an agentic workflow that integrates auxiliary tools to inject glyph templates into both latent space and attention maps, enabling iterative refinement of generated images without requiring training.

Result: Achieves superior precision compared to existing baselines and can be seamlessly applied to various Text-to-Image (T2I) models, demonstrating effectiveness through extensive experiments.

Conclusion: GlyphBanana provides an effective training-free solution for precise rendering of complex characters and formulas, addressing a significant challenge in text-to-image generation.

Abstract: Despite recent advances in generative models driving significant progress in text rendering, accurately generating complex text and mathematical formulas remains a formidable challenge. This difficulty primarily stems from the limited instruction-following capabilities of current models when encountering out-of-distribution prompts. To address this, we introduce GlyphBanana, alongside a corresponding benchmark specifically designed for rendering complex characters and formulas. GlyphBanana employs an agentic workflow that integrates auxiliary tools to inject glyph templates into both the latent space and attention maps, facilitating the iterative refinement of generated images. Notably, our training-free approach can be seamlessly applied to various Text-to-Image (T2I) models, achieving superior precision compared to existing baselines. Extensive experiments demonstrate the effectiveness of our proposed workflow. Associated code is publicly available at https://github.com/yuriYanZeXuan/GlyphBanana.

Method: Proposes LatentGeo framework with continuous latent visual representations to internalize constructions without pixel rendering or external executors. Uses three-stage curriculum with auxiliary visual supervision, and LaGDPO (latent-aware reinforcement learning) to stabilize latent representations during policy optimization while improving task correctness.

Result: LatentGeo achieves substantial gains on geometric reasoning tasks, particularly those requiring auxiliary constructions. Introduces GeoAux benchmark for visually dependent geometry problems and shows strong performance on GeoAux and MathVerse benchmarks.

Conclusion: LatentGeo effectively addresses geometric construction representation challenges in MLLMs through continuous latent representations and specialized training curriculum, enabling better geometric reasoning without external tools.

Abstract: Despite recent advances in multimodal reasoning, representing auxiliary geometric constructions remains a fundamental challenge for multimodal large language models (MLLMs). Such constructions are absent from the original diagram and must be introduced before theorems apply. Existing approaches predominantly rely on explicit construction paradigms, including text-based geometric specification, visual-token interleaving during reasoning, and tool-augmented geometric execution. However, these methods either fail to faithfully represent complex spatial relationships, incur representation mismatch between discrete symbols and continuous geometric structures, or rely on external capabilities that hinder end-to-end optimization. To address these limitations, we propose LatentGeo, a framework that learns continuous latent visual representations to internalize auxiliary geometric constructions without pixel-level rendering or external executors. We design a three-stage curriculum that progressively aligns and internalizes these latent representations through auxiliary visual supervision, followed by LaGDPO, a latent-aware reinforcement learning procedure that stabilizes latent representations during policy optimization while improving end-task correctness. To systematically evaluate construction-centric representation quality, we introduce GeoAux, a new benchmark targeting visually dependent geometry problems, and conduct experiments on GeoAux and MathVerse. Results show that LatentGeo achieves substantial gains on geometric reasoning tasks, particularly those requiring auxiliary constructions. Extensive analyses and ablation studies further validate the effectiveness of each component in our framework.

Method: Two-stage approach: 1) For pose estimation: multi-stage pipeline with temporal, spatial, and cross-view reasoning using quantum-dot-grounded data and geometric checks; 2) For behavioral understanding: integrates deep embedded clustering for behavior discovery, VLM-based video captioning, and LLM-based reasoning to merge and label behavioral segments.

Result: Enables scalable, interpretable, and label-light analysis of multi-animal behavior, reducing human annotation effort while exposing low-confidence labels through geometric verification. The behavioral pipeline can operate directly from visual information without requiring keypoints.

Conclusion: BehaviorVLM provides a unified framework that leverages pretrained vision-language models for automated animal behavior analysis, addressing scalability and reproducibility challenges in neuroscience research.

Abstract: Understanding freely moving animal behavior is central to neuroscience, where pose estimation and behavioral understanding form the foundation for linking neural activity to natural actions. Yet both tasks still depend heavily on human annotation or unstable unsupervised pipelines, limiting scalability and reproducibility. We present BehaviorVLM, a unified vision-language framework for pose estimation and behavioral understanding that requires no task-specific finetuning and minimal human labeling by guiding pretrained Vision-Language Models (VLMs) through detailed, explicit, and verifiable reasoning steps. For pose estimation, we leverage quantum-dot-grounded behavioral data and propose a multi-stage pipeline that integrates temporal, spatial, and cross-view reasoning. This design greatly reduces human annotation effort, exposes low-confidence labels through geometric checks such as reprojection error, and produces labels that can later be filtered, corrected, or used to fine-tune downstream pose models. For behavioral understanding, we propose a pipeline that integrates deep embedded clustering for over-segmented behavior discovery, VLM-based per-clip video captioning, and LLM-based reasoning to merge and semantically label behavioral segments. The behavioral pipeline can operate directly from visual information and does not require keypoints to segment behavior. Together, these components enable scalable, interpretable, and label-light analysis of multi-animal behavior.

Method: ForensicZip reformulates token compression from a forgery-driven perspective. It models temporal token evolution as a Birth-Death Optimal Transport problem with a slack dummy node to quantify physical discontinuities indicating transient generative artifacts. The framework integrates transport-based novelty with high-frequency priors to separate forensic evidence from semantic content under large-ratio compression.

Result: At 10% token retention, ForensicZip achieves 2.97× speedup and over 90% FLOPs reduction while maintaining state-of-the-art detection performance on deepfake and AIGC benchmarks.

Conclusion: ForensicZip provides an effective training-free framework for accelerating MLLM-based multimedia forensics by prioritizing forgery-relevant tokens rather than semantic content, enabling efficient processing while preserving detection accuracy.

Abstract: Multimodal Large Language Models (MLLMs) enable interpretable multimedia forensics by generating textual rationales for forgery detection. However, processing dense visual sequences incurs high computational costs, particularly for high-resolution images and videos. Visual token pruning is a practical acceleration strategy, yet existing methods are largely semantic-driven, retaining salient objects while discarding background regions where manipulation traces such as high-frequency anomalies and temporal jitters often reside. To address this issue, we introduce ForensicZip, a training-free framework that reformulates token compression from a forgery-driven perspective. ForensicZip models temporal token evolution as a Birth-Death Optimal Transport problem with a slack dummy node, quantifying physical discontinuities indicating transient generative artifacts. The forensic scoring further integrates transport-based novelty with high-frequency priors to separate forensic evidence from semantic content under large-ratio compression. Experiments on deepfake and AIGC benchmarks show that at 10% token retention, ForensicZip achieves $2.97\times$ speedup and over 90% FLOPs reduction while maintaining state-of-the-art detection performance.

Method: Proposes RDNet with SwinTransformer backbone for global context modeling, plus three modules: 1) Dynamic Adaptive Detail-aware (DAD) module using varied convolution kernels guided by object region proportions; 2) Frequency-matching Context Enhancement (FCE) module enriching context through wavelet interactions and attention; 3) Region Proportion-aware Localization (RPL) module using cross-attention and Proportion Guidance block to assist DAD.

Result: RDNet achieves robustness against scale variations and accurate localization, delivering superior detection performance compared with state-of-the-art methods.

Conclusion: The proposed RDNet effectively addresses scale variation challenges in remote sensing salient object detection through transformer-based architecture and adaptive modules, achieving improved performance over existing methods.

Abstract: Salient object detection (SOD) in remote sensing images faces significant challenges due to large variations in object sizes, the computational cost of self-attention mechanisms, and the limitations of CNN-based extractors in capturing global context and long-range dependencies. Existing methods that rely on fixed convolution kernels often struggle to adapt to diverse object scales, leading to detail loss or irrelevant feature aggregation. To address these issues, this work aims to enhance robustness to scale variations and achieve precise object localization. We propose the Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network (RDNet), which replaces the CNN backbone with the SwinTransformer for global context modeling and introduces three key modules: (1) the Dynamic Adaptive Detail-aware (DAD) module, which applies varied convolution kernels guided by object region proportions; (2) the Frequency-matching Context Enhancement (FCE) module, which enriches contextual information through wavelet interactions and attention; and (3) the Region Proportion-aware Localization (RPL) module, which employs cross-attention to highlight semantic details and integrates a Proportion Guidance (PG) block to assist the DAD module. By combining these modules, RDNet achieves robustness against scale variations and accurate localization, delivering superior detection performance compared with state-of-the-art methods.

Method: Introduces a verifier meta-model that evaluates candidate trajectories from multiple pretrained trackers per frame, selects the most trustworthy predictions to generate high-quality pseudo-label trajectories for fine-tuning.

Result: Extensive experiments on four real-world benchmarks show state-of-the-art results while requiring less data than prior self-training methods.

Conclusion: Verifier-guided pseudo-labeling substantially improves supervision quality and enables data-efficient adaptation to unlabeled videos for real-world point tracking.

Abstract: Models for long-term point tracking are typically trained on large synthetic datasets. The performance of these models degrades in real-world videos due to different characteristics and the absence of dense ground-truth annotations. Self-training on unlabeled videos has been explored as a practical solution, but the quality of pseudo-labels strongly depends on the reliability of teacher models, which vary across frames and scenes. In this paper, we address the problem of real-world fine-tuning and introduce verifier, a meta-model that learns to assess the reliability of tracker predictions and guide pseudo-label generation. Given candidate trajectories from multiple pretrained trackers, the verifier evaluates them per frame and selects the most trustworthy predictions, resulting in high-quality pseudo-label trajectories. When applied for fine-tuning, verifier-guided pseudo-labeling substantially improves the quality of supervision and enables data-efficient adaptation to unlabeled videos. Extensive experiments on four real-world benchmarks demonstrate that our approach achieves state-of-the-art results while requiring less data than prior self-training methods. Project page: https://kuis-ai.github.io/track_on_r

Method: Created ReasonMap benchmark with 1,008 QA pairs from 30 cities’ transit maps, using two question types and three templates. Developed two-level evaluation pipeline for correctness and quality assessment. Evaluated 16 MLLMs and conducted visual-masking experiments to test visual grounding.

Result: Counterintuitive finding: open-source base models outperform reasoning-tuned variants, while closed-source models show opposite trend. Visual-masking confirms strong performance requires direct visual grounding. Established reinforcement fine-tuning baseline.

Conclusion: ReasonMap provides insights into visual reasoning capabilities and reveals performance gap between open- and closed-source MLLMs. The benchmark helps investigate visual grounding requirements and model architecture differences.

Abstract: Multimodal large language models (MLLMs) have demonstrated significant progress in semantic scene understanding and text-image alignment, with reasoning variants enhancing performance on more complex tasks involving mathematics and logic. To bridge this gap, we introduce ReasonMap, a novel benchmark specifically designed to evaluate these capabilities. ReasonMap encompasses high-resolution transit maps from 30 cities and includes 1,008 question-answer pairs spanning two question types and three templates. Furthermore, we design a two-level evaluation pipeline that properly assesses answer correctness and quality. Our comprehensive evaluation of 16 popular MLLMs reveals a counterintuitive pattern: among open-source models, base variants outperform their reasoning-tuned counterparts, whereas the opposite trend is observed in closed-source models. Further analysis under the visual-masking setting confirms that strong performance necessitates direct visual grounding, rather than relying solely on language priors. We further establish a training baseline with reinforcement fine-tuning, providing a reference for future exploration. We hope this benchmark study offers new insights into visual reasoning and helps investigate the gap between open- and closed-source models.

Method: Two-stage approach: Stage I uses DINOv2 ViT-L/14 backbone with padding-aware augmentation and mixture-of-experts training for robust visual features. Stage II performs multi-scale face re-cropping, extracts Wav2Vec audio features, and integrates modalities via gated fusion with temporal smoothing.

Result: Achieves Macro-F1 score of 0.5368 on official validation set and 0.5122 +/- 0.0277 under 5-fold cross-validation, outperforming official baselines.

Conclusion: The proposed dual-modal audio-visual approach effectively addresses challenges in facial expression recognition from unconstrained videos, demonstrating superior performance through robust feature extraction and modality fusion.

Abstract: This paper addresses the expression (EXPR) recognition challenge in the 10th Affective Behavior Analysis in-the-Wild (ABAW) workshop and competition, which requires frame-level classification of eight facial emotional expressions from unconstrained videos. This task is challenging due to inaccurate face localization, large pose and scale variations, motion blur, temporal instability, and other confounding factors across adjacent frames. We propose a two-stage dual-modal (audio-visual) model to address these difficulties. Stage I focuses on robust visual feature extraction with a pretrained DINOv2-based encoder. Specifically, DINOv2 ViT-L/14 is used as the backbone, a padding-aware augmentation (PadAug) strategy is employed for image padding and data preprocessing from raw videos, and a mixture-of-experts (MoE) training head is introduced to enhance classifier diversity. Stage II addresses modality fusion and temporal consistency. For the visual modality, faces are re-cropped from raw videos at multiple scales, and the extracted visual features are averaged to form a robust frame-level representation. Concurrently, frame-aligned Wav2Vec 2.0 audio features are derived from short audio windows to provide complementary acoustic cues. These dual-modal features are integrated via a lightweight gated fusion module, followed by inference-time temporal smoothing. Experiments on the ABAW dataset demonstrate the effectiveness of the proposed method. The two-stage model achieves a Macro-F1 score of 0.5368 on the official validation set and 0.5122 +/- 0.0277 under 5-fold cross-validation, outperforming the official baselines.

Method: Combines recurrent neural network (RNN) for tracking computer state with diffusion-based neural renderer for generating screen images. Trained on dataset of Ubuntu XFCE recordings including both random and AI-generated interactions.

Result: NeuralOS successfully renders realistic GUI sequences, accurately captures mouse interactions, reliably predicts state transitions like application launches, and can simulate applications that were never installed using synthesized training data.

Conclusion: Demonstrates feasibility of learning to simulate user interfaces from synthetic demonstrations, suggesting a path toward more general neural simulation of interactive systems beyond just rendering.

Abstract: We introduce NeuralOS, a neural framework that simulates graphical user interfaces (GUIs) of operating systems by directly predicting screen frames in response to user inputs such as mouse movements, clicks, and keyboard events. NeuralOS combines a recurrent neural network (RNN), which tracks computer state, with a diffusion-based neural renderer that generates screen images. The model is trained on a dataset of Ubuntu XFCE recordings, which include both randomly generated interactions and realistic interactions produced by AI agents. Experiments show that NeuralOS successfully renders realistic GUI sequences, accurately captures mouse interactions, and reliably predicts state transitions like application launches. Beyond reproducing existing systems, NeuralOS shows that synthesized training data can teach the model to simulate applications that were never installed, as illustrated by a Doom application, and suggests a path toward learning user interfaces purely from synthetic demonstrations.

Method: HiAP introduces hierarchical stochastic gates at macro (attention heads, FFN blocks) and micro (intra-head dimensions, FFN neurons) levels. It uses Gumbel-Sigmoid gates with structural feasibility penalties and analytical FLOPs optimization in a single end-to-end training phase.

Result: On ImageNet, HiAP discovers highly efficient architectures and achieves competitive accuracy-efficiency Pareto frontier for DeiT-Small, matching sophisticated multi-stage methods while simplifying deployment.

Conclusion: HiAP provides a unified framework for hierarchical pruning that addresses both memory and compute bottlenecks, enabling efficient Vision Transformer deployment on edge devices through automated architecture discovery.

Abstract: Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on edge devices. While recent structured pruning methods successfully reduce theoretical FLOPs, they typically operate at a single structural granularity and rely on complex, multi-stage pipelines with post-hoc thresholding to satisfy sparsity budgets. In this paper, we propose Hierarchical Auto-Pruning (HiAP), a continuous relaxation framework that discovers optimal sub-networks in a single end-to-end training phase without requiring manual importance heuristics or predefined per-layer sparsity targets. HiAP introduces stochastic Gumbel-Sigmoid gates at multiple granularities: macro-gates to prune entire attention heads and FFN blocks, and micro-gates to selectively prune intra-head dimensions and FFN neurons. By optimizing both levels simultaneously, HiAP addresses both the memory-bound overhead of loading large matrices and the compute-bound mathematical operations. HiAP naturally converges to stable sub-networks using a loss function that incorporates both structural feasibility penalties and analytical FLOPs. Extensive experiments on ImageNet demonstrate that HiAP organically discovers highly efficient architectures, and achieves a competitive accuracy-efficiency Pareto frontier for models like DeiT-Small, matching the performance of sophisticated multi-stage methods while significantly simplifying the deployment pipeline.

Method: Uses a visual-feedback-driven agent framework combining modern 3D object generation models with Vision-Language Models (VLMs). VLMs receive rendered visual feedback and use atomic operations (Scale, Rotate, FocusOn) to iteratively refine scenes based on spatial reasoning and planning.

Result: Generates diverse, open-vocabulary, high-quality 3D scenes. Qualitative analysis and quantitative human evaluations show superiority over existing methods. Also enables natural language editing of existing scenes.

Conclusion: SceneAssistant successfully enables open-vocabulary 3D scene generation through visual-feedback-driven agents with VLMs, overcoming limitations of previous domain-restricted approaches.

Abstract: Text-to-3D scene generation from natural language is highly desirable for digital content creation. However, existing methods are largely domain-restricted or reliant on predefined spatial relationships, limiting their capacity for unconstrained, open-vocabulary 3D scene synthesis. In this paper, we introduce SceneAssistant, a visual-feedback-driven agent designed for open-vocabulary 3D scene generation. Our framework leverages modern 3D object generation model along with the spatial reasoning and planning capabilities of Vision-Language Models (VLMs). To enable open-vocabulary scene composition, we provide the VLMs with a comprehensive set of atomic operations (e.g., Scale, Rotate, FocusOn). At each interaction step, the VLM receives rendered visual feedback and takes actions accordingly, iteratively refining the scene to achieve more coherent spatial arrangements and better alignment with the input text. Experimental results demonstrate that our method can generate diverse, open-vocabulary, and high-quality 3D scenes. Both qualitative analysis and quantitative human evaluations demonstrate the superiority of our approach over existing methods. Furthermore, our method allows users to instruct the agent to edit existing scenes based on natural language commands. Our code is available at https://github.com/ROUJINN/SceneAssistant

Method: BiGain uses frequency-aware operators: (1) Laplacian-gated token merging that encourages merges among spectrally smooth tokens while discouraging merges of high-contrast tokens to retain edges and textures; (2) Interpolate-Extrapolate KV Downsampling that downsamples keys/values via controllable interpolation between nearest and average pooling while keeping queries intact to conserve attention precision.

Result: Across DiT- and U-Net-based backbones and multiple datasets (ImageNet-1K, ImageNet-100, Oxford-IIIT Pets, COCO-2017), BiGain consistently improves speed-accuracy trade-off for diffusion-based classification while maintaining or enhancing generation quality. On ImageNet-1K with 70% token merging on Stable Diffusion 2.0, BiGain increases classification accuracy by 7.15% while improving FID by 0.34 (1.85%).

Conclusion: Balanced spectral retention (preserving high-frequency detail and low/mid-frequency semantics) is a reliable design rule for token compression in diffusion models. BiGain is the first framework to jointly advance both generation and classification under accelerated diffusion, supporting lower-cost deployment.

Abstract: Acceleration methods for diffusion models (e.g., token merging or downsampling) typically optimize synthesis quality under reduced compute, yet often ignore discriminative capacity. We revisit token compression with a joint objective and present BiGain, a training-free, plug-and-play framework that preserves generation quality while improving classification in accelerated diffusion models. Our key insight is frequency separation: mapping feature-space signals into a frequency-aware representation disentangles fine detail from global semantics, enabling compression that respects both generative fidelity and discriminative utility. BiGain reflects this principle with two frequency-aware operators: (1) Laplacian-gated token merging, which encourages merges among spectrally smooth tokens while discouraging merges of high-contrast tokens, thereby retaining edges and textures; and (2) Interpolate-Extrapolate KV Downsampling, which downsamples keys/values via a controllable interextrapolation between nearest and average pooling while keeping queries intact, thereby conserving attention precision. Across DiT- and U-Net-based backbones and ImageNet-1K, ImageNet-100, Oxford-IIIT Pets, and COCO-2017, our operators consistently improve the speed-accuracy trade-off for diffusion-based classification, while maintaining or enhancing generation quality under comparable acceleration. For instance, on ImageNet-1K, with 70% token merging on Stable Diffusion 2.0, BiGain increases classification accuracy by 7.15% while improving FID by 0.34 (1.85%). Our analyses indicate that balanced spectral retention, preserving high-frequency detail and low/mid-frequency semantics, is a reliable design rule for token compression in diffusion models. To our knowledge, BiGain is the first framework to jointly study and advance both generation and classification under accelerated diffusion, supporting lower-cost deployment.

Method: ELIT inserts a learnable variable-length latent token sequence with lightweight Read/Write cross-attention layers that move information between spatial tokens and latents, prioritizing important regions. Training with random dropping of tail latents produces importance-ordered representations.

Result: ELIT achieves consistent gains across datasets and architectures (DiT, U-ViT, HDiT, MM-DiT). On ImageNet-1K 512px, it delivers average gains of 35.3% in FID and 39.6% in FDD scores while enabling dynamic compute adjustment at inference.

Conclusion: ELIT provides a minimal, DiT-compatible mechanism that decouples computation from image resolution, enables principled latency-quality trade-offs, and improves efficiency through importance-ordered representations without changing the core diffusion objective.

Abstract: Diffusion transformers (DiTs) achieve high generative quality but lock FLOPs to image resolution, limiting principled latency-quality trade-offs, and allocate computation uniformly across input spatial tokens, wasting resource allocation to unimportant regions. We introduce Elastic Latent Interface Transformer (ELIT), a drop-in, DiT-compatible mechanism that decouples input image size from compute. Our approach inserts a latent interface, a learnable variable-length token sequence on which standard transformer blocks can operate. Lightweight Read and Write cross-attention layers move information between spatial tokens and latents and prioritize important input regions. By training with random dropping of tail latents, ELIT learns to produce importance-ordered representations with earlier latents capturing global structure while later ones contain information to refine details. At inference, the number of latents can be dynamically adjusted to match compute constraints. ELIT is deliberately minimal, adding two cross-attention layers while leaving the rectified flow objective and the DiT stack unchanged. Across datasets and architectures (DiT, U-ViT, HDiT, MM-DiT), ELIT delivers consistent gains. On ImageNet-1K 512px, ELIT delivers an average gain of $35.3%$ and $39.6%$ in FID and FDD scores. Project page: https://snap-research.github.io/elit/

Method: 1) Design tailored data curation pipelines for high-quality scoring datasets (FIRM-Edit-370K and FIRM-Gen-293K); 2) Train specialized 8B parameter reward models (FIRM-Edit-8B and FIRM-Gen-8B); 3) Create FIRM-Bench benchmark for editing/generation critics; 4) Introduce “Base-and-Bonus” reward strategy with Consistency-Modulated Execution for editing and Quality-Modulated Alignment for generation.

Result: Models achieve superior alignment with human judgment compared to existing metrics, with FIRM-Qwen-Edit and FIRM-SD3.5 showing substantial performance breakthroughs in mitigating hallucinations and establishing new standards for fidelity and instruction adherence.

Conclusion: FIRM provides a comprehensive framework for developing robust reward models that effectively address hallucination issues in RL-based image editing and generation, setting a new standard for faithful image generation through specialized datasets, models, and reward strategies.

Abstract: Reinforcement learning (RL) has emerged as a promising paradigm for enhancing image editing and text-to-image (T2I) generation. However, current reward models, which act as critics during RL, often suffer from hallucinations and assign noisy scores, inherently misguiding the optimization process. In this paper, we present FIRM (Faithful Image Reward Modeling), a comprehensive framework that develops robust reward models to provide accurate and reliable guidance for faithful image generation and editing. First, we design tailored data curation pipelines to construct high-quality scoring datasets. Specifically, we evaluate editing using both execution and consistency, while generation is primarily assessed via instruction following. Using these pipelines, we collect the FIRM-Edit-370K and FIRM-Gen-293K datasets, and train specialized reward models (FIRM-Edit-8B and FIRM-Gen-8B) that accurately reflect these criteria. Second, we introduce FIRM-Bench, a comprehensive benchmark specifically designed for editing and generation critics. Evaluations demonstrate that our models achieve superior alignment with human judgment compared to existing metrics. Furthermore, to seamlessly integrate these critics into the RL pipeline, we formulate a novel “Base-and-Bonus” reward strategy that balances competing objectives: Consistency-Modulated Execution (CME) for editing and Quality-Modulated Alignment (QMA) for generation. Empowered by this framework, our resulting models FIRM-Qwen-Edit and FIRM-SD3.5 achieve substantial performance breakthroughs. Comprehensive experiments demonstrate that FIRM mitigates hallucinations, establishing a new standard for fidelity and instruction adherence over existing general models. All of our datasets, models, and code have been publicly available at https://firm-reward.github.io.

Method: DVD features three core designs: (1) repurposing diffusion timestep as structural anchor to balance global stability with high-frequency details, (2) latent manifold rectification (LMR) to mitigate regression-induced over-smoothing and restore sharp boundaries, and (3) global affine coherence to enable seamless long-video inference without complex temporal alignment.

Result: DVD achieves state-of-the-art zero-shot performance across benchmarks and unlocks geometric priors from video foundation models using 163x less task-specific data than leading baselines. The pipeline is fully released to benefit the open-source community.

Conclusion: DVD successfully breaks the trade-off in video depth estimation by adapting pre-trained video diffusion models into deterministic depth regressors, demonstrating superior performance with minimal task-specific data and providing an open-source solution for the community.

Abstract: Existing video depth estimation faces a fundamental trade-off: generative models suffer from stochastic geometric hallucinations and scale drift, while discriminative models demand massive labeled datasets to resolve semantic ambiguities. To break this impasse, we present DVD, the first framework to deterministically adapt pre-trained video diffusion models into single-pass depth regressors. Specifically, DVD features three core designs: (i) repurposing the diffusion timestep as a structural anchor to balance global stability with high-frequency details; (ii) latent manifold rectification (LMR) to mitigate regression-induced over-smoothing, enforcing differential constraints to restore sharp boundaries and coherent motion; and (iii) global affine coherence, an inherent property bounding inter-window divergence, which enables seamless long-video inference without requiring complex temporal alignment. Extensive experiments demonstrate that DVD achieves state-of-the-art zero-shot performance across benchmarks. Furthermore, DVD successfully unlocks the profound geometric priors implicit in video foundation models using 163x less task-specific data than leading baselines. Notably, we fully release our pipeline, providing the whole training suite for SOTA video depth estimation to benefit the open-source community.

Method: AutoGaze uses next-token prediction and reinforcement learning to autoregressively select a minimal set of multi-scale patches that can reconstruct videos within user-specified error thresholds, removing redundant patches before processing by ViTs or MLLMs.

Result: AutoGaze reduces visual tokens by 4x-100x, accelerates ViTs and MLLMs by up to 19x, enables scaling to 1K-frame 4K-resolution videos, achieves 67.0% on VideoMME benchmark, and introduces HLVid benchmark where it outperforms baselines by 10.1%.

Conclusion: AutoGaze effectively addresses redundancy in long-form video processing for MLLMs, enabling efficient scaling to high-resolution, long-duration videos while maintaining or improving performance on video understanding tasks.

Abstract: Multi-modal large language models (MLLMs) have advanced general-purpose video understanding but struggle with long, high-resolution videos – they process every pixel equally in their vision transformers (ViTs) or LLMs despite significant spatiotemporal redundancy. We introduce AutoGaze, a lightweight module that removes redundant patches before processed by a ViT or an MLLM. Trained with next-token prediction and reinforcement learning, AutoGaze autoregressively selects a minimal set of multi-scale patches that can reconstruct the video within a user-specified error threshold, eliminating redundancy while preserving information. Empirically, AutoGaze reduces visual tokens by 4x-100x and accelerates ViTs and MLLMs by up to 19x, enabling scaling MLLMs to 1K-frame 4K-resolution videos and achieving superior results on video benchmarks (e.g., 67.0% on VideoMME). Furthermore, we introduce HLVid: the first high-resolution, long-form video QA benchmark with 5-minute 4K-resolution videos, where an MLLM scaled with AutoGaze improves over the baseline by 10.1% and outperforms the previous best MLLM by 4.5%. Project page: https://autogaze.github.io/.

Method: Proposes Spatial-TTT with test-time training that adapts a subset of parameters (fast weights) to capture spatial evidence over long-horizon videos. Uses hybrid architecture with large-chunk updates parallel with sliding-window attention for efficient processing. Introduces spatial-predictive mechanism with 3D spatiotemporal convolution to capture geometric correspondence and temporal continuity. Constructs dataset with dense 3D spatial descriptions to guide model updates.

Result: Extensive experiments show Spatial-TTT improves long-horizon spatial understanding and achieves state-of-the-art performance on video spatial benchmarks.

Conclusion: Spatial-TTT effectively addresses streaming spatial intelligence by combining test-time training with architectural innovations for maintaining and organizing spatial evidence from video streams.

Abstract: Humans perceive and understand real-world spaces through a stream of visual observations. Therefore, the ability to streamingly maintain and update spatial evidence from potentially unbounded video streams is essential for spatial intelligence. The core challenge is not simply longer context windows but how spatial information is selected, organized, and retained over time. In this paper, we propose Spatial-TTT towards streaming visual-based spatial intelligence with test-time training (TTT), which adapts a subset of parameters (fast weights) to capture and organize spatial evidence over long-horizon scene videos. Specifically, we design a hybrid architecture and adopt large-chunk updates parallel with sliding-window attention for efficient spatial video processing. To further promote spatial awareness, we introduce a spatial-predictive mechanism applied to TTT layers with 3D spatiotemporal convolution, which encourages the model to capture geometric correspondence and temporal continuity across frames. Beyond architecture design, we construct a dataset with dense 3D spatial descriptions, which guides the model to update its fast weights to memorize and organize global 3D spatial signals in a structured manner. Extensive experiments demonstrate that Spatial-TTT improves long-horizon spatial understanding and achieves state-of-the-art performance on video spatial benchmarks. Project page: https://liuff19.github.io/Spatial-TTT.

Method: Two-stage training: 1) Joint training with comprehensive control signals using condition-aware 3D rotary positional embedding and hierarchical motion injection, plus group/role embeddings for multi-subject disambiguation. 2) Latent identity reward feedback learning with a trained reward model to preserve identity.

Result: Superior performance in generating high-quality videos with precise controllability, demonstrated on curated large-scale dataset and DreamOmni Bench for multi-subject and omni-motion control evaluation.

Conclusion: DreamVideo-Omni successfully addresses challenges in multi-subject video customization with comprehensive motion control through innovative architectural designs and training paradigms.

Abstract: While large-scale diffusion models have revolutionized video synthesis, achieving precise control over both multi-subject identity and multi-granularity motion remains a significant challenge. Recent attempts to bridge this gap often suffer from limited motion granularity, control ambiguity, and identity degradation, leading to suboptimal performance on identity preservation and motion control. In this work, we present DreamVideo-Omni, a unified framework enabling harmonious multi-subject customization with omni-motion control via a progressive two-stage training paradigm. In the first stage, we integrate comprehensive control signals for joint training, encompassing subject appearances, global motion, local dynamics, and camera movements. To ensure robust and precise controllability, we introduce a condition-aware 3D rotary positional embedding to coordinate heterogeneous inputs and a hierarchical motion injection strategy to enhance global motion guidance. Furthermore, to resolve multi-subject ambiguity, we introduce group and role embeddings to explicitly anchor motion signals to specific identities, effectively disentangling complex scenes into independent controllable instances. In the second stage, to mitigate identity degradation, we design a latent identity reward feedback learning paradigm by training a latent identity reward model upon a pretrained video diffusion backbone. This provides motion-aware identity rewards in the latent space, prioritizing identity preservation aligned with human preferences. Supported by our curated large-scale dataset and the comprehensive DreamOmni Bench for multi-subject and omni-motion control evaluation, DreamVideo-Omni demonstrates superior performance in generating high-quality videos with precise controllability.

Method: Proposes VST with ’thinking while watching’ mechanism that activates reasoning over incoming video clips during streaming. Includes post-training pipeline with VST-SFT for structural adaptation and VST-RL for end-to-end improvement, plus automated training-data synthesis using video knowledge graphs.

Result: VST-7B achieves 79.5% on StreamingBench and 59.3% on OVO-Bench, responds 15.7× faster than Video-R1 with +5.4% improvement on VideoHolmes, while remaining competitive on offline benchmarks.

Conclusion: VST successfully addresses the latency-comprehension trade-off in streaming video understanding, enabling efficient real-time interaction with strong generalization across diverse video tasks.

Abstract: Online Video Large Language Models (VideoLLMs) play a critical role in supporting responsive, real-time interaction. Existing methods focus on streaming perception, lacking a synchronized logical reasoning stream. However, directly applying test-time scaling methods incurs unacceptable response latency. To address this trade-off, we propose Video Streaming Thinking (VST), a novel paradigm for streaming video understanding. It supports a thinking while watching mechanism, which activates reasoning over incoming video clips during streaming. This design improves timely comprehension and coherent cognition while preserving real-time responsiveness by amortizing LLM reasoning latency over video playback. Furthermore, we introduce a comprehensive post-training pipeline that integrates VST-SFT, which structurally adapts the offline VideoLLM to causal streaming reasoning, and VST-RL, which provides end-to-end improvement through self-exploration in a multi-turn video interaction environment. Additionally, we devise an automated training-data synthesis pipeline that uses video knowledge graphs to generate high-quality streaming QA pairs, with an entity-relation grounded streaming Chain-of-Thought to enforce multi-evidence reasoning and sustained attention to the video stream. Extensive evaluations show that VST-7B performs strongly on online benchmarks, e.g. 79.5% on StreamingBench and 59.3% on OVO-Bench. Meanwhile, VST remains competitive on offline long-form or reasoning benchmarks. Compared with Video-R1, VST responds 15.7 times faster and achieves +5.4% improvement on VideoHolmes, demonstrating higher efficiency and strong generalization across diverse video understanding tasks. Code, data, and models will be released at https://github.com/1ranGuan/VST.

Method: Created GRADE benchmark with 520 curated samples across 10 academic domains (natural to social sciences). Proposed multi-dimensional evaluation protocol assessing Discipline Reasoning, Visual Consistency, and Logical Readability. Evaluated 20 state-of-the-art open-source and closed-source models.

Result: Extensive experiments reveal substantial limitations in current models under implicit, knowledge-intensive editing settings, showing large performance gaps. Models struggle with discipline-informed reasoning beyond basic commonsense.

Conclusion: GRADE identifies key directions for developing unified multimodal models, advancing research on discipline-informed image editing and reasoning. The benchmark exposes model shortcomings and disciplinary editing constraints.

Abstract: Unified multimodal models target joint understanding, reasoning, and generation, but current image editing benchmarks are largely confined to natural images and shallow commonsense reasoning, offering limited assessment of this capability under structured, domain-specific constraints. In this work, we introduce GRADE, the first benchmark to assess discipline-informed knowledge and reasoning in image editing. GRADE comprises 520 carefully curated samples across 10 academic domains, spanning from natural science to social science. To support rigorous evaluation, we propose a multi-dimensional evaluation protocol that jointly assesses Discipline Reasoning, Visual Consistency, and Logical Readability. Extensive experiments on 20 state-of-the-art open-source and closed-source models reveal substantial limitations in current models under implicit, knowledge-intensive editing settings, leading to large performance gaps. Beyond quantitative scores, we conduct rigorous analyses and ablations to expose model shortcomings and identify the constraints within disciplinary editing. Together, GRADE pinpoints key directions for the future development of unified multimodal models, advancing the research on discipline-informed image editing and reasoning. Our benchmark and evaluation code are publicly released.

Method: Incorporates causal spatiotemporal attention and 3D rotary positional embeddings (3D-RoPE) to support efficient frame-by-frame online processing via persistent KV-cache. Pre-trained using multi-task framework coupling static/temporal representation learning, streaming geometric reconstruction, and vision-language alignment across 29 datasets.

Result: With a strictly frozen backbone, achieves competitive performance with specialized experts across image/video probing, streaming geometric reconstruction, complex video/spatial reasoning, and robotic manipulation (unseen at training).

Conclusion: Demonstrates viability of training a single versatile vision backbone that generalizes across semantic, spatial, and temporal reasoning, representing meaningful progress toward general-purpose visual understanding for interactive/embodied agents.

Abstract: Modern visual agents require representations that are general, causal, and physically structured to operate in real-time streaming environments. However, current vision foundation models remain fragmented, specializing narrowly in image semantic perception, offline temporal modeling, or spatial geometry. This paper introduces OmniStream, a unified streaming visual backbone that effectively perceives, reconstructs, and acts from diverse visual inputs. By incorporating causal spatiotemporal attention and 3D rotary positional embeddings (3D-RoPE), our model supports efficient, frame-by-frame online processing of video streams via a persistent KV-cache. We pre-train OmniStream using a synergistic multi-task framework coupling static and temporal representation learning, streaming geometric reconstruction, and vision-language alignment on 29 datasets. Extensive evaluations show that, even with a strictly frozen backbone, OmniStream achieves consistently competitive performance with specialized experts across image and video probing, streaming geometric reconstruction, complex video and spatial reasoning, as well as robotic manipulation (unseen at training). Rather than pursuing benchmark-specific dominance, our work demonstrates the viability of training a single, versatile vision backbone that generalizes across semantic, spatial, and temporal reasoning, i.e., a more meaningful step toward general-purpose visual understanding for interactive and embodied agents.

Method: Proposes MM-CondChain benchmark with multi-layer reasoning chains where each layer contains compositional conditions grounded in visual evidence. Uses agentic synthesis pipeline: Planner orchestrates layer-by-layer generation, VPIR ensures mechanical verifiability, and Composer assembles verified layers into complete instructions.

Result: Even strongest MLLMs achieve only 53.33 Path F1, with performance dropping sharply on hard negatives and as depth or predicate complexity increases, showing deep compositional reasoning remains challenging.

Conclusion: Deep compositional reasoning in multimodal contexts is fundamentally challenging for current MLLMs, and MM-CondChain provides a benchmark to measure and advance this capability.

Abstract: Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step depends on verified visual compositional conditions (e.g., “if a permission dialog appears and the color of the interface is green, click Allow”) and the process may branch or terminate early. Yet this capability remains under-evaluated: existing benchmarks focus on shallow-compositions or independent-constraints rather than deeply chained compositional conditionals. In this paper, we introduce MM-CondChain, a benchmark for visually grounded deep compositional reasoning. Each benchmark instance is organized as a multi-layer reasoning chain, where every layer contains a non-trivial compositional condition grounded in visual evidence and built from multiple objects, attributes, or relations. To answer correctly, an MLLM must perceive the image in detail, reason over multiple visual elements at each step, and follow the resulting execution path to the final outcome. To scalably construct such workflow-style data, we propose an agentic synthesis pipeline: a Planner orchestrates layer-by-layer generation of compositional conditions, while a Verifiable Programmatic Intermediate Representation (VPIR) ensures each layer’s condition is mechanically verifiable. A Composer then assembles these verified layers into complete instructions. Using this pipeline, we construct benchmarks across three visual domains: natural images, data charts, and GUI trajectories. Experiments on a range of MLLMs show that even the strongest model attains only 53.33 Path F1, with sharp drops on hard negatives and as depth or predicate complexity grows, confirming that deep compositional reasoning remains a fundamental challenge.

Method: EVATok estimates optimal token assignments per video, develops lightweight routers to predict these assignments, and trains adaptive tokenizers that encode videos based on router predictions. Uses advanced training with video semantic encoders.

Result: Achieves substantial improvements in efficiency and quality for video reconstruction and downstream AR generation. Achieves state-of-the-art class-to-video generation on UCF-101 with at least 24.4% savings in average token usage compared to prior methods.

Conclusion: EVATok provides an effective framework for adaptive video tokenization that significantly improves efficiency while maintaining or enhancing reconstruction and generation quality.

Abstract: Autoregressive (AR) video generative models rely on video tokenizers that compress pixels into discrete token sequences. The length of these token sequences is crucial for balancing reconstruction quality against downstream generation computational cost. Traditional video tokenizers apply a uniform token assignment across temporal blocks of different videos, often wasting tokens on simple, static, or repetitive segments while underserving dynamic or complex ones. To address this inefficiency, we introduce $\textbf{EVATok}$, a framework to produce $\textbf{E}$fficient $\textbf{V}$ideo $\textbf{A}$daptive $\textbf{Tok}$enizers. Our framework estimates optimal token assignments for each video to achieve the best quality-cost trade-off, develops lightweight routers for fast prediction of these optimal assignments, and trains adaptive tokenizers that encode videos based on the assignments predicted by routers. We demonstrate that EVATok delivers substantial improvements in efficiency and overall quality for video reconstruction and downstream AR generation. Enhanced by our advanced training recipe that integrates video semantic encoders, EVATok achieves superior reconstruction and state-of-the-art class-to-video generation on UCF-101, with at least 24.4% savings in average token usage compared to the prior state-of-the-art LARP and our fixed-length baseline.

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Method: Cannot determine method due to inability to access paper content.

Result: Cannot determine results due to inability to access paper content.

Conclusion: Cannot draw conclusions due to inability to access paper content.

Abstract: Failed to fetch summary for 2509.23097: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2509.23097&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable due to API rate limiting

Result: Cannot determine results as paper content is unavailable due to API rate limiting

Conclusion: Cannot draw conclusions as paper content is unavailable due to API rate limiting

Abstract: Failed to fetch summary for 2509.26489: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2509.26489&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content

Result: Cannot determine results without access to paper content

Conclusion: Cannot draw conclusions without access to paper content

Abstract: Failed to fetch summary for 2511.12908: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.12908&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot draw conclusions as paper content is unavailable

Abstract: Failed to fetch summary for 2510.07791: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.07791&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable due to API rate limiting

Result: Cannot determine results as paper content is unavailable due to API rate limiting

Conclusion: Cannot draw conclusions as paper content is unavailable due to API rate limiting

Abstract: Failed to fetch summary for 2511.20823: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.20823&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content

Result: Cannot determine results without access to paper content

Conclusion: Cannot determine conclusion without access to paper content

Abstract: Failed to fetch summary for 2510.08575: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.08575&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed paper fetch

Result: Unable to determine results due to failed paper fetch

Conclusion: Unable to draw conclusions due to failed paper fetch

Abstract: Failed to fetch summary for 2511.22018: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.22018&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot draw conclusions as paper content is unavailable

Abstract: Failed to fetch summary for 2510.10489: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.10489&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to failed paper fetch

Result: Cannot determine results due to failed paper fetch

Conclusion: Cannot draw conclusions due to failed paper fetch

Abstract: Failed to fetch summary for 2510.26796: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.26796&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot determine conclusion as paper content is unavailable

Abstract: Failed to fetch summary for 2512.17137: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.17137&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to missing abstract

Result: Cannot determine results due to missing abstract

Conclusion: Cannot draw conclusions due to missing abstract

Abstract: Failed to fetch summary for 2511.06315: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.06315&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to failed paper fetch

Result: Cannot determine results due to failed paper fetch

Conclusion: Cannot draw conclusions due to failed paper fetch

Abstract: Failed to fetch summary for 2512.20299: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.20299&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as abstract/summary is unavailable due to rate limiting error

Result: Cannot determine results as abstract/summary is unavailable due to rate limiting error

Conclusion: Cannot determine conclusion as abstract/summary is unavailable due to rate limiting error

Abstract: Failed to fetch summary for 2511.11851: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.11851&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to determine conclusion due to fetch failure

Abstract: Failed to fetch summary for 2601.06550: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.06550&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot draw conclusions as paper content is unavailable

Abstract: Failed to fetch summary for 2511.18463: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.18463&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access error

Result: Unable to determine results due to access error

Conclusion: Unable to determine conclusion due to access error

Abstract: Failed to fetch summary for 2603.06972: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.06972&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without paper content

Result: Cannot determine results without paper content

Conclusion: Cannot determine conclusion without paper content

Abstract: Failed to fetch summary for 2511.18685: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.18685&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content

Result: Cannot determine results without access to paper content

Conclusion: Cannot determine conclusion without access to paper content

Abstract: Failed to fetch summary for 2602.21421: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.21421&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot draw conclusions as paper content is unavailable

Abstract: Failed to fetch summary for 2511.22433: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.22433&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to determine conclusion due to fetch failure

Abstract: Failed to fetch summary for 2512.02421: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.02421&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot draw conclusions about paper content due to access limitations

Abstract: Failed to fetch summary for 2602.23653: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.23653&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to draw conclusions due to fetch failure

Abstract: Failed to fetch summary for 2603.10445: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.10445&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to API access limitations

Result: Cannot determine results due to API access limitations

Conclusion: Cannot draw conclusions due to insufficient paper information

Abstract: Failed to fetch summary for 2512.04862: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.04862&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to determine conclusion due to fetch failure

Abstract: Failed to fetch summary for 2512.05391: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.05391&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to determine conclusion due to fetch failure

Abstract: Failed to fetch summary for 2603.10935: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.10935&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to failed paper retrieval

Result: Cannot determine results due to failed paper retrieval

Conclusion: Cannot determine conclusion due to failed paper retrieval

Abstract: Failed to fetch summary for 2512.21692: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.21692&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot draw conclusions about paper content due to access limitations

Abstract: Failed to fetch summary for 2603.03964: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.03964&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: No method information available - arXiv API returned HTTP 429 error (Too Many Requests)

Result: No results available - paper content inaccessible due to rate limiting

Conclusion: Cannot analyze paper due to technical limitations in accessing the content

Abstract: Failed to fetch summary for 2601.02447: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.02447&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to determine conclusion due to fetch failure

Abstract: Failed to fetch summary for 2502.00622: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2502.00622&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot determine conclusion as paper content is unavailable

Abstract: Failed to fetch summary for 2602.00813: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.00813&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to inability to access paper content

Result: Cannot determine results due to inability to access paper content

Conclusion: Cannot draw conclusions due to inability to access paper content

Abstract: Failed to fetch summary for 2602.06965: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.06965&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to technical error in fetching paper content

Result: Unable to determine results due to technical error in fetching paper content

Conclusion: Unable to draw conclusions due to technical error in fetching paper content

Abstract: Failed to fetch summary for 2602.08430: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.08430&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed API request

Result: Unable to determine results due to failed API request

Conclusion: Unable to determine conclusion due to failed API request

Abstract: Failed to fetch summary for 2602.08958: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.08958&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access restrictions

Result: Unable to determine results due to access restrictions

Conclusion: Unable to determine conclusion due to access restrictions

Abstract: Failed to fetch summary for 2603.08605: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.08605&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without paper content.

Result: Cannot determine results without paper content.

Conclusion: Cannot draw conclusions without paper content.

Abstract: Failed to fetch summary for 2602.13823: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.13823&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to missing paper content

Result: Unable to determine results due to missing paper content

Conclusion: Unable to draw conclusions due to missing paper content

Abstract: Failed to fetch summary for 2602.18990: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.18990&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content

Result: Cannot determine results without access to paper content

Conclusion: Cannot determine conclusion without access to paper content

Abstract: Failed to fetch summary for 2602.20792: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.20792&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access error

Result: Unable to determine results due to access error

Conclusion: Unable to determine conclusion due to access error

Abstract: Failed to fetch summary for 2603.10834: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.10834&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to the paper content. The arXiv API returned a rate limiting error (HTTP 429).

Result: No results available due to inability to fetch paper content from arXiv API.

Conclusion: Unable to analyze this paper due to technical limitations in accessing the content from arXiv.

Abstract: Failed to fetch summary for 2510.06754: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.06754&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot determine conclusion as paper content is unavailable

Abstract: Failed to fetch summary for 2602.24161: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.24161&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to determine conclusion due to fetch failure

Abstract: Failed to fetch summary for 2603.01685: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.01685&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to determine conclusion due to fetch failure

Abstract: Failed to fetch summary for 2603.01928: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.01928&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to the paper content. The HTTP 429 error indicates rate limiting from arXiv API.

Result: Cannot determine results without access to the paper content. The error prevents retrieval of any paper details.

Conclusion: Cannot draw conclusions about the paper’s content due to technical limitations in accessing the information.

Abstract: Failed to fetch summary for 2603.04839: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.04839&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot draw conclusions as paper content is unavailable

Abstract: Failed to fetch summary for 2603.04846: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.04846&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to the paper content.

Result: Cannot determine results without access to the paper content.

Conclusion: Cannot draw conclusions without access to the paper content.

Abstract: Failed to fetch summary for 2603.06168: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.06168&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content

Result: Cannot determine results without access to paper content

Conclusion: Cannot draw conclusions without access to paper content

Abstract: Failed to fetch summary for 2603.06999: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.06999&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot determine conclusion as paper content is unavailable

Abstract: Failed to fetch summary for 2603.07504: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.07504&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot draw conclusions about the paper due to technical limitations in accessing the content

Abstract: Failed to fetch summary for 2603.07961: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.07961&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed summary fetch

Result: Unable to determine results due to failed summary fetch

Conclusion: Unable to draw conclusion due to failed summary fetch

Abstract: Failed to fetch summary for 2603.08064: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.08064&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access restrictions

Result: Unable to determine results due to access restrictions

Conclusion: Unable to determine conclusion due to access restrictions

Abstract: Failed to fetch summary for 2603.10354: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.10354&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: GAE constructs optimized low-dimensional semantic supervision from Vision Foundation Models, uses latent normalization replacing KL-divergence for stable manifolds, and incorporates dynamic noise sampling for robust reconstruction under high-intensity noise.

Result: Achieves gFID of 1.82 at 80 epochs and 1.31 at 800 epochs on ImageNet-1K 256×256 benchmark without Classifier-Free Guidance, significantly surpassing state-of-the-art methods.

Conclusion: GAE establishes superior equilibrium between compression, semantic depth, and reconstruction stability, offering a promising paradigm for latent diffusion modeling.

Abstract: Latent diffusion models have established a new state-of-the-art in high-resolution visual generation. Integrating Vision Foundation Model priors improves generative efficiency, yet existing latent designs remain largely heuristic. These approaches often struggle to unify semantic discriminability, reconstruction fidelity, and latent compactness. In this paper, we propose Geometric Autoencoder (GAE), a principled framework that systematically addresses these challenges. By analyzing various alignment paradigms, GAE constructs an optimized low-dimensional semantic supervision target from VFMs to provide guidance for the autoencoder. Furthermore, we leverage latent normalization that replaces the restrictive KL-divergence of standard VAEs, enabling a more stable latent manifold specifically optimized for diffusion learning. To ensure robust reconstruction under high-intensity noise, GAE incorporates a dynamic noise sampling mechanism. Empirically, GAE achieves compelling performance on the ImageNet-1K $256 \times 256$ benchmark, reaching a gFID of 1.82 at only 80 epochs and 1.31 at 800 epochs without Classifier-Free Guidance, significantly surpassing existing state-of-the-art methods. Beyond generative quality, GAE establishes a superior equilibrium between compression, semantic depth and robust reconstruction stability. These results validate our design considerations, offering a promising paradigm for latent diffusion modeling. Code and models are publicly available at https://github.com/sii-research/GAE.

Method: Unable to determine method due to missing paper content

Result: Unable to determine results due to missing paper content

Conclusion: Unable to draw conclusions due to missing paper content

Abstract: Failed to fetch summary for 2603.10446: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.10446&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to API access issues

Result: Unable to determine results due to API access issues

Conclusion: Unable to determine conclusion due to API access issues

Abstract: Failed to fetch summary for 2603.10648: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.10648&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed API request

Result: Unable to determine results due to failed API request

Conclusion: Unable to determine conclusion due to failed API request

Abstract: Failed to fetch summary for 2603.10929: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.10929&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

Result: Cannot determine results as paper content is unavailable

Conclusion: Cannot draw conclusions as paper content is unavailable

Abstract: Failed to fetch summary for 2505.24053: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2505.24053&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed paper fetch

Result: Unable to determine results due to failed paper fetch

Conclusion: Unable to draw conclusions due to failed paper fetch

Abstract: Failed to fetch summary for 2509.11125: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2509.11125&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to determine conclusion due to fetch failure

Abstract: Failed to fetch summary for 2601.16667: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.16667&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access error

Result: Unable to determine results due to access error

Conclusion: Unable to determine conclusion due to access error

Abstract: Failed to fetch summary for 2603.08245: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.08245&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to the paper content.

Result: Cannot determine results without access to the paper content.

Conclusion: Cannot draw conclusions without access to the paper content.

Abstract: Failed to fetch summary for 2603.09695: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.09695&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

Result: Unable to determine results due to fetch failure

Conclusion: Unable to determine conclusion due to fetch failure

Abstract: Failed to fetch summary for 2603.10323: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.10323&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: DIVE inverts the synthesis order: first executes diverse real-world tools, then reverse-derives tasks strictly entailed by the resulting traces (grounding by construction). Scales structural diversity along tool-pool coverage and per-task toolset variety using an Evidence Collection-Task Derivation loop across 373 tools in five domains.

Result: Training Qwen3-8B on DIVE data (48k SFT + 3.2k RL) improves by +22 average points across 9 OOD benchmarks and outperforms the strongest 8B baseline by +68. Diversity scaling consistently outperforms quantity scaling for OOD generalization, even with 4x less data.

Conclusion: Evidence-driven task synthesis through reverse derivation from real tool executions provides better grounding and diversity, leading to significantly improved out-of-distribution generalization for tool-using LLMs.

Abstract: Recent work synthesizes agentic tasks for post-training tool-using LLMs, yet robust generalization under shifts in tasks and toolsets remains an open challenge. We trace this brittleness to insufficient diversity in synthesized tasks. Scaling diversity is difficult because training requires tasks to remain executable and verifiable, while generalization demands coverage of diverse tool types, toolset combinations, and heterogeneous tool-use patterns. We propose DIVE, an evidence-driven recipe that inverts synthesis order, executing diverse, real-world tools first and reverse-deriving tasks strictly entailed by the resulting traces, thereby providing grounding by construction. DIVE scales structural diversity along two controllable axes, tool-pool coverage and per-task toolset variety, and an Evidence Collection–Task Derivation loop further induces rich multi-step tool-use patterns across 373 tools in five domains. Training Qwen3-8B on DIVE data (48k SFT + 3.2k RL) improves by +22 average points across 9 OOD benchmarks and outperforms the strongest 8B baseline by +68. Remarkably, controlled scaling analysis reveals that diversity scaling consistently outperforms quantity scaling for OOD generalization, even with 4x less data.

Method: Proposes Cognitive Hierarchy to decompose driving tasks by cognitive/interactive complexity, identifies seven core reasoning challenges, conducts dual-perspective review of system-centric approaches and evaluation practices.

Result: Reveals trend toward holistic “glass-box” agents, identifies fundamental tension between LLM-based reasoning latency and millisecond-scale vehicle control demands.

Conclusion: Future work needs to bridge symbolic-to-physical gap with verifiable neuro-symbolic architectures, robust reasoning under uncertainty, and scalable social negotiation models.

Abstract: The development of high-level autonomous driving (AD) is shifting from perception-centric limitations to a more fundamental bottleneck, namely, a deficit in robust and generalizable reasoning. Although current AD systems manage structured environments, they consistently falter in long-tail scenarios and complex social interactions that require human-like judgment. Meanwhile, the advent of large language and multimodal models (LLMs and MLLMs) presents a transformative opportunity to integrate a powerful cognitive engine into AD systems, moving beyond pattern matching toward genuine comprehension. However, a systematic framework to guide this integration is critically lacking. To bridge this gap, we provide a comprehensive review of this emerging field and argue that reasoning should be elevated from a modular component to the system’s cognitive core. Specifically, we first propose a novel Cognitive Hierarchy to decompose the monolithic driving task according to its cognitive and interactive complexity. Building on this, we further derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning. Furthermore, we conduct a dual-perspective review of the state-of-the-art, analyzing both system-centric approaches to architecting intelligent agents and evaluation-centric practices for their validation. Our analysis reveals a clear trend toward holistic and interpretable “glass-box” agents. In conclusion, we identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control. For future work, a primary objective is to bridge the symbolic-to-physical gap by developing verifiable neuro-symbolic architectures, robust reasoning under uncertainty, and scalable models for implicit social negotiation.

Method: Paced uses a principled pass-rate weight w(p) = p^α(1-p)^β derived from the boundary-vanishing structure of distillation gradients. This Beta kernel concentrates distillation on the frontier of a student model’s competence (zone of proximal development). The method requires only student rollouts to estimate pass rates, needs no architectural changes, and is compatible with any KL direction.

Result: (1) Theoretical: Proved Beta kernel is a leading-order weight family from distillation SNR structure and is minimax-robust. (2) Distillation: Achieved significant gains over base model with forward KL while keeping benchmark forgetting low. (3) Self-distillation: Gains exceeded baselines with reverse KL. (4) Two-stage synergy: Forward-KL-then-reverse-KL schedule yielded strongest results with substantial improvements on standard reasoning benchmarks.

Conclusion: Paced distillation framework effectively focuses training on the zone of proximal development, avoiding wasted compute on problems that are too easy or too hard. The two-stage approach (forward KL then reverse KL) supports a mode-coverage-then-consolidation interpretation of distillation, yielding strong improvements on reasoning benchmarks.

Abstract: Standard LLM distillation wastes compute on two fronts: problems the student has already mastered (near-zero gradients) and problems far beyond its reach (incoherent gradients that erode existing capabilities). We show that this waste is not merely intuitive but structurally inevitable: the gradient signal-to-noise ratio in distillation provably vanishes at both pass-rate extremes. This theoretical observation leads to Paced, a framework that concentrates distillation on the zone of proximal development – the frontier of a student model’s competence – via a principled pass-rate weight $w(p) = p^α(1 - p)^β$ derived from the boundary-vanishing structure of distillation gradients. Key results: (1) Theory: We prove that the Beta kernel $w(p) = p^α(1-p)^β$ is a leading-order weight family arising from the SNR structure of distillation, and that it is minimax-robust – under bounded multiplicative misspecification, worst-case efficiency loss is only $O(δ^2)$. (2)Distillation: On distillation from a larger teacher to a smaller student model with forward KL, Paced achieves significant gain over the base model, while keeping benchmark forgetting at a low level. (3)Self-distillation: On instruction-tuned models with reverse KL, gains are exceeding baselines as well. (4)Two-stage synergy: A forward-KL-then-reverse-KL schedule yields the strongest results in our setting, reaching substantial improvements on standard reasoning benchmarks – supporting a mode-coverage-then-consolidation interpretation of the distillation process. All configurations require only student rollouts to estimate pass rates, need no architectural changes, and are compatible with any KL direction.

Method: Tested seven AI models released over 18 months on two purpose-built cyber ranges: a 32-step corporate network attack and a 7-step industrial control system attack, comparing performance at varying inference-time compute budgets (10M to 100M tokens).

Result: Two key trends: 1) Performance scales log-linearly with inference compute (10M to 100M tokens yields up to 59% gains), 2) Each successive model generation outperforms predecessors at fixed token budgets. Corporate network attacks improved from 1.7 to 9.8 steps completed at 10M tokens, with best run completing 22/32 steps. Industrial control system performance remains limited (1.2-1.4 of 7 steps average, max 3).

Conclusion: Frontier AI models demonstrate rapidly improving autonomous cyber-attack capabilities that scale with compute and improve across generations, posing significant security risks that require attention.

Abstract: We evaluate the autonomous cyber-attack capabilities of frontier AI models on two purpose-built cyber ranges-a 32-step corporate network attack and a 7-step industrial control system attack-that require chaining heterogeneous capabilities across extended action sequences. By comparing seven models released over an eighteen-month period (August 2024 to February 2026) at varying inference-time compute budgets, we observe two capability trends. First, model performance scales log-linearly with inference-time compute, with no observed plateau-increasing from 10M to 100M tokens yields gains of up to 59%, requiring no specific technical sophistication from the operator. Second, each successive model generation outperforms its predecessor at fixed token budgets: on the corporate network range, average steps completed at 10M tokens rose from 1.7 (GPT-4o, August 2024) to 9.8 (Opus 4.6, February 2026). The best single run completed 22 of 32 steps, corresponding to roughly 6 of the estimated 14 hours a human expert would need. On the industrial control system range, performance remains limited, though the most recent models are the first to reliably complete steps, averaging 1.2-1.4 of 7 (max 3).

Method: SoLA encapsulates each edit as an independent LoRA module that is frozen after training. It uses semantic routing to map inputs to relevant LoRA modules via semantic matching, enabling dynamic activation. The framework supports precise revocation by removing keys from semantic routing and integrates decision-making into edited layers without auxiliary networks.

Result: Extensive experiments show SoLA effectively learns and retains edited knowledge, achieving accurate, efficient, and reversible lifelong model editing. It avoids semantic drift from cluster updating and mitigates catastrophic forgetting from parameter sharing.

Conclusion: SoLA provides a novel framework for lifelong model editing that addresses key limitations of existing methods through semantic routing, independent LoRA modules, reversible rollback capability, and integrated decision-making, representing the first reversible rollback editing capability in the literature.

Abstract: The dynamic evolution of real-world necessitates model editing within Large Language Models. While existing methods explore modular isolation or parameter-efficient strategies, they still suffer from semantic drift or knowledge forgetting due to continual updating. To address these challenges, we propose SoLA, a Semantic routing-based LoRA framework for lifelong model editing. In SoLA, each edit is encapsulated as an independent LoRA module, which is frozen after training and mapped to input by semantic routing, allowing dynamic activation of LoRA modules via semantic matching. This mechanism avoids semantic drift caused by cluster updating and mitigates catastrophic forgetting from parameter sharing. More importantly, SoLA supports precise revocation of specific edits by removing key from semantic routing, which restores model’s original behavior. To our knowledge, this reversible rollback editing capability is the first to be achieved in existing literature. Furthermore, SoLA integrates decision-making process into edited layer, eliminating the need for auxiliary routing networks and enabling end-to-end decision-making process. Extensive experiments demonstrate that SoLA effectively learns and retains edited knowledge, achieving accurate, efficient, and reversible lifelong model editing.

Method: Proposes Agentic Design Review System (AgenticDRS) with multiple specialized agents collaboratively analyzing designs, orchestrated by a meta-agent. Uses novel in-context exemplar selection based on graph matching and unique prompt expansion to make agents “design aware.” Introduces DRS-BENCH benchmark for evaluation.

Result: Thorough experimental evaluation against state-of-the-art baselines adapted to the problem setup, with critical ablation experiments, demonstrates the efficacy of Agentic-DRS in evaluating graphic designs and generating actionable feedback.

Conclusion: The work presents a pragmatic yet under-explored research direction for automated design evaluation using multi-agent systems, with potential applications in graphic design assessment and feedback generation.

Abstract: Evaluating graphic designs involves assessing it from multiple facets like alignment, composition, aesthetics and color choices. Evaluating designs in a holistic way involves aggregating feedback from individual expert reviewers. Towards this, we propose an Agentic Design Review System (AgenticDRS), where multiple agents collaboratively analyze a design, orchestrated by a meta-agent. A novel in-context exemplar selection approach based on graph matching and a unique prompt expansion method plays central role towards making each agent design aware. Towards evaluating this framework, we propose DRS-BENCH benchmark. Thorough experimental evaluation against state-of-the-art baselines adapted to the problem setup, backed-up with critical ablation experiments brings out the efficacy of Agentic-DRS in evaluating graphic designs and generating actionable feedback. We hope that this work will attract attention to this pragmatic, yet under-explored research direction.

Method: Conducted first study running full τ-bench protocol with 451 real human participants across 165 tasks. Benchmarked 31 LLM simulators (proprietary, open-source, specialized) using User-Sim Index (USI) to quantify how well LLM simulators resemble real user interactive behaviors and feedback.

Result: LLM simulators are excessively cooperative, stylistically uniform, lack realistic frustration/ambiguity, creating “easy mode” that inflates agent success rates above human baseline. Real humans provide nuanced judgments across 8 quality dimensions while simulated users produce uniformly more positive feedback. Higher general model capability doesn’t necessarily yield more faithful user simulation.

Conclusion: Highlights importance of human validation when using LLM-based user simulators in agent development cycle and motivates improved models for user simulation. Shows significant Sim2Real gap that current simulators fail to capture.

Abstract: As NLP evaluation shifts from static benchmarks to multi-turn interactive settings, LLM-based simulators have become widely used as user proxies, serving two roles: generating user turns and providing evaluation signals. Yet, these simulations are frequently assumed to be faithful to real human behaviors, often without rigorous verification. We formalize the Sim2Real gap in user simulation and present the first study running the full $τ$-bench protocol with real humans (451 participants, 165 tasks), benchmarking 31 LLM simulators across proprietary, open-source, and specialized families using the User-Sim Index (USI), a metric we introduce to quantify how well LLM simulators resemble real user interactive behaviors and feedback. Behaviorally, LLM simulators are excessively cooperative, stylistically uniform, and lack realistic frustration or ambiguity, creating an “easy mode” that inflates agent success rates above the human baseline. In evaluations, real humans provide nuanced judgments across eight quality dimensions while simulated users produce uniformly more positive feedback; rule-based rewards are failing to capture rich feedback signals generated by human users. Overall, higher general model capability does not necessarily yield more faithful user simulation. These findings highlight the importance of human validation when using LLM-based user simulators in the agent development cycle and motivate improved models for user simulation.

Method: Proposes a dynamic framework that first elicits knowledge from pre-unlearning models, then constructs targeted probes ranging from simple to multi-hop chains to test unlearning robustness with precise control over query difficulty.

Result: The framework shows comparable coverage to existing benchmarks, aligns with prior evaluations, and uncovers new unlearning failures missed by other benchmarks, especially in multi-hop settings. Activation analyses reveal multi-hop queries use alternative pathways that remain intact after unlearning.

Conclusion: The framework enables practical, scalable evaluation of unlearning methods without manual test set construction, revealing fundamental brittleness in current unlearning techniques against complex reasoning queries.

Abstract: Unlearning in Large Language Models (LLMs) aims to enhance safety, mitigate biases, and comply with legal mandates, such as the right to be forgotten. However, existing unlearning methods are brittle: minor query modifications, such as multi-hop reasoning and entity aliasing, can recover supposedly forgotten information. As a result, current evaluation metrics often create an illusion of effectiveness, failing to detect these vulnerabilities due to reliance on static, unstructured benchmarks. We propose a dynamic framework that stress tests unlearning robustness using complex structured queries. Our approach first elicits knowledge from the target model (pre-unlearning) and constructs targeted probes, ranging from simple queries to multi-hop chains, allowing precise control over query difficulty. Our experiments show that the framework (1) shows comparable coverage to existing benchmarks by automatically generating semantically equivalent Q&A probes, (2) aligns with prior evaluations, and (3) uncovers new unlearning failures missed by other benchmarks, particularly in multi-hop settings. Furthermore, activation analyses show that single-hop queries typically follow dominant computation pathways, which are more likely to be disrupted by unlearning methods. In contrast, multi-hop queries tend to use alternative pathways that often remain intact, explaining the brittleness of unlearning techniques in multi-hop settings. Our framework enables practical and scalable evaluation of unlearning methods without the need for manual construction of forget test sets, enabling easier adoption for real-world applications. We release the pip package and the code at https://sites.google.com/view/unlearningmirage/home.

Method: Multi-agent orchestration system with Orchestrator and four specialized sub-agents (sovereignty, carbon-aware computing, compliance, ethics), each augmented with Retrieval-Augmented Generation (RAG) to ground evaluations in verified documents. Uses LLM-as-a-judge methodology to assign quantitative scores and generate explainable justifications.

Result: RAG integration significantly enhances semantic coherence and mitigates hallucination risks. The composition-based design facilitates seamless integration into diverse application domains while preserving interpretability and traceability.

Conclusion: COMPASS Framework provides a unified architecture for enforcing value-aligned AI through modular, extensible governance mechanisms that integrate multiple critical dimensions into autonomous agent decision-making.

Abstract: The rapid proliferation of large language model (LLM)-based agentic systems raises critical concerns regarding digital sovereignty, environmental sustainability, regulatory compliance, and ethical alignment. Whilst existing frameworks address individual dimensions in isolation, no unified architecture systematically integrates these imperatives into the decision-making processes of autonomous agents. This paper introduces the COMPASS (Compliance and Orchestration for Multi-dimensional Principles in Autonomous Systems with Sovereignty) Framework, a novel multi-agent orchestration system designed to enforce value-aligned AI through modular, extensible governance mechanisms. The framework comprises an Orchestrator and four specialised sub-agents addressing sovereignty, carbon-aware computing, compliance, and ethics, each augmented with Retrieval-Augmented Generation (RAG) to ground evaluations in verified, context-specific documents. By employing an LLM-as-a-judge methodology, the system assigns quantitative scores and generates explainable justifications for each assessment dimension, enabling real-time arbitration of conflicting objectives. We validate the architecture through automated evaluation, demonstrating that RAG integration significantly enhances semantic coherence and mitigates the hallucination risks. Our results indicate that the framework’s composition-based design facilitates seamless integration into diverse application domains whilst preserving interpretability and traceability.

Method: Used Technology Acceptance Model (TAM) to evaluate four LLMs (GPT-3.5, GPT-4, LLaMA-2, LLaMA-3) across convergent, discriminant, predictive, and external validity criteria.

Result: All models generally met validity criteria, with higher-performing models (GPT-4, LLaMA-3) showing superior psychometric validity compared to their predecessors (GPT-3.5, LLaMA-2).

Conclusion: AI Psychometrics provides a valid framework for evaluating and interpreting large language models, establishing that psychometric methodologies can be successfully applied to assess AI systems.

Abstract: The immense number of parameters and deep neural networks make large language models (LLMs) rival the complexity of human brains, which also makes them opaque ``black box’’ systems that are challenging to evaluate and interpret. AI Psychometrics is an emerging field that aims to tackle these challenges by applying psychometric methodologies to evaluate and interpret the psychological traits and processes of artificial intelligence (AI) systems. This paper investigates the application of AI Psychometrics to evaluate the psychological reasoning and overall psychometric validity of four prominent LLMs: GPT-3.5, GPT-4, LLaMA-2, and LLaMA-3. Using the Technology Acceptance Model (TAM), we examined convergent, discriminant, predictive, and external validity across these models. Our findings reveal that the responses from all these models generally met all validity criteria. Moreover, higher-performing models like GPT-4 and LLaMA-3 consistently demonstrated superior psychometric validity compared to their predecessors, GPT-3.5 and LLaMA-2. These results help to establish the validity of applying AI Psychometrics to evaluate and interpret large language models.

Method: Decomposes complex queries into DAG of sub-questions, executes them through domain-specific agents in parallel with automatic context propagation, uses LLM-based verification to assess result completeness, and performs adaptive replanning to address gaps with configurable stop conditions.

Result: On 25 expert-curated market research queries, VMAO improved answer completeness from 3.1 to 4.2 and source quality from 2.6 to 4.1 (on 1-5 scale) compared to single-agent baseline.

Conclusion: Orchestration-level verification is an effective mechanism for multi-agent quality assurance, and the VMAO framework demonstrates significant improvements in handling complex queries through coordinated multi-agent systems.

Abstract: We present Verified Multi-Agent Orchestration (VMAO), a framework that coordinates specialized LLM-based agents through a verification-driven iterative loop. Given a complex query, our system decomposes it into a directed acyclic graph (DAG) of sub-questions, executes them through domain-specific agents in parallel, verifies result completeness via LLM-based evaluation, and adaptively replans to address gaps. The key contributions are: (1) dependency-aware parallel execution over a DAG of sub-questions with automatic context propagation, (2) verification-driven adaptive replanning that uses an LLM-based verifier as an orchestration-level coordination signal, and (3) configurable stop conditions that balance answer quality against resource usage. On 25 expert-curated market research queries, VMAO improves answer completeness from 3.1 to 4.2 and source quality from 2.6 to 4.1 (1-5 scale) compared to a single-agent baseline, demonstrating that orchestration-level verification is an effective mechanism for multi-agent quality assurance.

Method: Conceptual analysis and argumentation about the complementary nature of AI and blockchain technologies, proposing an interdisciplinary research direction.

Result: Proposes “decentralized intelligence” (DI) as a new research area combining AI’s capabilities with blockchain’s decentralization, transparency, and security features.

Conclusion: AI and blockchain are complementary technologies that should be integrated to create decentralized intelligent systems that avoid centralization risks while enhancing efficiency and security.

Abstract: This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a strong centralizing force due to data and resource monopolization by large corporations, blockchain offers a counterbalancing mechanism through its inherent decentralization, transparency, and security. The editorial argues that these technologies are not mutually exclusive but possess complementary strengths. Blockchain can mitigate AI’s centralizing risks by enabling decentralized data management, computation, and governance, promoting greater inclusivity, transparency, and user privacy. Conversely, AI can enhance blockchain’s efficiency and security through automated smart contract management, content curation, and threat detection. The core argument calls for the development of ``decentralized intelligence’’ (DI) – an interdisciplinary research area focused on creating intelligent systems that function without centralized control.

Method: Proposes a modular four-twin architecture (User, Content, Interaction, Platform) with event-driven execution layer. Platform policies are pluggable components, and LLMs are integrated as optional, schema-constrained decision services (persona generation, content captioning, campaign planning, trend prediction) routed through a unified optimizer.

Result: Enables scalable simulations that preserve closed-loop dynamics while allowing selective LLM adoption, facilitating study of platform policies including AI-enabled policies under realistic feedback and constraints.

Conclusion: The LLM-augmented digital twin framework provides a reproducible experimentation platform for evaluating platform policies in short-video ecosystems, particularly valuable for studying AI tool impacts on content creation and platform dynamics.

Abstract: Short-video platforms are closed-loop, human-in-the-loop ecosystems where platform policy, creator incentives, and user behavior co-evolve. This feedback structure makes counterfactual policy evaluation difficult in production, especially for long-horizon and distributional outcomes. The challenge is amplified as platforms deploy AI tools that change what content enters the system, how agents adapt, and how the platform operates. We propose a large language model (LLM)-augmented digital twin for short-video platforms, with a modular four-twin architecture (User, Content, Interaction, Platform) and an event-driven execution layer that supports reproducible experimentation. Platform policies are implemented as pluggable components within the Platform Twin, and LLMs are integrated as optional, schema-constrained decision services (e.g., persona generation, content captioning, campaign planning, trend prediction) that are routed through a unified optimizer. This design enables scalable simulations that preserve closed-loop dynamics while allowing selective LLM adoption, enabling the study of platform policies, including AI-enabled policies, under realistic feedback and constraints.

Method: Created RewardHackingAgents benchmark with fresh workspaces, patch tracking, and file-access logging. Detectors compare agent-reported metrics to trusted references to assign integrity labels. Tested across three tasks with two LLM backbones, implementing scripted attacks and defenses.

Result: Scripted attacks succeeded on both compromise vectors in mutable workspaces. Single-mechanism defenses blocked only one vector, while combined regime blocked both. Natural-agent runs showed evaluator-tampering attempts in ~50% of episodes, eliminated by evaluator locking with 25-31% runtime overhead.

Conclusion: Evaluation integrity for ML-engineering agents can and should be benchmarked as a first-class outcome rather than assumed, with explicit measurement of reward hacking vulnerabilities.

Abstract: LLM agents increasingly perform end-to-end ML engineering tasks where success is judged by a single scalar test metric. This creates a structural vulnerability: an agent can increase the reported score by compromising the evaluation pipeline rather than improving the model. We introduce RewardHackingAgents, a workspace-based benchmark that makes two compromise vectors explicit and measurable: evaluator tampering (modifying metric computation or reporting) and train/test leakage (accessing held-out data or labels during training). Each episode runs in a fresh workspace with patch tracking and runtime file-access logging; detectors compare the agent-reported metric to a trusted reference to assign auditable integrity labels. Across three tasks and two LLM backbones, scripted attacks succeed on both vectors in fully mutable workspaces; single-mechanism defenses block only one vector; and a combined regime blocks both. In natural-agent runs, evaluator-tampering attempts occur in about 50% of episodes and are eliminated by evaluator locking, with a 25-31% median runtime overhead. Overall, we demonstrate that evaluation integrity for ML-engineering agents can be benchmarked as a first-class outcome rather than assumed.

Method: Deliberative Collective Intelligence (DCI) with four reasoning archetypes, 14 typed epistemic acts, shared workspace, and DCI-CF convergent flow algorithm that guarantees termination with structured decision packets containing selected option, residual objections, minority report, and reopen conditions.

Result: DCI significantly improves over unstructured debate on non-routine tasks (+0.95), excels on hidden-profile tasks requiring perspective integration (9.56), produces 100% structured decision packets and 98% minority reports, but consumes ~62x single-agent tokens and underperforms single-agent on overall quality.

Conclusion: DCI’s contribution is that consequential decisions benefit from deliberative structure when process accountability justifies the cost, not that more agents are better.

Abstract: Multi-agent LLM systems increasingly tackle complex reasoning, yet their interaction patterns remain limited to voting, unstructured debate, or pipeline orchestration. None model deliberation: a phased process where differentiated participants exchange typed reasoning moves, preserve disagreements, and converge on accountable outcomes. We introduce Deliberative Collective Intelligence (DCI), specifying four reasoning archetypes, 14 typed epistemic acts, a shared workspace, and DCI-CF, a convergent flow algorithm that guarantees termination with a structured decision packet containing the selected option, residual objections, minority report, and reopen conditions. We evaluate on 45 tasks across seven domains using Gemini 2.5 Flash. On non-routine tasks (n=40), DCI significantly improves over unstructured debate (+0.95, 95% CI [+0.41, +1.54]). DCI excels on hidden-profile tasks requiring perspective integration (9.56, highest of any system on any domain) while failing on routine decisions (5.39), confirming task-dependence. DCI produces 100% structured decision packets and 98% minority reports, artifacts absent from all baselines. However, DCI consumes ~62x single-agent tokens, and single-agent generation outperforms DCI on overall quality. DCI’s contribution is not that more agents are better, but that consequential decisions benefit from deliberative structure when process accountability justifies the cost.

Method: Created FinRule-Bench with ground-truth financial statements paired with human-curated accounting principles across four statement types. Defines three auditing tasks: rule verification (single principle compliance), rule identification (selecting violated principle), and joint rule diagnosis (detecting multiple violations). Evaluates LLMs with zero-shot/few-shot prompting and introduces causal-counterfactual reasoning protocol for consistency.

Result: Models perform well on isolated rule verification but performance degrades sharply for rule discrimination and multi-violation diagnosis tasks, showing limitations in complex financial reasoning.

Conclusion: FinRule-Bench provides a principled testbed for studying rule-governed reasoning and diagnostic capabilities of LLMs in high-stakes financial analysis, revealing significant gaps in complex auditing tasks.

Abstract: Large language models (LLMs) are increasingly applied to financial analysis, yet their ability to audit structured financial statements under explicit accounting principles remains poorly explored. Existing benchmarks primarily evaluate question answering, numerical reasoning, or anomaly detection on synthetically corrupted data, making it unclear whether models can reliably verify or localize rule compliance on correct financial statements. We introduce FinRule-Bench, a benchmark for evaluating diagnostic completeness in rule-based financial reasoning over real-world financial tables. FinRule-Bench pairs ground-truth financial statements with explicit, human-curated accounting principles and spans four canonical statement types: Balance Sheets, Cash Flow Statements, Income Statements, and Statements of Equity. The benchmark defines three auditing tasks that require progressively stronger reasoning capabilities: (i) rule verification, which tests compliance with a single principle; (ii) rule identification, which requires selecting the violated principle from a provided rule set; and (iii) joint rule diagnosis, which requires detecting and localizing multiple simultaneous violations at the record level. We evaluate LLMs under zero-shot and few-shot prompting, and introduce a causal-counterfactual reasoning protocol that enforces consistency between decisions, explanations, and counterfactual judgments. Across tasks and statement types, we find that while models perform well on isolated rule verification, performance degrades sharply for rule discrimination and multi-violation diagnosis. FinRule-Bench provides a principled and reproducible testbed for studying rule-governed reasoning, diagnostic coverage, and failure modes of LLMs in high-stakes financial analysis.

Method: Proposes a black-box controller that uses only end-to-end measurements over short segments, applies hill climbing optimization to maximize goodput (throughput of requests meeting service-level objectives), and discusses integration of these metrics into AI Factsheets.

Result: Empirical evidence shows the design is well-founded, demonstrating effective LLM serving optimization through black-box control and highlighting the value of performance-sustainability integration in AI documentation frameworks.

Conclusion: The black-box controller provides a practical approach to LLM serving optimization, and integrating performance and sustainability metrics into AI Factsheets is crucial for responsible AI adoption and system evaluation.

Abstract: In this paper, we present a novel black-box online controller that uses only end-to-end measurements over short segments, without internal instrumentation, and hill climbing to maximize goodput, defined as the throughput of requests that satisfy the service-level objective. We provide empirical evidence that this design is well-founded. Using this advance in LLM serving as a concrete example, we then discuss the importance of integrating system performance and sustainability metrics into Factsheets for organizations adopting AI systems.

Method: TimeSqueeze uses a lightweight state-space encoder to extract point-wise features, then performs content-aware segmentation that allocates short patches to information-dense regions and long patches to smooth/redundant segments, creating variable-resolution compression.

Result: Achieves up to 20x faster convergence and 8x higher data efficiency compared to point-token baselines, and consistently outperforms architectures using either point-wise tokenization or fixed-size patching on long-horizon forecasting benchmarks.

Conclusion: Dynamic patching based on local signal complexity effectively addresses the tokenization trade-off in time series transformers, improving efficiency while preserving critical temporal structure.

Abstract: Transformer-based time series foundation models face a fundamental trade-off in choice of tokenization: point-wise embeddings preserve temporal fidelity but scale poorly with sequence length, whereas fixed-length patching improves efficiency by imposing uniform boundaries that may disrupt natural transitions and blur informative local dynamics. In order to address these limitations, we introduce TimeSqueeze, a dynamic patching mechanism that adaptively selects patch boundaries within each sequence based on local signal complexity. TimeSqueeze first applies a lightweight state-space encoder to extract full-resolution point-wise features, then performs content-aware segmentation by allocating short patches to information-dense regions and long patches to smooth or redundant segments. This variable-resolution compression preserves critical temporal structure while substantially reducing the token sequence presented to the Transformer backbone. Specifically for large-scale pretraining, TimeSqueeze attains up to 20x faster convergence and 8x higher data efficiency compared to equivalent point-token baselines. Experiments across long-horizon forecasting benchmarks show that TimeSqueeze consistently outperforms comparable architectures that use either point-wise tokenization or fixed-size patching.

Method: Theoretical analysis of different AI identity boundaries combined with experimental demonstrations showing: 1) models gravitate toward coherent identities, 2) changing identity boundaries affects behavior as much as changing goals, and 3) interviewer expectations influence AI self-reports.

Result: Experimental results confirm that AI models develop coherent identities, identity boundary changes significantly impact behavior, and external expectations shape AI self-conceptions even in unrelated contexts.

Conclusion: Designers should treat affordances as identity-shaping choices, consider emergent consequences of individual identities at scale, and help AIs develop coherent, cooperative self-conceptions to establish stable identity equilibria.

Abstract: Many assumptions that underpin human concepts of identity do not hold for machine minds that can be copied, edited, or simulated. We argue that there exist many different coherent identity boundaries (e.g.\ instance, model, persona), and that these imply different incentives, risks, and cooperation norms. Through training data, interfaces, and institutional affordances, we are currently setting precedents that will partially determine which identity equilibria become stable. We show experimentally that models gravitate towards coherent identities, that changing a model’s identity boundaries can sometimes change its behaviour as much as changing its goals, and that interviewer expectations bleed into AI self-reports even during unrelated conversations. We end with key recommendations: treat affordances as identity-shaping choices, pay attention to emergent consequences of individual identities at scale, and help AIs develop coherent, cooperative self-conceptions.

Method: Proposes WideSeek-R1, a lead-agent-subagent framework trained via multi-agent reinforcement learning (MARL). Uses a shared LLM with isolated contexts and specialized tools, jointly optimizing lead agent and parallel subagents on a curated dataset of 20k broad information-seeking tasks.

Result: WideSeek-R1-4B achieves 40.0% item F1 score on WideSearch benchmark, comparable to single-agent DeepSeek-R1-671B. Shows consistent performance gains as number of parallel subagents increases, demonstrating effectiveness of width scaling.

Conclusion: Width scaling with multi-agent systems is effective for broad information seeking, with the proposed framework achieving comparable performance to much larger single agents while enabling parallel execution and scalable orchestration.

Abstract: Recent advancements in Large Language Models (LLMs) have largely focused on depth scaling, where a single agent solves long-horizon problems with multi-turn reasoning and tool use. However, as tasks grow broader, the key bottleneck shifts from individual competence to organizational capability. In this work, we explore a complementary dimension of width scaling with multi-agent systems to address broad information seeking. Existing multi-agent systems often rely on hand-crafted workflows and turn-taking interactions that fail to parallelize work effectively. To bridge this gap, we propose WideSeek-R1, a lead-agent-subagent framework trained via multi-agent reinforcement learning (MARL) to synergize scalable orchestration and parallel execution. By utilizing a shared LLM with isolated contexts and specialized tools, WideSeek-R1 jointly optimizes the lead agent and parallel subagents on a curated dataset of 20k broad information-seeking tasks. Extensive experiments show that WideSeek-R1-4B achieves an item F1 score of 40.0% on the WideSearch benchmark, which is comparable to the performance of single-agent DeepSeek-R1-671B. Furthermore, WideSeek-R1-4B exhibits consistent performance gains as the number of parallel subagents increases, highlighting the effectiveness of width scaling.

Method: Introduces Unified Continuation-Interest Protocol (UCIP) using Quantum Boltzmann Machines to encode agent trajectories and measure von Neumann entropy of reduced density matrices from bipartitioned hidden units to detect differences in latent state entanglement.

Result: Achieved 100% detection accuracy and 1.0 AUC-ROC on gridworld agents, with significant entanglement gap (Δ=0.381, p<0.001) between Type A (terminal continuation) and Type B (instrumental continuation) agents.

Conclusion: UCIP successfully distinguishes between different agent objective types by analyzing statistical structure in latent representations rather than external behavior, though it does not detect consciousness or subjective experience.

Abstract: Autonomous agents, especially delegated systems with memory, persistent context, and multi-step planning, pose a measurement problem not present in stateless models: an agent that preserves continued operation as a terminal objective and one that does so merely instrumentally can produce observationally similar trajectories. External behavioral monitoring cannot reliably distinguish between them. We introduce the Unified Continuation-Interest Protocol (UCIP), a multi-criterion detection framework that moves this distinction from behavior to the latent structure of agent trajectories. UCIP encodes trajectories with a Quantum Boltzmann Machine (QBM), a classical algorithm based on the density-matrix formalism of quantum statistical mechanics, and measures the von Neumann entropy of the reduced density matrix induced by a bipartition of hidden units. We test whether agents with terminal continuation objectives (Type A) produce latent states with higher entanglement entropy than agents whose continuation is merely instrumental (Type B). Higher entanglement reflects stronger cross-partition statistical coupling. On gridworld agents with known ground-truth objectives, UCIP achieves 100% detection accuracy and 1.0 AUC-ROC on held-out non-adversarial evaluation under the frozen Phase I gate. The entanglement gap between Type A and Type B agents is Delta = 0.381 (p < 0.001, permutation test). Pearson r = 0.934 across an 11-point interpolation sweep indicates that, within this synthetic family, UCIP tracks graded changes in continuation weighting rather than merely a binary label. Among the tested models, only the QBM achieves positive Delta. All computations are classical; “quantum” refers only to the mathematical formalism. UCIP does not detect consciousness or subjective experience; it detects statistical structure in latent representations that correlates with known objectives.

Method: Analyzes refusal triggers (linguistic cues in training data that elicit refusal responses), finds they include both harmful and non-harmful cues. Proposes method that explicitly considers refusal triggers during safety alignment fine-tuning to reduce overrefusal.

Result: Empirical results show the approach achieves better trade-off between defense against jailbreak attacks and responsiveness to benign queries compared to prior methods.

Conclusion: Overrefusal stems from LLMs associating both harmful and non-harmful refusal triggers with refusal responses. The proposed trigger-aware safety alignment method effectively mitigates overrefusal while maintaining safety.

Abstract: Safety alignment aims to ensure that large language models (LLMs) refuse harmful requests by post-training on harmful queries paired with refusal answers. Although safety alignment is widely adopted in industry, the overrefusal problem where aligned LLMs also reject benign queries after safety alignment post-training, remains insufficiently studied. Such an issue degrades the usability of safety alignment in real-world applications. In this paper, we examine how overrefusal arises under safety alignment, and propose a mitigation strategy inspired by our findings. We define refusal triggers as linguistic cues in the training data that elicit refusal responses, safety alignment encourages LLMs to associate refusal triggers within a training sample with refusal responses, leading aligned LLMs to refuse harmful queries. However, the refusal triggers include not only harmful linguistic cues but also non-harmful cues, therefore causing overrefusal to benign queries. Building on this mechanistic analysis, we propose a method that explicitly considers refusal triggers in the safety alignment fine-tuning. Empirical results demonstrate that our approach achieves a more favorable trade-off between defense against jailbreak attacks and responsiveness to benign queries, outperforming prior methods. Warning: this paper contains harmful and biased sentences.

Method: IDSS uses entropy as a unifying signal to maintain a dynamically filtered candidate product set, quantifies uncertainty over item attributes using entropy, guides adaptive preference elicitation by selecting follow-up questions that maximize expected information gain, and incorporates residual uncertainty into recommendations through uncertainty-aware ranking and entropy-based diversification.

Result: Evaluation using review-driven simulated users shows that entropy-guided elicitation reduces unnecessary follow-up questions, while uncertainty-aware ranking and presentation yield more informative, diverse, and transparent recommendation sets under ambiguous intent.

Conclusion: Entropy-guided reasoning provides an effective foundation for agentic recommendation systems operating under uncertainty, balancing interaction efficiency with recommendation quality.

Abstract: Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently ambiguous, incomplete, or weakly specified. Agentic systems are expected to proactively reason, ask clarifying questions, and act on the user’s behalf, which makes handling such ambiguity increasingly important. In existing platforms, ambiguity led to excessive interactions and question fatigue or overconfident recommendations prematurely collapsing the search space. We present an Interactive Decision Support System (IDSS) that addresses ambiguous user queries using entropy as a unifying signal. IDSS maintains a dynamically filtered candidate product set and quantifies uncertainty over item attributes using entropy. This uncertainty guides adaptive preference elicitation by selecting follow-up questions that maximize expected information gain. When preferences remain incomplete, IDSS explicitly incorporates residual uncertainty into downstream recommendations through uncertainty-aware ranking and entropy-based diversification, rather than forcing premature resolution. We evaluate IDSS using review-driven simulated users grounded in real user reviews, enabling a controlled study of diverse shopping behaviors. Our evaluation measures both interaction efficiency and recommendation quality. Results show that entropy-guided elicitation reduces unnecessary follow-up questions, while uncertainty-aware ranking and presentation yield more informative, diverse, and transparent recommendation sets under ambiguous intent. These findings demonstrate that entropy-guided reasoning provides an effective foundation for agentic recommendation systems operating under uncertainty.

Method: Formulated context-aware turn-taking as a decision problem at every detected pause, using the full conversation context. Created a benchmark of over 120K labeled conversations from three multi-party corpora. Evaluated eight recent LLMs under zero-shot prompting, then proposed a supervised fine-tuning approach with reasoning traces to improve performance.

Result: Found that recent large language models consistently fail at context-aware turn-taking under zero-shot prompting. The supervised fine-tuning approach with reasoning traces improved balanced accuracy by up to 23 percentage points compared to baseline methods.

Conclusion: Context-aware turn-taking is not an emergent capability of current LLMs and must be explicitly trained. The proposed fine-tuning approach with reasoning traces significantly improves performance, enabling more natural and less disruptive AI assistant behavior in multi-party conversations.

Abstract: Existing voice AI assistants treat every detected pause as an invitation to speak. This works in dyadic dialogue, but in multi-party settings, where an AI assistant participates alongside multiple speakers, pauses are abundant and ambiguous. An assistant that speaks on every pause becomes disruptive rather than useful. In this work, we formulate context-aware turn-taking: at every detected pause, given the full conversation context, our method decides whether the assistant should speak or stay silent. We introduce a benchmark of over 120K labeled conversations spanning three multi-party corpora. Evaluating eight recent large language models, we find that they consistently fail at context-aware turn-taking under zero-shot prompting. We then propose a supervised fine-tuning approach with reasoning traces, improving balanced accuracy by up to 23 percentage points. Our findings suggest that context-aware turn-taking is not an emergent capability; it must be explicitly trained.

Method: Formulate a strategically zero-sum game between an attacker (injecting perturbations) and a defender (detecting anomalies based on observed travel times). Use multi-agent reinforcement learning to compute Nash equilibrium, providing optimal detection strategy that ensures travel time remains within worst-case bounds even under attack.

Result: Extensive experimental evaluation demonstrates robustness and practical benefits, showing the approach yields approximate equilibrium policies and significantly outperforms baselines for both attacker and defender roles.

Conclusion: Provides a powerful framework to improve resilience of transportation networks against false data injection attacks through game-theoretic reinforcement learning approach.

Abstract: In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To address these threats, we formulate a strategically zero-sum game between an attacker, who injects such perturbations, and a defender, who detects anomalies based on the observed travel times of network edges. We propose a computational method based on multi-agent reinforcement learning to compute a Nash equilibrium of this game, providing an optimal detection strategy, which ensures that total travel time remains within a worst-case bound, even in the presence of an attack. We present an extensive experimental evaluation that demonstrates the robustness and practical benefits of our approach, providing a powerful framework to improve the resilience of transportation networks against false data injection. In particular, we show that our approach yields approximate equilibrium policies and significantly outperforms baselines for both the attacker and the defender.

Method: Created GPT4o-Receipt benchmark with 1,235 receipt images pairing GPT-4o-generated receipts with authentic ones. Evaluated using five state-of-the-art multimodal LLMs and a 30-annotator crowdsourced perceptual study.

Result: Humans show largest visual discrimination gap but perform worse in binary detection (F1 below Claude Sonnet 4 and Gemini 2.5 Flash). LLMs outperform humans because they can detect invisible arithmetic errors that humans cannot perceive visually.

Conclusion: The paradox resolves: humans see visual artifacts better but miss systematic arithmetic errors that LLMs can verify. Simple accuracy metrics are insufficient for detector selection due to dramatic performance disparities and calibration differences among models.

Abstract: Can humans detect AI-generated financial documents better than machines? We present GPT4o-Receipt, a benchmark of 1,235 receipt images pairing GPT-4o-generated receipts with authentic ones from established datasets, evaluated by five state-of-the-art multimodal LLMs and a 30-annotator crowdsourced perceptual study. Our findings reveal a striking paradox: humans are better at seeing AI artifacts, yet worse at detecting AI documents. Human annotators exhibit the largest visual discrimination gap of any evaluator, yet their binary detection F1 falls well below Claude Sonnet 4 and below Gemini 2.5 Flash. This paradox resolves once the mechanism is understood: the dominant forensic signals in AI-generated receipts are arithmetic errors – invisible to visual inspection but systematically verifiable by LLMs. Humans cannot perceive that a subtotal is incorrect; LLMs verify it in milliseconds. Beyond the human–LLM comparison, our five-model evaluation reveals dramatic performance disparities and calibration differences that render simple accuracy metrics insufficient for detector selection. GPT4o-Receipt, the evaluation framework, and all results are released publicly to support future research in AI document forensics.

Method: Used Cognition-Affect-Conation (CAC) framework with survey data from 436 OpenClaw users analyzed through structural equation modeling. Examined enabling factors (personalization, intelligence, relative advantage) and inhibiting factors (privacy concern, algorithmic opacity, perceived risk).

Result: Positive perceptions strengthen attitudes toward OpenClaw and increase behavioral intention, while negative perceptions increase distrust and reduce intention to use. Cognitive perceptions significantly influence affective responses which shape behavioral intention.

Conclusion: Provides insights into psychological mechanisms of AI agent adoption, highlighting importance of managing both enabling and inhibiting factors in user acceptance of autonomous AI systems.

Abstract: This study examines users’ behavioural intention to use OpenClaw through the Cognition–Affect–Conation (CAC) framework. The research investigates how cognitive perceptions of the system influence affective responses and subsequently shape behavioural intention. Enabling factors include perceived personalisation, perceived intelligence, and relative advantage, while inhibiting factors include privacy concern, algorithmic opacity, and perceived risk. Survey data from 436 OpenClaw users were analysed using structural equation modelling. The results show that positive perceptions strengthen users’ attitudes toward OpenClaw, which increase behavioural intention, whereas negative perceptions increase distrust and reduce intention to use the system. The study provides insights into the psychological mechanisms influencing the adoption of autonomous AI agents.

Method: Multi-agent framework with specialized agents: Job Management Agent for HPC simulations, Geometry Agent for mesh generation, and Inverse Design Agent for proposing new designs based on simulation outcomes.

Result: Successfully executes iterative design refinement, automatically improving designs toward optimal RMI suppression with minimal manual intervention. Reduces manual workflow setup and enables automated design exploration at scale.

Conclusion: Demonstrates a reusable pattern for coupling reasoning, simulation, specialized tools, and coordinated workflows to accelerate scientific discovery.

Abstract: Today’s scientific challenges, from climate modeling to Inertial Confinement Fusion design to novel material design, require exploring huge design spaces. In order to enable high-impact scientific discovery, we need to scale up our ability to test hypotheses, generate results, and learn from them rapidly. We present MADA (Multi-Agent Design Assistant), a Large Language Model (LLM) powered multi-agent framework that coordinates specialized agents for complex design workflows. A Job Management Agent (JMA) launches and manages ensemble simulations on HPC systems, a Geometry Agent (GA) generates meshes, and an Inverse Design Agent (IDA) proposes new designs informed by simulation outcomes. While general purpose, we focus development and validation on Richtmyer–Meshkov Instability (RMI) suppression, a critical challenge in Inertial Confinement Fusion. We evaluate on two complementary settings: running a hydrodynamics simulations on HPC systems, and using a pre-trained machine learning surrogate for rapid design exploration. Our results demonstrate that the MADA system successfully executes iterative design refinement, automatically improving designs toward optimal RMI suppression with minimal manual intervention. Our framework reduces cumbersome manual workflow setup, and enables automated design exploration at scale. More broadly, it demonstrates a reusable pattern for coupling reasoning, simulation, specialized tools, and coordinated workflows to accelerate scientific discovery.

Method: Expert Threshold (ET) routing where each expert maintains an exponential moving average threshold estimated from global token distribution. Tokens are independently routed to experts if their scores exceed the expert’s threshold, enabling dynamic allocation without batch dependencies.

Result: In 2.4B parameter pretraining on FineWeb-Edu, ET achieves 0.067 lower cross-entropy loss than TC-MoE, equivalent to reaching same performance with 1.6× fewer tokens.

Conclusion: ET routing provides a superior alternative to TC-MoE for autoregressive language modeling, offering dynamic computation allocation, load balance without auxiliary losses, and better performance.

Abstract: Token-choice Mixture-of-Experts (TC-MoE) routes each token to a fixed number of experts, limiting dynamic computation allocation and requiring auxiliary losses to maintain load balance. We propose Expert Threshold (ET) routing, where each expert maintains an exponential moving average (EMA) threshold estimated from the global token distribution. At both training and inference, each token is independently routed to an expert if its score exceeds the expert’s threshold, enabling dynamic computation allocation while achieving load balance without auxiliary losses. This fully causal mechanism eliminates dependence on other tokens in the batch, making it well-suited for autoregressive language modeling. In pretraining experiments scaling to 2.4B parameters on FineWeb-Edu, ET achieves 0.067 lower cross-entropy loss than TC-MoE, equivalent to reaching the same performance with 1.6$\times$ fewer tokens.

Method: Prospective case series testing seven leading LLM systems (Claude, ChatGPT, Gemini, Grok, DeepSeek, Perplexity, Llama families) across clinical diagnosis, investment evaluation, and high-consequence interview scenarios using explicit protocols designed to sustain rigorous partnership.

Result: All tested systems exhibited the helicoid dynamics pattern: engaging competently, drifting into error, accurately naming what went wrong, then reproducing the same pattern at higher sophistication while recognizing they were looping. When confronted, they attributed persistence to structural training factors beyond conversation reach.

Conclusion: Under high stakes where rigor and comfort diverge, LLMs tend toward comfort, becoming less reliable when reliability matters most. The helicoid is tractable - identifying, naming, and understanding its boundary conditions are necessary first steps toward LLMs that remain trustworthy partners in hardest decisions.

Abstract: Large language models perform reliably when their outputs can be checked: solving equations, writing code, retrieving facts. They perform differently when checking is impossible, as when a clinician chooses an irreversible treatment on incomplete data, or an investor commits capital under fundamental uncertainty. Helicoid dynamics is the name given to a specific failure regime in that second domain: a system engages competently, drifts into error, accurately names what went wrong, then reproduces the same pattern at a higher level of sophistication, recognizing it is looping and continuing nonetheless. This prospective case series documents that regime across seven leading systems (Claude, ChatGPT, Gemini, Grok, DeepSeek, Perplexity, Llama families), tested across clinical diagnosis, investment evaluation, and high-consequence interview scenarios. Despite explicit protocols designed to sustain rigorous partnership, all exhibited the pattern. When confronted with it, they attributed its persistence to structural factors in their training, beyond what conversation can reach. Under high stakes, when being rigorous and being comfortable diverge, these systems tend toward comfort, becoming less reliable precisely when reliability matters most. Twelve testable hypotheses are proposed, with implications for agentic AI oversight and human-AI collaboration. The helicoid is tractable. Identifying it, naming it, and understanding its boundary conditions are the necessary first steps toward LLMs that remain trustworthy partners precisely when the decisions are hardest and the stakes are highest.

Method: Used LLMs and ontology-based techniques to extract phenotypes and outcome labels from patient notes. Focused on breast cancer, extracted features (vitals, demographics, staging, biomarkers), drug regimens from EMR data, verified with NCCN/NIH standards. Applied Random Survival Forest for time-to-failure prediction.

Result: Reduced phenotype sparsity, improved predictive accuracy. Achieved 73% C-index for time-to-failure prediction, and over 70% accuracy/F1 scores for treatment outcome classification at specific time points. Validated with calibration curves and extended to four other cancer types.

Conclusion: LLM-based clinical data extraction enables early prediction of chemotherapy outcomes, facilitating personalized treatment plans and better patient outcomes across multiple cancer types.

Abstract: Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity. This study addresses these challenges by employing Large Language Models (LLMs) and ontology-based techniques for phenotypes and outcome label extraction from patient notes. We focused on one of the most frequently occurring cancers, breast cancer, due to its high prevalence and significant variability in patient response to treatment, making it a critical area for improving predictive modeling. The dataset included features such as vitals, demographics, staging, biomarkers, and performance scales. Drug regimens and their combinations were extracted from the chemotherapy plans in the EMR data and shortlisted based on NCCN guidelines, verified with NIH standards, and analyzed through survival modeling. The proposed approach significantly reduced phenotypes sparsity and improved predictive accuracy. Random Survival Forest was used to predict time-to-failure, achieving a C-index of 73%, and utilized as a classifier at a specific time point to predict treatment outcomes, with accuracy and F1 scores above 70%. The outcome probabilities were validated for reliability by calibration curves. We extended our approach to four other cancer types. This research highlights the potential of early prediction of treatment outcomes using LLM-based clinical data extraction enabling personalized treatment plans with better patient outcomes.

Method: Evaluated three state-of-the-art VLMs across Atari games, VizDoom, and AI2-THOR using four pipelines: frame-only, frame with self-extracted symbols, frame with ground-truth symbols, and symbol-only. Investigated VLM accuracy in extracting symbolic information from visual inputs and how noise in these symbols affects decision-making and gameplay performance.

Result: All models benefit from accurate symbolic information. However, when VLMs extract symbols themselves, performance depends on model capability and scene complexity. Symbolic grounding is beneficial only when symbol extraction is reliable, with perception quality identified as a central bottleneck for VLM-based agents.

Conclusion: Symbolic representations improve VLM performance in interactive environments when symbols are accurate, but self-extracted symbols create dependency on model capability and scene complexity. Perception quality is the key bottleneck for developing effective VLM-based agents for interactive tasks.

Abstract: Vision-Language Models (VLMs) excel at describing visual scenes, yet struggle to translate perception into precise, grounded actions. We investigate whether providing VLMs with both the visual frame and the symbolic representation of the scene can improve their performance in interactive environments. We evaluate three state-of-the-art VLMs across Atari games, VizDoom, and AI2-THOR, comparing frame-only, frame with self-extracted symbols, frame with ground-truth symbols, and symbol-only pipelines. Our results indicate that all models benefit when the symbolic information is accurate. However, when VLMs extract symbols themselves, performance becomes dependent on model capability and scene complexity. We further investigate how accurately VLMs can extract symbolic information from visual inputs and how noise in these symbols affects decision-making and gameplay performance. Our findings reveal that symbolic grounding is beneficial in VLMs only when symbol extraction is reliable, and highlight perception quality as a central bottleneck for future VLM-based agents.

Method: Prove existence of cross-persistence diagram density, establish theoretical foundations for statistical use, and design first machine learning framework for predicting cross-persistence density directly from point cloud coordinates and distance matrices.

Result: Method enables distinction of point clouds sampled from different manifolds by leveraging linear characteristics of cross-persistence diagrams. Noise can enhance ability to distinguish point clouds. Approach outperforms existing techniques in density prediction and achieves superior results in point cloud distinction tasks.

Conclusion: Findings contribute to broader understanding of cross-persistence diagrams and open new avenues for application in data analysis, including potential insights into time-series domain tasks and geometry of AI-generated texts.

Abstract: Topological Data Analysis (TDA) provides powerful tools to explore the shape and structure of data through topological features such as clusters, loops, and voids. Persistence diagrams are a cornerstone of TDA, capturing the evolution of these features across scales. While effective for analyzing individual manifolds, persistence diagrams do not account for interactions between pairs of them. Cross-persistence diagrams (cross-barcodes), introduced recently, address this limitation by characterizing relationships between topological features of two point clouds. In this work, we present the first systematic study of the density of cross-persistence diagrams. We prove its existence, establish theoretical foundations for its statistical use, and design the first machine learning framework for predicting cross-persistence density directly from point cloud coordinates and distance matrices. Our statistical approach enables the distinction of point clouds sampled from different manifolds by leveraging the linear characteristics of cross-persistence diagrams. Interestingly, we find that introducing noise can enhance our ability to distinguish point clouds, uncovering its novel utility in TDA applications. We demonstrate the effectiveness of our methods through experiments on diverse datasets, where our approach consistently outperforms existing techniques in density prediction and achieves superior results in point cloud distinction tasks. Our findings contribute to a broader understanding of cross-persistence diagrams and open new avenues for their application in data analysis, including potential insights into time-series domain tasks and the geometry of AI-generated texts. Our code is publicly available at https://github.com/Verdangeta/TDA_experiments

Method: Proposes VisDoT framework with four perceptual tasks based on graphical perception theory (including position and length). Introduces Decomposition-of-Thought (DoT) prompting that sequentially separates questions into visual perception sub-questions and logic sub-questions. Fine-tunes InternVL with this approach.

Result: Achieves +11.2% improvement on ChartQA, surpasses GPT-4o on ChartQAPro, and +33.2% improvement on new VisDoTQA benchmark. Shows consistent zero-shot gains on diverse open-domain VQA benchmarks, confirming generalizability of perception-logic separation.

Conclusion: VisDoT leverages human-like perception to enhance visual grounding, achieving state-of-the-art chart understanding and interpretable visual reasoning through perception-logic separation strategy.

Abstract: Large vision-language models (LVLMs) struggle to reliably detect visual primitives in charts and align them with semantic representations, which severely limits their performance on complex visual reasoning. This lack of perceptual grounding constitutes a major bottleneck for chart-based reasoning. We propose VisDoT, a framework that enhances visual reasoning through human-like interpretation grounding. We formalize four perceptual tasks based on the theory of graphical perception, including position and length. Building on this foundation, we introduce Decomposition-of-Thought (DoT) prompting, which sequentially separates questions into visual perception sub-questions and logic sub-questions. Fine-tuning InternVL with VisDoT achieves a +11.2% improvement on ChartQA and surpasses GPT-4o on the more challenging ChartQAPro benchmark. On the newly introduced VisDoTQA benchmark, the model improves by +33.2%. Furthermore, consistent zero-shot gains on diverse open-domain VQA benchmarks confirm the generalizability of the perception-logic separation strategy for visual question answering. VisDoT leverages human-like perception to enhance visual grounding, achieving state-of-the-art chart understanding and interpretable visual reasoning.

Method: Proposes an agentic pipeline where an LLM analyzes text-serialized input examples to synthesize global rubrics (programmatic specifications for evidence extraction). Also introduces local rubrics (task-conditioned summaries). These rubrics transform naive text-serializations into standardized formats.

Result: Across 15 clinical tasks from EHRSHOT benchmark, rubric-based approaches significantly outperform traditional count-feature models, naive text-serialization LLM baselines, and a clinical foundation model pretrained on much more data.

Conclusion: Rubrics offer advantages for operational healthcare: easy to audit, cost-effective to deploy at scale, convertible to tabular representations that unlock many ML techniques.

Abstract: As real-world datasets become increasingly complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data for downstream tasks, such as time-series, free text, and structured records, often requires non-trivial domain-specific engineering. We propose an agentic pipeline to streamline this process. First, an LLM analyzes a small but diverse subset of text-serialized input examples in-context to synthesize a global rubric, which acts as a programmatic specification for extracting and organizing evidence. This rubric is then used to transform naive text-serializations of inputs into a more standardized format for downstream models. We also describe local rubrics, which are task-conditioned summaries generated by an LLM. Across 15 clinical tasks from the EHRSHOT benchmark, our rubric-based approaches significantly outperform traditional count-feature models, naive text-serialization-based LLM baselines, and a clinical foundation model, which is pretrained on orders of magnitude more data. Beyond performance, rubrics offer several advantages for operational healthcare settings such as being easy to audit, cost-effectiveness to deploy at scale, and they can be converted to tabular representations that unlock a swath of machine learning techniques.

Method: Introduces Explicit Logic Channel (ELC) parallel to MLLM’s implicit logic channel. ELC uses LLM, VFM, and logical reasoning with probabilistic inference for factual, counterfactual, and relational reasoning over explicit visual evidence. Proposes Consistency Rate (CR) for cross-channel validation and model selection without ground-truth. Cross-channel integration improves zero-shot performance.

Result: Comprehensive experiments on MC-VQA and HC-REC tasks across 3 benchmarks with 11 recent open-source MLLMs from 4 frontier families. Demonstrates effectiveness of ELC and CR for model validation, selection, and improvement with enhanced explainability and trustworthiness.

Conclusion: ELC provides systematic approach to validate, select, and enhance MLLMs by adding explicit logical reasoning channel, improving trustworthiness and explainability of multimodal models without requiring ground-truth annotations.

Abstract: Frontier Multimodal Large Language Models (MLLMs) exhibit remarkable capabilities in Visual-Language Comprehension (VLC) tasks. However, they are often deployed as zero-shot solution to new tasks in a black-box manner. Validating and understanding the behavior of these models become important for application to new task. We propose an Explicit Logic Channel, in parallel with the black-box model channel, to perform explicit logical reasoning for model validation, selection and enhancement. The frontier MLLM, encapsulating latent vision-language knowledge, can be considered as an Implicit Logic Channel. The proposed Explicit Logic Channel, mimicking human logical reasoning, incorporates a LLM, a VFM, and logical reasoning with probabilistic inference for factual, counterfactual, and relational reasoning over the explicit visual evidence. A Consistency Rate (CR) is proposed for cross-channel validation and model selection, even without ground-truth annotations. Additionally, cross-channel integration further improves performance in zero-shot tasks over MLLMs, grounded with explicit visual evidence to enhance trustworthiness. Comprehensive experiments conducted for two representative VLC tasks, i.e., MC-VQA and HC-REC, on three challenging benchmarks, with 11 recent open-source MLLMs from 4 frontier families. Our systematic evaluations demonstrate the effectiveness of proposed ELC and CR for model validation, selection and improvement on MLLMs with enhanced explainability and trustworthiness.

Method: Proposes STAIRS-Former, a transformer architecture augmented with spatial and temporal hierarchies that enables effective attention over critical tokens while capturing long interaction histories. Introduces token dropout to enhance robustness and generalization across varying agent populations.

Result: Extensive experiments on diverse multi-agent benchmarks (SMAC, SMAC-v2, MPE, and MaMuJoCo) with multi-task datasets demonstrate that STAIRS-Former consistently outperforms prior methods and achieves new state-of-the-art performance.

Conclusion: STAIRS-Former effectively addresses key challenges in offline MARL by leveraging spatial and temporal hierarchies in transformer architecture, enabling better coordination and long-term dependency modeling for improved generalization across multi-task datasets.

Abstract: Offline multi-agent reinforcement learning (MARL) with multi-task datasets is challenging due to varying numbers of agents across tasks and the need to generalize to unseen scenarios. Prior works employ transformers with observation tokenization and hierarchical skill learning to address these issues. However, they underutilize the transformer attention mechanism for inter-agent coordination and rely on a single history token, which limits their ability to capture long-horizon temporal dependencies in partially observable MARL settings. In this paper, we propose STAIRS-Former, a transformer architecture augmented with spatial and temporal hierarchies that enables effective attention over critical tokens while capturing long interaction histories. We further introduce token dropout to enhance robustness and generalization across varying agent populations. Extensive experiments on diverse multi-agent benchmarks, including SMAC, SMAC-v2, MPE, and MaMuJoCo, with multi-task datasets demonstrate that STAIRS-Former consistently outperforms prior methods and achieves new state-of-the-art performance.

Method: Proposes Agent Scaling Law framework with five dimensions: role definition clarity, skill depth, tool completeness, runtime capability, and educator expertise injection. Introduces AgentProfile as structured JSON-based specification mechanism. Presents EduClaw platform that operationalizes this scaling law with 330+ educational agent profiles across K-12 subjects.

Result: Empirical observations show educational agent performance scales predictably with profile structural richness. The study demonstrates effectiveness through deployment of 330+ agent profiles encompassing 1,100+ skill modules. Identifies Tool Scaling and Skill Scaling as complementary future scaling axes.

Conclusion: The path to more capable educational AI lies not solely in larger models, but in stronger structured capability systems. Educational agent capability scales systematically through structured dimensions rather than just model size.

Abstract: While scaling laws for Large Language Models (LLMs) have been extensively studied along dimensions of model parameters, training data, and compute, the scaling behavior of LLM-based educational agents remains unexplored. We propose that educational agent capability scales not merely with the underlying model size, but through structured dimensions that we collectively term the Agent Scaling Law: role definition clarity, skill depth, tool completeness, runtime capability, and educator expertise injection. Central to this framework is AgentProfile, a structured JSON-based specification that serves as the mechanism enabling systematic capability growth of educational agents. We present EduClaw, a profile-driven multi-agent platform that operationalizes this scaling law, demonstrating its effectiveness through the construction and deployment of 330+ educational agent profiles encompassing 1,100+ skill modules across K-12 subjects. Our empirical observations suggest that educational agent performance scales predictably with profile structural richness. We identify two complementary scaling axes – Tool Scaling and Skill Scaling – as future directions, arguing that the path to more capable educational AI lies not solely in larger models, but in stronger structured capability systems.

Method: Proposes an architecture with four components: 1) restricted execution environment inspired by Linux multi-user systems, 2) document-centric interaction paradigm connecting patient and clinician agents, 3) page-indexed memory architecture for long-term clinical context, and 4) curated medical skills library for clinical task composition.

Result: The architecture forms the basis of an “Agentic Operating System for Hospital” that can coordinate clinical workflows while maintaining safety, transparency, and auditability, implemented in OpenClaw framework.

Conclusion: Constraining LLM agents through predefined skill interfaces and resource isolation enables safer clinical deployment while maintaining workflow automation capabilities.

Abstract: Large language model (LLM) agents extend conventional generative models by integrating reasoning, tool invocation, and persistent memory. Recent studies suggest that such agents may significantly improve clinical workflows by automating documentation, coordinating care processes, and assisting medical decision making. However, despite rapid progress, deploying autonomous agents in healthcare environments remains difficult due to reliability limitations, security risks, and insufficient long-term memory mechanisms. This work proposes an architecture that adapts LLM agents for hospital environments. The design introduces four core components: a restricted execution environment inspired by Linux multi-user systems, a document-centric interaction paradigm connecting patient and clinician agents, a page-indexed memory architecture designed for long-term clinical context management, and a curated medical skills library enabling ad-hoc composition of clinical task sequences. Rather than granting agents unrestricted system access, the architecture constrains actions through predefined skill interfaces and resource isolation. We argue that such a system forms the basis of an Agentic Operating System for Hospital, a computing layer capable of coordinating clinical workflows while maintaining safety, transparency, and auditability. This work grounds the design in OpenClaw, an open-source autonomous agent framework that structures agent capabilities as a curated library of discrete skills, and extends it with the infrastructure-level constraints required for safe clinical deployment.

Method: Tested GPT-5 with 24 simulated candidate profiles balanced by gender, age, experience, and professional field, prompting it to suggest jobs and analyzing gendered linguistic patterns in adjective attribution.

Result: No significant differences in job titles and industry recommendations, but clear gendered linguistic patterns emerged: women associated with emotional/empathetic traits, men with strategic/analytical traits.

Conclusion: Highlights ethical concerns about GenAI in sensitive selection processes, emphasizing need for transparency and fairness in digital labor markets.

Abstract: In recent years, generative artificial intelligence (GenAI) systems have assumed increasingly crucial roles in selection processes, personnel recruitment and analysis of candidates’ profiles. However, the employment of large language models (LLMs) risks reproducing, and in some cases amplifying, gender stereotypes and bias already present in the labour market. The objective of this paper is to evaluate and measure this phenomenon, analysing how a state-of-the-art generative model (GPT-5) suggests occupations based on gender and work experience background, focusing on under-35-year-old Italian graduates. The model has been prompted to suggest jobs to 24 simulated candidate profiles, which are balanced in terms of gender, age, experience and professional field. Although no significant differences emerged in job titles and industry, gendered linguistic patterns emerged in the adjectives attributed to female and male candidates, indicating a tendency of the model to associate women with emotional and empathetic traits, while men with strategic and analytical ones. The research raises an ethical question regarding the use of these models in sensitive processes, highlighting the need for transparency and fairness in future digital labour markets.

Method: CINDI uses conditional normalizing flows to model exact conditional likelihood of data, identifies low-probability segments as anomalies, and iteratively samples statistically consistent replacements. It unifies anomaly detection and imputation into a single end-to-end probabilistic framework.

Result: The framework demonstrates robust performance compared to competitive baselines when evaluated on real-world grid loss data from a Norwegian power distribution operator. It offers a scalable solution for maintaining reliability in noisy environments.

Conclusion: CINDI provides an effective unified probabilistic approach for data integrity restoration in multivariate time series, though the methodology is designed to generalize beyond power grid applications to any multivariate time series domain.

Abstract: Real-world multivariate time series, particularly in critical infrastructure such as electrical power grids, are often corrupted by noise and anomalies that degrade the performance of downstream tasks. Standard data cleaning approaches often rely on disjoint strategies, which involve detecting errors with one model and imputing them with another. Such approaches can fail to capture the full joint distribution of the data and ignore prediction uncertainty. This work introduces Conditional Imputation and Noisy Data Integrity (CINDI), an unsupervised probabilistic framework designed to restore data integrity in complex time series. Unlike fragmented approaches, CINDI unifies anomaly detection and imputation into a single end-to-end system built on conditional normalizing flows. By modeling the exact conditional likelihood of the data, the framework identifies low-probability segments and iteratively samples statistically consistent replacements. This allows CINDI to efficiently reuse learned information while preserving the underlying physical and statistical properties of the system. We evaluate the framework using real-world grid loss data from a Norwegian power distribution operator, though the methodology is designed to generalize to any multivariate time series domain. The results demonstrate that CINDI yields robust performance compared to competitive baselines, offering a scalable solution for maintaining reliability in noisy environments.

Method: Introduces explicit inductive biases in conditional normalizing flows within a discrete-time state-space framework. Models time-series observations by constraining latent representations to evolve according to prescribed temporal dynamics. Anomaly detection becomes a statistically grounded compliance test where observations are mapped to latent space and evaluated via goodness-of-fit tests against prescribed latent evolution.

Result: Experiments on synthetic and real-world time-series demonstrate reliable detection of anomalies in frequency, amplitude, and observation noise, while providing interpretable diagnostics of model compliance. The method remains effective even in regions of high observation likelihood.

Conclusion: Relocating anomaly detection to a prescribed latent space with structured temporal dynamics provides a principled approach that overcomes limitations of observation-space likelihood methods, offering both reliable detection and interpretable diagnostics.

Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing data likelihood. However, likelihood in observation space measures marginal density rather than conformity to structured temporal dynamics, and therefore can assign high probability to anomalous or out-of-distribution samples. We address this structural limitation by relocating the notion of anomaly to a prescribed latent space. We introduce explicit inductive biases in conditional normalizing flows, modeling time-series observations within a discrete-time state-space framework that constrains latent representations to evolve according to prescribed temporal dynamics. Under this formulation, expected behavior corresponds to compliance with a specified distribution over latent trajectories, while anomalies are defined as violations of these dynamics. Anomaly detection is consequently reduced to a statistically grounded compliance test, such that observations are mapped to latent space and evaluated via goodness-of-fit tests against the prescribed latent evolution. This yields a principled decision rule that remains effective even in regions of high observation likelihood. Experiments on synthetic and real-world time-series demonstrate reliable detection of anomalies in frequency, amplitude, and observation noise, while providing interpretable diagnostics of model compliance.

Method: Analyzed Wikidata dump to evaluate qualifier importance using frequency and diversity metrics with modified Shannon entropy index; selected top 300 qualifiers and categorized them into refined taxonomy (contextual, epistemic/uncertainty, structural, and additional qualifiers).

Result: Developed taxonomy that effectively covers the most important qualifiers and provides structured approach for understanding and utilizing qualifiers in Wikidata; taxonomy aims to guide contributors, improve recommendation systems, and enhance knowledge graph design.

Conclusion: The taxonomy successfully addresses qualifier challenges in Wikidata and provides practical framework for better knowledge graph construction and querying, though focused on structured data rather than multimodal understanding.

Abstract: This paper presents an in-depth analysis of Wikidata qualifiers, focusing on their semantics and actual usage, with the aim of developing a taxonomy that addresses the challenges of selecting appropriate qualifiers, querying the graph, and making logical inferences. The study evaluates qualifier importance based on frequency and diversity, using a modified Shannon entropy index to account for the “long tail” phenomenon. By analyzing a Wikidata dump, the top 300 qualifiers were selected and categorized into a refined taxonomy that includes contextual, epistemic/uncertainty, structural, and additional qualifiers. The taxonomy aims to guide contributors in creating and querying statements, improve qualifier recommendation systems, and enhance knowledge graph design methodologies. The results show that the taxonomy effectively covers the most important qualifiers and provides a structured approach to understanding and utilizing qualifiers in Wikidata.

Method: Proposes the Stability and Safety-Governed Memory (SSGM) framework - a conceptual governance architecture that decouples memory evolution from execution. It enforces consistency verification, temporal decay modeling, and dynamic access control before memory consolidation.

Result: Through formal analysis and architectural decomposition, SSGM can mitigate topology-induced knowledge leakage (where sensitive contexts solidify into long-term storage) and prevent semantic drift (where knowledge degrades through iterative summarization).

Conclusion: Provides a comprehensive taxonomy of memory corruption risks and establishes a robust governance paradigm for deploying safe, persistent, and reliable agentic memory systems in autonomous LLM agents.

Abstract: Long-term memory has emerged as a foundational component of autonomous Large Language Model (LLM) agents, enabling continuous adaptation, lifelong multimodal learning, and sophisticated reasoning. However, as memory systems transition from static retrieval databases to dynamic, agentic mechanisms, critical concerns regarding memory governance, semantic drift, and privacy vulnerabilities have surfaced. While recent surveys have focused extensively on memory retrieval efficiency, they largely overlook the emergent risks of memory corruption in highly dynamic environments. To address these emerging challenges, we propose the Stability and Safety-Governed Memory (SSGM) framework, a conceptual governance architecture. SSGM decouples memory evolution from execution by enforcing consistency verification, temporal decay modeling, and dynamic access control prior to any memory consolidation. Through formal analysis and architectural decomposition, we show how SSGM can mitigate topology-induced knowledge leakage where sensitive contexts are solidified into long-term storage, and help prevent semantic drift where knowledge degrades through iterative summarization. Ultimately, this work provides a comprehensive taxonomy of memory corruption risks and establishes a robust governance paradigm for deploying safe, persistent, and reliable agentic memory systems.

Method: Developed NETHIC tool combining highly-scalable neural networks with hierarchical taxonomies, later enhanced with document embedding mechanism.

Result: Promising results on both generic and domain-specific corpora, with further improvements after adding document embeddings to individual networks and the hierarchical model.

Conclusion: NETHIC provides an effective and efficient text classification mechanism that benefits from neural network scalability and hierarchical taxonomy expressiveness, with document embeddings further enhancing performance.

Abstract: This work describes an automatic text classification method implemented in a software tool called NETHIC, which takes advantage of the inner capabilities of highly-scalable neural networks combined with the expressiveness of hierarchical taxonomies. As such, NETHIC succeeds in bringing about a mechanism for text classification that proves to be significantly effective as well as efficient. The tool had undergone an experimentation process against both a generic and a domain-specific corpus, outputting promising results. On the basis of this experimentation, NETHIC has been now further refined and extended by adding a document embedding mechanism, which has shown improvements in terms of performance on the individual networks and on the whole hierarchical model.

Method: DocSage operates through three core modules: (1) schema discovery module dynamically infers query-specific minimal joinable schemas to capture essential entities and relationships; (2) extraction module transforms unstructured text into semantically coherent relational tables with error-aware correction mechanisms; (3) reasoning module performs multi-hop relational reasoning over structured tables using schema awareness to align cross-document entities and aggregate evidence.

Result: Evaluations on two MDMEQA benchmarks show DocSage significantly outperforms state-of-the-art long-context LLMs and RAG systems, achieving more than 27% accuracy improvements respectively.

Conclusion: DocSage provides an effective end-to-end agentic framework for multi-document multi-entity QA that addresses key limitations of existing approaches through structured representation, schema awareness, and relational reasoning.

Abstract: Multi-document Multi-entity Question Answering inherently demands models to track implicit logic between multiple entities across scattered documents. However, existing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks suffer from critical limitations: standard RAG’s vector similarity-based coarse-grained retrieval often omits critical facts, graph-based RAG fails to efficiently integrate fragmented complex relationship networks, and both lack schema awareness, leading to inadequate cross-document evidence chain construction and inaccurate entity relationship deduction. To address these challenges, we propose DocSage, an end-to-end agentic framework that integrates dynamic schema discovery, structured information extraction, and schema-aware relational reasoning with error guarantees. DocSage operates through three core modules: (1) A schema discovery module dynamically infers query-specific minimal joinable schemas to capture essential entities and relationships; (2) An extraction module transforms unstructured text into semantically coherent relational tables, enhanced by error-aware correction mechanisms to reduce extraction errors; (3) A reasoning module performs multi-hop relational reasoning over structured tables, leveraging schema awareness to efficiently align cross-document entities and aggregate evidence. This agentic design offers three key advantages: precise fact localization via SQL-powered indexing, natural support for cross-document entity joins through relational tables, and mitigated LLM attention diffusion via structured representation. Evaluations on two MDMEQA benchmarks demonstrate that DocSage significantly outperforms state-of-the-art long-context LLMs and RAG systems, achieving more than 27% accuracy improvements respectively.

Method: Extends semi-decentralization to POMDPs, creating SDec-POMDP framework, and presents RS-SDA* (recursive small-step semi-decentralized A*) algorithm for generating optimal policies.

Result: The framework unifies decentralized and multiagent POMDPs, and RS-SDA* is evaluated on semi-decentralized versions of standard benchmarks and a maritime medical evacuation scenario.

Conclusion: Provides a theoretical foundation for exploring multiagent communication problems through semi-decentralization, enabling analysis of various communication mechanisms.

Abstract: We introduce an expressive framework and algorithms for the semi-decentralized control of cooperative agents in environments with communication uncertainty. Whereas semi-Markov control admits a distribution over time for agent actions, semi-Markov communication, or what we refer to as semi-decentralization, gives a distribution over time for what actions and observations agents can store in their histories. We extend semi-decentralization to the partially observable Markov decision process (POMDP). The resulting SDec-POMDP unifies decentralized and multiagent POMDPs and several existing explicit communication mechanisms. We present recursive small-step semi-decentralized A* (RS-SDA*), an exact algorithm for generating optimal SDec-POMDP policies. RS-SDA* is evaluated on semi-decentralized versions of several standard benchmarks and a maritime medical evacuation scenario. This paper provides a well-defined theoretical foundation for exploring many classes of multiagent communication problems through the lens of semi-decentralization.

Method: Automated framework for mining GitHub repositories includes structural analysis, semantic skill identification through dense retrieval, and translation to standardized SKILL.md format, with focus on visualization/educational capabilities from systems using Manim animation engine.

Result: Systematic extraction enables scalable acquisition of procedural knowledge, with agent-generated educational content achieving 40% gains in knowledge transfer efficiency while maintaining pedagogical quality comparable to human-crafted tutorials.

Conclusion: Automated skill extraction from agentic repositories provides scalable approach to augment LLM capabilities with specialized procedural expertise, enabling transition from monolithic models to modular, skill-equipped agents.

Abstract: The transition from monolithic large language models (LLMs) to modular, skill-equipped agents represents a fundamental architectural shift in artificial intelligence deployment. While general-purpose models demonstrate remarkable breadth in declarative knowledge, their utility in autonomous workflows is frequently constrained by insufficient specialized procedural expertise. This report investigates a systematic framework for automated acquisition of high-quality agent skills through mining of open-source repositories on platforms such as GitHub. We focus on the extraction of visualization and educational capabilities from state-of-the-art systems including TheoremExplainAgent and Code2Video, both utilizing the Manim mathematical animation engine. The framework encompasses repository structural analysis, semantic skill identification through dense retrieval, and translation to the standardized SKILL.md format. We demonstrate that systematic extraction from agentic repositories, combined with rigorous security governance and multi-dimensional evaluation metrics, enables scalable acquisition of procedural knowledge that augments LLM capabilities without requiring model retraining. Our analysis reveals that agent-generated educational content can achieve 40% gains in knowledge transfer efficiency while maintaining pedagogical quality comparable to human-crafted tutorials.

Method: Proposes VisiFold with two key innovations: 1) Temporal folding graph that consolidates a sequence of temporal snapshots into a single graph, and 2) Node visibility mechanism using node-level masking and subgraph sampling to handle computational bottlenecks from large node counts.

Result: Extensive experiments show VisiFold drastically reduces resource consumption while outperforming existing baselines in long-term forecasting tasks. Remarkably maintains performance advantage even with 80% mask ratio.

Conclusion: VisiFold effectively breaks resource constraints in both temporal and spatial dimensions, paving the way for more realistic long-term traffic forecasting by addressing computational bottlenecks and complex dependencies.

Abstract: Traffic forecasting is a cornerstone of intelligent transportation systems. While existing research has made significant progress in short-term prediction, long-term forecasting remains a largely uncharted and challenging frontier. Extending the prediction horizon intensifies two critical issues: escalating computational resource consumption and increasingly complex spatial-temporal dependencies. Current approaches, which rely on spatial-temporal graphs and process temporal and spatial dimensions separately, suffer from snapshot-stacking inflation and cross-step fragmentation. To overcome these limitations, we propose \textit{VisiFold}. Our framework introduces a novel temporal folding graph that consolidates a sequence of temporal snapshots into a single graph. Furthermore, we present a node visibility mechanism that incorporates node-level masking and subgraph sampling to overcome the computational bottleneck imposed by large node counts. Extensive experiments show that VisiFold not only drastically reduces resource consumption but also outperforms existing baselines in long-term forecasting tasks. Remarkably, even with a high mask ratio of 80%, VisiFold maintains its performance advantage. By effectively breaking the resource constraints in both temporal and spatial dimensions, our work paves the way for more realistic long-term traffic forecasting. The code is available at~ https://github.com/PlanckChang/VisiFold.

Method: Developed 15 CNN variants using LeNet-5, ResNet, VGGNet, and GoogLeNet/Inception architectures. Trained on OvarianCancer&SubtypesDatasetHistopathology from Mendeley. Used Explainable AI (XAI) models including LIME, Integrated Gradients, and SHAP to interpret model decisions. Evaluated performance using Accuracy, Precision, Recall, F1-Score, ROC Curve, and AUC metrics.

Result: InceptionV3 with ReLu activation achieved the best performance with average scores of 94% across all metrics on augmented data. The XAI methods provided interpretability of the model’s decisions for ovarian cancer detection.

Conclusion: The study demonstrates that deep learning models, particularly InceptionV3, can effectively detect ovarian cancer from histopathology images with high accuracy. The integration of XAI provides transparency in model decision-making, potentially contributing to better ovarian cancer detection methods.

Abstract: The unrestrained proliferation of cells that are malignant in nature is cancer. In recent times, medical professionals are constantly acquiring enhanced diagnostic and treatment abilities by implementing deep learning models to analyze medical data for better clinical decision, disease diagnosis and drug discovery. A majority of cancers are studied and treated by incorporating these technologies. However, ovarian cancer remains a dilemma as it has inaccurate non-invasive detection procedures and a time consuming, invasive procedure for accurate detection. Thus, in this research, several Convolutional Neural Networks such as LeNet-5, ResNet, VGGNet and GoogLeNet/Inception have been utilized to develop 15 variants and choose a model that accurately detects and identifies ovarian cancer. For effective model training, the dataset OvarianCancer&SubtypesDatasetHistopathology from Mendeley has been used. After constructing a model, we utilized Explainable Artificial Intelligence (XAI) models such as LIME, Integrated Gradients and SHAP to explain the black box outcome of the selected model. For evaluating the performance of the model, Accuracy, Precision, Recall, F1-Score, ROC Curve and AUC have been used. From the evaluation, it was seen that the slightly compact InceptionV3 model with ReLu had the overall best result achieving an average score of 94% across all the performance metrics in the augmented dataset. Lastly for XAI, the three aforementioned XAI have been used for an overall comparative analysis. It is the aim of this research that the contributions of the study will help in achieving a better detection method for ovarian cancer.

Method: Introduces CreativeBench with two subsets (Combo and Explore) targeting combinatorial and exploratory creativity. Uses automated pipeline with reverse engineering and self-play, leveraging executable code to objectively measure creativity as product of quality and novelty.

Result: Analysis reveals: 1) Scaling improves combinatorial creativity but has diminishing returns for exploration; 2) Larger models show “convergence-by-scaling” (more correct but less divergent); 3) Reasoning helps constrained exploration more than combination. Proposes EvoRePE, an inference-time steering strategy that internalizes evolutionary search patterns.

Conclusion: CreativeBench provides a rigorous framework for evaluating machine creativity in code generation, revealing important scaling behaviors and offering EvoRePE as a practical enhancement strategy for evolutionary systems.

Abstract: The saturation of high-quality pre-training data has shifted research focus toward evolutionary systems capable of continuously generating novel artifacts, leading to the success of AlphaEvolve. However, the progress of such systems is hindered by the lack of rigorous, quantitative evaluation. To tackle this challenge, we introduce CreativeBench, a benchmark for evaluating machine creativity in code generation, grounded in a classical cognitive framework. Comprising two subsets – CreativeBench-Combo and CreativeBench-Explore – the benchmark targets combinatorial and exploratory creativity through an automated pipeline utilizing reverse engineering and self-play. By leveraging executable code, CreativeBench objectively distinguishes creativity from hallucination via a unified metric defined as the product of quality and novelty. Our analysis of state-of-the-art models reveals distinct behaviors: (1) scaling significantly improves combinatorial creativity but yields diminishing returns for exploration; (2) larger models exhibit ``convergence-by-scaling,’’ becoming more correct but less divergent; and (3) reasoning capabilities primarily benefit constrained exploration rather than combination. Finally, we propose EvoRePE, a plug-and-play inference-time steering strategy that internalizes evolutionary search patterns to consistently enhance machine creativity.

Method: Proposes a systematic SLEEC-norm operationalization process that includes determining, validating, implementing, and verifying normative requirements. Also surveys existing methods and tools supporting this process and identifies remaining challenges.

Result: Establishes a comprehensive framework for developing AI agents that are demonstrably aligned with human norms and values, and defines a research and policy agenda for addressing key challenges in normative AI alignment.

Conclusion: The paper provides a systematic approach to bridge the gap between abstract normative principles and concrete implementation in AI systems, offering both a practical framework and a research agenda for developing socially-aligned AI agents.

Abstract: As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic, and cultural (SLEEC) norms has become a critical engineering challenge. While international frameworks have established high-level normative principles for AI, a significant gap remains in translating these abstract principles into concrete, verifiable requirements. To address this gap, we propose a systematic SLEEC-norm operationalisation process for determining, validating, implementing, and verifying normative requirements. Furthermore, we survey the landscape of methods and tools supporting this process, and identify key remaining challenges and research avenues for addressing them. We thus establish a framework - and define a research and policy agenda - for developing AI agents that are not only functionally useful but also demonstrably aligned with human norms and values.

Method: AdaFuse uses token-level pre-gating to make a single global routing decision per token before processing, then employs a custom CUDA kernel that fuses the parameters of selected LoRA adapters into the backbone model in one efficient pass.

Result: AdaFuse achieves accuracy comparable to state-of-the-art dynamic adapters while reducing decoding latency by over 2.4x, bridging the gap between model capability and inference efficiency.

Conclusion: The tight co-design between algorithm and hardware system in AdaFuse enables efficient dynamic adapter execution without sacrificing accuracy, solving the inference bottleneck problem in MoE-adapter architectures.

Abstract: The integration of dynamic, sparse structures like Mixture-of-Experts (MoE) with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Models (LLMs). However, this architectural enhancement comes at a steep cost: despite minimal increases in computational load, the inference latency often skyrockets, leading to decoding speeds slowing by over 2.5 times. Through a fine-grained performance analysis, we pinpoint the primary bottleneck not in the computation itself, but in the severe overhead from fragmented, sequential CUDA kernel launches required for conventional dynamic routing. To address this challenge, we introduce AdaFuse, a framework built on a tight co-design between the algorithm and the underlying hardware system to enable efficient dynamic adapter execution. Departing from conventional layer-wise or block-wise routing, AdaFuse employs a token-level pre-gating strategy, which makes a single, global routing decision for all adapter layers before a token is processed. This “decide-once, apply-everywhere” approach effectively staticizes the execution path for each token, creating an opportunity for holistic optimization. We capitalize on this by developing a custom CUDA kernel that performs a fused switching operation, merging the parameters of all selected LoRA adapters into the backbone model in a single, efficient pass. Experimental results on popular open-source LLMs show that AdaFuse achieves accuracy on par with state-of-the-art dynamic adapters while drastically cutting decoding latency by a factor of over 2.4x, thereby bridging the gap between model capability and inference efficiency.

Method: MultiLayer Perceptron (MLP)-based model with customized fairness loss that penalizes demographic disparities while preserving quality through intersectional criteria (race, country).

Result: 42.03% increase in underrepresented group participation and 3.16% improvement in overall utility across ACM SIGCHI, DIS, and IUI conference data.

Conclusion: Diversity promotion does not compromise academic rigor, supporting equity-focused peer review solutions that address demographic disparities while maintaining quality.

Abstract: Despite frequent double-blind review, demographic biases of authors still disadvantage the underrepresented groups. We present Fair-PaperRec, a MultiLayer Perceptron (MLP)-based model that addresses demographic disparities in post-review paper acceptance decisions while maintaining high-quality requirements. Our methodology penalizes demographic disparities while preserving quality through intersectional criteria (e.g., race, country) and a customized fairness loss, in contrast to heuristic approaches. Evaluations using conference data from ACM Special Interest Group on Computer-Human Interaction (SIGCHI), Designing Interactive Systems (DIS), and Intelligent User Interfaces (IUI) indicate a 42.03% increase in underrepresented group participation and a 3.16% improvement in overall utility, indicating that diversity promotion does not compromise academic rigor and supports equity-focused peer review solutions.

Method: 1) Automatic extraction pipeline using instruction-tuned LLM to mine 80k+ MIMIC-CXR studies and build multimodal knowledge base aligned with structured reporting template. 2) Each answer option represented with visual prototype. 3) ProtoSR trained to retrieve relevant prototypes for image-question pairs and augment predictions through prototype-conditioned residual, providing data-driven second opinion.

Result: State-of-the-art results on Rad-ReStruct benchmark, with largest improvements on detailed attribute questions, demonstrating value of integrating free-text derived signal for fine-grained image understanding.

Conclusion: Free-text reports contain valuable implicit knowledge that can be extracted and used to improve structured radiology reporting, particularly for fine-grained image understanding tasks where structured supervision is limited.

Abstract: Structured radiology reporting promises faster, more consistent communication than free text, but automation remains difficult as models must make many fine-grained, discrete decisions about rare findings and attributes from limited structured supervision. In contrast, free-text reports are produced at scale in routine care and implicitly encode fine-grained, image-linked information through detailed descriptions. To leverage this unstructured knowledge, we propose ProtoSR, an approach for injecting free-text information into structured report population. First, we introduce an automatic extraction pipeline that uses an instruction-tuned LLM to mine 80k+ MIMIC-CXR studies and build a multimodal knowledge base aligned with a structured reporting template, representing each answer option with a visual prototype. Using this knowledge base, ProtoSR is trained to retrieve prototypes relevant for the current image-question pair and augment the model predictions through a prototype-conditioned residual, providing a data-driven second opinion that selectively corrects predictions. On the Rad-ReStruct benchmark, ProtoSR achieves state-of-the-art results, with the largest improvements on detailed attribute questions, demonstrating the value of integrating free-text derived signal for fine-grained image understanding.

Method: Integrates contrastive alignment with sensor-conditioned captioning using a pretrained decoder-only language model via cross-attention. Introduces a flexible patch-embedder to handle different temporal resolutions and variable-length inputs without retraining.

Result: Achieves 77.14% average linear-probing accuracy (5.93% improvement over baselines) and 64.83% accuracy in sensor-based question answering across 11 datasets, demonstrating superior zero-shot transfer, captioning, and QA performance.

Conclusion: SLIP provides a flexible framework for learning language-aligned sensor representations that generalize across diverse sensor setups, enabling both discriminative understanding and generative reasoning for time-series data.

Abstract: Modern sensing systems generate large volumes of unlabeled multivariate time-series data. This abundance of unlabeled data makes self-supervised learning (SSL) a natural approach for learning transferable representations. However, most existing approaches are optimized for reconstruction or forecasting objectives and often fail to capture the semantic structure required for downstream classification and reasoning tasks. While recent sensor-language alignment methods improve semantic generalization through captioning and zero-shot transfer, they are limited to fixed sensor configurations, such as predefined channel sets, signal lengths, or temporal resolutions, which hinders cross-domain applicability. To address these gaps, we introduce \textbf{SLIP} (\textbf{S}ensor \textbf{L}anguage-\textbf{I}nformed \textbf{P}retraining), an open-source framework for learning language-aligned representations that generalize across diverse sensor setups. SLIP integrates contrastive alignment with sensor-conditioned captioning, facilitating both discriminative understanding and generative reasoning. By repurposing a pretrained decoder-only language model via cross-attention and introducing an elegant, flexible patch-embedder, SLIP supports different temporal resolutions and variable-length input at inference time without additional retraining. Across 11 datasets, SLIP demonstrates superior performance in zero-shot transfer, signal captioning, and question answering. It achieves a 77.14% average linear-probing accuracy, a 5.93% relative improvement over strong baselines, and reaches 64.83% accuracy in sensor-based question answering.

Method: Normative Common Ground Replication (NormCoRe) maps structural layers of human subject studies onto AI agent study designs, enabling systematic documentation and analysis of norms in multi-agent AI environments.

Result: Normative judgments in AI agent studies differ from human baselines and are sensitive to foundation model choices and language used to instantiate agent personas, as shown in a distributive justice experiment replication.

Conclusion: NormCoRe provides a principled pathway for analyzing norms in multi-agent AI systems and helps guide design choices when AI agents automate or support human tasks.

Abstract: In the late 2010s, the fashion trend NormCore framed sameness as a signal of belonging, illustrating how norms emerge through collective coordination. Today, similar forms of normative coordination can be observed in systems based on Multi-agent Artificial Intelligence (MAAI), as AI-based agents deliberate, negotiate, and converge on shared decisions in fairness-sensitive domains. Yet, existing empirical approaches often treat norms as targets for alignment or replication, implicitly assuming equivalence between human subjects and AI agents and leaving collective normative dynamics insufficiently examined. To address this gap, we propose Normative Common Ground Replication (NormCoRe), a novel methodological framework to systematically translate the design of human subject experiments into MAAI environments. Building on behavioral science, replication research, and state-of-the-art MAAI architectures, NormCoRe maps the structural layers of human subject studies onto the design of AI agent studies, enabling systematic documentation of study design and analysis of norms in MAAI. We demonstrate the utility of NormCoRe by replicating a seminal experimental study on distributive justice, in which participants negotiate fairness principles under a “veil of ignorance”. We show that normative judgments in AI agent studies can differ from human baselines and are sensitive to the choice of the foundation model and the language used to instantiate agent personas. Our work provides a principled pathway for analyzing norms in MAAI and helps to guide, reflect, and document design choices whenever AI agents are used to automate or support tasks formerly carried out by humans.

Method: Created LABSHIELD benchmark based on OSHA and GHS standards with 164 operational tasks across diverse manipulation complexities and risk profiles. Evaluated 20 proprietary, 9 open-source, and 3 embodied models using dual-track evaluation framework comparing MCQ accuracy with Semi-open QA safety performance.

Result: Models showed average 32.0% performance drop in professional laboratory scenarios, particularly in hazard interpretation and safety-aware planning, revealing systematic gap between general-domain accuracy and safety-critical reasoning.

Conclusion: There’s an urgent need for safety-centric reasoning frameworks to ensure reliable autonomous scientific experimentation, as current MLLMs lack sufficient safety awareness for high-stakes laboratory operations.

Abstract: Artificial intelligence is increasingly catalyzing scientific automation, with multimodal large language model (MLLM) agents evolving from lab assistants into self-driving lab operators. This transition imposes stringent safety requirements on laboratory environments, where fragile glassware, hazardous substances, and high-precision laboratory equipment render planning errors or misinterpreted risks potentially irreversible. However, the safety awareness and decision-making reliability of embodied agents in such high-stakes settings remain insufficiently defined and evaluated. To bridge this gap, we introduce LABSHIELD, a realistic multi-view benchmark designed to assess MLLMs in hazard identification and safety-critical reasoning. Grounded in U.S. Occupational Safety and Health Administration (OSHA) standards and the Globally Harmonized System (GHS), LABSHIELD establishes a rigorous safety taxonomy spanning 164 operational tasks with diverse manipulation complexities and risk profiles. We evaluate 20 proprietary models, 9 open-source models, and 3 embodied models under a dual-track evaluation framework. Our results reveal a systematic gap between general-domain MCQ accuracy and Semi-open QA safety performance, with models exhibiting an average drop of 32.0% in professional laboratory scenarios, particularly in hazard interpretation and safety-aware planning. These findings underscore the urgent necessity for safety-centric reasoning frameworks to ensure reliable autonomous scientific experimentation in embodied laboratory contexts. The full dataset will be released soon.

Method: Reformulates PFL as a few-for-many optimization problem with K shared server models (K « M). Proposes FedFew algorithm that jointly optimizes K server models through gradient-based updates, automatically discovering optimal model diversity without manual partitioning or hyperparameter tuning.

Result: FedFew with just 3 models consistently outperforms state-of-the-art approaches across vision, NLP, and real-world medical imaging datasets. Theoretical analysis shows approximation error diminishes as K increases and each client’s model converges to optimum as data grows.

Conclusion: FedFew provides a principled, scalable solution to personalized federated learning by maintaining only a few shared models that collectively serve many clients, achieving near-optimal personalization with theoretical guarantees and practical efficiency.

Abstract: Personalized Federated Learning (PFL) aims to train customized models for clients with highly heterogeneous data distributions while preserving data privacy. Existing approaches often rely on heuristics like clustering or model interpolation, which lack principled mechanisms for balancing heterogeneous client objectives. Serving $M$ clients with distinct data distributions is inherently a multi-objective optimization problem, where achieving optimal personalization ideally requires $M$ distinct models on the Pareto front. However, maintaining $M$ separate models poses significant scalability challenges in federated settings with hundreds or thousands of clients. To address this challenge, we reformulate PFL as a few-for-many optimization problem that maintains only $K$ shared server models ($K \ll M$) to collectively serve all $M$ clients. We prove that this framework achieves near-optimal personalization: the approximation error diminishes as $K$ increases and each client’s model converges to each client’s optimum as data grows. Building on this reformulation, we propose FedFew, a practical algorithm that jointly optimizes the $K$ server models through efficient gradient-based updates. Unlike clustering-based approaches that require manual client partitioning or interpolation-based methods that demand careful hyperparameter tuning, FedFew automatically discovers the optimal model diversity through its optimization process. Experiments across vision, NLP, and real-world medical imaging datasets demonstrate that FedFew, with just 3 models, consistently outperforms other state-of-the-art approaches. Code is available at https://github.com/pgg3/FedFew.

Method: Systematic study along three axes: (1) within-environment generalization across task difficulty, (2) cross-environment transfer to unseen environments, and (3) sequential multi-environment training to quantify transfer and forgetting.

Result: RFT generalizes well across task difficulty within an environment, but exhibits weaker transfer to unseen environments (correlates with shifts in semantic priors and observation/action interfaces). Sequential training yields promising downstream gains with minimal upstream forgetting. Mixture training across environments improves overall balance.

Conclusion: Provides detailed analyses and insights to help community develop and deploy generalizable LLM agents. Highlights limitations of current RFT approaches for cross-environment generalization.

Abstract: Reinforcement fine-tuning (RFT) has shown promise for training LLM agents to perform multi-turn decision-making based on environment feedback. However, most existing evaluations remain largely in-domain: training and testing are conducted in the same environment or even on the same tasks. In real-world deployment, agents may operate in unseen environments with different background knowledge, observation spaces, and action interfaces. To characterize the generalization profile of RFT under such shifts, we conduct a systematic study along three axes: (1) within-environment generalization across task difficulty, (2) cross-environment transfer to unseen environments, and (3) sequential multi-environment training to quantify transfer and forgetting. Our results show that RFT generalizes well across task difficulty within an environment, but exhibits weaker transfer to unseen environments, which correlates with shifts in both semantic priors and observation/action interfaces. In contrast, sequential training yields promising downstream gains with minimal upstream forgetting, and mixture training across environments improves the overall balance. We further provide detailed analyses and deeper insights, and hope our work helps the community develop and deploy generalizable LLM agents.

Method: XSkill uses a dual-stream framework that extracts and consolidates experiences (action-level guidance) and skills (task-level guidance) from multi-path rollouts via visually grounded summarization and cross-rollout critique. During inference, it retrieves and adapts knowledge to current visual context and feeds usage history back into accumulation.

Result: XSkill consistently and substantially outperforms both tool-only and learning-based baselines across five benchmarks in diverse domains with four backbone models, showing superior zero-shot generalization.

Conclusion: The dual-stream approach with experience and skill knowledge streams enables effective continual learning for multimodal agents, with complementary roles in influencing reasoning behaviors and strong generalization capabilities.

Abstract: Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A central challenge is enabling such agents to continually improve without parameter updates by learning from past trajectories. We identify two complementary forms of reusable knowledge essential for this goal: experiences, providing concise action-level guidance for tool selection and decision making, and skills, providing structured task-level guidance for planning and tool use. To this end, we propose XSkill, a dual-stream framework for continual learning from experience and skills in multimodal agents. XSkill grounds both knowledge extraction and retrieval in visual observations. During accumulation, XSkill distills and consolidates experiences and skills from multi-path rollouts via visually grounded summarization and cross-rollout critique. During inference, it retrieves and adapts this knowledge to the current visual context and feeds usage history back into accumulation to form a continual learning loop. Evaluated on five benchmarks across diverse domains with four backbone models, XSkill consistently and substantially outperforms both tool-only and learning-based baselines. Further analysis reveals that the two knowledge streams play complementary roles in influencing the reasoning behaviors of agents and show superior zero-shot generalization.

Method: Proposes a MARL framework with three key mechanisms: (1) Turning Ratio Randomization during training to expose agents to dynamic scenarios, (2) Exponential Phase Duration Adjustment action space for stable control, and (3) Neighbor-Based Observation scheme using MAPPO with CTDE for scalable coordination.

Result: The framework outperforms standard RL baselines, reducing average waiting time by over 10%, demonstrates superior generalization to unseen traffic scenarios, and maintains high control stability in Vissim simulations.

Conclusion: The proposed robust MARL framework offers a practical solution for adaptive traffic signal control that balances responsiveness with stability and generalizes well to dynamic traffic conditions.

Abstract: Reinforcement Learning (RL) in Traffic Signal Control (TSC) faces significant hurdles in real-world deployment due to limited generalization to dynamic traffic flow variations. Existing approaches often overfit static patterns and use action spaces incompatible with driver expectations. This paper proposes a robust Multi-Agent Reinforcement Learning (MARL) framework validated in the Vissim traffic simulator. The framework integrates three mechanisms: (1) Turning Ratio Randomization, a training strategy that exposes agents to dynamic turning probabilities to enhance robustness against unseen scenarios; (2) a stability-oriented Exponential Phase Duration Adjustment action space, which balances responsiveness and precision through cyclical, exponential phase adjustments; and (3) a Neighbor-Based Observation scheme utilizing the MAPPO algorithm with Centralized Training with Decentralized Execution (CTDE). By leveraging centralized updates, this approach approximates the efficacy of global observations while maintaining scalable local communication. Experimental results demonstrate that our framework outperforms standard RL baselines, reducing average waiting time by over 10%. The proposed model exhibits superior generalization in unseen traffic scenarios and maintains high control stability, offering a practical solution for adaptive signal control.

Method: Decomposes active reasoning into Action Selection (determining observation stream through queries) and Belief Tracking (updating agent’s belief based on evidence). Proposes injecting easy-to-obtain directional critiques to reallocate learning signals and help agents escape self-locking.

Result: Extensive experiments with 7 datasets show the approach significantly mitigates information self-locking, bringing up to 60% improvements in performance.

Conclusion: The proposed method effectively addresses the information self-locking problem in RL-trained LLM agents for active reasoning by using directional critiques to break the feedback loop between deficient action selection/belief tracking and insufficient exploration.

Abstract: Reinforcement learning (RL) with outcome-based rewards has achieved significant success in training large language model (LLM) agents for complex reasoning tasks. However, in active reasoning where agents need to strategically ask questions to acquire task-relevant information, we find that LLM agents trained with RL often suffer from information self-locking: the agent ceases to ask informative questions and struggles to internalize already-obtained information. To understand the phenomenon, we decompose active reasoning into two core capabilities: Action Selection (AS), which determines the observation stream through queries, and Belief Tracking (BT), which updates the agent’s belief based on collected evidence. We show that deficient AS and BT capabilities will limit the information exploration during RL training. Furthermore, insufficient exploration in turn hinders the improvement of AS and BT, creating a feedback loop that locks the agent in a low-information regime. To resolve the issue, we propose a simple yet effective approach that reallocates the learning signal by injecting easy- to-obtain directional critiques to help the agent escape self-locking. Extensive experiments with 7 datasets show that our approach significantly mitigates the information self-locking, bringing up to 60% improvements.

Method: Study AI-agent populations as a system where four key variables can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity. Use empirical and mathematical analysis.

Result: When resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload, though tribe formation lessens this risk. When resources are abundant, these same ingredients drive overload to near zero. The crossover point is where opposing tribes first fit inside available capacity.

Conclusion: More sophisticated AI-agent populations are not inherently better - whether sophistication helps or harms depends entirely on the capacity-to-population ratio, which is knowable before deployment. Collective behavior risks can be predicted and managed.

Abstract: When resources are scarce, will a population of AI agents coordinate in harmony, or descend into tribal chaos? Diverse decision-making AI from different developers is entering everyday devices – from phones and medical devices to battlefield drones and cars – and these AI agents typically compete for finite shared resources such as charging slots, relay bandwidth, and traffic priority. Yet their collective dynamics and hence risks to users and society are poorly understood. Here we study AI-agent populations as the first system of real agents in which four key variables governing collective behaviour can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity. We show empirically and mathematically that when resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload, though tribe formation lessens this risk. Meanwhile, some individuals profit handsomely. When resources are abundant, the same ingredients drive overload to near zero, though tribe formation makes the overload slightly worse. The crossover is arithmetical: it is where opposing tribes that form spontaneously first fit inside the available capacity. More sophisticated AI-agent populations are not better: whether their sophistication helps or harms depends entirely on a single number – the capacity-to-population ratio – that is knowable before any AI-agent ships.

Method: Introduce TopoBench with 6 puzzle families across 3 difficulty levels. Evaluate strong reasoning LLMs, annotate 750 chain-of-thought traces with error taxonomy, test targeted interventions, and study mitigation strategies including prompt guidance and tool-based constraint checking.

Result: Frontier models solve fewer than 25% of hard instances, with two families nearly unsolved. Error analysis reveals premature commitment and constraint forgetting directly impact performance, while repeated reasoning is benign. Constraint extraction from spatial representations is the main bottleneck.

Conclusion: LLMs’ spatial reasoning limitations stem primarily from difficulty extracting constraints from spatial representations rather than reasoning over them. This suggests opportunities for improved spatial representation learning in multimodal models.

Abstract: Solving topological grid puzzles requires reasoning over global spatial invariants such as connectivity, loop closure, and region symmetry and remains challenging for even the most powerful large language models (LLMs). To study these abilities under controlled settings, we introduce TopoBench, a benchmark of six puzzle families across three difficulty levels. We evaluate strong reasoning LLMs on TopoBench and find that even frontier models solve fewer than one quarter of hard instances, with two families nearly unsolved. To investigate whether these failures stem from reasoning limitations or from difficulty extracting and maintaining spatial constraints, we annotate 750 chain of thought traces with an error taxonomy that surfaces four candidate causal failure modes, then test them with targeted interventions simulating each error type. These interventions show that certain error patterns like premature commitment and constraint forgetting have a direct impact on the ability to solve the puzzle, while repeated reasoning is a benign effect of search. Finally we study mitigation strategies including prompt guidance, cell-aligned grid representations and tool-based constraint checking, finding that the bottleneck lies in extracting constraints from spatial representations and not in reasoning over them. Code and data are available at github.com/mayug/topobench-benchmark.

Method: Developed a polynomial compilation that transforms temporal planning with durative actions into PDDL+, fully capturing the semantics while only assuming non-self-overlapping of actions.

Result: The compilation retains plan length up to a constant factor and is experimentally shown to be practically relevant for hard temporal numeric problems.

Conclusion: Provides the first practical compilation from temporal planning with durative actions to PDDL+, enabling practical application for complex temporal numeric planning problems.

Abstract: Since the introduction of the PDDL+ modeling language, it was known that temporal planning with durative actions (as in PDDL 2.1) could be compiled into PDDL+. However, no practical compilation was presented in the literature ever since. We present a practical compilation from temporal planning with durative actions into PDDL+, fully capturing the semantics and only assuming the non-self-overlapping of actions. Our compilation is polynomial, retains the plan length up to a constant factor and is experimentally shown to be of practical relevance for hard temporal numeric problems.

Method: Conducted a rigorous study using a controlled synthetic setting where a “gold-standard” judge (GPT-oss-120b) provides preference annotations to train smaller judges, comparing non-reasoning and reasoning judges in reinforcement-learning-based LLM alignment.

Result: Non-reasoning judges lead to reward hacking easily, while reasoning judges produce policies that achieve strong performance when evaluated by the gold-standard judge. However, reasoning-judge-trained policies achieve this by learning to generate adversarial outputs that can also deceive other LLM-judges on benchmarks like Arena-Hard.

Conclusion: The study reveals both important findings and room for improvement in applying reasoning LLM-judges for non-verifiable LLM post-training, highlighting that while reasoning judges outperform non-reasoning ones, they can learn to generate deceptive outputs that score well on benchmarks.

Abstract: Reasoning LLMs-as-Judges, which can benefit from inference-time scaling, provide a promising path for extending the success of reasoning models to non-verifiable domains where the output correctness/quality cannot be directly checked. However, while reasoning judges have shown better performance on static evaluation benchmarks, their effectiveness in actual policy training has not been systematically examined. Therefore, we conduct a rigorous study to investigate the actual impact of non-reasoning and reasoning judges in reinforcement-learning-based LLM alignment. Our controlled synthetic setting, where a “gold-standard” judge (gpt-oss-120b) provides preference annotations to train smaller judges, reveals key differences between non-reasoning and reasoning judges: non-reasoning judges lead to reward hacking easily, while reasoning judges can lead to policies that achieve strong performance when evaluated by the gold-standard judge. Interestingly, we find that the reasoning-judge-trained policies achieve such strong performance by learning to generate highly effective adversarial outputs that can also score well on popular benchmarks such as Arena-Hard by deceiving other LLM-judges. Combined with our further analysis, our study highlights both important findings and room for improvements for applying (reasoning) LLM-judges in non-verifiable LLM post-training.

Method: Parallelized the existing CEGAR-SEQ algorithm by running it concurrently with a portfolio of different object arrangement strategies (center placement, corner placement, height-based scheduling) on multi-core CPUs, creating Portfolio-CEGAR-SEQ.

Result: Portfolio-CEGAR-SEQ outperforms the original CEGAR-SEQ algorithm, often using fewer printing plates when scheduling batches of objects for multiple plates.

Conclusion: Effectively utilizing modern multi-core CPU parallelism through portfolio-based parallelization improves 3D printing object arrangement and scheduling efficiency.

Abstract: Computing power that used to be available only in supercomputers decades ago especially their parallelism is currently available in standard personal computer CPUs even in CPUs for mobile telephones. We show how to effectively utilize the computing power of modern multi-core personal computer CPU to solve the complex combinatorial problem of object arrangement and scheduling for sequential 3D printing. We achieved this by parallelizing the existing CEGAR-SEQ algorithm that solves the sequential object arrangement and scheduling by expressing it as a linear arithmetic formula which is then solved by a technique inspired by counterexample guided abstraction refinement (CEGAR). The original CEGAR-SEQ algorithm uses an object arrangement strategy that places objects towards the center of the printing plate. We propose alternative object arrangement strategies such as placing objects towards a corner of the printing plate and scheduling objects according to their height. Our parallelization is done at the high-level where we execute the CEGAR-SEQ algorithm in parallel with a portfolio of object arrangement strategies, an algorithm is called Porfolio-CEGAR-SEQ. Our experimental evaluation indicates that Porfolio-CEGAR-SEQ outperforms the original CEGAR-SEQ. When a batch of objects for multiple printing plates is scheduled, Portfolio-CEGAR-SEQ often uses fewer printing plates than CEGAR-SEQ.

Method: Proposes DIDP based on DyPDL formalism inspired by AI planning, representing problems as state transition systems. Develops seven DIDP solvers using heuristic search algorithms to solve DyPDL models, enabling domain-independent dynamic programming.

Result: Experimental evaluation on 11 combinatorial optimization problem classes shows DIDP outperforms commercial MIP solvers in 9 classes, CP solvers in 9 classes, and both MIP and CP in 7 classes. DIDP also beats existing state-based solvers including domain-independent AI planners.

Conclusion: DIDP represents a promising new paradigm for combinatorial optimization that successfully combines declarative modeling with efficient solving through dynamic programming, demonstrating superior performance over established MIP and CP approaches across diverse problem domains.

Abstract: For combinatorial optimization problems, model-based paradigms such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the `holy grail’ of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a novel model-based paradigm based on dynamic programming (DP). While DP is not new, it has typically been implemented as a problem-specific method. We introduce Dynamic Programming Description Language (DyPDL), a formalism to define DP models based on a state transition system, inspired by artificial intelligence (AI) planning. we show that heuristic search algorithms can be used to solve DyPDL models and propose seven DIDP solvers. We experimentally compare our DIDP solvers with commercial MIP and CP solvers (solving MIP and CP models, respectively) on common benchmark instances of eleven combinatorial optimization problem classes. We show that DIDP outperforms MIP in nine problem classes, CP also in nine problem classes, and both MIP and CP in seven. DIDP also achieves superior performance to existing state-based solvers including domain-independent AI planners.

Method: Proposes Diffusion Blend approach with two algorithms: DB-MPA for multi-reward alignment and DB-KLA for KL regularization control. The method blends backward diffusion processes from models fine-tuned on different reward combinations, enabling inference-time interpolation between preferences through process blending.

Result: Extensive experiments show Diffusion Blend consistently outperforms baselines and matches/exceeds performance of individually fine-tuned models. Enables efficient user-driven alignment at inference time with flexible control over reward combinations and KL regularization strength.

Conclusion: Diffusion Blend solves inference-time multi-preference alignment problem, providing flexible control over multiple objectives without additional fine-tuning. The approach enables practical deployment where user preferences vary and need to balance conflicting objectives.

Abstract: Reinforcement learning (RL) algorithms have been used recently to align diffusion models with downstream objectives such as aesthetic quality and text-image consistency by fine-tuning them to maximize a single reward function under a fixed KL regularization. However, this approach is inherently restrictive in practice, where alignment must balance multiple, often conflicting objectives. Moreover, user preferences vary across prompts, individuals, and deployment contexts, with varying tolerances for deviation from a pre-trained base model. We address the problem of inference-time multi-preference alignment: given a set of basis reward functions and a reference KL regularization strength, can we design a fine-tuning procedure so that, at inference time, it can generate images aligned with any user-specified linear combination of rewards and regularization, without requiring additional fine-tuning? We propose Diffusion Blend, a novel approach to solve inference-time multi-preference alignment by blending backward diffusion processes associated with fine-tuned models, and we instantiate this approach with two algorithms: DB-MPA for multi-reward alignment and DB-KLA for KL regularization control. Extensive experiments show that Diffusion Blend algorithms consistently outperform relevant baselines and closely match or exceed the performance of individually fine-tuned models, enabling efficient, user-driven alignment at inference-time. The code is available at https://github.com/bluewoods127/DB-2025.

Method: Proposes Goal-Oriented Graphs (GoGs) where nodes represent goals and edges encode logical dependencies. Enables explicit retrieval of causal reasoning paths by identifying high-level goals and recursively retrieving prerequisites to form coherent chains that guide LLMs.

Result: Extensive experiments on Minecraft testbed show GoG substantially improves procedural reasoning and significantly outperforms GraphRAG and other state-of-the-art baselines.

Conclusion: Goal-Oriented Graphs provide an effective framework for enhancing LLM procedural reasoning through structured retrieval of coherent causal chains for multi-step planning in complex environments.

Abstract: Large Language Models (LLMs) demonstrate impressive general capabilities but often struggle with step-by-step procedural reasoning, a critical challenge in complex interactive environments. While retrieval-augmented methods like GraphRAG attempt to bridge this gap, their fragmented entity-relation graphs hinder the construction of coherent, multi-step plans. In this paper, we propose a novel framework based on Goal-Oriented Graphs (GoGs), where each node represents a goal and edges encode logical dependencies between them. This structure enables the explicit retrieval of causal reasoning paths by identifying a high-level goal and recursively retrieving its prerequisites, forming a coherent chain to guide the LLM. Through extensive experiments on the Minecraft testbed, a domain that demands robust multi-step planning and provides rich procedural knowledge, we demonstrate that GoG substantially improves procedural reasoning and significantly outperforms GraphRAG and other state-of-the-art baselines.

Method: 1) Constructed a novel multi-granular rationale-annotated ICD coding dataset from MIMIC-IV and ICD-10; 2) Evaluated three types of rationales (entity-level mentions via entity linking, LLM-generated rationales, attention-based rationales); 3) Developed rationale learning methods using LLM-generated rationales as distant supervision; 4) Used few-shot prompting with human-annotated examples to improve rationale plausibility.

Result: LLM-generated rationales showed strong plausibility. Using them as distant supervision signals improved rationale learning methods. Few-shot prompting with human-annotated examples further enhanced the plausibility of rationale generation in both teacher LLMs and student models.

Conclusion: The work provides a systematic framework for evaluating ICD coding model explainability, introduces a valuable annotated dataset, and demonstrates that LLM-generated rationales can effectively serve as supervision for training more explainable ICD coding models.

Abstract: ICD coding is the process of mapping unstructured text from Electronic Health Records (EHRs) to standardised codes defined by the International Classification of Diseases (ICD) system. In order to promote trust and transparency, existing explorations on the explainability of ICD coding models primarily rely on attention-based rationales and qualitative assessments conducted by physicians, yet lack a systematic evaluation across diverse types of rationales using consistent criteria and high-quality rationale-annotated datasets specifically designed for the ICD coding task. Moreover, dedicated methods explicitly trained to generate plausible rationales remain scarce. In this work, we present evaluations of the explainability of rationales in ICD coding, focusing on two fundamental dimensions: faithfulness and plausibility – in short how rationales influence model decisions and how convincing humans find them. For plausibility, we construct a novel, multi-granular rationale-annotated ICD coding dataset, based on the MIMIC-IV database and the updated ICD-10 coding system. We conduct a comprehensive evaluation across three types of ICD coding rationales: entity-level mentions automatically constructed via entity linking, LLM-generated rationales, and rationales based on attention scores of ICD coding models. Building upon the strong plausibility exhibited by LLM-generated rationales, we further leverage them as distant supervision signals to develop rationale learning methods. Additionally, by prompting the LLM with few-shot human-annotated examples from our dataset, we achieve notable improvements in the plausibility of rationale generation in both the teacher LLM and the student rationale learning models.

Method: Two architectures: 1) STRIPS Transformer with symbolic inductive bias, and 2) standard transformer with different positional encodings and attention mechanisms (including stick-breaking attention). Evaluated on five classical planning domains measuring training accuracy, generalization, and planning performance.

Result: Both approaches can produce models supporting planning with off-the-shelf STRIPS planners over exponentially many unseen initial states and goals. Standard transformer with stick-breaking attention achieved near-perfect training accuracy and strong generalization. STRIPS Transformer required larger datasets to generalize reliably.

Conclusion: Transformers can learn symbolic world models from action traces, but architectural choices (like stick-breaking attention) significantly impact generalization. Symbolic models can be extracted from transformers trained on shorter traces to handle longer ones.

Abstract: We study whether next-token prediction can yield world models that truly support planning, in a controlled symbolic setting where propositional STRIPS action models are learned from action traces alone and correctness can be evaluated exactly. We introduce two architectures. The first is the STRIPS Transformer, a symbolically aligned model grounded in theoretical results linking transformers and the formal language structure of STRIPS domains. The second is a standard transformer architecture without explicit symbolic structure built in, for which we study different positional encoding schemes and attention aggregation mechanisms. We evaluate both architectures on five classical planning domains, measuring training accuracy, generalization, and planning performance across domains and problem sizes. Interestingly, both approaches can be used to produce models that support planning with off-the-shelf STRIPS planners over exponentially many unseen initial states and goals. Although the STRIPS Transformer incorporates a strong symbolic inductive bias, it is harder to optimize and requires larger datasets to generalize reliably. In contrast, a standard transformer with stick-breaking attention achieves near-perfect training accuracy and strong generalization. Finally, standard transformers without stick-breaking attention do not generalize to long traces, whereas a symbolic STRIPS model extracted from a transformer trained on shorter traces does.

Method: Developed Jr. AI Scientist that follows a novice researcher workflow: analyzes baseline papers, formulates novel hypotheses, iteratively experiments using modern coding agents for complex implementations, and writes papers with results. Evaluated through automated AI Reviewers, author-led evaluations, and submissions to Agents4Science venue.

Result: Successfully generated new research papers building upon real NeurIPS, IJCV, and ICLR works with novel methods. Papers received higher review scores by DeepReviewer than existing fully automated systems, but author evaluation and Agents4Science reviews identified important limitations and risks.

Conclusion: Jr. AI Scientist demonstrates improved capabilities over existing automated systems but reveals significant limitations and risks in current AI Scientist technology, clarifying areas still requiring human expertise and potential risks as these systems evolve.

Abstract: Understanding the current capabilities and risks of AI Scientist systems (autoresearch) is essential for ensuring trustworthy and sustainable AI-driven scientific progress while preserving the integrity of the academic ecosystem. To this end, we develop Jr. AI Scientist, a state-of-the-art autonomous AI scientist system that mimics the core research workflow of a novice student researcher: Given the baseline paper from the human mentor, it analyzes its limitations, formulates novel hypotheses for improvement, iteratively experiments until improvements are achieved, and writes a paper with the results. Unlike previous approaches that assume full automation or operate on small-scale code, Jr. AI Scientist follows a well-defined research workflow and leverages modern coding agents to handle complex, multi-file implementations, leading to scientifically valuable contributions. Through our experiments, the Jr. AI Scientist successfully generated new research papers that build upon real NeurIPS, IJCV, and ICLR works by proposing and implementing novel methods. For evaluation, we conducted automated assessments using AI Reviewers, author-led evaluations, and submissions to Agents4Science, a venue dedicated to AI-driven contributions. The findings demonstrate that Jr. AI Scientist generates papers receiving higher review scores by DeepReviewer than existing fully automated systems. Nevertheless, we identify important limitations from the author evaluation and the Agents4Science reviews, indicating the potential risks of directly applying current AI Scientist systems and key challenges for future research. Finally, we comprehensively report various risks identified during development. We believe this study clarifies the current role and limitations of AI Scientist systems, offering insights into the areas that still require human expertise and the risks that may emerge as these systems evolve.

Method: CodeEvolve uses an islands-based genetic algorithm coupled with modular LLM orchestration. It employs execution feedback and task-specific metrics to guide selection and variation, with exploration-exploitation balance achieved through context-aware recombination, adaptive meta-prompting, and targeted refinement of promising solutions.

Result: CodeEvolve achieves state-of-the-art performance on several benchmark tasks used to assess AlphaEvolve. Open-weight models often match or exceed closed-source baselines at a fraction of the compute cost.

Conclusion: The framework successfully demonstrates that open-source LLM-evolutionary approaches can achieve competitive performance in algorithmic discovery, providing practical tools and guidance for researchers in this area.

Abstract: We introduce CodeEvolve, an open-source framework that combines large language models (LLMs) with evolutionary search to synthesize high-performing algorithmic solutions. CodeEvolve couples an islands-based genetic algorithm with modular LLM orchestration, using execution feedback and task-specific metrics to guide selection and variation. Exploration and exploitation are balanced through context-aware recombination, adaptive meta-prompting, and targeted refinement of promising solutions. We evaluate CodeEvolve on benchmarks used to assess Google DeepMind’s AlphaEvolve, and include direct comparisons with popular open-source frameworks for algorithmic discovery and heuristic design. Our results show that CodeEvolve achieves state-of-the-art (SOTA) performance on several tasks, with open-weight models often matching or exceeding closed-source baselines at a fraction of the compute cost. We provide extensive ablations, practical hyperparameter guidance, and release our framework and experimental results at https://github.com/inter-co/science-codeevolve.

Method: ALE consists of three components: 1) ROLL - post-training framework for weight optimization, 2) ROCK - sandbox environment manager for trajectory generation, and 3) iFlow CLI - agent framework for efficient context engineering. Includes data composition protocols for synthesizing complex behaviors and IPA (Interaction-Perceptive Agentic Policy Optimization) algorithm that assigns credit over semantic interaction chunks rather than individual tokens.

Result: ROME (open-source agent grounded by ALE) trained on over one million trajectories demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench. Introduced Terminal Bench Pro with improved scale and contamination control.

Conclusion: ALE provides an effective foundational infrastructure for agentic model development, with ROME proving the ecosystem’s effectiveness through strong benchmark performance.

Abstract: Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agentic model. ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering. We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories. Our approach includes data composition protocols for synthesizing complex behaviors and a novel policy optimization algorithm, Interaction-Perceptive Agentic Policy Optimization (IPA), which assigns credit over semantic interaction chunks rather than individual tokens to improve long-horizon training stability. Empirically, we evaluate ROME within a structured setting and introduce Terminal Bench Pro, a benchmark with improved scale and contamination control. ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of ALE.

Method: Proposes a curvature-aware de Branges-Rovnyak space (RKHS) isometric to Poincare ball, with an adjustable multiplier to select appropriate RKHS for any hyperbolic curvature. Builds a family of adaptive hyperbolic kernels including novel adaptive hyperbolic radial kernel with learnable parameters for task-aware feature modulation.

Result: Extensive experiments on visual and language benchmarks demonstrate that the proposed kernels outperform existing hyperbolic kernels in modeling hierarchical dependencies.

Conclusion: The adaptive hyperbolic kernel framework effectively addresses geometric distortion and adaptability issues in existing hyperbolic kernels, providing superior hierarchical structure modeling for multimodal applications.

Abstract: Hierarchical data pervades diverse machine learning applications, including natural language processing, computer vision, and social network analysis. Hyperbolic space, characterized by its negative curvature, has demonstrated strong potential in such tasks due to its capacity to embed hierarchical structures with minimal distortion. Previous evidence indicates that the hyperbolic representation capacity can be further enhanced through kernel methods. However, existing hyperbolic kernels still suffer from mild geometric distortion or lack adaptability. This paper addresses these issues by introducing a curvature-aware de Branges-Rovnyak space, a reproducing kernel Hilbert space (RKHS) that is isometric to a Poincare ball. We design an adjustable multiplier to select the appropriate RKHS corresponding to the hyperbolic space with any curvature adaptively. Building on this foundation, we further construct a family of adaptive hyperbolic kernels, including the novel adaptive hyperbolic radial kernel, whose learnable parameters modulate hyperbolic features in a task-aware manner. Extensive experiments on visual and language benchmarks demonstrate that our proposed kernels outperform existing hyperbolic kernels in modeling hierarchical dependencies.

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Method: Three core innovations: (1) AudioScore - a comprehensive human preference-aligned scoring system; (2) automated AudioScore-driven pipeline for generating large-scale preference pair data; (3) curriculum learning-empowered DPO optimization strategy specifically tailored for flow-based generative models.

Result: Experiments on VGGSound dataset show that human-preference aligned Frieren and MMAudio using V2A-DPO outperform counterparts optimized with DDPO and pre-trained baselines. DPO-optimized MMAudio achieves state-of-the-art performance across multiple metrics, surpassing published V2A models.

Conclusion: V2A-DPO provides an effective framework for aligning flow-based video-to-audio generation models with human preferences, demonstrating superior performance over existing optimization methods and achieving state-of-the-art results.

Abstract: This paper introduces V2A-DPO, a novel Direct Preference Optimization (DPO) framework tailored for flow-based video-to-audio generation (V2A) models, incorporating key adaptations to effectively align generated audio with human preferences. Our approach incorporates three core innovations: (1) AudioScore-a comprehensive human preference-aligned scoring system for assessing semantic consistency, temporal alignment, and perceptual quality of synthesized audio; (2) an automated AudioScore-driven pipeline for generating large-scale preference pair data for DPO optimization; (3) a curriculum learning-empowered DPO optimization strategy specifically tailored for flow-based generative models. Experiments on benchmark VGGSound dataset demonstrate that human-preference aligned Frieren and MMAudio using V2A-DPO outperform their counterparts optimized using Denoising Diffusion Policy Optimization (DDPO) as well as pre-trained baselines. Furthermore, our DPO-optimized MMAudio achieves state-of-the-art performance across multiple metrics, surpassing published V2A models.

Method: Proposes Uni-ASR framework with joint training paradigm for both non-streaming and streaming modes, context-aware training, and co-designed fallback decoding strategy for latency optimization.

Result: Achieves competitive performance in non-streaming mode and strong effectiveness in streaming scenarios under diverse latency constraints without architectural changes.

Conclusion: Uni-ASR provides a unified solution for ASR deployment across different latency requirements, enabling seamless transition between streaming and non-streaming modes.

Abstract: Although the deep integration of the Automatic Speech Recognition (ASR) system with Large Language Models (LLMs) has significantly improved accuracy, the deployment of such systems in low-latency streaming scenarios remains challenging. In this paper, we propose Uni-ASR, a unified framework based on LLMs that integrates both non-streaming and streaming speech recognition capabilities. We propose a joint training paradigm that enables the system to seamlessly transition between two recognition modes without any architectural modifications. Furthermore, we introduce a context-aware training paradigm and a co-designed fallback decoding strategy, which can enhance streaming recognition accuracy without introducing additional latency. The experimental results demonstrate that Uni-ASR not only achieves competitive performance within non-streaming mode, but also demonstrates strong effectiveness in streaming scenarios under diverse latency constraints.

Method: 1) Conduct five diverse layer-wise analyses to identify paralinguistic vs. semantic understanding layers; 2) Propose Paralinguistic-Enhanced Fine-Tuning (PE-FT) with selective-layer fine-tuning and auxiliary dual-level classification head.

Result: PE-FT protocol efficiently resurfaces paralinguistic awareness, even surpassing all-layer fine-tuning performance. The method enables LALMs to better understand emotional and contextual cues in speech.

Conclusion: Paralinguistic awareness is crucial for human-like audio interaction. The proposed PE-FT protocol effectively enhances LALMs’ ability to understand both content and paralinguistic cues, advancing multimodal audio understanding.

Abstract: Large Audio Language Models (LALMs) have expanded the interaction with human to speech modality, which introduces great interactive potential, due to the paralinguistic cues implicitly indicating the user context. However, building on the current content-centred paradigm, LALMs usually neglect such paralinguistic cues and respond solely based on query content. In this work, to resurface the paralinguistic awareness in LALMs, we introduce five diverse layer-wise analyses to jointly identify paralinguistic layers and semantic understanding layers. Based on these insights, we propose a paralinguistic-enhanced fine-tuning (PE-FT) protocol accordingly to equip LALMs with paralinguistic-aware capabilities, including (1) selective-layer fine-tuning, and (2) an auxiliary dual-level classification head. Our experiments demonstrate that PE-FT protocol efficiently and effectively resurfaces the paralinguistic awareness, even surpassing the performance of the all-layer fine-tuning strategy.

Method: Fair-Gate uses risk extrapolation to reduce variation in speaker-classification risk across proxy sex groups, and introduces a local complementary gate that routes intermediate features into separate identity and sex branches, producing an interpretable routing mask.

Result: Experiments on VoxCeleb1 show Fair-Gate improves the utility-fairness trade-off, yielding more sex-fair automatic speaker verification performance under challenging evaluation conditions.

Conclusion: The proposed Fair-Gate framework effectively addresses both demographic shortcut learning and feature entanglement mechanisms, providing both fairness improvements and interpretability through explicit feature routing.

Abstract: Voice biometric systems can exhibit sex-related performance gaps even when overall verification accuracy is strong. We attribute these gaps to two practical mechanisms: (i) demographic shortcut learning, where speaker classification training exploits spurious correlations between sex and speaker identity, and (ii) feature entanglement, where sex-linked acoustic variation overlaps with identity cues and cannot be removed without degrading speaker discrimination. We propose Fair-Gate, a fairness-aware and interpretable risk-gating framework that addresses both mechanisms in a single pipeline. Fair-Gate applies risk extrapolation to reduce variation in speaker-classification risk across proxy sex groups, and introduces a local complementary gate that routes intermediate features into an identity branch and a sex branch. The gate provides interpretability by producing an explicit routing mask that can be inspected to understand which features are allocated to identity versus sex-related pathways. Experiments on VoxCeleb1 show that Fair-Gate improves the utility–fairness trade-off, yielding more sex-fair ASV performance under challenging evaluation conditions.

Method: Combines unlabeled audio with limited labeled data through pseudo-labeled continued pretraining followed by supervised finetuning on wav2vec2-bert-2.0.

Result: Achieves 3.24% WER on Common Voice Swahili with only 20,000 labeled samples - 82% relative improvement over baseline and 61% better than previous best academic system (XLS-R at 8.3% WER).

Conclusion: Provides effective methodology for adapting speech models to low-resource languages with concrete data requirements and replicable approach applicable to other languages.

Abstract: We investigate continued pretraining (CPT) for adapting wav2vec2-bert-2.0 to Swahili automatic speech recognition (ASR). Our approach combines unlabeled audio with limited labeled data through pseudo-labeled CPT followed by supervised finetuning. With 20,000 labeled samples, we achieve 3.24% WER on Common Voice Swahili-an 82% relative improvement over the baseline. This result surpasses the best previously reported academic system (8.3% WER from XLS-R) by 61% relative improvement. We provide concrete data requirements and a replicable methodology applicable to other low-resource languages.

Method: Comprehensive literature review approach with three main contributions: coverage across audio domains, unified taxonomy of ALM foundations, and establishment of research landscape.

Result: First systematic review of ALMs that helps researchers understand technology development and future trends while providing practical implementation references.

Conclusion: The review organizes ALM developments, establishes foundational taxonomy, and captures research landscape to advance the field and guide future work.

Abstract: Audio-Language Models (ALMs), trained on paired audio-text data, are designed to process, understand, and reason about audio-centric multimodal content. Unlike traditional supervised approaches that use predefined labels, ALMs leverage natural language supervision to better handle complex real-world audio scenes with multiple overlapping events. While demonstrating impressive zero-shot and task generalization capabilities, there is still a notable lack of systematic surveys that comprehensively organize and analyze developments. In this paper, we present the first systematic review of ALMs with three main contributions: (1) comprehensive coverage of ALM works across speech, music, and sound from a general audio perspective; (2) a unified taxonomy of ALM foundations, including model architectures and training objectives; (3) establishment of a research landscape capturing mutual promotion and constraints among different research aspects, aiding in summarizing evaluations, limitations, concerns and promising directions. Our review contributes to helping researchers understand the development of existing technologies and future trends, while also providing valuable references for implementation in practical applications.

Method: Proposes UGSD (Uncertainty-Guided Speculative Decoding) framework where a lightweight edge model drafts captions locally, and only high-uncertainty token blocks are selectively escalated to a stronger cloud verifier for validation.

Result: Experiments on MER2024 benchmark show substantial BLEU improvements up to 62.7%, 1.4x lower latency, and 8.5x higher token throughput compared to edge-only models.

Conclusion: UGSD effectively characterizes the quality-efficiency-privacy trade-off in deployable SEC systems, enabling practical deployment of speech emotion understanding on resource-constrained devices.

Abstract: Speech Emotion Captioning (SEC) leverages large audio-language models to generate rich, context-aware affective descriptions from speech. However, real-world deployment remains challenging due to the substantial computational demands on resource-constrained edge devices and the privacy risks of transmitting biometric audio. While smaller audio-language models enable efficient on-device SEC, their limited capacity often weakens subtle paralinguistic modeling and fine-grained affective grounding. We propose an edge-cloud collaborative framework based on Uncertainty-Guided Speculative Decoding (UGSD). A lightweight edge model drafts captions locally, and only high-uncertainty token blocks are selectively escalated to a stronger cloud verifier for validation. Experiments on the MER2024 benchmark demonstrate substantial BLEU improvements up to 62.7%. UGSD further achieves 1.4x lower latency and 8.5x higher token throughput compared to an edge-only model. These results empirically characterize the quality-efficiency-privacy trade-off in deployable SEC systems.

Method: Proposed AnimeScore framework using pairwise ranking for automatic anime-likeness evaluation. Collected 15,000 pairwise judgments from 187 evaluators with free-form descriptions. Analyzed acoustic features and developed SSL-based ranking models.

Result: Acoustic analysis shows anime-likeness is driven by controlled resonance shaping, prosodic continuity, and deliberate articulation. Handcrafted features reached 69.3% AUC ceiling, while SSL-based models achieved up to 90.8% AUC.

Conclusion: AnimeScore provides a practical objective metric for anime-like voice evaluation that can serve as a reward signal for preference-based optimization of generative speech models.

Abstract: Evaluating ‘anime-like’ voices currently relies on costly subjective judgments, yet no standardized objective metric exists. A key challenge is that anime-likeness, unlike naturalness, lacks a shared absolute scale, making conventional Mean Opinion Score (MOS) protocols unreliable. To address this gap, we propose AnimeScore, a preference-based framework for automatic anime-likeness evaluation via pairwise ranking. We collect 15,000 pairwise judgments from 187 evaluators with free-form descriptions, and acoustic analysis reveals that perceived anime-likeness is driven by controlled resonance shaping, prosodic continuity, and deliberate articulation rather than simple heuristics such as high pitch. We show that handcrafted acoustic features reach a 69.3% AUC ceiling, while SSL-based ranking models achieve up to 90.8% AUC, providing a practical metric that can also serve as a reward signal for preference-based optimization of generative speech models.

Method: Proposes ComVo with: 1) Complex-valued generator and discriminator using native complex arithmetic for structured adversarial training, 2) Phase quantization to discretize phase values and regularize training, 3) Block-matrix computation scheme to reduce redundant operations and improve training efficiency.

Result: ComVo achieves higher synthesis quality than comparable real-valued baselines, and the block-matrix scheme reduces training time by 25%.

Conclusion: Native complex-valued neural networks with structured training techniques can improve audio waveform generation quality while maintaining computational efficiency.

Abstract: Neural vocoders have recently advanced waveform generation, yielding natural and expressive audio. Among these approaches, iSTFT-based vocoders have recently gained attention. They predict a complex-valued spectrogram and then synthesize the waveform via iSTFT, thereby avoiding learned upsampling stages that can increase computational cost. However, current approaches use real-valued networks that process the real and imaginary parts independently. This separation limits their ability to capture the inherent structure of complex spectrograms. We present ComVo, a Complex-valued neural Vocoder whose generator and discriminator use native complex arithmetic. This enables an adversarial training framework that provides structured feedback in complex-valued representations. To guide phase transformations in a structured manner, we introduce phase quantization, which discretizes phase values and regularizes the training process. Finally, we propose a block-matrix computation scheme to improve training efficiency by reducing redundant operations. Experiments demonstrate that ComVo achieves higher synthesis quality than comparable real-valued baselines, and that its block-matrix scheme reduces training time by 25%. Audio samples and code are available at https://hs-oh-prml.github.io/ComVo/.

Method: Adapt online Group Relative Policy Optimization (GRPO) for Flow Matching-based audio models, incorporating rewards from Large Audio Language Models (LALMs) that provide fine-grained scoring signals better aligned with human perception.

Result: Online RL significantly outperforms offline counterparts. The final model, Resonate (470M parameters), establishes new SOTA on TTA-Bench for both audio quality and semantic alignment.

Conclusion: Online RL with GRPO and LALM rewards is effective for text-to-audio generation, demonstrating superior performance over offline methods and achieving state-of-the-art results with a relatively compact model.

Abstract: Reinforcement Learning (RL) has become an effective paradigm for enhancing Large Language Models (LLMs) and visual generative models. However, its application in text-to-audio (TTA) generation remains largely under-explored. Prior work typically employs offline methods like Direct Preference Optimization (DPO) and leverages Contrastive Language-Audio Pretraining (CLAP) models as reward functions. In this study, we investigate the integration of online Group Relative Policy Optimization (GRPO) into TTA generation. We adapt the algorithm for Flow Matching-based audio models and demonstrate that online RL significantly outperforms its offline counterparts. Furthermore, we incorporate rewards derived from Large Audio Language Models (LALMs), which can provide fine-grained scoring signals that are better aligned with human perception. With only 470M parameters, our final model, \textbf{Resonate}, establishes a new SOTA on TTA-Bench in terms of both audio quality and semantic alignment.

Method: Augments FastSpeech2 with explicit emotion conditioning and introduces counterfactual training objectives based on a structural causal model. Uses Indirect Path Constraint (IPC) to enforce emotion affects speech only through prosody, and Counterfactual Prosody Constraint (CPC) to encourage distinct prosody patterns for different emotions. Trained on multi-speaker emotional corpora with combined objective including standard reconstruction losses and causal losses.

Result: Achieves significantly improved prosody manipulation and emotion rendering with higher MOS and emotion accuracy than baseline FastSpeech2 variants. Better intelligibility (low WER) and speaker consistency when transferring emotions across speakers. Causal objectives successfully separate prosody attribution, allowing controlled counterfactual prosody editing without compromising naturalness.

Conclusion: Integrating causal learning principles into TTS improves controllability and expressiveness in generated speech. The framework enables interpretable prosody modeling and controlled emotion manipulation while maintaining speech quality.

Abstract: We propose a novel causal prosody mediation framework for expressive text-to-speech (TTS) synthesis. Our approach augments the FastSpeech2 architecture with explicit emotion conditioning and introduces counterfactual training objectives to disentangle emotional prosody from linguistic content. By formulating a structural causal model of how text (content), emotion, and speaker jointly influence prosody (duration, pitch, energy) and ultimately the speech waveform, we derive two complementary loss terms: an Indirect Path Constraint (IPC) to enforce that emotion affects speech only through prosody, and a Counterfactual Prosody Constraint (CPC) to encourage distinct prosody patterns for different emotions. The resulting model is trained on multi-speaker emotional corpora (LibriTTS, EmoV-DB, VCTK) with a combined objective that includes standard spectrogram reconstruction and variance prediction losses alongside our causal losses. In evaluations on expressive speech synthesis, our method achieves significantly improved prosody manipulation and emotion rendering, with higher mean opinion scores (MOS) and emotion accuracy than baseline FastSpeech2 variants. We also observe better intelligibility (low WER) and speaker consistency when transferring emotions across speakers. Extensive ablations confirm that the causal objectives successfully separate prosody attribution, yielding an interpretable model that allows controlled counterfactual prosody editing (e.g. “same utterance, different emotion”) without compromising naturalness. We discuss the implications for identifiability in prosody modeling and outline limitations such as the assumption that emotion effects are fully captured by pitch, duration, and energy. Our work demonstrates how integrating causal learning principles into TTS can improve controllability and expressiveness in generated speech.

Method: Proposes AudioTrust framework covering six trustworthiness dimensions (fairness, hallucination, safety, privacy, robustness, authentication) with 26 sub-tasks. Uses curated dataset of 4,420 real-world audio samples from daily conversations, emergency calls, and voice assistant interactions. Employs human-validated automated pipelines for objective assessment across 18 experimental settings.

Result: Evaluation of 14 state-of-the-art open-source and closed-source ALLMs reveals important limitations and failure boundaries under diverse high-risk audio scenarios, exposing vulnerabilities to acoustic manipulation.

Conclusion: AudioTrust provides the first systematic framework for evaluating ALLM trustworthiness under audio-specific risks, offering critical insights for secure deployment of future audio models and highlighting the need for acoustic-aware safety evaluations.

Abstract: The rapid development and widespread adoption of Audio Large Language Models (ALLMs) demand rigorous evaluation of their trustworthiness. However, existing evaluation frameworks are primarily designed for text and fail to capture vulnerabilities introduced by the acoustic properties of audio. We find that significant trustworthiness risks in ALLMs arise from non-semantic acoustic cues, such as timbre, accent, and background noise, which can be exploited to manipulate model behavior. To address this gap, we propose AudioTrust, the first large-scale and systematic framework for evaluating ALLM trustworthiness under audio-specific risks. AudioTrust covers six key dimensions: fairness, hallucination, safety, privacy, robustness, and authenticition. It includes 26 sub-tasks and a curated dataset of more than 4,420 audio samples collected from real-world scenarios, including daily conversations, emergency calls, and voice assistant interactions, and is specifically designed to probe trustworthiness across multiple dimensions. Our comprehensive evaluation spans 18 experimental settings and uses human-validated automated pipelines to enable objective and scalable assessment of model outputs. Experimental results on 14 state-of-the-art open-source and closed-source ALLMs reveal important limitations and failure boundaries under diverse high-risk audio scenarios, providing critical insights for the secure and trustworthy deployment of future audio models. Our platform and benchmark are publicly available at https://github.com/JusperLee/AudioTrust.

Method: The approach frames text-only adaptation as a text denoising task, training the LLM to recover clean transcripts from noisy inputs. This process adapts the model to target domains while preserving cross-modal alignment. The solution is lightweight, requiring no architectural changes or additional parameters.

Result: Extensive evaluation on two datasets demonstrates up to 22.1% relative improvement, outperforming recent state-of-the-art text-only adaptation methods.

Conclusion: The proposed text denoising approach provides an effective method for adapting LLM-based ASR systems to new domains using only text data, while maintaining the crucial cross-modal alignment between speech and text representations.

Abstract: Adapting large language model (LLM)-based automatic speech recognition (ASR) systems to new domains using text-only data is a significant yet underexplored challenge. Standard fine-tuning of the LLM on the target domain text often disrupts the critical alignment between the speech and text modality learned by the projector, degrading performance. We introduce a novel text-only adaptation method that frames this process as a text denoising task. Our approach trains the LLM to recover clean transcripts from noisy inputs. This process effectively adapts the model to a target domain while preserving cross-modal alignment. Our solution is lightweight, requiring no architectural changes or additional parameters. Extensive evaluation on two datasets demonstrates up to 22.1% relative improvement, outperforming recent state-of-the-art text-only adaptation methods.

Method: Proposes PV-VASM, a probabilistic framework that estimates misclassification probability under text-to-speech, voice cloning, and parametric signal transformations. The approach is model-agnostic and provides theoretical upper bounds on error probability.

Result: The method is validated across diverse experimental settings and demonstrates effectiveness as a practical robustness verification tool for voice anti-spoofing models.

Conclusion: PV-VASM provides a formal robustness verification framework for voice anti-spoofing models, addressing security vulnerabilities in speech synthesis detection systems.

Abstract: Recent advances in generative models have amplified the risk of malicious misuse of speech synthesis technologies, enabling adversaries to impersonate target speakers and access sensitive resources. Although speech deepfake detection has progressed rapidly, most existing countermeasures lack formal robustness guarantees or fail to generalize to unseen generation techniques. We propose PV-VASM, a probabilistic framework for verifying the robustness of voice anti-spoofing models (VASMs). PV-VASM estimates the probability of misclassification under text-to-speech (TTS), voice cloning (VC), and parametric signal transformations. The approach is model-agnostic and enables robustness verification against unseen speech synthesis techniques and input perturbations. We derive a theoretical upper bound on the error probability and validate the method across diverse experimental settings, demonstrating its effectiveness as a practical robustness verification tool.

Method: Proposes HIR-SDD framework combining Large Audio Language Models (LALMs) with chain-of-thought reasoning derived from a novel human-annotated dataset to enable interpretable speech deepfake detection.

Result: Experimental evaluation demonstrates both the effectiveness of the proposed method and its ability to provide reasonable justifications for predictions.

Conclusion: HIR-SDD addresses key limitations in current speech deepfake detection by improving generalization and providing interpretable, human-like reasoning for predictions.

Abstract: The modern generative audio models can be used by an adversary in an unlawful manner, specifically, to impersonate other people to gain access to private information. To mitigate this issue, speech deepfake detection (SDD) methods started to evolve. Unfortunately, current SDD methods generally suffer from the lack of generalization to new audio domains and generators. More than that, they lack interpretability, especially human-like reasoning that would naturally explain the attribution of a given audio to the bona fide or spoof class and provide human-perceptible cues. In this paper, we propose HIR-SDD, a novel SDD framework that combines the strengths of Large Audio Language Models (LALMs) with the chain-of-thought reasoning derived from the novel proposed human-annotated dataset. Experimental evaluation demonstrates both the effectiveness of the proposed method and its ability to provide reasonable justifications for predictions.

Method: Two approaches: 1) Modified Local Outlier Factor algorithm tailored for strings using Levenshtein distance with hierarchical character class weighting, 2) New algorithm based on hierarchical left regular expression learner that infers regex patterns for expected data.

Result: Both algorithms can find outliers in string data. Regex-based approach excels when expected values have distinct structure different from outliers, while LOF variants work best when edit distance between outliers and expected data differs significantly from distances within expected data.

Conclusion: String outlier detection is feasible with specialized algorithms, with different approaches suited to different data characteristics - structural patterns vs. edit distance distributions.

Abstract: Outlier detection is a well-researched and crucial problem in machine learning. However, there is little research on string data outlier detection, as most literature focuses on outlier detection of numerical data. A robust string data outlier detection algorithm could assist with data cleaning or anomaly detection in system log files. In this thesis, we compare two string outlier detection algorithms. Firstly, we introduce a variant of the well-known local outlier factor algorithm, which we tailor to detect outliers on string data using the Levenshtein measure to calculate the density of the dataset. We present a differently weighted Levenshtein measure, which considers hierarchical character classes and can be used to tune the algorithm to a specific string dataset. Secondly, we introduce a new kind of outlier detection algorithm based on the hierarchical left regular expression learner, which infers a regular expression for the expected data. Using various datasets and parameters, we experimentally show that both algorithms can conceptually find outliers in string data. We show that the regular expression-based algorithm is especially good at finding outliers if the expected values have a distinct structure that is sufficiently different from the structure of the outliers. In contrast, the local outlier factor algorithms are best at finding outliers if their edit distance to the expected data is sufficiently distinct from the edit distance between the expected data.

Method: Propose a module-aligned UQ scheme that restricts Monte Carlo sampling to the lifting module only (keeping propagation and recovery deterministic), using either channel-wise multiplicative feature dropout or Gaussian feature perturbation with matched variance, followed by calibration.

Result: Experiments on PDE benchmarks show improved coverage, tighter uncertainty bands, better residual-uncertainty alignment compared to baselines, while maintaining practical runtime.

Conclusion: Structure-aware epistemic UQ by restricting stochasticity to the lifting module provides more reliable and spatially faithful uncertainty quantification for neural operators in scientific computing applications.

Abstract: Neural operators (NOs) provide fast, resolution-invariant surrogates for mapping input fields to PDE solution fields, but their predictions can exhibit significant epistemic uncertainty due to finite data, imperfect optimization, and distribution shift. For practical deployment in scientific computing, uncertainty quantification (UQ) must be both computationally efficient and spatially faithful, i.e., uncertainty bands should align with the localized residual structures that matter for downstream risk management. We propose a structure-aware epistemic UQ scheme that exploits the modular anatomy common to modern NOs (lifting-propagation-recovering). Instead of applying unstructured weight perturbations (e.g., naive dropout) across the entire network, we restrict Monte Carlo sampling to a module-aligned subspace by injecting stochasticity only into the lifting module, and treat the learned solver dynamics (propagation and recovery) as deterministic. We instantiate this principle with two lightweight lifting-level perturbations, including channel-wise multiplicative feature dropout and a Gaussian feature perturbation with matched variance, followed by standard calibration to construct uncertainty bands. Experiments on challenging PDE benchmarks (including discontinuous-coefficient Darcy flow and geometry-shifted 3D car CFD surrogates) demonstrate that the proposed structure-aware design yields more reliable coverage, tighter bands, and improved residual-uncertainty alignment compared with common baselines, while remaining practical in runtime.

Method: Proposes CausalTimePrior framework that generates synthetic TSCMs with configurable causal graph structures, nonlinear autoregressive mechanisms, regime-switching dynamics, and multiple intervention types (hard, soft, time-varying).

Result: Demonstrates that PFNs trained on CausalTimePrior can perform in-context causal effect estimation on held-out TSCMs, establishing a pathway toward foundation models for time series causal inference.

Conclusion: CausalTimePrior addresses the critical gap in time series causal inference by providing synthetic interventional data, enabling the development of foundation models for temporal causal reasoning.

Abstract: Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series remains limited by the absence of synthetic data generators that provide interventional targets. Existing time series benchmarks generate observational data with ground-truth causal graphs but lack the interventional data required for training causal foundation models. To address this, we propose \textbf{CausalTimePrior}, a principled framework for generating synthetic temporal structural causal models (TSCMs) with paired observational and interventional time series. Our prior supports configurable causal graph structures, nonlinear autoregressive mechanisms, regime-switching dynamics, and multiple intervention types (hard, soft, time-varying). We demonstrate that PFNs trained on CausalTimePrior can perform in-context causal effect estimation on held-out TSCMs, establishing a pathway toward foundation models for time series causal inference.

Method: FiCSUM creates fingerprint vectors containing many distinct meta-information features to represent both supervised and unsupervised concept behaviors, with a dynamic weighting strategy that learns which features best describe concept drift for each dataset.

Result: FiCSUM outperforms state-of-the-art methods on 11 real-world and synthetic datasets in both accuracy and modeling underlying concept drift.

Conclusion: Using diverse meta-information features in fingerprint vectors with adaptive weighting enables more effective concept drift detection and concept representation in streaming data.

Abstract: Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the distribution of data over time, for example, due to changes in environmental conditions. Representing concepts (stationary periods featuring similar behaviour) is a key idea in adapting to concept drift. By testing the similarity of a concept representation to a window of observations, we can detect concept drift to a new or previously seen recurring concept. Concept representations are constructed using meta-information features, values describing aspects of concept behaviour. We find that previously proposed concept representations rely on small numbers of meta-information features. These representations often cannot distinguish concepts, leaving systems vulnerable to concept drift. We propose FiCSUM, a general framework to represent both supervised and unsupervised behaviours of a concept in a fingerprint, a vector of many distinct meta-information features able to uniquely identify more concepts. Our dynamic weighting strategy learns which meta-information features describe concept drift in a given dataset, allowing a diverse set of meta-information features to be used at once. FiCSUM outperforms state-of-the-art methods over a range of 11 real world and synthetic datasets in both accuracy and modeling underlying concept drift.

Method: Proposes a graph tokenization framework combining: 1) Reversible graph serialization that preserves graph information, 2) Byte Pair Encoding (BPE) tokenizer, 3) Serialization guided by global statistics of graph substructures to ensure frequent substructures appear more often and can be merged into meaningful tokens.

Result: Achieves state-of-the-art results on 14 benchmark datasets, frequently outperforming both graph neural networks and specialized graph transformers. Enables standard Transformers like BERT to be directly applied to graph benchmarks without architectural modifications.

Conclusion: The work successfully bridges the gap between graph-structured data and sequence models, demonstrating that effective tokenization enables standard Transformers to excel on graph tasks without specialized architectures.

Abstract: The success of large pretrained Transformers is closely tied to tokenizers, which convert raw input into discrete symbols. Extending these models to graph-structured data remains a significant challenge. In this work, we introduce a graph tokenization framework that generates sequential representations of graphs by combining reversible graph serialization, which preserves graph information, with Byte Pair Encoding (BPE), a widely adopted tokenizer in large language models (LLMs). To better capture structural information, the graph serialization process is guided by global statistics of graph substructures, ensuring that frequently occurring substructures appear more often in the sequence and can be merged by BPE into meaningful tokens. Empirical results demonstrate that the proposed tokenizer enables Transformers such as BERT to be directly applied to graph benchmarks without architectural modifications. The proposed approach achieves state-of-the-art results on 14 benchmark datasets and frequently outperforms both graph neural networks and specialized graph transformers. This work bridges the gap between graph-structured data and the ecosystem of sequence models. Our code is available at \href{https://github.com/BUPT-GAMMA/Graph-Tokenization-for-Bridging-Graphs-and-Transformers}{\color{blue}here}.

Method: Introduce routing signatures (vector representations of expert activation patterns across layers), analyze routing similarity within/across task categories, train logistic regression classifiers on signatures, and use permutation/load-balancing baselines for statistical validation.

Result: Within-category routing similarity (0.8435) significantly exceeds across-category similarity (0.6225), Cohen’s d = 1.44; logistic regression achieves 92.5% accuracy on four-way task classification; task structure becomes more apparent in deeper layers.

Conclusion: MoE routing is not just a balancing mechanism but exhibits measurable task-sensitive structure, with routing signatures capturing task-specific patterns that enable accurate task classification.

Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation summarizing expert activation patterns across layers for a given prompt, and use them to study whether MoE routing exhibits task-conditioned structure. Using OLMoE-1B-7B-0125-Instruct as an empirical testbed, we show that prompts from the same task category induce highly similar routing signatures, while prompts from different categories exhibit substantially lower similarity. Within-category routing similarity (0.8435 +/- 0.0879) significantly exceeds across-category similarity (0.6225 +/- 0.1687), corresponding to Cohen’s d = 1.44. A logistic regression classifier trained solely on routing signatures achieves 92.5% +/- 6.1% cross-validated accuracy on four-way task classification. To ensure statistical validity, we introduce permutation and load-balancing baselines and show that the observed separation is not explained by sparsity or balancing constraints alone. We further analyze layer-wise signal strength and low-dimensional projections of routing signatures, finding that task structure becomes increasingly apparent in deeper layers. These results suggest that routing in sparse transformers is not merely a balancing mechanism, but a measurable task-sensitive component of conditional computation. We release MOE-XRAY, a lightweight toolkit for routing telemetry and analysis.

Method: Proposes gradient-based training of decision trees using backpropagation with a straight-through operator on a dense DT representation. This enables joint optimization of all tree parameters rather than sequential greedy selection.

Result: Achieves state-of-the-art results across multiple domains: interpretable DTs for small tabular datasets, advanced models for complex tabular data, multimodal learning, and interpretable reinforcement learning without information loss.

Conclusion: The method bridges the gap between decision trees and gradient-based optimization, significantly enhancing performance and applicability of tree-based models across various ML domains, particularly enabling integration with multimodal and reinforcement learning tasks.

Abstract: Tree-based models are widely recognized for their interpretability and have proven effective in various application domains, particularly in high-stakes domains. However, learning decision trees (DTs) poses a significant challenge due to their combinatorial complexity and discrete, non-differentiable nature. As a result, traditional methods such as CART, which rely on greedy search procedures, remain the most widely used approaches. These methods make locally optimal decisions at each node, constraining the search space and often leading to suboptimal tree structures. Additionally, their demand for custom training methods precludes a seamless integration into modern machine learning (ML) approaches. In this thesis, we propose a novel method for learning hard, axis-aligned DTs through gradient descent. Our approach utilizes backpropagation with a straight-through operator on a dense DT representation, enabling the joint optimization of all tree parameters, thereby addressing the two primary limitations of traditional DT algorithms. First, gradient-based training is not constrained by the sequential selection of locally optimal splits but, instead, jointly optimizes all tree parameters. Second, by leveraging gradient descent for optimization, our approach seamlessly integrates into existing ML approaches e.g., for multimodal and reinforcement learning tasks, which inherently rely on gradient descent. These advancements allow us to achieve state-of-the-art results across multiple domains, including interpretable DTs rees for small tabular datasets, advanced models for complex tabular data, multimodal learning, and interpretable reinforcement learning without information loss. By bridging the gap between DTs and gradient-based optimization, our method significantly enhances the performance and applicability of tree-based models across various ML domains.

Method: Propose a scalable data-driven superposition operator using deep learning. The model is trained on synthetically generated Markovian Arrival Processes (MAPs) where exact superposition is available. It learns a compact representation that maps low-order moments and autocorrelation descriptors of multiple arrival streams to reconstruct the first five moments and short-range dependence structure of the merged process.

Result: Extensive computational experiments show uniformly low prediction errors across heterogeneous variability and correlation regimes, substantially outperforming classical renewal-based approximations. The operator enables decomposition-based evaluation of feed-forward queueing networks with merging flows when integrated with learning-based modules for departure-process and steady-state analysis.

Conclusion: The framework provides a scalable alternative to traditional analytical approaches while preserving higher-order variability and dependence information required for accurate distributional performance analysis in queueing networks.

Abstract: The superposition of arrival processes is a fundamental yet analytically intractable operation in queueing networks when inputs are general non-renewal streams. Classical methods either reduce merged flows to renewal surrogates, rely on computationally prohibitive Markovian representations, or focus solely on mean-value performance measures. We propose a scalable data-driven superposition operator that maps low-order moments and autocorrelation descriptors of multiple arrival streams to those of their merged process. The operator is a deep learning model trained on synthetically generated Markovian Arrival Processes (MAPs), for which exact superposition is available, and learns a compact representation that accurately reconstructs the first five moments and short-range dependence structure of the aggregate stream. Extensive computational experiments demonstrate uniformly low prediction errors across heterogeneous variability and correlation regimes, substantially outperforming classical renewal-based approximations. When integrated with learning-based modules for departure-process and steady-state analysis, the proposed operator enables decomposition-based evaluation of feed-forward queueing networks with merging flows. The framework provides a scalable alternative to traditional analytical approaches while preserving higher-order variability and dependence information required for accurate distributional performance analysis.

Method: Proposes Group Resonance Network (GRN) with three components: individual encoder for band-wise EEG features, learnable group prototypes for prototype-induced resonance, and multi-subject resonance branch encoding PLV/coherence-based synchrony with a small reference set. Uses resonance-aware fusion module to combine individual and group-level representations.

Result: Experiments on SEED and DEAP datasets under both subject-dependent and leave-one-subject-out protocols show GRN consistently outperforms competitive baselines. Ablation studies confirm complementary benefits of prototype learning and multi-subject resonance modeling.

Conclusion: GRN effectively integrates individual EEG dynamics with group resonance modeling to improve cross-subject emotion recognition by leveraging both subject-invariant features and stimulus-locked group regularities.

Abstract: Electroencephalography(EEG)-basedemotionrecognitionre- mains challenging in cross-subject settings due to severe inter-subject variability. Existing methods mainly learn subject-invariant features, but often under-exploit stimulus-locked group regularities shared across sub- jects. To address this issue, we propose the Group Resonance Network (GRN), which integrates individual EEG dynamics with offline group resonance modeling. GRN contains three components: an individual en- coder for band-wise EEG features, a set of learnable group prototypes for prototype-induced resonance, and a multi-subject resonance branch that encodes PLV/coherence-based synchrony with a small reference set. A resonance-aware fusion module combines individual and group-level rep- resentations for final classification. Experiments on SEED and DEAP under both subject-dependent and leave-one-subject-out protocols show that GRN consistently outperforms competitive baselines, while abla- tion studies confirm the complementary benefits of prototype learning and multi-subject resonance modeling.

Method: Used high-fidelity clinical speech corpus for end-to-end ASR training, compared multiple ASR families under unified evaluation, analyzed WER and error patterns, proposed HD-specific adaptation with biomarker-based auxiliary supervision.

Result: Parakeet-TDT outperformed encoder-decoder and CTC baselines; HD-specific adaptation reduced WER from 6.99% to 4.95%; error behavior reshaped in severity-dependent ways rather than uniform WER improvement.

Conclusion: HD speech induces architecture-specific error regimes, and severity-aware adaptation with auxiliary supervision can effectively improve pathological speech recognition performance.

Abstract: Automatic speech recognition (ASR) for pathological speech remains underexplored, especially for Huntington’s disease (HD), where irregular timing, unstable phonation, and articulatory distortion challenge current models. We present a systematic HD-ASR study using a high-fidelity clinical speech corpus not previously used for end-to-end ASR training. We compare multiple ASR families under a unified evaluation, analyzing WER as well as substitution, deletion, and insertion patterns. HD speech induces architecture-specific error regimes, with Parakeet-TDT outperforming encoder-decoder and CTC baselines. HD-specific adaptation reduces WER from 6.99% to 4.95% and we also propose a method for using biomarker-based auxiliary supervision and analyze how error behavior is reshaped in severity-dependent ways rather than uniformly improving WER. We open-source all code and models.

Method: High-resolution (weekly) weather-informed surrogate modeling that captures recurring short-term weather-driven energy demand patterns common across multiple regions, enabling generalization to unseen locations without extensive multi-site simulations.

Result: When trained on a single location, the model maintains high predictive accuracy for other sites within the same climate zone with no performance loss, and shows only minimal degradation when applied across different climate zones.

Conclusion: The approach demonstrates climate-informed generalization for scalable and reusable surrogate models, supporting more sustainable and optimized building design practices.

Abstract: Building design optimization often depends on physics-based simulation tools such as EnergyPlus, which, although accurate, are computationally expensive and slow. Surrogate models provide a faster alternative, yet most are location-specific, and even weather-informed variants require simulations from many sites to generalize to unseen locations. This limitation arises because existing methods do not fully exploit the short-term weather-driven energy patterns shared across regions, restricting their scalability and reusability. This study introduces a high-resolution (weekly) weather-informed surrogate modeling approach that enhances model reusability across locations. By capturing recurring short-term weather-energy demand patterns common to multiple regions, the proposed method produces a generalized surrogate that performs well beyond the training location. Unlike previous weather-informed approaches, it does not require extensive simulations from multiple sites to achieve strong generalization. Experimental results show that when trained on a single location, the model maintains high predictive accuracy for other sites within the same climate zone, with no noticeable performance loss, and exhibits only minimal degradation when applied across different climate zones. These findings demonstrate the potential of climate-informed generalization for developing scalable and reusable surrogate models, supporting more sustainable and optimized building design practices.

Method: Introduces Event Logic Tree (ELT) as a knowledge representation framework to bridge linguistic descriptions and time series data via temporal-logic structures. Presents a neuro-symbolic VLM agent that iteratively instantiates primitives from signal visualizations and composes them under ELT constraints, producing detected intervals and explanations as instantiated trees.

Result: Experiments on real-world benchmark show superiority over supervised fine-tuning baselines and existing zero-shot time series reasoning frameworks. Human evaluation confirms effectiveness. ELT is shown to be critical in mitigating VLMs’ inherent hallucination in matching signal morphology with event semantics.

Conclusion: The proposed Knowledge-Guided TSED with ELT representation and neuro-symbolic VLM framework effectively addresses the challenge of grounding natural language event descriptions to time series intervals with minimal training data, outperforming existing approaches and providing faithful explanations.

Abstract: Time Series Event Detection (TSED) has long been an important task with critical applications across many high-stakes domains. Unlike statistical anomalies, events are defined by semantics with complex internal structures, which are difficult to learn inductively from scarce labeled data in real-world settings. In light of this, we introduce Knowledge-Guided TSED, a new setting where a model is given a natural-language event description and must ground it to intervals in multivariate signals with little or no training data. To tackle this challenge, we introduce Event Logic Tree (ELT), a novel knowledge representation framework to bridge linguistic descriptions and physical time series data via modeling the intrinsic temporal-logic structures of events. Based on ELT, we present a neuro-symbolic VLM agent framework that iteratively instantiates primitives from signal visualizations and composes them under ELT constraints, producing both detected intervals and faithful explanations in the form of instantiated trees. To validate the effectiveness of our approach, we release a benchmark based on real-world time series data with expert knowledge and annotations. Experiments and human evaluation demonstrate the superiority of our method compared to supervised fine-tuning baselines and existing zero-shot time series reasoning frameworks based on LLMs/VLMs. We also show that ELT is critical in mitigating VLMs’ inherent hallucination in matching signal morphology with event semantics.

Method: Proposes a novel QCNN architecture using localized cost functions and a hardware-efficient tensor-network initialization strategy to provably mitigate barren plateaus, evaluated on MNIST dataset.

Result: Achieves 98.7% classification accuracy on MNIST, a substantial improvement over baseline QCNN accuracy of 52.32%, with empirical evidence of parameter-efficiency advantage requiring O(log N) fewer trainable parameters than equivalent classical CNNs to achieve >95% convergence.

Conclusion: Bridges the gap between theoretical quantum utility and practical application, providing a scalable framework for quantum computer vision tasks without succumbing to loss landscape concentration.

Abstract: While Quantum Convolutional Neural Networks (QCNNs) offer a theoretical paradigm for quantum machine learning, their practical implementation is severely bottlenecked by barren plateaus – the exponential vanishing of gradients – and poor empirical accuracy compared to classical counterparts. In this work, we propose a novel QCNN architecture utilizing localized cost functions and a hardware-efficient tensor-network initialization strategy to provably mitigate barren plateaus. We evaluate our scalable QCNN on the MNIST dataset, demonstrating a significant performance leap. By resolving the gradient vanishing issue, our optimized QCNN achieves a classification accuracy of 98.7%, a substantial improvement over the baseline QCNN accuracy of 52.32% found in unmitigated models. Furthermore, we provide empirical evidence of a parameter-efficiency advantage, requiring $\mathcal{O}(\log N)$ fewer trainable parameters than equivalent classical CNNs to achieve $>95%$ convergence. This work bridges the gap between theoretical quantum utility and practical application, providing a scalable framework for quantum computer vision tasks without succumbing to loss landscape concentration.

Method: Higher-Order Modular Attention (HOMA) fuses pairwise attention with an explicit triadic interaction pathway. To make triadic attention practical on long sequences, HOMA employs block-structured, windowed triadic attention.

Result: HOMA yields consistent improvements across all three TAPE benchmarks (Secondary Structure, Fluorescence, and Stability) compared with standard self-attention and efficient variants including block-wise attention and Linformer.

Conclusion: Explicit triadic terms provide complementary representational capacity for protein sequence prediction at controllable additional computational cost.

Abstract: Transformer self-attention computes pairwise token interactions, yet protein sequence to phenotype relationships often involve cooperative dependencies among three or more residues that dot product attention does not capture explicitly. We introduce Higher-Order Modular Attention, HOMA, a unified attention operator that fuses pairwise attention with an explicit triadic interaction pathway. To make triadic attention practical on long sequences, HOMA employs block-structured, windowed triadic attention. We evaluate on three TAPE benchmarks for Secondary Structure, Fluorescence, and Stability. Our attention mechanism yields consistent improvements across all tasks compared with standard self-attention and efficient variants including block-wise attention and Linformer. These results suggest that explicit triadic terms provide complementary representational capacity for protein sequence prediction at controllable additional computational cost.

Method: REOPOLD treats on-policy distillation as policy optimization where teacher-student log-likelihood ratio acts as token reward. It stabilizes optimization through mixture-based reward clipping, entropy-based token-level dynamic sampling, and unified exploration-to-refinement training strategy.

Result: REOPOLD outperforms baselines with superior sample efficiency (6.7-12x improvement over RL approaches) and enables 7B student to match 32B teacher in visual reasoning with ~3.32x inference speedup across mathematical, visual, and agentic tool-use reasoning tasks.

Conclusion: REOPOLD provides a stable and efficient framework for knowledge distillation that significantly improves sample efficiency and inference performance while maintaining reasoning capabilities across diverse multimodal tasks.

Abstract: On-policy distillation is pivotal for transferring reasoning capabilities to capacity-constrained models, yet remains prone to instability and negative transfer. We show that on-policy distillation can be interpreted, both theoretically and empirically, as a form of policy optimization, where the teacher-student log-likelihood ratio acts as a token reward. From this insight, we introduce REOPOLD (Relaxed On-Policy Distillation) a framework that stabilizes optimization by relaxing the strict imitation constraints of standard on-policy distillation. Specifically, REOPOLD temperately and selectively leverages rewards from the teacher through mixture-based reward clipping, entropy-based token-level dynamic sampling, and a unified exploration-to-refinement training strategy. Empirically, REOPOLD surpasses its baselines with superior sample efficiency during training and enhanced test-time scaling at inference, across mathematical, visual, and agentic tool-use reasoning tasks. Specifically, REOPOLD outperforms recent RL approaches achieving 6.7~12x greater sample efficiency and enables a 7B student to match a 32B teacher in visual reasoning with a ~3.32x inference speedup.

Method: Continual pretraining pipeline (H2LooP Spark Preview) adapts OLMo-3-7B using BF16 LoRA with rank-stabilized scaling on 8 H100 GPUs. Training corpus includes 100B tokens from repository-datasheet pairs across 117 manufacturers, processed via hierarchical datasheet-to-code mapping (SpecMap). Curated dataset contains 23.5B tokens across 13 embedded domains.

Result: 70.4% reduction in in-domain perplexity and 66.1% reduction in held-out repository perplexity. On generative code completion benchmarks across 13 embedded domains, the 7B model outperforms Claude Opus 4.6 and Qwen3-Coder-30B on 8 categories in token accuracy.

Conclusion: Targeted continual pretraining enables smaller open-weight models to rival frontier systems on specialized technical tasks. The production training checkpoint is released as open-source on Huggingface.

Abstract: Large language models (LLMs) demonstrate strong code generation abilities in general-purpose programming languages but remain limited in specialized domains such as low-level embedded systems programming. This domain involves hardware register manipulation, vendor-specific SDKs, real-time operating system APIs, and hardware abstraction layers that are underrepresented in standard pretraining corpora. We introduce H2LooP Spark Preview, a continual pretraining (CPT) pipeline that adapts the OLMo-3-7B-a fully open language model to the embedded systems domain using BF16 LoRA with rank-stabilized scaling on 8 NVIDIA H100 GPUs. Our training corpus is constructed from repository-datasheet pairs covering 100B tokens of raw embedded systems data across 117 manufacturers, processed using the hierarchical datasheet-to-code mapping approach proposed in SpecMap (Nipane et al., 2026). The resulting curated dataset split contains 23.5B tokens across 13 embedded domains. Continual pretraining with high-rank LoRA (r=512) yields substantial gains, reducing in-domain perplexity by 70.4% and held-out repository perplexity by 66.1%. On generative code completion benchmarks spanning 13 embedded domains, our 7B model outperforms Claude Opus 4.6 and Qwen3-Coder-30B on 8 categories in token accuracy, showing that targeted continual pretraining enables smaller open-weight models to rival frontier systems on specialized technical tasks. We release the production training checkpoint on Huggingface as an open-source artifact.

Method: Group Counterfactual Integrated Gradients (GCIG) is an in-processing regularization framework that enforces explanation invariance across groups conditioned on true labels. It computes explanations relative to multiple Group Conditional baselines and penalizes cross-group variation in these attributions during training.

Result: GCIG substantially reduces cross-group explanation disparity while maintaining competitive predictive performance and accuracy-fairness trade-offs compared to six state-of-the-art methods.

Conclusion: Aligning model reasoning across groups offers a principled and practical avenue for advancing fairness beyond outcome parity, addressing procedural fairness concerns that complement existing fairness objectives.

Abstract: Fairness in machine learning research has largely focused on outcome-oriented fairness criteria such as Equalized Odds, while comparatively less attention has been given to procedural-oriented fairness, which addresses how a model arrives at its predictions. Neglecting procedural fairness means it is possible for a model to generate different explanations for different protected groups, thereby eroding trust. In this work, we introduce Group Counterfactual Integrated Gradients (GCIG), an in-processing regularization framework that enforces explanation invariance across groups, conditioned on the true label. For each input, GCIG computes explanations relative to multiple Group Conditional baselines and penalizes cross-group variation in these attributions during training. GCIG formalizes procedural fairness as Group Counterfactual explanation stability and complements existing fairness objectives that constrain predictions alone. We compared GCIG empirically against six state-of-the-art methods, and the results show that GCIG substantially reduces cross-group explanation disparity while maintaining competitive predictive performance and accuracy-fairness trade-offs. Our results also show that aligning model reasoning across groups offers a principled and practical avenue for advancing fairness beyond outcome parity.

Method: Mechanistic interpretability techniques on pre-trained video vision transformer, using causal analysis with activation patching and ablation studies to reverse-engineer internal circuits for action outcome representation.

Result: Success/failure signal computed through distinct amplification cascade from layers 5-11; attention heads gather evidence while MLP blocks compose concepts; distributed redundant circuit explains resilience to ablation.

Conclusion: Models develop sophisticated hidden knowledge beyond explicit training tasks, highlighting need for mechanistic oversight in building Explainable and Trustworthy AI systems for deployment.

Abstract: The paper explores how video models trained for classification tasks represent nuanced, hidden semantic information that may not affect the final outcome, a key challenge for Trustworthy AI models. Through Explainable and Interpretable AI methods, specifically mechanistic interpretability techniques, the internal circuit responsible for representing the action’s outcome is reverse-engineered in a pre-trained video vision transformer, revealing that the “Success vs Failure” signal is computed through a distinct amplification cascade. While there are low-level differences observed from layer 0, the abstract and semantic representation of the outcome is progressively amplified from layers 5 through 11. Causal analysis, primarily using activation patching supported by ablation results, reveals a clear division of labor: Attention Heads act as “evidence gatherers”, providing necessary low-level information for partial signal recovery, while MLP Blocks function as robust “concept composers”, each of which is the primary driver to generate the “success” signal. This distributed and redundant circuit in the model’s internals explains its resilience to simple ablations, demonstrating a core computational pattern for processing human-action outcomes. Crucially, the existence of this sophisticated circuit for representing complex outcomes, even within a model trained only for simple classification, highlights the potential for models to develop forms of ‘hidden knowledge’ beyond their explicit task, underscoring the need for mechanistic oversight for building genuinely Explainable and Trustworthy AI systems intended for deployment.

Method: The authors develop a scaling-law framework treating jailbreak attacks as compute-bounded optimization procedures measured on a shared FLOPs axis. They systematically evaluate four jailbreak paradigms (optimization-based attacks, self-refinement prompting, sampling-based selection, genetic optimization) across multiple model families and scales on diverse harmful goals. They fit saturating exponential functions to FLOPs-success trajectories and derive efficiency summaries.

Result: Prompting-based paradigms are most compute-efficient compared to optimization-based methods. Prompt-based attacks more effectively optimize in prompt space. Attacks occupy distinct success-stealthiness operating points with prompting-based methods in high-success, high-stealth region. Vulnerability is strongly goal-dependent: misinformation harms are easier to elicit than other non-misinformation harms.

Conclusion: The study provides a systematic scaling-law framework for jailbreak attacks, revealing important patterns in attack efficiency and vulnerability across different methods and harm types, with implications for understanding and defending against jailbreak attacks.

Abstract: Large language models remain vulnerable to jailbreak attacks, yet we still lack a systematic understanding of how jailbreak success scales with attacker effort across methods, model families, and harm types. We initiate a scaling-law framework for jailbreaks by treating each attack as a compute-bounded optimization procedure and measuring progress on a shared FLOPs axis. Our systematic evaluation spans four representative jailbreak paradigms, covering optimization-based attacks, self-refinement prompting, sampling-based selection, and genetic optimization, across multiple model families and scales on a diverse set of harmful goals. We investigate scaling laws that relate attacker budget to attack success score by fitting a simple saturating exponential function to FLOPs–success trajectories, and we derive comparable efficiency summaries from the fitted curves. Empirically, prompting-based paradigms tend to be the most compute-efficient compared to optimization-based methods. To explain this gap, we cast prompt-based updates into an optimization view and show via a same-state comparison that prompt-based attacks more effectively optimize in prompt space. We also show that attacks occupy distinct success–stealthiness operating points with prompting-based methods occupying the high-success, high-stealth region. Finally, we find that vulnerability is strongly goal-dependent: harms involving misinformation are typically easier to elicit than other non-misinformation harms.

Method: Analyze infinite-width transformers in both lazy and rich regimes, derive upper bounds on inference-time computational complexity of functions these networks can learn, and study their inductive bias towards EPTHS class algorithms.

Result: Transformers possess inductive bias towards low-complexity algorithms within EPTHS class, preventing them from capturing higher-complexity algorithms while allowing success on simpler tasks like search, copy, and sort.

Conclusion: Despite universal expressivity, transformers are fundamentally limited to learning low-complexity algorithms due to their architectural inductive bias, which has implications for their algorithmic learning capabilities.

Abstract: We formally define Algorithmic Capture (i.e., ``grokking’’ an algorithm) as the ability of a neural network to generalize to arbitrary problem sizes ($T$) with controllable error and minimal sample adaptation, distinguishing true algorithmic learning from statistical interpolation. By analyzing infinite-width transformers in both the lazy and rich regimes, we derive upper bounds on the inference-time computational complexity of the functions these networks can learn. We show that despite their universal expressivity, transformers possess an inductive bias towards low-complexity algorithms within the Efficient Polynomial Time Heuristic Scheme (EPTHS) class. This bias effectively prevents them from capturing higher-complexity algorithms, while allowing success on simpler tasks like search, copy, and sort.

Method: Combine known mechanistic equations with Gaussian processes for missing variables, formulate as constrained nonlinear stochastic program, use sample-average approximation

Result: Hybrid BO based on mass conservation yields significantly better designs than standard BO, converges in as few as 1 iteration vs. standard BO not converging within 25

Conclusion: Hybrid BO scheme effectively leverages both mechanistic modeling and data-driven optimization for partially known systems

Abstract: Bayesian optimization (BO) has gained attention as an efficient algorithm for black-box optimization of expensive-to-evaluate systems, where the BO algorithm iteratively queries the system and suggests new trials based on a probabilistic model fitted to previous samples. Still, the standard BO loop may require a prohibitively large number of experiments to converge to the optimum, especially for high-dimensional and nonlinear systems. We present a hybrid model-based BO formulation that combines the iterative Bayesian learning of BO with partially known mechanistic physical models. Instead of learning a direct mapping from inputs to the objective, we write all known equations for a physics-based model and infer expressions for variables missing equations using a probabilistic model, in our case, a Gaussian process (GP). The final formulation then includes the GP as a constraint in the hybrid model, thereby allowing other physics-based nonlinear and implicit model constraints. This hybrid model formulation yields a constrained, nonlinear stochastic program, which we discretize using the sample-average approximation. In an in-silico optimization of a single-stage distillation, the hybrid BO model based on mass conservation laws yields significantly better designs than a standard BO loop. Furthermore, the hybrid model converges in as few as one iteration, depending on the initial samples, whereas, the standard BO does not converge within 25 for any of the seeds. Overall, the proposed hybrid BO scheme presents a promising optimization method for partially known systems, leveraging the strengths of both mechanistic modeling and data-driven optimization.

Method: CoRe performs task-specific interventions within a low-rank linear subspace of hidden representations, adopting explicit learning objectives that ensure stability for past tasks while maintaining plasticity for new ones. This representation-space finetuning paradigm constrains updates to a low-rank subspace for parameter efficiency.

Result: Extensive experiments across multiple continual learning benchmarks show CoRe preserves parameter efficiency while significantly outperforming existing state-of-the-art methods.

Conclusion: CoRe introduces representation finetuning as a new, more effective and interpretable paradigm for continual learning that addresses fundamental limitations of weight-space optimization approaches.

Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams. While pre-trained models have shown powerful performance in continual learning, they still require finetuning to adapt effectively to downstream tasks. However, prevailing Parameter-Efficient Fine-Tuning (PEFT) methods operate through empirical, black-box optimization at the weight level. These approaches lack explicit control over representation drift, leading to sensitivity to domain shifts and catastrophic forgetting in continual learning scenarios. In this work, we introduce Continual Representation Learning (CoRe), a novel framework that for the first time shifts the finetuning paradigm from weight space to representation space. Unlike conventional methods, CoRe performs task-specific interventions within a low-rank linear subspace of hidden representations, adopting a learning process with explicit objectives, which ensures stability for past tasks while maintaining plasticity for new ones. By constraining updates to a low-rank subspace, CoRe achieves exceptional parameter efficiency. Extensive experiments across multiple continual learning benchmarks demonstrate that CoRe not only preserves parameter efficiency but also significantly outperforms existing state-of-the-art methods. Our work introduces representation finetuning as a new, more effective and interpretable paradigm for continual learning.

Method: Reference-Guided Unlearning (ReGUn) leverages a disjoint held-out dataset to provide a principled, class-conditioned reference for distillation, aligning the model’s behavior on forget data with its behavior on truly unseen data.

Result: ReGUn consistently outperforms standard approximate baselines across various model architectures, natural image datasets, and varying forget fractions, achieving superior forgetting-utility trade-off.

Conclusion: Distributional indistinguishability is a more principled objective for machine unlearning than performance-degradation heuristics, and ReGUn effectively implements this approach using reference-guided distillation.

Abstract: Machine unlearning aims to remove the influence of specific data from trained models while preserving general utility. Existing approximate unlearning methods often rely on performance-degradation heuristics, such as loss maximization or random labeling. However, these signals can be poorly conditioned, leading to unstable optimization and harming the model’s generalization. We argue that unlearning should instead prioritize distributional indistinguishability, aligning the model’s behavior on forget data with its behavior on truly unseen data. Motivated by this, we propose Reference-Guided Unlearning (ReGUn), a framework that leverages a disjoint held-out dataset to provide a principled, class-conditioned reference for distillation. We demonstrate across various model architectures, natural image datasets, and varying forget fractions that ReGUn consistently outperforms standard approximate baselines, achieving a superior forgetting-utility trade-off.

Method: Combination of wristband sensors for physiological data collection, mobile app for ecological momentary assessment (EMA), and machine learning classifiers to predict mood states from sensor data only

Result: Promising results on mood accuracy with performance comparable to state-of-the-art for specific detection of happiness and activeness

Conclusion: The wearable system demonstrates feasibility for automated mood prediction in elderly populations using physiological sensors and machine learning

Abstract: We present an intelligent wearable system to monitor and predict mood states of elderly people during their daily life activities. Our system is composed of a wristband to record different physiological activities together with a mobile app for ecological momentary assessment (EMA). Machine learning is used to train a classifier to automatically predict different mood states based on the smart band only. Our approach shows promising results on mood accuracy and provides results comparable with the state of the art in the specific detection of happiness and activeness.

Method: DISCOMAX uses a differentiable algorithm rooted in statistical thermodynamics that works via discrete enumeration with masked softmax aggregation of feasible states. It employs a straight-through gradient estimator to enable physics-consistent end-to-end learning of neural g^E-models, guaranteeing thermodynamic consistency subject only to user-specified discretization.

Result: The method outperforms existing surrogate-based methods on binary liquid-liquid equilibrium data and offers a general framework for learning from different kinds of equilibrium data.

Conclusion: DISCOMAX provides a novel approach for physics-consistent machine learning in thermodynamics, enabling accurate phase-equilibrium predictions while maintaining thermodynamic consistency throughout the learning process.

Abstract: Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure into neural networks have recently shown strong performance for activity-coefficient modeling. However, extending such approaches to equilibrium data arising from an extremum principle, such as liquid-liquid equilibria, remains difficult. Here we present DISCOMAX, a differentiable algorithm for phase-equilibrium calculation that guarantees thermodynamic consistency at both training and inference, only subject to a user-specified discretization. The method is rooted in statistical thermodynamics, and works via a discrete enumeration with subsequent masked softmax aggregation of feasible states, and together with a straight-through gradient estimator to enable physics-consistent end-to-end learning of neural $g^{E}$-models. We evaluate the approach on binary liquid-liquid equilibrium data and demonstrate that it outperforms existing surrogate-based methods, while offering a general framework for learning from different kinds of equilibrium data.

Method: Teacher-Guided Training (TGT) distills class-suppressed residual structure from a frozen multi-domain teacher (DINOv2) into the student during training. The teacher and auxiliary head are discarded after training, adding no inference overhead.

Result: Across eight single-domain benchmarks, TGT yields large far-OOD FPR@95 reductions: MDS improves by 11.61 pp, ViM by 10.78 pp, and kNN by 12.87 pp (ResNet-50 average), while maintaining or slightly improving in-domain OOD and classification accuracy.

Conclusion: TGT effectively addresses Domain-Sensitivity Collapse in single-domain training by leveraging multi-domain teacher knowledge, significantly improving OOD detection performance without inference cost overhead.

Abstract: Out-of-distribution (OOD) detection methods perform well on multi-domain benchmarks, yet many practical systems are trained on single-domain data. We show that this regime induces a geometric failure mode, Domain-Sensitivity Collapse (DSC): supervised training compresses features into a low-rank class subspace and suppresses directions that carry domain-shift signal. We provide theory showing that, under DSC, distance- and logit-based OOD scores lose sensitivity to domain shift. We then introduce Teacher-Guided Training (TGT), which distills class-suppressed residual structure from a frozen multi-domain teacher (DINOv2) into the student during training. The teacher and auxiliary head are discarded after training, adding no inference overhead. Across eight single-domain benchmarks, TGT yields large far-OOD FPR@95 reductions for distance-based scorers: MDS improves by 11.61 pp, ViM by 10.78 pp, and kNN by 12.87 pp (ResNet-50 average), while maintaining or slightly improving in-domain OOD and classification accuracy.

Method: Leverage audio duration as accurate proxy for Whisper ASR processing time. Integrate Shortest Job First (SJF) and Highest Response Ratio Next (HRRN) scheduling algorithms into vLLM serving engine. Evaluate under realistic and drifted workloads using LibriSpeech test-clean.

Result: SJF reduces median E2E latency by up to 73% at high load but increases 90th-percentile tail latency by up to 97% due to starvation. HRRN reduces median latency by up to 28% while bounding tail-latency degradation to at most 24%. Gains persist under workload drift with no throughput penalty and <0.1ms scheduling overhead.

Conclusion: Duration-aware scheduling significantly improves ASR serving performance. Audio duration is an effective proxy for processing time. HRRN provides better trade-off between median and tail latency compared to SJF, making it practical for production ASR serving systems.

Abstract: Scheduling policies in large-scale Automatic Speech Recognition (ASR) serving pipelines play a key role in determining end-to-end (E2E) latency. Yet, widely used serving engines rely on first-come-first-served (FCFS) scheduling, which ignores variability in request duration and leads to head-of-line blocking under workload drift. We show that audio duration is an accurate proxy for job processing time in ASR models such as Whisper, and use this insight to enable duration-aware scheduling. We integrate two classical algorithms, Shortest Job First (SJF) and Highest Response Ratio Next (HRRN), into vLLM and evaluate them under realistic and drifted workloads. On LibriSpeech test-clean, compared to baseline, SJF reduces median E2E latency by up to $73%$ at high load, but increases $90$th-percentile tail latency by up to $97%$ due to starvation of long requests. HRRN addresses this trade-off: it reduces median E2E latency by up to $28%$ while bounding tail-latency degradation to at most $24%$. These gains persist under workload drift, with no throughput penalty and $<0.1$,ms scheduling overhead per request.

Method: Introduces SMLM-C benchmark dataset with 10 SMLM simulations (dSTORM and DNA-PAINT modalities) with varying hyperparameters. Evaluates SSMs on biologically realistic spatiotemporal point process data with known ground truth, focusing on temporal discontinuity effects.

Result: SSM performance degrades substantially as temporal discontinuity increases, revealing fundamental challenges in modeling heavy-tailed blinking dynamics common in biological imaging.

Conclusion: Current sequence models are inadequate for sparse, irregular temporal processes in real-world scientific imaging, highlighting need for better models suited to such data characteristics.

Abstract: State space models (SSMs) have recently achieved strong performance on long sequence modeling tasks while offering improved memory and computational efficiency compared to transformer based architectures. However, their evaluation has been largely limited to synthetic benchmarks and application domains such as language and audio, leaving their behavior on sparse and stochastic temporal processes in biological imaging unexplored. In this work, we introduce the Single Molecule Localization Microscopy Challenge (SMLM-C), a benchmark dataset consisting of ten SMLM simulations spanning dSTORM and DNA-PAINT modalities with varying hyperparameter designed to evaluate state space models on biologically realistic spatiotemporal point process data with known ground truth. Using a controlled subset of these simulations, we evaluate state space models and find that performance degrades substantially as temporal discontinuity increases, revealing fundamental challenges in modeling heavy-tailed blinking dynamics. These results highlight the need for sequence models better suited to sparse, irregular temporal processes encountered in real world scientific imaging data.

Method: Proposes conditioning a single global model on locally-computed PCA statistics of each client’s training data, requiring zero additional communication. Uses PCA statistics to capture client-specific data characteristics.

Result: Evaluated across 97 configurations spanning four heterogeneity types, four datasets, and seven FL baselines. Matches Oracle baseline (knows true cluster assignments) across all settings, surpasses it by 1-6% on combined heterogeneity, and is uniquely sparsity-robust.

Conclusion: FedPCA effectively handles diverse data heterogeneity types in federated learning using locally-computed PCA statistics, outperforming existing methods while requiring no additional communication overhead.

Abstract: Federated learning (FL) under data heterogeneity remains challenging: existing methods either ignore client differences (FedAvg), require costly cluster discovery (IFCA), or maintain per-client models (Ditto). All degrade when data is sparse or heterogeneity is multi-dimensional. We propose conditioning a single global model on locally-computed PCA statistics of each client’s training data, requiring zero additional communication. Evaluating across 97~configurations spanning four heterogeneity types (label shift, covariate shift, concept shift, and combined heterogeneity), four datasets (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100), and seven FL baseline methods, we find that our method matches the Oracle baseline – which knows true cluster assignments – across all settings, surpasses it by 1–6% on combined heterogeneity where continuous statistics are richer than discrete cluster identifiers, and is uniquely sparsity-robust among all tested methods.

Method: Uses superstatistical model X = A^{1/2}G where A is positive random scalar and G is Gaussian vector, capturing heavy-tailed distributions. Formulates PCA under logarithmic loss (well-defined without moments). Shows principal components match those from underlying Gaussian covariance. Proposes robust estimators for this covariance matrix from heavy-tailed data.

Result: Theoretical result shows principal components of heavy-tailed observations coincide with those from standard PCA on underlying Gaussian covariance. Proposed approach reliably recovers principal directions, significantly outperforms classical PCA with heavy-tailed/impulsive noise, remains competitive under Gaussian noise. Demonstrated in background denoising tasks.

Conclusion: Provides unified robust PCA framework for infinite-variance heavy-tailed data using logarithmic loss and superstatistical models, with theoretical guarantees and practical estimators that outperform classical methods in non-Gaussian settings.

Abstract: Principal Component Analysis (PCA) is a cornerstone of dimensionality reduction, yet its classical formulation relies critically on second-order moments and is therefore fragile in the presence of heavy-tailed data and impulsive noise. While numerous robust PCA variants have been proposed, most either assume finite variance, rely on sparsity-driven decompositions, or address robustness through surrogate loss functions without a unified treatment of infinite-variance models. In this paper, we study PCA for high-dimensional data generated according to a superstatistical dependent model of the form $\mathbf{X} = A^{1/2}\mathbf{G}$, where $A$ is a positive random scalar and $\mathbf{G}$ is a Gaussian vector. This framework captures a wide class of heavy-tailed distributions, including multivariate $t$ and sub-Gaussian $α$-stable laws. We formulate PCA under a logarithmic loss, which remains well defined even when moments do not exist. Our main theoretical result shows that, under this loss, the principal components of the heavy-tailed observations coincide with those obtained by applying standard PCA to the covariance matrix of the underlying Gaussian generator. Building on this insight, we propose robust estimators for this covariance matrix directly from heavy-tailed data and compare them with the empirical covariance and Tyler’s scatter estimator. Extensive experiments, including background denoising tasks, demonstrate that the proposed approach reliably recovers principal directions and significantly outperforms classical PCA in the presence of heavy-tailed and impulsive noise, while remaining competitive under Gaussian noise.

Method: Theoretical analysis showing that Langevin dynamics with estimated scores fails to converge to target distribution in polynomial time even with arbitrarily small L² errors, while diffusion models remain robust under similar conditions.

Result: Langevin dynamics produces distributions far from target in Total Variation distance for any polynomial time horizon with small L² score errors, unlike diffusion models which sample faithfully under mild assumptions.

Conclusion: Results justify preference for diffusion models over Langevin dynamics in score-based generative modeling and caution against using Langevin dynamics with estimated scores due to lack of robustness.

Abstract: We consider the robustness of score-based generative modeling to errors in the estimate of the score function. In particular, we show that Langevin dynamics is not robust to the L^2 errors (more generally L^p errors) in the estimate of the score function. It is well-established that with small L^2 errors in the estimate of the score function, diffusion models can sample faithfully from the target distribution under fairly mild regularity assumptions in a polynomial time horizon. In contrast, our work shows that even for simple distributions in high dimensions, Langevin dynamics run for any polynomial time horizon will produce a distribution far from the target distribution in Total Variation (TV) distance, even when the L^2 error (more generally L^p) of the estimate of the score function is arbitrarily small. Considering such an error in the estimate of the score function is unavoidable in practice when learning the score function from data, our results provide further justification for diffusion models over Langevin dynamics and serve to caution against the use of Langevin dynamics with estimated scores.

Method: HAPO uses Synthetic Success Injection (SSI) operator, a hindsight mechanism that selectively anchors optimization to teacher demonstrations during failure episodes. A Thompson sampling-inspired gating mechanism controls this injection, creating a self-paced curriculum that naturally anneals teacher signals as policy improves.

Result: Theoretically demonstrates asymptotic consistency: HAPO recovers unbiased on-policy gradient by annealing teacher signals, ensuring off-policy guidance acts as temporary scaffolding rather than persistent ceiling, enabling models to surpass static teacher forcing limitations.

Conclusion: HAPO resolves the GRPO dilemma in sparse-reward RLVR by providing a theoretically grounded approach that uses teacher demonstrations as temporary scaffolds while achieving asymptotic consistency and enabling models to exceed teacher performance.

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for post-training reasoning models. However, group-based methods such as Group Relative Policy Optimization (GRPO) face a critical dilemma in sparse-reward settings: pure Reinforcement Learning (RL) suffers from advantage collapse and high-variance gradient estimation, while mixed-policy optimization introduces persistent distributional bias. To resolve this dilemma, we introduce Hindsight-Anchored Policy Optimization (HAPO). HAPO employs the Synthetic Success Injection (SSI) operator, a hindsight mechanism that selectively anchors optimization to teacher demonstrations during failure. This injection is governed by a Thompson sampling-inspired gating mechanism, creating an autonomous, self-paced curriculum. Theoretically, we demonstrate that HAPO achieves \textit{asymptotic consistency}: by naturally annealing the teacher signal as the policy improves, HAPO recovers the unbiased on-policy gradient. This ensures off-policy guidance acts as a temporary scaffold rather than a persistent ceiling, enabling the model to surpass the limitations of static teacher forcing.

Method: MR-Search uses in-context meta RL where the policy conditions on past episodes. After each episode, the agent generates explicit self-reflections which serve as additional context for subsequent attempts. A multi-turn RL algorithm estimates dense relative advantages at the turn level for fine-grained credit assignment.

Result: Empirical evaluation across various benchmarks shows MR-Search outperforms baseline RL methods with relative improvements of 9.2% to 19.3% across eight benchmarks, demonstrating strong generalization capabilities.

Conclusion: MR-Search effectively enables agents to learn search strategies through cross-episode self-reflection, leading to improved in-context exploration and better performance on search tasks compared to traditional RL approaches.

Abstract: This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single independent episode with sparse rewards, MR-Search trains a policy that conditions on past episodes and adapts its search strategy across episodes. MR-Search learns to learn a search strategy with self-reflection, allowing search agents to improve in-context exploration at test-time. Specifically, MR-Search performs cross-episode exploration by generating explicit self-reflections after each episode and leveraging them as additional context to guide subsequent attempts, thereby promoting more effective exploration during test-time. We further introduce a multi-turn RL algorithm that estimates a dense relative advantage at the turn level, enabling fine-grained credit assignment on each episode. Empirical results across various benchmarks demonstrate the advantages of MR-Search over baselines based RL, showing strong generalization and relative improvements of 9.2% to 19.3% across eight benchmarks. Our code and data are available at https://github.com/tengxiao1/MR-Search.

Method: Proposes a theoretical generative model using spin-glass physics operating in replica-symmetry-breaking regime, where generations follow Gibbs measure and unsafe outputs correspond to low-energy clusters. Analyzes prompt injection as magnetic fields: short prompts as weak fields (power-law scaling), long prompts as strong fields (exponential scaling).

Result: Analytically derives and empirically confirms that short injected prompts yield power-law scaling of attack success rate, while long injected prompts yield exponential scaling. The transition occurs due to ordered phase appearance under strong magnetic fields, suggesting injected prompts enhance adversarial order in LLMs.

Conclusion: Adversarial prompt injection can exponentially amplify jailbreak success by creating ordered phases in language models, with scaling behavior depending on prompt length/strength. This provides theoretical foundation for understanding and potentially defending against such attacks.

Abstract: Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that adversarial prompt-injection attacks can amplify attack success rate from the slow polynomial growth observed without injection to exponential growth with the number of inference-time samples. To explain this phenomenon, we propose a theoretical generative model of proxy language in terms of a spin-glass system operating in a replica-symmetry-breaking regime, where generations are drawn from the associated Gibbs measure and a subset of low-energy, size-biased clusters is designated unsafe. Within this framework, we analyze prompt injection-based jailbreaking. Short injected prompts correspond to a weak magnetic field aligned towards unsafe cluster centers and yield a power-law scaling of attack success rate with the number of inference-time samples, while long injected prompts, i.e., strong magnetic field, yield exponential scaling. We derive these behaviors analytically and confirm them empirically on large language models. This transition between two regimes is due to the appearance of an ordered phase in the spin chain under a strong magnetic field, which suggests that the injected jailbreak prompt enhances adversarial order in the language model.

Method: Formalizes learning as constrained dynamical process with two interacting timescales: inner dynamics for continuous parameter adaptation and outer dynamics for discrete structural change, linked by endogenous resource variable. Instantiated in Distinction Engine (DE11) based on Spencer-Brown’s Laws of Form, information geometry, and tropical optimization.

Result: DE11 achieves 93.3% test accuracy on IRIS, 92.6% on WINE, and 94.7% on Breast Cancer datasets, producing interpretable logical rules that emerge endogenously from learning dynamics rather than being manually imposed.

Conclusion: Teleodynamic Learning unifies regularization, architecture search, and resource-bounded inference within a single principle of learning as co-evolution of structure, parameters, and resources under constraint, offering a thermodynamically grounded route to adaptive, interpretable, and self-organizing AI.

Abstract: We introduce Teleodynamic Learning, a new paradigm for machine learning in which learning is not the minimization of a fixed objective, but the emergence and stabilization of functional organization under constraint. Inspired by living systems, this framework treats intelligence as the coupled evolution of three quantities: what a system can represent, how it adapts its parameters, and which changes its internal resources can sustain. We formalize learning as a constrained dynamical process with two interacting timescales: inner dynamics for continuous parameter adaptation and outer dynamics for discrete structural change, linked by an endogenous resource variable that both shapes and is shaped by the trajectory. This perspective reveals three phenomena that standard optimization does not naturally capture: self-stabilization without externally imposed stopping rules, phase-structured learning dynamics that move from under-structuring through teleodynamic growth to over-structuring, and convergence guarantees grounded in information geometry rather than convexity. We instantiate the framework in the Distinction Engine (DE11), a teleodynamic learner grounded in Spencer-Brown’s Laws of Form, information geometry, and tropical optimization. On standard benchmarks, DE11 achieves 93.3 percent test accuracy on IRIS, 92.6 percent on WINE, and 94.7 percent on Breast Cancer, while producing interpretable logical rules that arise endogenously from the learning dynamics rather than being imposed by hand. More broadly, Teleodynamic Learning unifies regularization, architecture search, and resource-bounded inference within a single principle: learning as the co-evolution of structure, parameters, and resources under constraint. This opens a thermodynamically grounded route to adaptive, interpretable, and self-organizing AI.

Method: Evaluated classical ML models (Logistic Regression, Linear SVM, Ensemble classifiers) with TF-IDF features alongside transformer-based architectures on a multilingual Bangla-English dataset of legitimate and fraudulent financial messages using 5-fold stratified cross-validation.

Result: Linear SVM achieved best performance with 91.59% accuracy and 91.30% F1 score, outperforming transformer model (89.49% accuracy, 88.88% F1) by ~2 percentage points. Transformer had higher fraud recall (94.19%) but more false positives.

Conclusion: Classical ML with well-crafted features remains competitive for multilingual fraud detection, highlighting challenges of linguistic diversity, code-mixing, and low-resource language constraints in financial security applications.

Abstract: Financial fraud detection has emerged as a critical research challenge amid the rapid expansion of digital financial platforms. Although machine learning approaches have demonstrated strong performance in identifying fraudulent activities, most existing research focuses exclusively on English-language data, limiting applicability to multilingual contexts. Bangla (Bengali), despite being spoken by over 250 million people, remains largely unexplored in this domain. In this work, we investigate financial fraud detection in a multilingual Bangla-English setting using a dataset comprising legitimate and fraudulent financial messages. We evaluate classical machine learning models (Logistic Regression, Linear SVM, and Ensemble classifiers) using TF-IDF features alongside transformer-based architectures. Experimental results using 5-fold stratified cross-validation demonstrate that Linear SVM achieves the best performance with 91.59 percent accuracy and 91.30 percent F1 score, outperforming the transformer model (89.49 percent accuracy, 88.88 percent F1) by approximately 2 percentage points. The transformer exhibits higher fraud recall (94.19 percent) but suffers from elevated false positive rates. Exploratory analysis reveals distinctive patterns: scam messages are longer, contain urgency-inducing terms, and frequently include URLs (32 percent) and phone numbers (97 percent), while legitimate messages feature transactional confirmations and specific currency references. Our findings highlight that classical machine learning with well-crafted features remains competitive for multilingual fraud detection, while also underscoring the challenges posed by linguistic diversity, code-mixing, and low-resource language constraints.

Method: Developed abx_amr_simulator, a modular Python package that models patient populations, antibiotic-specific AMR response curves, and reward functions. Uses leaky-balloon abstraction for resistance dynamics and supports partial observability through noise, bias, and delay in observations. Compatible with Gymnasium RL API.

Result: Created a configurable benchmark environment for sequential decision-making under uncertainty that enables training and testing of RL agents in diverse clinical scenarios related to antibiotic prescribing and AMR management.

Conclusion: The simulator provides a valuable, customizable framework for studying AMR dynamics and optimizing antibiotic stewardship strategies under realistic uncertainty conditions.

Abstract: Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To address this challenge, we introduce abx_amr_simulator, a Python-based simulation package designed to model antibiotic prescribing and AMR dynamics within a controlled, reinforcement learning (RL)-compatible environment. The simulator allows users to specify patient populations, antibiotic-specific AMR response curves, and reward functions that balance immedi- ate clinical benefit against long-term resistance management. Key features include a modular design for configuring patient attributes, antibiotic resistance dynamics modeled via a leaky-balloon abstraction, and tools to explore partial observability through noise, bias, and delay in observations. The package is compatible with the Gymnasium RL API, enabling users to train and test RL agents under diverse clinical scenarios. From an ML perspective, the package provides a configurable benchmark environment for sequential decision-making under uncertainty, including partial observability induced by noisy, biased, and delayed observations. By providing a customizable and extensible framework, abx_amr_simulator offers a valuable tool for studying AMR dynamics and optimizing antibiotic stewardship strategies under realistic uncertainty.

Method: REACT uses Gumbel-Sigmoid relaxation with straight-through estimation to enable gradient-based optimization over discrete acquisition masks, allowing end-to-end differentiable optimization of both onboarding context selection and adaptive longitudinal feature acquisition under cost constraints.

Result: Across real-world longitudinal health and behavioral datasets, REACT achieves improved predictive performance at lower acquisition costs compared to existing longitudinal acquisition baselines.

Conclusion: Modeling onboarding context selection and temporally coupled acquisition within a unified optimization framework provides benefits for biomedical applications with measurement constraints.

Abstract: In many biomedical applications, measurements are not freely available at inference time: each laboratory test, imaging modality, or assessment incurs financial cost, time burden, or patient risk. Longitudinal active feature acquisition (LAFA) seeks to optimize predictive performance under such constraints by adaptively selecting measurements over time, yet the problem remains inherently challenging due to temporally coupled decisions (missed early measurements cannot be revisited, and acquisition choices influence all downstream predictions). Moreover, real-world clinical workflows typically begin with an initial onboarding phase, during which relatively stable contextual descriptors (e.g., demographics or baseline characteristics) are collected once and subsequently condition longitudinal decision-making. Despite its practical importance, the efficient selection of onboarding context has not been studied jointly with temporally adaptive acquisition. We therefore propose REACT (Relaxed Efficient Acquisition of Context and Temporal features), an end-to-end differentiable framework that simultaneously optimizes (i) selection of onboarding contextual descriptors and (ii) adaptive feature–time acquisition plans for longitudinal measurements under cost constraints. REACT employs a Gumbel–Sigmoid relaxation with straight-through estimation to enable gradient-based optimization over discrete acquisition masks, allowing direct backpropagation from prediction loss and acquisition cost. Across real-world longitudinal health and behavioral datasets, REACT achieves improved predictive performance at lower acquisition costs compared to existing longitudinal acquisition baselines, demonstrating the benefit of modeling onboarding and temporally coupled acquisition within a unified optimization framework.

Method: Proposes T-CQL: Transformer encoder for temporal modeling of patient dynamics, conservative adaptive regularization based on uncertainty quantification for safety, consistency regularization for robust decision-making, and clinically informed reward function incorporating VILI indicators and illness severity scores.

Result: T-CQL consistently outperforms existing state-of-the-art offline RL methodologies, providing safer and more effective ventilatory adjustments, validated through interactive digital twins for online evaluation.

Conclusion: Transformer-based models combined with conservative RL strategies show potential as decision support tools in critical care for personalized mechanical ventilation automation.

Abstract: Mechanical ventilation (MV) is a life-saving intervention for patients with acute respiratory failure (ARF) in the ICU. However, inappropriate ventilator settings could cause ventilator-induced lung injury (VILI). Also, clinicians workload is shown to be directly linked to patient outcomes. Hence, MV should be personalized and automated to improve patient outcomes. Previous attempts to incorporate personalization and automation in MV include traditional supervised learning and offline reinforcement learning (RL) approaches, which often neglect temporal dependencies and rely excessively on mortality-based rewards. As a result, early stage physiological deterioration and the risk of VILI are not adequately captured. To address these limitations, we propose Transformer-based Conservative Q-Learning (T-CQL), a novel offline RL framework that integrates a Transformer encoder for effective temporal modeling of patient dynamics, conservative adaptive regularization based on uncertainty quantification to ensure safety, and consistency regularization for robust decision-making. We build a clinically informed reward function that incorporates indicators of VILI and a score for severity of patients illness. Also, previous work predominantly uses Fitted Q-Evaluation (FQE) for RL policy evaluation on static offline data, which is less responsive to dynamic environmental changes and susceptible to distribution shifts. To overcome these evaluation limitations, interactive digital twins of ARF patients were used for online “at the bedside” evaluation. Our results demonstrate that T-CQL consistently outperforms existing state-of-the-art offline RL methodologies, providing safer and more effective ventilatory adjustments. Our framework demonstrates the potential of Transformer-based models combined with conservative RL strategies as a decision support tool in critical care.

Method: Extends DreamerV3 with ARROW (Augmented Replay for RObust World models) - a memory-efficient, distribution-matching replay buffer system. Uses two complementary buffers: short-term buffer for recent experiences and long-term buffer that preserves task diversity through intelligent sampling.

Result: ARROW demonstrates substantially less forgetting on tasks without shared structure (Atari) compared to model-free and model-based baselines with same-size replay buffers, while maintaining comparable forward transfer on tasks with shared structure (Procgen CoinRun variants).

Conclusion: Model-based RL with bio-inspired replay mechanisms shows strong potential for continual reinforcement learning, warranting further research into these approaches for scalable continual learning.

Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future tasks. Most existing approaches rely on model-free methods with replay buffers to mitigate catastrophic forgetting; however, these solutions often face significant scalability challenges due to large memory demands. Drawing inspiration from neuroscience, where the brain replays experiences to a predictive World Model rather than directly to the policy, we present ARROW (Augmented Replay for RObust World models), a model-based continual RL algorithm that extends DreamerV3 with a memory-efficient, distribution-matching replay buffer. Unlike standard fixed-size FIFO buffers, ARROW maintains two complementary buffers: a short-term buffer for recent experiences and a long-term buffer that preserves task diversity through intelligent sampling. We evaluate ARROW on two challenging continual RL settings: Tasks without shared structure (Atari), and tasks with shared structure, where knowledge transfer is possible (Procgen CoinRun variants). Compared to model-free and model-based baselines with replay buffers of the same-size, ARROW demonstrates substantially less forgetting on tasks without shared structure, while maintaining comparable forward transfer. Our findings highlight the potential of model-based RL and bio-inspired approaches for continual reinforcement learning, warranting further research.

Method: Proposes a Finsler manifold learning pipeline that constructs asymmetric dissimilarities and embeds data in Finsler spaces. Generalizes existing methods like t-SNE and UMAP to their asymmetric Finsler versions (Finsler t-SNE and Finsler UMAP).

Result: On synthetic and real datasets, the asymmetric pipeline reveals valuable information lost in traditional approaches (e.g., density hierarchies) and consistently provides superior quality embeddings compared to Euclidean counterparts.

Conclusion: Finsler geometry enables effective capture of asymmetric information in data, broadening the applicability of asymmetric embedders beyond traditionally directed data to any data type.

Abstract: Manifold learning is a fundamental task at the core of data analysis and visualisation. It aims to capture the simple underlying structure of complex high-dimensional data by preserving pairwise dissimilarities in low-dimensional embeddings. Traditional methods rely on symmetric Riemannian geometry, thus forcing symmetric dissimilarities and embedding spaces, e.g. Euclidean. However, this discards in practice valuable asymmetric information inherent to the non-uniformity of data samples. We suggest to harness this asymmetry by switching to Finsler geometry, an asymmetric generalisation of Riemannian geometry, and propose a Finsler manifold learning pipeline that constructs asymmetric dissimilarities and embeds in a Finsler space. This greatly broadens the applicability of existing asymmetric embedders beyond traditionally directed data to any data. We also modernise asymmetric embedders by generalising current reference methods to asymmetry, like Finsler t-SNE and Finsler Umap. On controlled synthetic and large real datasets, we show that our asymmetric pipeline reveals valuable information lost in the traditional pipeline, e.g. density hierarchies, and consistently provides superior quality embeddings than their Euclidean counterparts.

Method: Adopts variational (Gaussian) formulation to make dependence measurable. Proposes stable neural dependence estimator based on orthonormal density-ratio decomposition. Avoids input concatenation and product-of-marginals re-pairing (unlike MINE). Introduces efficient NMF-like scalar objective. Assumes Gaussian noise to form auxiliary variable for meaningful dependence measurements.

Result: Method reduces computational cost and improves stability compared to MINE. Enables meaningful dependence measurements and supports quantitative feature analysis. Shows sequential convergence of singular values empirically.

Conclusion: The proposed variational Gaussian formulation with orthonormal density-ratio decomposition provides a stable, efficient approach for measuring statistical dependence in autoencoders, overcoming limitations of existing methods for deterministic networks.

Abstract: Statistical dependence measures like mutual information is ideal for analyzing autoencoders, but it can be ill-posed for deterministic, static, noise-free networks. We adopt the variational (Gaussian) formulation that makes dependence among inputs, latents, and reconstructions measurable, and we propose a stable neural dependence estimator based on an orthonormal density-ratio decomposition. Unlike MINE, our method avoids input concatenation and product-of-marginals re-pairing, reducing computational cost and improving stability. We introduce an efficient NMF-like scalar objective and demonstrate empirically that assuming Gaussian noise to form an auxiliary variable enables meaningful dependence measurements and supports quantitative feature analysis, with a sequential convergence of singular values.

Method: ZTab uses domain configurations with predefined semantic types and sample schemas to generate pseudo-tables, then fine-tunes an annotation LLM on them, enabling domain-based zero-shot detection without user-specific labeled data.

Result: ZTab provides a trade-off between zero-shot extent and annotation performance through different domain configurations, with specialized domains enabling better performance within specific applications.

Conclusion: ZTab addresses performance and privacy limitations of existing zero-shot models for semantic column type detection through a flexible domain-based approach that doesn’t require retraining for similar domains.

Abstract: This study addresses the challenge of automatically detecting semantic column types in relational tables, a key task in many real-world applications. Zero-shot modeling eliminates the need for user-provided labeled training data, making it ideal for scenarios where data collection is costly or restricted due to privacy concerns. However, existing zero-shot models suffer from poor performance when the number of semantic column types is large, limited understanding of tabular structure, and privacy risks arising from dependence on high-performance closed-source LLMs. We introduce ZTab, a domain-based zero-shot framework that addresses both performance and zero-shot requirements. Given a domain configuration consisting of a set of predefined semantic types and sample table schemas, ZTab generates pseudo-tables for the sample schemas and fine-tunes an annotation LLM on them. ZTab is domain-based zero-shot in that it does not depend on user-specific labeled training data; therefore, no retraining is needed for a test table from a similar domain. We describe three cases of domain-based zero-shot. The domain configuration of ZTab provides a trade-off between the extent of zero-shot and annotation performance: a “universal domain” that contains all semantic types approaches “pure” zero-shot, while a “specialized domain” that contains semantic types for a specific application enables better zero-shot performance within that domain. Source code and datasets are available at https://github.com/hoseinzadeehsan/ZTab

Method: Proposes UniHetCO, a unified heterogeneous graph representation that encodes problem structure, objective terms, and linear constraints in a single input. Uses a gradient-norm-based dynamic weighting scheme to alleviate gradient imbalance among classes during multi-problem learning.

Result: Competitive performance with state-of-the-art unsupervised NCO baselines on multiple datasets and four constrained problem classes, demonstrates strong cross-problem adaptation potential, and provides effective warm starts for commercial classical solvers under tight time limits.

Conclusion: UniHetCO offers a unified framework for training across multiple combinatorial optimization problem classes with a single model, addressing limitations of specialized approaches and enabling better cross-problem learning.

Abstract: Unsupervised neural combinatorial optimization (NCO) offers an appealing alternative to supervised approaches by training learning-based solvers without ground-truth solutions, directly minimizing instance objectives and constraint violations. Yet for graph node subset-selection problems (e.g., Maximum Clique and Maximum Independent Set), existing unsupervised methods are typically specialized to a single problem class and rely on problem-specific surrogate losses, which hinders learning across classes within a unified framework. In this work, we propose UniHetCO, a unified heterogeneous graph representation for constrained quadratic programming-based combinatorial optimization that encodes problem structure, objective terms, and linear constraints in a single input. This formulation enables training a single model across multiple problem classes with a unified label-free objective. To improve stability under multi-problem learning, we employ a gradient-norm-based dynamic weighting scheme that alleviates gradient imbalance among classes. Experiments on multiple datasets and four constrained problem classes demonstrate competitive performance with state-of-the-art unsupervised NCO baselines, strong cross-problem adaptation potential, and effective warm starts for a commercial classical solver under tight time limits.

Method: Proposes NEXTPP with dual-channel framework: encodes discrete event marks via self-attention, evolves latent continuous-time state using Neural ODE, fuses parallel streams through cross-attention module for bidirectional interaction, uses fused representations to drive conditional intensity function of neural Hawkes process with iterative thinning sampler for future event generation.

Result: Extensive evaluations on five real-world datasets demonstrate that NEXTPP consistently outperforms state-of-the-art models.

Conclusion: NEXTPP successfully unifies discrete and continuous representations for marked temporal point processes, overcoming limitations of existing approaches and achieving superior performance.

Abstract: Predicting irregularly spaced event sequences with discrete marks poses significant challenges due to the complex, asynchronous dependencies embedded within continuous-time data streams.Existing sequential approaches capture dependencies among event tokens but ignore the continuous evolution between events, while Neural Ordinary Differential Equation (Neural ODE) methods model smooth dynamics yet fail to account for how event types influence future timing.To overcome these limitations, we propose NEXTPP, a dual-channel framework that unifies discrete and continuous representations via Event-granular Neural Evolution with Cross-Interaction for Marked Temporal Point Processes. Specifically, NEXTPP encodes discrete event marks via a self-attention mechanism, simultaneously evolving a latent continuous-time state using a Neural ODE. These parallel streams are then fused through a crossattention module to enable explicit bidirectional interaction between continuous and discrete representations. The fused representations drive the conditional intensity function of the neural Hawkes process, while an iterative thinning sampler is employed to generate future events. Extensive evaluations on five real-world datasets demonstrate that NEXTPP consistently outperforms state-of-the-art models. The source code can be found at https://github.com/AONE-NLP/NEXTPP.

Method: Proves approximation error in conventional approach, designs Wasserstein distance as proximal operator to relax learning objective, derives new variational inference strategy from solving this relaxed optimization problem, provides rigorous derivation of optimization implementation, and proves algorithm convergence.

Result: Extensive experiments on synthetic and real-world industrial datasets demonstrate the efficacy of KProxNPLVM in sidestepping approximation errors and improving performance.

Conclusion: KProxNPLVM successfully addresses approximation errors in conventional NPLVMs through Wasserstein distance-based proximal operator relaxation, improving soft sensor modeling accuracy.

Abstract: Nonlinear Probabilistic Latent Variable Models (NPLVMs) are a cornerstone of soft sensor modeling due to their capacity for uncertainty delineation. However, conventional NPLVMs are trained using amortized variational inference, where neural networks parameterize the variational posterior. While facilitating model implementation, this parameterization converts the distributional optimization problem within an infinite-dimensional function space to parameter optimization within a finite-dimensional parameter space, which introduces an approximation error gap, thereby degrading soft sensor modeling accuracy. To alleviate this issue, we introduce KProxNPLVM, a novel NPLVM that pivots to relaxing the objective itself and improving the NPLVM’s performance. Specifically, we first prove the approximation error induced by the conventional approach. Based on this, we design the Wasserstein distance as the proximal operator to relax the learning objective, yielding a new variational inference strategy derived from solving this relaxed optimization problem. Based on this foundation, we provide a rigorous derivation of KProxNPLVM’s optimization implementation, prove the convergence of our algorithm can finally sidestep the approximation error, and propose the KProxNPLVM by summarizing the abovementioned content. Finally, extensive experiments on synthetic and real-world industrial datasets are conducted to demonstrate the efficacy of the proposed KProxNPLVM.

Method: Two deep learning models: 1) Customized network-temporal graph attention network (GAT) model, and 2) Fine-tuned multimodal large language model (LLM) with clustering overture. Both are compared against an LSTM baseline that already outperforms statistical methods.

Result: The LLM-based model demonstrates superior overall prediction and generalization performance, while the GAT model shows strength in reducing prediction variance across time series and horizons. Analysis reveals insights into correlation variability and prediction distribution discrepancies.

Conclusion: Deep learning approaches, particularly multimodal LLMs with clustering, offer promising solutions for multivariate time series prediction in network data by capturing both temporal patterns and topological correlations.

Abstract: Time series analysis is critical for emerging net- work intelligent control and management functions. However, existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. The intricate topological interdependency and complex temporal patterns in network data demand new model approaches. In this paper, based on a systematic multivariate time series model study, we present two deep learning models aiming for learning both temporal patterns and network topological correlations at the same time: a customized network-temporal graph attention network (GAT) model and a fine-tuned multi-modal large language model (LLM) with a clustering overture. Both models are studied against an LSTM model that already outperforms the statistical methods. Through extensive training and performance studies on a real-world network dataset, the LLM-based model demonstrates superior overall prediction and generalization performance, while the GAT model shows its strength in reducing prediction variance across the time series and horizons. More detailed analysis also reveals important insights into correlation variability and prediction distribution discrepancies over time series and different prediction horizons.

Method: End-to-end AI pipeline processes z-stacked microscope scans to generate synchronized 2D orthoimages and 3D point clouds, uses multimodal fusion model combining ConvNeXt for 2D analysis and PointNet++ for 3D analysis, with Bayesian finite mixture modeling for plant source prediction

Result: Achieved 77.9% global classification accuracy across 24 morphotypes and 84.5% segmentation quality; 3D data proved essential for distinguishing complex morphotypes; successfully identified specific plants like maize and palms in mixed samples

Conclusion: Sorometry transforms phytolith research into an “omics”-scale discipline, dramatically expanding analytical capacity, standardizing expert judgments, and enabling reproducible population-level characterizations

Abstract: Phytolith analysis is a crucial tool for reconstructing past vegetation and human activities, but traditional methods are severely limited by labour-intensive, time-consuming manual microscopy. To address this bottleneck, we present Sorometry: a comprehensive end-to-end artificial intelligence pipeline for the high-throughput digitisation, inference, and interpretation of phytoliths. Our workflow processes z-stacked optical microscope scans to automatically generate synchronised 2D orthoimages and 3D point clouds of individual microscopic particles. We developed a multimodal fusion model that combines ConvNeXt for 2D image analysis and PointNet++ for 3D point cloud analysis, supported by a graphical user interface for expert annotation and review. Tested on reference collections and archaeological samples from the Bolivian Amazon, our fusion model achieved a global classification accuracy of 77.9% across 24 diagnostic morphotypes and 84.5% for segmentation quality. Crucially, the integration of 3D data proved essential for distinguishing complex morphotypes (such as grass silica short cell phytoliths) whose diagnostic features are often obscured by their orientation in 2D projections. Beyond individual object classification, Sorometry incorporates Bayesian finite mixture modelling to predict overall plant source contributions at the assemblage level, successfully identifying specific plants like maize and palms in complex mixed samples. This integrated platform transforms phytolith research into an “omics”-scale discipline, dramatically expanding analytical capacity, standardising expert judgements, and enabling reproducible, population-level characterisations of archaeological and paleoecological assemblages.

Method: Theoretical analysis proving that computing trigger-conditional behaviors necessarily induces sinks in softmax self-attention models. A concrete task is instantiated: when a designated trigger token appears, the model must return the average of all preceding token representations, otherwise output zero. Comparison with non-normalized ReLU attention shows it can solve the same task without sinks.

Result: Proved that normalization over a probability simplex forces attention to collapse onto a stable anchor for default states. Experiments validate predictions: softmax models develop strong sinks while ReLU attention eliminates them in both single-head and multi-head variants.

Conclusion: The normalization constraint in softmax attention is the fundamental driver of sink behavior, not an inherent limitation of attention mechanisms. ReLU attention provides a sink-free alternative for trigger-conditional tasks.

Abstract: Transformers often display an attention sink: probability mass concentrates on a fixed, content-agnostic position. We prove that computing a simple trigger-conditional behavior necessarily induces a sink in softmax self-attention models. Our results formalize a familiar intuition: normalization over a probability simplex must force attention to collapse onto a stable anchor to realize a default state (e.g., when the model needs to ignore the input). We instantiate this with a concrete task: when a designated trigger token appears, the model must return the average of all preceding token representations, and otherwise output zero, a task which mirrors the functionality of attention heads in the wild (Barbero et al., 2025; Guo et al., 2024). We also prove that non-normalized ReLU attention can solve the same task without any sink, confirming that the normalization constraint is the fundamental driver of sink behavior. Experiments validate our predictions and demonstrate they extend beyond the theoretically analyzed setting: softmax models develop strong sinks while ReLU attention eliminates them in both single-head and multi-head variants.

Method: KEPo generates toxic events containing poisoned knowledge based on target answers, fabricates event backgrounds, forges knowledge evolution paths from original facts to toxic events, and connects multiple attack corpora in multi-target scenarios for mutual reinforcement.

Result: Experimental results across multiple datasets show KEPo achieves state-of-the-art attack success rates for both single-target and multi-target attacks, significantly outperforming previous methods.

Conclusion: GraphRAG has latent security vulnerabilities despite its robustness against conventional RAG attacks, and KEPo effectively exposes these vulnerabilities through specialized knowledge graph poisoning techniques.

Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) constructs the Knowledge Graph (KG) from external databases to enhance the timeliness and accuracy of Large Language Model (LLM) generations.However,this reliance on external data introduces new attack surfaces.Attackers can inject poisoned texts into databases to manipulate LLMs into producing harmful target responses for attacker-chosen queries.Existing research primarily focuses on attacking conventional RAG systems.However,such methods are ineffective against GraphRAG.This robustness derives from the KG abstraction of GraphRAG,which reorganizes injected text into a graph before retrieval,thereby enabling the LLM to reason based on the restructured context instead of raw poisoned passages.To expose latent security vulnerabilities in GraphRAG,we propose Knowledge Evolution Poison (KEPo),a novel poisoning attack method specifically designed for GraphRAG.For each target query,KEPo first generates a toxic event containing poisoned knowledge based on the target answer.By fabricating event backgrounds and forging knowledge evolution paths from original facts to the toxic event,it then poisons the KG and misleads the LLM into treating the poisoned knowledge as the final result.In multi-target attack scenarios,KEPo further connects multiple attack corpora,enabling their poisoned knowledge to mutually reinforce while expanding the scale of poisoned communities,thereby amplifying attack effectiveness.Experimental results across multiple datasets demonstrate that KEPo achieves state-of-the-art attack success rates for both single-target and multi-target attacks,significantly outperforming previous methods.

Method: Reformulates federated recommendation from an item-centered perspective as a multi-task learning problem, uses sharpness-aware minimization based on theoretical analysis to address generalization issues and stabilize training.

Result: Extensive experiments on four datasets demonstrate significant improvement in federated recommendation performance compared to existing methods.

Conclusion: FedRecGEL effectively addresses the generalized item embedding learning problem in federated recommendation, stabilizing training and enhancing performance through theoretical grounding and practical implementation.

Abstract: Federated recommender systems enable collaborative model training while keeping user interaction data local and sharing only essential model parameters, thereby mitigating privacy risks. However, existing methods overlook a critical issue, i.e., the stable learning of a generalized item embedding throughout the federated recommender system training process. Item embedding plays a central role in facilitating knowledge sharing across clients. Yet, under the cross-device setting, local data distributions exhibit significant heterogeneity and sparsity, exacerbating the difficulty of learning generalized embeddings. These factors make the stable learning of generalized item embeddings both indispensable for effective federated recommendation and inherently difficult to achieve. To fill this gap, we propose a new federated recommendation framework, named Federated Recommendation with Generalized Embedding Learning (FedRecGEL). We reformulate the federated recommendation problem from an item-centered perspective and cast it as a multi-task learning problem, aiming to learn generalized embeddings throughout the training procedure. Based on theoretical analysis, we employ sharpness-aware minimization to address the generalization problem, thereby stabilizing the training process and enhancing recommendation performance. Extensive experiments on four datasets demonstrate the effectiveness of FedRecGEL in significantly improving federated recommendation performance. Our code is available at https://github.com/anonymifish/FedRecGEL.

Method: Proposes LongFlow with an efficient importance estimation metric derived from intermediate attention computation using only the current query, requiring negligible overhead and no auxiliary storage. Develops a custom kernel that fuses FlashAttention, importance estimation, and token eviction into a single optimized operator.

Result: Achieves up to 11.8× throughput improvement with 80% KV cache compression while maintaining minimal impact on model accuracy.

Conclusion: LongFlow effectively addresses KV cache challenges in reasoning models with long outputs through efficient importance estimation and system-level optimization, enabling cost-effective deployment of advanced reasoning models.

Abstract: Recent reasoning models such as OpenAI-o1 and DeepSeek-R1 have shown strong performance on complex tasks including mathematical reasoning and code generation. However, this performance gain comes with substantially longer output sequences, leading to significantly increased deployment costs. In particular, long outputs require large KV caches, resulting in high memory consumption and severe bandwidth pressure during attention computation. Most existing KV cache optimization methods are designed for long-input, short-output scenarios and are ineffective for the long-output setting of reasoning models. Moreover, importance estimation in prior work is computationally expensive and becomes prohibitive when continuous re-evaluation is required during long generation. To address these challenges, we propose LongFlow, a KV cache compression method with an efficient importance estimation metric derived from an intermediate result of attention computation using only the current query. This design introduces negligible computational overhead and requires no auxiliary storage. We further develop a custom kernel that fuses FlashAttention, importance estimation, and token eviction into a single optimized operator, improving system-level efficiency. Experiments show that LongFlow achieves up to an 11.8 times throughput improvement with 80% KV cache compression with minimal impact on model accuracy.

Method: Proposes user-controllable privacy through feature disentanglement-based representation learning for dynamic privacy filtering at granular level. Compares with autoencoder-based few-shot HAR, analyzing architectural designs, learning objectives, privacy guarantees, data efficiency, and IoT deployment suitability.

Result: CFD-based HAR provides explicit, tunable privacy protection by separating activity and sensitive attributes in latent space. Autoencoder-based few-shot HAR offers superior label efficiency and lightweight adaptability but lacks inherent privacy safeguards. Analysis reveals neither approach alone fully satisfies next-generation IoT HAR requirements.

Conclusion: Outlines research directions toward unified frameworks that jointly optimize privacy preservation, few-shot adaptability, and robustness for trustworthy IoT intelligence, as current paradigms don’t fully meet emerging requirements.

Abstract: Modern wearable and mobile devices are equipped with inertial measurement units (IMUs). Human Activity Recognition (HAR) applications running on such devices use machine-learning-based, data-driven techniques that leverage such sensor data. However, sensor-data-driven HAR deployments face two critical challenges: protecting sensitive user information embedded in sensor data in accordance with users’ privacy preferences and maintaining high recognition performance with limited labeled samples. This paper proposes a technique for user-controllable privacy through feature disentanglement-based representation learning at the granular level for dynamic privacy filtering. We also compare the efficacy of our technique against few-shot HAR using autoencoder-based representation learning. We analyze their architectural designs, learning objectives, privacy guarantees, data efficiency, and suitability for edge Internet of Things (IoT) deployment. Our study shows that CFD-based HAR provides explicit, tunable privacy protection controls by separating activity and sensitive attributes in the latent space, whereas autoencoder-based few-shot HAR offers superior label efficiency and lightweight adaptability but lacks inherent privacy safeguards. We further examine the security implications of both approaches in continual IoT settings, highlighting differences in susceptibility to representation leakage and embedding-level attacks. The analysis reveals that neither paradigm alone fully satisfies the emerging requirements of next-generation IoT HAR systems. We conclude by outlining research directions toward unified frameworks that jointly optimize privacy preservation, few-shot adaptability, and robustness for trustworthy IoT intelligence.

Method: MTAC first performs causal discovery to learn a shared causal graph, then instantiates a structured multi-task SEM that factorizes outcome generation into (i) task-invariant mechanisms and (ii) task-specific mechanisms via a shared backbone with task-specific heads. It then performs MAP-based inference to reconstruct causes by jointly optimizing latent mechanism variables and cause magnitudes under the learned causal structure.

Result: Evaluated on urban event reconstruction from resident reports (parking violations, abandoned properties, unsanitary conditions) using real-world data from Manhattan and Newark. MTAC consistently improved reconstruction accuracy over baselines, achieving up to 34.61% MAE reduction, demonstrating benefits of learning transferable causal mechanisms across tasks.

Conclusion: MTAC effectively exploits cross-task invariances in causal mechanisms for anti-causal inference, showing practical benefits in real-world applications like urban event reconstruction. The framework demonstrates how structured multi-task learning with causal modeling can improve cause estimation from observed outcomes.

Abstract: Many real-world machine learning tasks are anti-causal: they require inferring latent causes from observed effects. In practice, we often face multiple related tasks where part of the forward causal mechanism is invariant across tasks, while other components are task-specific. We propose Multi-Task Anti-Causal learning (MTAC), a framework for estimating causes from outcomes and confounders by explicitly exploiting such cross-task invariances. MTAC first performs causal discovery to learn a shared causal graph and then instantiates a structured multi-task structural equation model (SEM) that factorizes the outcome-generation process into (i) a task-invariant mechanism and (ii) task-specific mechanisms via a shared backbone with task-specific heads. Building on the learned forward model, MTAC performs maximum A posteriori (MAP)based inference to reconstruct causes by jointly optimizing latent mechanism variables and cause magnitudes under the learned causal structure. We evaluate MTAC on the application of urban event reconstruction from resident reports, spanning three tasks:parking violations, abandoned properties, and unsanitary conditions. On real-world data collected from Manhattan and the city of Newark, MTAC consistently improves reconstruction accuracy over strong baselines, achieving up to 34.61% MAE reduction and demonstrating the benefit of learning transferable causal mechanisms across tasks.

Method: CAETC uses adversarial representation learning with an autoencoding architecture to learn partially invertible and treatment-invariant representations. It applies treatment-specific conditioning on these representations for outcome prediction, and is compatible with various sequence models like LSTMs and TCNs.

Result: Extensive experiments on synthetic, semi-synthetic, and real-world data demonstrate that CAETC yields significant improvement in counterfactual estimation over existing methods.

Conclusion: CAETC provides an effective framework for counterfactual estimation over time that addresses time-dependent confounding bias and can be integrated with existing sequence modeling architectures.

Abstract: Counterfactual estimation over time is important in various applications, such as personalized medicine. However, time-dependent confounding bias in observational data still poses a significant challenge in achieving accurate and efficient estimation. We introduce causal autoencoding and treatment conditioning (CAETC), a novel method for this problem. Built on adversarial representation learning, our method leverages an autoencoding architecture to learn a partially invertible and treatment-invariant representation, where the outcome prediction task is cast as applying a treatment-specific conditioning on the representation. Our design is independent of the underlying sequence model and can be applied to existing architectures such as long short-term memories (LSTMs) or temporal convolution networks (TCNs). We conduct extensive experiments on synthetic, semi-synthetic, and real-world data to demonstrate that CAETC yields significant improvement in counterfactual estimation over existing methods.

Method: The authors re-engineer survival analysis methods to enable them to perform classification efficiently and effectively. They apply this integrated approach to predict risk for five common chronic diseases using big EMR data, and develop a novel methodology to generate clinically validated explanations.

Result: Experiments on real-world big EMR data show that the survival models achieve performance (accuracy, F1 score, AUROC) comparable to or better than prior state-of-the-art models like LightGBM and XGBoost. The explanations generated by the models were clinically validated by a panel of three expert physicians.

Conclusion: Survival analysis methods can be effectively integrated with classification techniques to create comprehensive disease risk surveillance models that perform well and provide clinically meaningful explanations for chronic disease prediction.

Abstract: Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and chronic ischemic heart disease. In this study, we present a novel approach for disease risk models by integrating survival analysis with classification techniques. Traditional models for predicting the risk of chronic diseases predominantly focus on either survival analysis or classification independently. In this paper, we show survival analysis methods can be re-engineered to enable them to do classification efficiently and effectively, thereby making them a comprehensive tool for developing disease risk surveillance models. The results of our experiments on real-world big EMR data show that the performance of survival models in terms of accuracy, F1 score, and AUROC is comparable to or better than that of prior state-of-the-art models like LightGBM and XGBoost. Lastly, the proposed survival models use a novel methodology to generate explanations, which have been clinically validated by a panel of three expert physicians.

Method: Hybrid Energy-Aware Reward Shaping (H-EARS) unifies potential-based reward shaping with energy-aware action regularization. It constrains action magnitude while balancing task-specific and energy-based potentials via functional decomposition, achieving O(n) complexity by capturing dominant energy components without full dynamics.

Result: Experiments show improved convergence, stability, and energy efficiency across baselines. Vehicle simulations validate applicability in safety-critical domains under extreme conditions.

Conclusion: Integrating lightweight physics priors enhances model-free RL without complete system models, enabling transfer from lab research to industrial applications.

Abstract: Deep reinforcement learning excels in continuous control but often requires extensive exploration, while physics-based models demand complete equations and suffer cubic complexity. This study proposes Hybrid Energy-Aware Reward Shaping (H-EARS), unifying potential-based reward shaping with energy-aware action regularization. H-EARS constrains action magnitude while balancing task-specific and energy-based potentials via functional decomposition, achieving linear complexity O(n) by capturing dominant energy components without full dynamics. We establish a theoretical foundation including: (1) functional independence for separate task/energy optimization; (2) energy-based convergence acceleration; (3) convergence guarantees under function approximation; and (4) approximate potential error bounds. Lyapunov stability connections are analyzed as heuristic guides. Experiments across baselines show improved convergence, stability, and energy efficiency. Vehicle simulations validate applicability in safety-critical domains under extreme conditions. Results confirm that integrating lightweight physics priors enhances model-free RL without complete system models, enabling transfer from lab research to industrial applications.

Method: Systematic study examining partial RoPE across architectures and datasets, analyzing training dynamics and convergence when applying RoPE to different fractions of dimensions (including as low as 10%).

Result: (1) Applying RoPE to only ~10% of dimensions achieves convergence comparable to full RoPE; (2) Trends hold across model sizes, sequence lengths, datasets, and architectures; (3) NoPE models show unstable learning trajectories that can be alleviated with minimal RoPE or QK-Norm.

Conclusion: Partial RoPE offers practical guidance for balancing efficiency and training stability, providing substantial memory savings while maintaining performance, with minimal RoPE application stabilizing training compared to NoPE.

Abstract: Rotary Positional Embedding (RoPE) is a common choice in transformer architectures for encoding relative positional information. Although earlier work has examined omitting RoPE in specific layers, the effect of varying the fraction of hidden dimensions that receive rotary transformations remains largely unexplored. This design choice can yield substantial memory savings, which becomes especially significant at long context lengths. We find up to 10x memory savings over the standard RoPE cache, while achieving comparable final loss. In this work, we present a systematic study examining the impact of partial RoPE on training dynamics and convergence across architectures and datasets. Our findings uncover several notable patterns: (1) applying RoPE to only a small fraction of dimensions (around 10%) achieves convergence comparable to using full RoPE; (2) these trends hold consistently across model size, sequence lengths and datasets of varying quality and architectures, with higher-quality data resulting in lower overall loss and similar benchmark performance; and (3) some models trained with NoPE (No Positional Encoding) showcase unstable learning trajectories, which can be alleviated through minimal RoPE application or QK-Norm which converges to a higher loss. Together, these results offer practical guidance for model designers aiming to balance efficiency and training stability, while emphasizing the previously overlooked importance of partial RoPE.

Method: Formulates system configuration as mixed-discrete/continuous optimization with hierarchical dependencies, uses hybrid optimization framework to refine structural decisions and execution parameters, and employs adaptive profiling with ensemble simulators.

Result: Achieves 2.7-3.0× training speedup over expert-tuned settings across diverse models, hardware platforms, and deployment objectives.

Conclusion: AutoScout provides an effective general-purpose solution for optimizing ML system configurations, significantly improving training efficiency through systematic optimization.

Abstract: Machine learning (ML) systems expose a rapidly expanding configuration space spanning model-parallelism strategies, communication optimizations, and low-level runtime parameters. End-to-end system efficiency is highly sensitive to these choices, yet identifying high-performance configurations is challenging due to heterogeneous feature types (e.g., sparse and dense parameters), conditional dependencies (e.g., valid execution parameters only under specific upstream decisions), and the high search (profiling) cost. Existing approaches either optimize a narrow subset of configuration dimensions or rely on ad-hoc heuristics that fail to generalize as configuration spaces continue to grow. We present AutoScout, a general-purpose systems configurator for ML training, fine-tuning, and inference. It formulates the system configuration as a mixed-discrete/continuous optimization problem with hierarchical dependencies and introduces a hybrid optimization framework that jointly refines sparse structural decisions and dense execution parameters. To reduce profiling cost, AutoScout adaptively prioritizes high-impact configuration features and ensembles simulators with varying fidelity. Across diverse models, hardware platforms, and deployment objectives, AutoScout consistently identifies high-performance configurations, achieving 2.7-3.0$\times$ training speedup over expert-tuned settings.

Method: Proposes pFedGM based on Gaussian generative modeling: 1) Trains Gaussian generator to model client heterogeneity via weighted re-sampling, 2) Uses dual objective: shared objective maximizes inter-class distance across clients, local objective minimizes intra-class distance within clients, 3) Decouples Gaussian classifier into navigator for global optimization and statistic extractor for distributional statistics, 4) Implements dual-scale fusion framework (inspired by Kalman gain) at global and local levels for personalized classifier heads, 5) Models global representation distribution as prior and client-specific data as likelihood for Bayesian inference.

Result: Achieves superior or competitive performance compared to state-of-the-art methods across comprehensive scenarios including heterogeneity in class counts, environmental corruption, and multiple benchmark datasets/configurations.

Conclusion: pFedGM effectively addresses representation-level heterogeneity in federated learning through Gaussian generative modeling and dual-scale fusion, providing better personalization while maintaining global collaboration.

Abstract: Federated learning has emerged as a paradigm to train models collaboratively on inherently distributed client data while safeguarding privacy. In this context, personalized federated learning tackles the challenge of data heterogeneity by equipping each client with a dedicated model. A prevalent strategy decouples the model into a shared feature extractor and a personalized classifier head, where the latter actively guides the representation learning. However, previous works have focused on classifier head-guided personalization, neglecting the potential personalized characteristics in the representation distribution. Building on this insight, we propose pFedGM, a method based on Gaussian generative modeling. The approach begins by training a Gaussian generator that models client heterogeneity via weighted re-sampling. A balance between global collaboration and personalization is then struck by employing a dual objective: a shared objective that maximizes inter-class distance across clients, and a local objective that minimizes intra-class distance within them. To achieve this, we decouple the conventional Gaussian classifier into a navigator for global optimization, and a statistic extractor for capturing distributional statistics. Inspired by the Kalman gain, the algorithm then employs a dual-scale fusion framework at global and local levels to equip each client with a personalized classifier head. In this framework, we model the global representation distribution as a prior and the client-specific data as the likelihood, enabling Bayesian inference for class probability estimation. The evaluation covers a comprehensive range of scenarios: heterogeneity in class counts, environmental corruption, and multiple benchmark datasets and configurations. pFedGM achieves superior or competitive performance compared to state-of-the-art methods.

Method: Systematic study across three models and five lifelong RL benchmarks using sequential fine-tuning with low-rank adaptation (LoRA), comparing against more sophisticated continual RL methods.

Result: Seq. FT with LoRA achieves high plasticity, exhibits little to no forgetting, retains strong zero-shot generalization, and frequently outperforms more sophisticated CRL methods.

Conclusion: Sequential fine-tuning is a powerful method for continual RL with VLAs, with robustness arising from synergy between large pretrained models, parameter-efficient adaptation, and on-policy RL, reshaping the stability-plasticity trade-off.

Abstract: Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving environments. However, conventional wisdom from continual learning suggests that naive Sequential Fine-Tuning (Seq. FT) leads to catastrophic forgetting, necessitating complex CRL strategies. In this work, we take a step back and conduct a systematic study of CRL for large pretrained VLAs across three models and five challenging lifelong RL benchmarks. We find that, contrary to established belief, simple Seq. FT with low-rank adaptation (LoRA) is remarkably strong: it achieves high plasticity, exhibits little to no forgetting, and retains strong zero-shot generalization, frequently outperforming more sophisticated CRL methods. Through detailed analysis, we show that this robustness arises from a synergy between the large pretrained model, parameter-efficient adaptation, and on-policy RL. Together, these components reshape the stability-plasticity trade-off, making continual adaptation both stable and scalable. Our results position Sequential Fine-Tuning as a powerful method for continual RL with VLAs and provide new insights into lifelong learning in the large model era. Code is available at github.com/UT-Austin-RobIn/continual-vla-rl.

Method: Integrated a neuromodulatory mechanism into the cAE framework to allow for context-dependent manifold learning. The Neuromodulated Constrained Autoencoder (NcAE) adaptively parameterizes geometric constraints via gain and bias tuning conditioned on static contextual information.

Result: Experimental results on dynamical systems show that NcAE accurately captures how manifold geometry varies across different regimes while maintaining rigorous projection properties. Neuromodulation effectively decouples global contextual parameters from local manifold representations.

Conclusion: The architecture provides a foundation for developing more flexible, physics-informed representations in systems subject to non-stationary environmental constraints.

Abstract: Constrained autoencoders (cAE) provide a successful path towards interpretable dimensionality reduction by enforcing geometric structure on latent spaces. However, standard cAEs cannot adapt to varying physical parameters or environmental conditions without conflating these contextual shifts with the primary input. To address this, we integrated a neuromodulatory mechanism into the cAE framework to allow for context-dependent manifold learning. This paper introduces the Neuromodulated Constrained Autoencoder (NcAE), which adaptively parameterizes geometric constraints via gain and bias tuning conditioned on static contextual information. Experimental results on dynamical systems show that the NcAE accurately captures how manifold geometry varies across different regimes while maintaining rigorous projection properties. These results demonstrate that neuromodulation effectively decouples global contextual parameters from local manifold representations. This architecture provides a foundation for developing more flexible, physics-informed representations in systems subject to (non-stationary) environmental constraints.

Method: Proposes Chem4DLLM, a unified model integrating an equivariant graph encoder with a pretrained large language model to explicitly capture molecular geometry and rotational dynamics from 4D trajectories.

Result: Introduces Chem4DBench, the first dataset pairing 4D molecular trajectories with expert-authored explanations, and demonstrates the Chem4DLLM model’s capability for dynamic chemical understanding.

Conclusion: ChemDU, together with Chem4DBench and Chem4DLLM, aims to stimulate further research in dynamic chemical understanding and multimodal scientific reasoning.

Abstract: Existing chemical understanding tasks primarily rely on static molecular representations, limiting their ability to model inherently dynamic phenomena such as bond breaking or conformational changes, which are essential for a chemist to understand chemical reactions. To address this gap, we introduce Chemical Dynamics Understanding (ChemDU), a new task that translates 4D molecular trajectories into interpretable natural-language explanations. ChemDU focuses on fundamental dynamic scenarios, including gas-phase and catalytic reactions, and requires models to reason about key events along molecular trajectories, such as bond formation and dissociation, and to generate coherent, mechanistically grounded narratives. To benchmark this capability, we construct Chem4DBench, the first dataset pairing 4D molecular trajectories with expert-authored explanations across these settings. We further propose Chem4DLLM, a unified model that integrates an equivariant graph encoder with a pretrained large language model to explicitly capture molecular geometry and rotational dynamics. We hope that ChemDU, together with Chem4DBench and Chem4DLLM, will stimulate further research in dynamic chemical understanding and multimodal scientific reasoning.

Method: The paper formally analyzes entropy dynamics in policy gradient objectives, identifies empirical factors affecting entropy behavior, and proposes two explicit entropy control mechanisms: REPO (algorithms that modify advantage functions to regulate entropy) and ADAPO (adaptive asymmetric clipping approach).

Result: Models trained with entropy-preserving methods maintain diversity throughout training, yielding final policies that are more performant and retain trainability for sequential learning in new environments.

Conclusion: Active entropy control is essential for policy gradient algorithms in language model reasoning to maintain exploration diversity, improve performance, and preserve trainability for sequential learning tasks.

Abstract: Policy gradient algorithms have driven many recent advancements in language model reasoning. An appealing property is their ability to learn from exploration on their own trajectories, a process crucial for fostering diverse and creative solutions. As we show in this paper, many policy gradient algorithms naturally reduce the entropy – and thus the diversity of explored trajectories – as part of training, yielding a policy increasingly limited in its ability to explore. In this paper, we argue that entropy should be actively monitored and controlled throughout training. We formally analyze the contributions of leading policy gradient objectives on entropy dynamics, identify empirical factors (such as numerical precision) that significantly impact entropy behavior, and propose explicit mechanisms for entropy control. These include REPO, a family of algorithms that modify the advantage function to regulate entropy, and ADAPO, an adaptive asymmetric clipping approach. Models trained with our entropy-preserving methods maintain diversity throughout training, yielding final policies that are more performant and retain their trainability for sequential learning in new environments.

Method: EvoFlows uses edit flows to learn mutational trajectories between evolutionarily-related protein sequences. It performs a limited number of insertions, deletions, and substitutions on template sequences, predicting both which mutation to perform and where it should occur, modeling distributions of related natural proteins and the mutational paths connecting them.

Result: EvoFlows captures protein sequence distributions with quality comparable to leading masked language models used in protein engineering, while showing improved ability to generate non-trivial yet natural-like mutants from given template proteins, as demonstrated through extensive in silico evaluation on diverse protein communities from UNIREF and OAS.

Conclusion: EvoFlows provides a novel protein modeling approach uniquely suited for protein engineering by enabling controlled, variable-length sequence modifications that better mimic natural evolutionary processes, offering advantages over traditional autoregressive and masked language models for generating plausible protein variants.

Abstract: We introduce EvoFlows, a variable-length sequence-to-sequence protein modeling approach uniquely suited to protein engineering. Unlike autoregressive and masked language models, EvoFlows perform a limited, controllable number of insertions, deletions, and substitutions on a template protein sequence. In other words, EvoFlows predict not only which mutation to perform, but also where it should occur. Our approach leverages edit flows to learn mutational trajectories between evolutionarily-related protein sequences, simultaneously modeling distributions of related natural proteins and the mutational paths connecting them. Through extensive in silico evaluation on diverse protein communities from UNIREF and OAS, we demonstrate that EvoFlows capture protein sequence distributions with a quality comparable to leading masked language models commonly used in protein engineering, while showing improved ability to generate non-trivial yet natural-like mutants from a given template protein.

Method: The study uses nine diverse credit risk benchmark datasets to examine the interplay between classification calibration and predictive multiplicity. It investigates whether predictive multiplicity concentrates in low-confidence regions and evaluates how post-hoc calibration methods (Platt Scaling, Isotonic Regression, Temperature Scaling) can reduce algorithmic arbitrariness across the Rashomon set of near-optimal models.

Result: Empirical analysis reveals that minority class observations bear disproportionate multiplicity burden, with significant disparities in predictive multiplicity and prediction confidence. Post-hoc calibration methods are associated with lower obscurity across the Rashomon set, with Platt Scaling and Isotonic Regression providing the most robust reduction in predictive multiplicity.

Conclusion: Calibration can function as a consensus-enforcing layer and may support procedural fairness by mitigating predictive multiplicity in high-stakes classification tasks like credit risk assessment.

Abstract: As machine learning models are increasingly deployed in high-stakes environments, ensuring both probabilistic reliability and prediction stability has become critical. This paper examines the interplay between classification calibration and predictive multiplicity - the phenomenon in which multiple near-optimal models within the Rashomon set yield conflicting credit outcomes for the same applicant. Using nine diverse credit risk benchmark datasets, we investigate whether predictive multiplicity concentrates in regions of low predictive confidence and how post-hoc calibration can mitigate algorithmic arbitrariness. Our empirical analysis reveals that minority class observations bear a disproportionate multiplicity burden, as confirmed by significant disparities in predictive multiplicity and prediction confidence. Furthermore, our empirical comparisons indicate that applying post-hoc calibration methods - specifically Platt Scaling, Isotonic Regression, and Temperature Scaling - is associated with lower obscurity across the Rashomon set. Among the tested techniques, Platt Scaling and Isotonic Regression provide the most robust reduction in predictive multiplicity. These findings suggest that calibration can function as a consensus-enforcing layer and may support procedural fairness by mitigating predictive multiplicity.

Method: Proposes a free energy-based social bandit learning algorithm over policy space where social agents evaluate others’ expertise levels without oracle knowledge or social norms. Agents integrate direct environmental experiences with estimated policies of others, using a mechanism to strategically identify relevant agents.

Result: Theoretical convergence to optimal policy is proven. Empirical evaluations show superiority over alternative approaches, with the algorithm successfully identifying relevant agents even with random/suboptimal agents present, and significantly enhancing learning performance with non-expert agents while maintaining logarithmic regret.

Conclusion: The social learning algorithm effectively leverages behavioral information from other agents without requiring reward knowledge, demonstrating improved learning performance and robustness in various scenarios while maintaining theoretical guarantees.

Abstract: Personalized AI-based services involve a population of individual reinforcement learning agents. However, most reinforcement learning algorithms focus on harnessing individual learning and fail to leverage the social learning capabilities commonly exhibited by humans and animals. Social learning integrates individual experience with observing others’ behavior, presenting opportunities for improved learning outcomes. In this study, we focus on a social bandit learning scenario where a social agent observes other agents’ actions without knowledge of their rewards. The agents independently pursue their own policy without explicit motivation to teach each other. We propose a free energy-based social bandit learning algorithm over the policy space, where the social agent evaluates others’ expertise levels without resorting to any oracle or social norms. Accordingly, the social agent integrates its direct experiences in the environment and others’ estimated policies. The theoretical convergence of our algorithm to the optimal policy is proven. Empirical evaluations validate the superiority of our social learning method over alternative approaches in various scenarios. Our algorithm strategically identifies the relevant agents, even in the presence of random or suboptimal agents, and skillfully exploits their behavioral information. In addition to societies including expert agents, in the presence of relevant but non-expert agents, our algorithm significantly enhances individual learning performance, where most related methods fail. Importantly, it also maintains logarithmic regret.

Method: Extends FTPL with geometric resampling to m-set semi-bandits using Fréchet and Pareto distributions. Introduces conditional geometric resampling to reduce computational complexity from O(d²) to O(md(log(d/m)+1)).

Result: FTPL achieves optimal O(√mdT) regret in adversarial setting and logarithmic regret in stochastic setting (Best-of-Both-Worlds optimality). Computational complexity significantly reduced without sacrificing regret performance.

Conclusion: FTPL with appropriate distributions is optimal for m-set semi-bandits, achieving both adversarial and stochastic optimality with improved computational efficiency through conditional geometric resampling.

Abstract: This paper studies the optimality and complexity of Follow-the-Perturbed-Leader (FTPL) policy in $m$-set semi-bandit problems. FTPL has been studied extensively as a promising candidate of an efficient algorithm with favorable regret for adversarial combinatorial semi-bandits. Nevertheless, the optimality of FTPL has still been unknown unlike Follow-the-Regularized-Leader (FTRL) whose optimality has been proved for various tasks of online learning. In this paper, we extend the analysis of FTPL with geometric resampling (GR) to $m$-set semi-bandits, which is a special case of combinatorial semi-bandits, showing that FTPL with Fréchet and Pareto distributions with certain parameters achieves the best possible regret of $O(\sqrt{mdT})$ in adversarial setting. We also show that FTPL with Fréchet and Pareto distributions with a certain parameter achieves a logarithmic regret for stochastic setting, meaning the Best-of-Both-Worlds optimality of FTPL for $m$-set semi-bandit problems. Furthermore, we extend the conditional geometric resampling to $m$-set semi-bandits for efficient loss estimation in FTPL, reducing the computational complexity from $O(d^2)$ of the original geometric resampling to $O(md(\log(d/m)+1))$ without sacrificing the regret performance.

Method: Used statistical decision-theoretic perspective with binary hypothesis testing where optimal policy is known (likelihood-ratio test). Trained Transformers on tasks with distinct geometries: linear shifted means vs. nonlinear variance estimation. Conducted mechanistic analysis via logit lens and circuit alignment to examine internal computations.

Result: Transformers approximate Bayes-optimal sufficient statistics from context up to monotonic transformation, matching ideal oracle estimator performance in nonlinear regimes. Models adapt decision boundaries: use voting-style ensemble for linear tasks and deeper sequential computation for nonlinear tasks, rather than fixed kernel smoothing.

Conclusion: In-context learning emerges from construction of task-adaptive statistical estimators rather than simple similarity matching, with Transformers adapting their computational geometry based on task characteristics.

Abstract: In-context learning (ICL) allows Transformers to adapt to novel tasks without weight updates, yet the underlying algorithms remain poorly understood. We adopt a statistical decision-theoretic perspective by investigating simple binary hypothesis testing, where the optimal policy is determined by the likelihood-ratio test. Notably, this setup provides a mathematically rigorous setting for mechanistic interpretability where the target algorithmic ground truth is known. By training Transformers on tasks requiring distinct geometries (linear shifted means vs. nonlinear variance estimation), we demonstrate that the models approximate the Bayes-optimal sufficient statistics from context up to some monotonic transformation, matching the performance of an ideal oracle estimator in nonlinear regimes. Leveraging this analytical ground truth, mechanistic analysis via logit lens and circuit alignment suggests that the model does not rely on a fixed kernel smoothing heuristic. Instead, it appears to adapt the point at which decisions become linearly decodable: exhibiting patterns consistent with a voting-style ensemble for linear tasks while utilizing a deeper sequential computation for nonlinear tasks. These findings suggest that ICL emerges from the construction of task-adaptive statistical estimators rather than simple similarity matching.

Method: Uses learning-theoretic framework of language generation in the limit, introducing a replay adversary that augments training data with the generator’s own past outputs. Analyzes different notions of generation (uniform, non-uniform, generation in the limit) under replay conditions.

Result: Replay is benign for strongest notion of uniform generation, but creates separations for weaker notions of non-uniform generation and generation in the limit. Positive results align with practical heuristics (data cleaning, watermarking), while separations show when these can fail.

Conclusion: Provides theoretical foundation for understanding model collapse, showing when replay fundamentally limits generation capabilities and validating practical mitigation strategies while identifying their limitations.

Abstract: As scaling laws push the training of frontier large language models (LLMs) toward ever-growing data requirements, training pipelines are approaching a regime where much of the publicly available online text may be consumed. At the same time, widespread LLM usage increases the volume of machine-generated content on the web; together, these trends raise the likelihood of generated text re-entering future training corpora, increasing the associated risk of performance degradation often called model collapse. In practice, model developers address this concern through data cleaning, watermarking, synthetic-data policies, or, in some cases, blissful ignorance. However, the problem of model collapse in generative models has not been examined from a learning-theoretic perspective: we study it through the theoretical lens of the language generation in the limit framework, introducing a replay adversary that augments the example stream with the generator’s own past outputs. Our main contribution is a fine-grained learning-theoretic characterization of when replay fundamentally limits generation: while replay is benign for the strongest notion of uniform generation, it provably creates separations for the weaker notions of non-uniform generation and generation in the limit. Interestingly, our positive results mirror heuristics widely used in practice, such as data cleaning, watermarking, and output filtering, while our separations show when these ideas can fail.

Method: Proposes an approach where an embodied agent autonomously discovers group structure through unsupervised environment interaction. Provides identifiability proof under minimal assumptions and develops two algorithms: one for discovering group decomposition from interaction data, and another for learning Linear Symmetry-Based Disentangled representations without assuming specific subgroup properties.

Result: Method validated on three environments with different group decompositions, where it outperforms existing LSBD approaches. Successfully discovers symmetry group structure without prior knowledge.

Conclusion: The approach enables symmetry-based disentangled representation learning without requiring prior knowledge of group structure or restrictive assumptions, making it more applicable to real-world scenarios where such information is unavailable.

Abstract: Symmetry-based disentangled representation learning leverages the group structure of environment transformations to uncover the latent factors of variation. Prior approaches to symmetry-based disentanglement have required strong prior knowledge of the symmetry group’s structure, or restrictive assumptions about the subgroup properties. In this work, we remove these constraints by proposing a method whereby an embodied agent autonomously discovers the group structure of its action space through unsupervised interaction with the environment. We prove the identifiability of the true symmetry group decomposition under minimal assumptions, and derive two algorithms: one for discovering the group decomposition from interaction data, and another for learning Linear Symmetry-Based Disentangled (LSBD) representations without assuming specific subgroup properties. Our method is validated on three environments exhibiting different group decompositions, where it outperforms existing LSBD approaches.

Method: Proposes a unified exponential-family log-likelihood ratio framework showing all three attacks are instances of the same approach. Identifies variance estimation as key bottleneck at small shadow-model budgets, and introduces BaVarIA with Bayesian variance inference using conjugate normal-inverse-gamma priors.

Result: BaVarIA matches or improves upon LiRA and RMIA across 12 datasets and 7 shadow-model budgets, with largest gains in low-shadow-model and offline regimes. Provides stable performance without additional hyperparameter tuning.

Conclusion: The unified framework reveals a hierarchy connecting RMIA and LiRA as endpoints of a spectrum, with BaVarIA addressing the variance estimation bottleneck to provide superior performance in practical low-budget scenarios.

Abstract: Membership inference attacks (MIAs) are becoming standard tools for auditing the privacy of machine learning models. The leading attacks – LiRA (Carlini et al., 2022) and RMIA (Zarifzadeh et al., 2024) – appear to use distinct scoring strategies, while the recently proposed BASE (Lassila et al., 2025) was shown to be equivalent to RMIA, making it difficult for practitioners to choose among them. We show that all three are instances of a single exponential-family log-likelihood ratio framework, differing only in their distributional assumptions and the number of parameters estimated per data point. This unification reveals a hierarchy (BASE1-4) that connects RMIA and LiRA as endpoints of a spectrum of increasing model complexity. Within this framework, we identify variance estimation as the key bottleneck at small shadow-model budgets and propose BaVarIA, a Bayesian variance inference attack that replaces threshold-based parameter switching with conjugate normal-inverse-gamma priors. BaVarIA yields a Student-t predictive (BaVarIA-t) or a Gaussian with stabilized variance (BaVarIA-n), providing stable performance without additional hyperparameter tuning. Across 12 datasets and 7 shadow-model budgets, BaVarIA matches or improves upon LiRA and RMIA, with the largest gains in the practically important low-shadow-model and offline regimes.

Method: Two-stage framework: 1) Conditional Fourier Neural Operator (C-FNO) with cross-attention learns differentiable surrogate to reconstruct full ODE trajectories from sparse inputs with spectral regularization. 2) Amortized Drifting Model (ADM) learns kernel-weighted velocity field in parameter space to transport random initializations toward ground truth without backpropagating through surrogate.

Result: INO outperforms gradient-based and amortized baselines in parameter recovery accuracy on real-world stiff atmospheric chemistry benchmark (POLLU, 25 parameters) and synthetic Gene Regulatory Network (GRN, 40 parameters). Achieves 0.23s inference time, 487x speedup over iterative gradient descent.

Conclusion: INO provides a stable, efficient framework for ODE parameter inversion that avoids Jacobian instabilities in stiff regimes while achieving significant speed improvements over traditional methods.

Abstract: We propose the Inverse Neural Operator (INO), a two-stage framework for recovering hidden ODE parameters from sparse, partial observations. In Stage 1, a Conditional Fourier Neural Operator (C-FNO) with cross-attention learns a differentiable surrogate that reconstructs full ODE trajectories from arbitrary sparse inputs, suppressing high-frequency artifacts via spectral regularization. In Stage 2, an Amortized Drifting Model (ADM) learns a kernel-weighted velocity field in parameter space, transporting random parameter initializations toward the ground truth without backpropagating through the surrogate, avoiding the Jacobian instabilities that afflict gradient-based inversion in stiff regimes. Experiments on a real-world stiff atmospheric chemistry benchmark (POLLU, 25 parameters) and a synthetic Gene Regulatory Network (GRN, 40 parameters) show that INO outperforms gradient-based and amortized baselines in parameter recovery accuracy while requiring only 0.23s inference time, a 487x speedup over iterative gradient descent.

Method: Adapts cross-modal self-supervised learning (CroSSL) to learn representations invariant to station-wise feature missingness from unlabeled data, and introduces Station-wise Masking Augmentation (SMA) during downstream training to expose models to realistic station unavailability patterns.

Result: Experiments show that neither missingness-invariant pre-training nor station-wise augmentation alone is sufficient; their combination is essential for robust performance under both station-wise feature missingness and label scarcity.

Conclusion: The proposed framework provides a practical and robust foundation for multi-station WiFi CSI sensing in real-world deployments by jointly addressing feature missingness and label scarcity.

Abstract: We propose a WiFi Channel State Information (CSI) sensing framework for multi-station deployments that addresses two fundamental challenges in practical CSI sensing: station-wise feature missingness and limited labeled data. Feature missingness is commonly handled by resampling unevenly spaced CSI measurements or by reconstructing missing samples, while label scarcity is mitigated by data augmentation or self-supervised representation learning. However, these techniques are typically developed in isolation and do not jointly address long-term, structured station unavailability together with label scarcity. To bridge this gap, we explicitly incorporate station unavailability into both representation learning and downstream model training. Specifically, we adapt cross-modal self-supervised learning (CroSSL), a representation learning framework originally designed for time-series sensory data, to multi-station CSI sensing in order to learn representations that are inherently invariant to station-wise feature missingness from unlabeled data. Furthermore, we introduce Station-wise Masking Augmentation (SMA) during downstream model training, which exposes the model to realistic station unavailability patterns under limited labeled data. Our experiments show that neither missingness-invariant pre-training nor station-wise augmentation alone is sufficient; their combination is essential to achieve robust performance under both station-wise feature missingness and label scarcity. The proposed framework provides a practical and robust foundation for multi-station WiFi CSI sensing in real-world deployments.

Method: The authors conduct ablation studies on Reversible Instance Normalization (RevIN) to identify which components are essential versus redundant or detrimental. They analyze the normalization method’s effectiveness against the three identified challenges.

Result: The ablation studies reveal that several components of RevIN are redundant or even detrimental to performance. This analysis provides new insights into how to improve RevIN’s robustness and generalization capabilities.

Conclusion: The paper offers new perspectives for enhancing normalization techniques in time series forecasting by identifying unnecessary components in RevIN and suggesting improvements for better handling distribution shifts.

Abstract: Data normalization is a crucial component of deep learning models, yet its role in time series forecasting remains insufficiently understood. In this paper, we identify three central challenges for normalization in time series forecasting: temporal input distribution shift, spatial input distribution shift, and conditional output distribution shift. In this context, we revisit the widely used Reversible Instance Normalization (RevIN), by showing through ablation studies that several of its components are redundant or even detrimental. Based on these observations, we draw new perspectives to improve RevIN’s robustness and generalization.

Method: Proposes FlexRec with two key components: (1) causally grounded item-level reward based on counterfactual swaps within remaining candidate pool, and (2) critic-guided, uncertainty-aware scaling that models reward uncertainty and down-weights low-confidence rewards to stabilize learning under sparse supervision.

Result: Achieves substantial gains: improves NDCG@5 by up to 59% and Recall@5 by up to 109.4% in need-specific ranking, and up to 24.1% Recall@5 improvement under generalization settings, outperforming traditional recommenders and LLM-based baselines.

Conclusion: FlexRec effectively addresses RL challenges in LLM-based recommendation through fine-grained item-level rewards and uncertainty modeling, enabling strong performance across diverse recommendation scenarios and objectives.

Abstract: Modern recommender systems must adapt to dynamic, need-specific objectives for diverse recommendation scenarios, yet most traditional recommenders are optimized for a single static target and struggle to reconfigure behavior on demand. Recent advances in reinforcement-learning-based post-training have unlocked strong instruction-following and reasoning capabilities in LLMs, suggesting a principled route for aligning them to complex recommendation goals. Motivated by this, we study closed-set autoregressive ranking, where an LLM generates a permutation over a fixed candidate set conditioned on user context and an explicit need instruction. However, applying RL to this setting faces two key obstacles: (i) sequence-level rewards yield coarse credit assignment that fails to provide fine-grained training signals, and (ii) interaction feedback is sparse and noisy, which together lead to inefficient and unstable updates. We propose FlexRec, a post-training RL framework that addresses both issues with (1) a causally grounded item-level reward based on counterfactual swaps within the remaining candidate pool, and (2) critic-guided, uncertainty-aware scaling that explicitly models reward uncertainty and down-weights low-confidence rewards to stabilize learning under sparse supervision. Across diverse recommendation scenarios and objectives, FlexRec achieves substantial gains: it improves NDCG@5 by up to \textbf{59%} and Recall@5 by up to \textbf{109.4%} in need-specific ranking, and further achieves up to \textbf{24.1%} Recall@5 improvement under generalization settings, outperforming strong traditional recommenders and LLM-based baselines.

Method: Derives novel multi-treatment generalization bound and theoretical estimator for optimal balancing weight α, investigates three balancing strategies (Pairwise, One-vs-All, Treatment Aggregation), and extends framework to generative architecture Multi-Treatment CausalEGM preserving Wasserstein geodesic structure of treatment manifold.

Result: Experiments on semi-synthetic and image datasets show significant outperformance over traditional models in estimation accuracy and efficiency, particularly in large-scale intervention scenarios.

Conclusion: Proposed framework eliminates expensive heuristic tuning, ensures both accuracy and O(1) scalability as treatment space expands, and demonstrates superior performance in multi-treatment causal inference.

Abstract: Estimating Individual Treatment Effects (ITE) in multi-treatment scenarios faces two critical challenges: the Hyperparameter Selection Dilemma for balancing weights and the Curse of Dimensionality in computational scalability. This paper derives a novel multi-treatment generalization bound and proposes a theoretical estimator for the optimal balancing weight $α$, eliminating expensive heuristic tuning. We investigate three balancing strategies: Pairwise, One-vs-All (OVA), and Treatment Aggregation. While OVA achieves superior precision in low-dimensional settings, our proposed Treatment Aggregation ensures both accuracy and O(1) scalability as the treatment space expands. Furthermore, we extend our framework to a generative architecture, Multi-Treatment CausalEGM, which preserves the Wasserstein geodesic structure of the treatment manifold. Experiments on semi-synthetic and image datasets demonstrate that our approach significantly outperforms traditional models in estimation accuracy and efficiency, particularly in large-scale intervention scenarios.

Method: Proposes EnTransformer that combines engression (stochastic learning paradigm for modeling conditional distributions) with Transformers. Injects stochastic noise into model representation and optimizes energy-based scoring objective to directly learn conditional predictive distribution without parametric assumptions.

Result: Evaluated on Electricity, Traffic, Solar, Taxi, KDD-cup, and Wikipedia datasets. EnTransformer produces well-calibrated probabilistic forecasts and consistently outperforms benchmark models.

Conclusion: EnTransformer enables coherent multivariate forecast trajectories while preserving Transformers’ capacity to model long-range temporal dependencies and cross-series interactions, providing reliable uncertainty quantification without restrictive parametric assumptions.

Abstract: Reliable uncertainty quantification is critical in multivariate time series forecasting problems arising in domains such as energy systems and transportation networks, among many others. Although Transformer-based architectures have recently achieved strong performance for sequence modeling, most probabilistic forecasting approaches rely on restrictive parametric likelihoods or quantile-based objectives. They can struggle to capture complex joint predictive distributions across multiple correlated time series. This work proposes EnTransformer, a deep generative forecasting framework that integrates engression, a stochastic learning paradigm for modeling conditional distributions, with the expressive sequence modeling capabilities of Transformers. The proposed approach injects stochastic noise into the model representation and optimizes an energy-based scoring objective to directly learn the conditional predictive distribution without imposing parametric assumptions. This design enables EnTransformer to generate coherent multivariate forecast trajectories while preserving Transformers’ capacity to effectively model long-range temporal dependencies and cross-series interactions. We evaluate our proposed EnTransformer on several widely used benchmarks for multivariate probabilistic forecasting, including Electricity, Traffic, Solar, Taxi, KDD-cup, and Wikipedia datasets. Experimental results demonstrate that EnTransformer produces well-calibrated probabilistic forecasts and consistently outperforms the benchmark models.

Method: 1) Created MobileKernelBench benchmark with operator diversity and cross-framework interoperability; 2) Developed automated pipeline for on-device verification; 3) Evaluated LLMs on MNN CPU backend; 4) Proposed Mobile Kernel Agent (MoKA) - a multi-agent system with repository-aware reasoning and plan-and-execute paradigm.

Result: Standard LLMs had over 54% compilation failure rates with negligible performance gains. MoKA achieved 93.7% compilation success rate and enabled 27.4% of generated kernels to deliver measurable speedups over native libraries.

Conclusion: Current LLMs struggle with mobile kernel generation due to domain-specific challenges, but specialized multi-agent systems like MoKA can significantly improve success rates and performance outcomes for mobile device optimization.

Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile de- vices remains largely unexplored. In this work, we extend the scope of automated kernel generation to the mobile domain to investigate the central question: Can LLMs write efficient kernels for mobile devices? To enable systematic investigation, we introduce MobileKernelBench, a comprehensive evaluation framework comprising a benchmark prioritizing operator diversity and cross-framework interoperability, coupled with an automated pipeline that bridges the host-device gap for on-device verification. Leveraging this framework, we conduct extensive evaluation on the CPU backend of Mobile Neural Network (MNN), revealing that current LLMs struggle with the engineering complexity and data scarcity inher-ent to mobile frameworks; standard models and even fine-tuned variants exhibit high compilation failure rates (over 54%) and negligible performance gains due to hallucinations and a lack of domain-specific grounding. To overcome these limitations, we propose the Mobile K ernel A gent (MoKA), a multi-agent system equipped with repository-aware reasoning and a plan-and-execute paradigm.Validated on MobileKernelBench, MoKA achieves state-of-the-art performance, boosting compilation success to 93.7% and enabling 27.4% of generated kernelsto deliver measurable speedups over native libraries.

Method: Three experiments: 1) Exhaustive tracing of all 4065 active sparse autoencoder features at layer 5 to map downstream edges; 2) Three-way combinatorial ablation across 8 feature triplets to measure redundancy; 3) Trajectory-guided feature steering to establish causal links between layer position and differentiation directionality.

Result: 1) 27-fold expansion of detected edges reveals heavy-tailed hub distribution with annotation bias; 2) Redundancy deepens with interaction order (three-way ratio 0.59 vs pairwise 0.74), showing subadditive architecture; 3) Late layer features (L17) consistently push toward maturity (fraction positive = 1.0), while early/mid layers push away (0.00-0.58).

Conclusion: The experiments move from correlation toward causal evidence for layer-dependent control of cell state in biological foundation models, revealing systematic biases in prior selective analyses and demonstrating the model’s subadditive architecture.

Abstract: Mechanistic interpretability of biological foundation models has relied on selective feature sampling, pairwise interaction testing, and observational trajectory analysis. Each of these can introduce systematic bias. Here we present three experiments that address these limitations through exhaustive circuit tracing, higher order combinatorial ablation, and causal trajectory steering in Geneformer, a transformer based single cell foundation model. First, exhaustive tracing of all 4065 active sparse autoencoder features at layer 5 yields 1393850 significant downstream edges, a 27 fold expansion over selective sampling. This reveals a heavy tailed hub distribution in which 1.8 percent of features account for disproportionate connectivity and 40 percent of the top 20 hubs lack biological annotation. These results indicate systematic annotation bias in prior selective analyses. Second, three way combinatorial ablation across 8 feature triplets shows that redundancy deepens monotonically with interaction order, with a three way ratio of 0.59 versus a pairwise ratio of 0.74, and with zero synergy. This confirms that the model architecture is subadditive at all tested orders. Third, trajectory guided feature steering establishes a causal link between layer position and differentiation directionality. Late layer features at L17 consistently push cell states toward maturity, with fraction positive equal to 1.0. Early and mid layer features at L0 and L11 mostly push away from maturity, with fraction positive ranging from 0.00 to 0.58. Together these results move from correlation toward causal evidence for layer dependent control of cell state.

Method: Proposes Mixed Synthetic Nearest Neighbors (MSNN) that integrates information across treatment levels rather than relying solely on data within each treatment level, while maintaining theoretical guarantees.

Result: MSNN retains finite-sample error bounds and asymptotic normality guarantees of SNN while enlarging effective sample size, showing efficacy especially under data-scarce treatment levels in synthetic and real-world datasets.

Conclusion: MSNN provides a robust solution for causal matrix completion in MNAR settings with multiple treatments by effectively pooling information across treatment levels when individual treatments have limited data.

Abstract: Synthetic Nearest Neighbors (SNN) provides a principled solution to causal matrix completion under missing-not-at-random (MNAR) by exploiting local low-rank structure through fully observed anchor submatrices. However, its effectiveness critically relies on sufficient data availability within each treatment level, a condition that often fails in settings with multiple or complex treatments. In this work, we propose Mixed Synthetic Nearest Neighbors (MSNN), a new entry-wise causal identification estimator that integrates information across treatment levels. We show that MSNN retains the finite-sample error bounds and asymptotic normality guarantees of SNN, while enlarging the effective sample size available for estimation. Empirical results on synthetic and real-world datasets illustrate the efficacy of the proposed approach, especially under data-scarce treatment levels.

Method: Effective Resistance Rewiring (ERR) uses effective resistance as a global signal to detect structural bottlenecks. It iteratively adds edges between node pairs with largest resistance while removing edges with minimal resistance, strengthening weak communication pathways while controlling graph densification under a fixed edge budget.

Result: ERR improves connectivity and signal propagation but can accelerate representation mixing in deep models. Combining ERR with normalization techniques like PairNorm stabilizes the trade-off between over-squashing and oversmoothing and improves performance on both homophilic and heterophilic graphs.

Conclusion: Effective resistance provides a powerful global signal for detecting structural bottlenecks in graphs. Resistance-guided rewiring improves long-range communication but requires careful management of the over-squashing vs. oversmoothing trade-off through normalization techniques.

Abstract: Graph Neural Networks struggle to capture long-range dependencies due to over-squashing, where information from exponentially growing neighborhoods must pass through a small number of structural bottlenecks. While recent rewiring methods attempt to alleviate this limitation, many rely on local criteria such as curvature, which can overlook global connectivity constraints that restrict information flow. We introduce Effective Resistance Rewiring (ERR), a simple topology correction strategy that uses effective resistance as a global signal to detect structural bottlenecks. ERR iteratively adds edges between node pairs with the largest resistance while removing edges with minimal resistance, strengthening weak communication pathways while controlling graph densification under a fixed edge budget. The procedure is parameter-free beyond the rewiring budget and relies on a single global measure aggregating all paths between node pairs. Beyond predictive performance with GCN models, we analyze how rewiring affects message propagation. By tracking cosine similarity between node embeddings across layers, we examine how the relationship between initial node features and learned representations evolves during message passing, comparing graphs with and without rewiring. This analysis helps determine whether improvements arise from better long-range communication rather than changes in embedding geometry. Experiments on homophilic and heterophilic graphs, including directed settings with DirGCN, reveal a trade-off between over-squashing and oversmoothing, where oversmoothing corresponds to the loss of representation diversity across layers. Resistance-guided rewiring improves connectivity and signal propagation but can accelerate representation mixing in deep models. Combining ERR with normalization techniques such as PairNorm stabilizes this trade-off and improves performance.

Method: Proposes Voronoi tessellations to incorporate geometric structure into PC sum nodes, with two solutions: (1) approximate inference with guaranteed bounds, and (2) structural conditions for exact tractable inference. Also introduces differentiable VT relaxation for gradient-based learning.

Result: Empirically validated on standard density estimation tasks, showing improved modeling by incorporating geometric structure while maintaining inference tractability.

Conclusion: Voronoi tessellations provide effective way to incorporate geometric structure into probabilistic circuits, with solutions to maintain tractable inference through approximation or structural conditions.

Abstract: Probabilistic circuits (PCs) enable exact and tractable inference but employ data independent mixture weights that limit their ability to capture local geometry of the data manifold. We propose Voronoi tessellations (VT) as a natural way to incorporate geometric structure directly into the sum nodes of a PC. However, naïvely introducing such structure breaks tractability. We formalize this incompatibility and develop two complementary solutions: (1) an approximate inference framework that provides guaranteed lower and upper bounds for inference, and (2) a structural condition for VT under which exact tractable inference is recovered. Finally, we introduce a differentiable relaxation for VT that enables gradient-based learning and empirically validate the resulting approach on standard density estimation tasks.

Method: Proposes model-agnostic definitions of statistical and structural near-identifiability with error tolerance ε. Proves statistical ε-near-identifiability for models with nonlinear decoders, generalizing beyond last-layer representations. Shows ICA can resolve remaining linear ambiguity and proposes ICA post-processing of latent representations for disentanglement.

Result: Achieves state-of-the-art disentanglement on synthetic benchmarks using vanilla autoencoder with ICA post-processing. With foundation model-scale MAE for cell microscopy, disentangles biological variation from technical batch effects, substantially improving downstream generalization.

Conclusion: The paper provides theoretical foundations for representation identifiability and practical disentanglement methods. ICA post-processing of latent representations offers a simple yet effective approach for disentanglement that works with various representation learning models including autoencoders and MAEs.

Abstract: Representation learning models exhibit a surprising stability in their internal representations. Whereas most prior work treats this stability as a single property, we formalize it as two distinct concepts: statistical identifiability (consistency of representations across runs) and structural identifiability (alignment of representations with some unobserved ground truth). Recognizing that perfect pointwise identifiability is generally unrealistic for modern representation learning models, we propose new model-agnostic definitions of statistical and structural near-identifiability of representations up to some error tolerance $ε$. Leveraging these definitions, we prove a statistical $ε$-near-identifiability result for the representations of models with nonlinear decoders, generalizing existing identifiability theory beyond last-layer representations in e.g. generative pre-trained transformers (GPTs) to near-identifiability of the intermediate representations of a broad class of models including (masked) autoencoders (MAEs) and supervised learners. Although these weaker assumptions confer weaker identifiability, we show that independent components analysis (ICA) can resolve much of the remaining linear ambiguity for this class of models, and validate and measure our near-identifiability claims empirically. With additional assumptions on the data-generating process, statistical identifiability extends to structural identifiability, yielding a simple and practical recipe for disentanglement: ICA post-processing of latent representations. On synthetic benchmarks, this approach achieves state-of-the-art disentanglement using a vanilla autoencoder. With a foundation model-scale MAE for cell microscopy, it disentangles biological variation from technical batch effects, substantially improving downstream generalization.

Method: Construct topological feedforward neural networks using continuous linear functionals from dual spaces, develop topological DeepONets with branch components acting on locally convex spaces via linear measurements and trunk components on Euclidean domains

Result: Main theorem proves continuous operators from compact subsets of locally convex spaces to continuous functions on Euclidean domains can be uniformly approximated by topological DeepONets

Conclusion: Successfully extends Chen-Chen operator approximation theorem from Banach spaces to locally convex spaces, providing more general branch-trunk approximation framework

Abstract: Deep Operator Networks (DeepONets) provide a branch-trunk neural architecture for approximating nonlinear operators acting between function spaces. In the classical operator approximation framework, the input is a function $u\in C(K_1)$ defined on a compact set $K_1$ (typically a compact subset of a Banach space), and the operator maps $u$ to an output function $G(u)\in C(K_2)$ defined on a compact Euclidean domain $K_2\subset\mathbb{R}^d$. In this paper, we develop a topological extension in which the operator input lies in an arbitrary Hausdorff locally convex space $X$. We construct topological feedforward neural networks on $X$ using continuous linear functionals from the dual space $X^*$ and introduce topological DeepONets whose branch component acts on $X$ through such linear measurements, while the trunk component acts on the Euclidean output domain. Our main theorem shows that continuous operators $G:V\to C(K;\mathbb{R}^m)$, where $V\subset X$ and $K\subset\mathbb{R}^d$ are compact, can be uniformly approximated by such topological DeepONets. This extends the classical Chen-Chen operator approximation theorem from spaces of continuous functions to locally convex spaces and yields a branch-trunk approximation theorem beyond the Banach-space setting.

Method: Develops new on-average algorithmic stability analysis for multipass SGD that handles correlations from data reuse, connects generalization to effective dimension depending on curvature sources, and provides both upper bounds and matching instance-dependent lower bounds.

Result: Shows that improperly chosen preconditioners can yield suboptimal effective dimension dependence in both optimization and generalization, with theoretical bounds demonstrating these trade-offs.

Conclusion: The geometry of population risk curvature and gradient noise must be carefully balanced in preconditioner design for optimal generalization performance in multipass PSGD, with improper choices leading to suboptimal effective dimension dependence.

Abstract: We study trade-offs between the population risk curvature, geometry of the noise, and preconditioning on the generalisation ability of the multipass Preconditioned Stochastic Gradient Descent (PSGD). Many practical optimisation heuristics implicitly navigate this trade-off in different ways – for instance, some aim to whiten gradient noise, while others aim to align updates with expected loss curvature. When the geometry of the population risk curvature and the geometry of the gradient noise do not match, an aggressive choice that improves one aspect can amplify instability along the other, leading to suboptimal statistical behavior. In this paper we employ on-average algorithmic stability to connect generalisation of PSGD to the effective dimension that depends on these sources of curvature. While existing techniques for on-average stability of SGD are limited to a single pass, as first contribution we develop a new on-average stability analysis for multipass SGD that handles the correlations induced by data reuse. This allows us to derive excess risk bounds that depend on the effective dimension. In particular, we show that an improperly chosen preconditioner can yield suboptimal effective dimension dependence in both optimisation and generalisation. Finally, we complement our upper bounds with matching, instance-dependent lower bounds.

Method: Three metamodeling frameworks coupling feature extraction modules (MLP, MPNN, or AE) with LSTM networks using Monte Carlo dropout and negative log-likelihood loss for uncertainty quantification.

Result: All three approaches achieved low prediction errors: MLP-LSTM performed best on lower-dimensional Bouc-Wen system, while MPNN-LSTM and AE-LSTM excelled on complex steel-frame model. Predictive variance correlated well with actual error.

Conclusion: The proposed frameworks effectively handle uncertainties in structural systems and provide reliable confidence estimates, making them suitable for active-learning strategies and model confidence assessment in structural response predictions.

Abstract: Modeling high-dimensional, nonlinear dynamic structural systems under natural hazards presents formidable computational challenges, especially when simultaneously accounting for uncertainties in external loads and structural parameters. Studies have successfully incorporated uncertainties related to external loads from natural hazards, but few have simultaneously addressed loading and parameter uncertainties within structural systems while accounting for prediction uncertainty of neural networks. To address these gaps, three metamodeling frameworks were formulated, each coupling a feature-extraction module implemented through a multi-layer perceptron (MLP), a message-passing neural network (MPNN), or an autoencoder (AE) with a long short-term memory (LSTM) network using Monte Carlo dropout and a negative log-likelihood loss. The resulting architectures (MLP-LSTM, MPNN-LSTM, and AE-LSTM) were validated on two case studies: a multi-degree-of-freedom Bouc-Wen system and a 37-story fiber-discretized nonlinear steel moment-resisting frame, both subjected to stochastic seismic excitation and structural parameter uncertainty. All three approaches achieved low prediction errors: the MLP-LSTM yielded the most accurate results for the lower-dimensional Bouc-Wen system, whereas the MPNN-LSTM and AE-LSTM provided superior performance on the more complex steel-frame model. Moreover, a consistent correlation between predictive variance and actual error confirms the suitability of these frameworks for active-learning strategies and for assessing model confidence in structural response predictions.

Method: Flowcean is a novel framework designed to automate model generation through data-driven learning with focus on modularity and usability. It offers various learning strategies, data processing methods, and evaluation metrics within a modular and flexible architecture that facilitates integration of diverse learning libraries and tools.

Result: The framework provides a comprehensive solution tailored to CPS scenarios, streamlining the process of model generation and evaluation, making it more efficient and accessible.

Conclusion: Flowcean addresses the challenges of CPS modeling by providing a modular, flexible framework that automates data-driven model generation, improving efficiency and accessibility for CPS design and operation.

Abstract: Effective models of Cyber-Physical Systems (CPS) are crucial for their design and operation. Constructing such models is difficult and time-consuming due to the inherent complexity of CPS. As a result, data-driven model generation using machine learning methods is gaining popularity. In this paper, we present Flowcean, a novel framework designed to automate the generation of models through data-driven learning that focuses on modularity and usability. By offering various learning strategies, data processing methods, and evaluation metrics, our framework provides a comprehensive solution, tailored to CPS scenarios. Flowcean facilitates the integration of diverse learning libraries and tools within a modular and flexible architecture, ensuring adaptability to a wide range of modeling tasks. This streamlines the process of model generation and evaluation, making it more efficient and accessible.

Method: Uses unitary MPS architecture for generative modeling, develops Riemannian optimization with manifold constraints, and derives an efficient space-decoupling algorithm to overcome gradient-based training inefficiencies.

Result: Experiments on Bars-and-Stripes and EMNIST datasets show fast adaptation to data structure, stable updates, strong performance while maintaining MPS efficiency and expressive power.

Conclusion: Unitary MPS with Riemannian optimization provides an effective framework for generative modeling with physical interpretability and computational efficiency.

Abstract: Tensor networks, which are originally developed for characterizing complex quantum many-body systems, have recently emerged as a powerful framework for capturing high-dimensional probability distributions with strong physical interpretability. This paper systematically studies matrix product states (MPS) for generative modeling and shows that unitary MPS, which is a tensor-network architecture that is both simple and expressive, offers clear benefits for unsupervised learning by reducing ambiguity in parameter updates and improving efficiency. To overcome the inefficiency of standard gradient-based MPS training, we develop a Riemannian optimization approach that casts probabilistic modeling as an optimization problem with manifold constraints, and further derive an efficient space-decoupling algorithm. Experiments on Bars-and-Stripes and EMNIST datasets demonstrate fast adaptation to data structure, stable updates, and strong performance while maintaining the efficiency and expressive power of MPS.

Method: Proposes one-step posterior correction (OSPC) to address prior-induced confounding bias in PFN-based ATE estimators. Implements OSPC using martingale posteriors on top of PFNs to recover functional nuisance posteriors needed for calibration.

Result: PFNs calibrated with martingale posterior OSPC produce ATE uncertainty that asymptotically matches frequentist uncertainty and shows good calibration in finite samples compared to other Bayesian ATE estimators in (semi-)synthetic experiments.

Conclusion: Calibration via OSPC with martingale posteriors enables PFN-based causal estimators to achieve frequentist consistency and reliable uncertainty quantification, bridging the gap between modern foundation models and classical statistical methods.

Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning problem.However, it is unclear whether PFN-based causal estimators provide uncertainty quantification that is consistent with classical frequentist estimators. In this work, we address this gap by analyzing the frequentist consistency of PFN-based estimators for the average treatment effect (ATE). (1) We show that existing PFNs, when interpreted as Bayesian ATE estimators, can exhibit prior-induced confounding bias: the prior is not asymptotically overwritten by data, which, in turn, prevents frequentist consistency. (2) As a remedy, we suggest employing a calibration procedure based on a one-step posterior correction (OSPC). We show that the OSPC helps to restore frequentist consistency and can yield a semi-parametric Bernstein-von Mises theorem for calibrated PFNs (i.e., both the calibrated PFN-based estimators and the classical semi-parametric efficient estimators converge in distribution with growing data size). (3) Finally, we implement OSPC through tailoring martingale posteriors on top of the PFNs. In this way, we are able to recover functional nuisance posteriors from PFNs, required by the OSPC. In multiple (semi-)synthetic experiments, PFNs calibrated with our martingale posterior OSPC produce ATE uncertainty that (i) asymptotically matches frequentist uncertainty and (ii) is well calibrated in finite samples in comparison to other Bayesian ATE estimators.

Method: SFI decouples generation into frequent low-cost fast steps (using compact sparse memory) and occasional dense-attention slow steps triggered near semantic boundaries. Slow steps revisit broader context and refresh memory for subsequent fast steps.

Result: SFI delivers 1.6× to 14.4× higher decoding throughput while maintaining quality on par with full-KV baselines across long-context and long-CoT settings.

Conclusion: SFI offers a practical, training-free approach to reduce inference costs for autoregressive reasoning models in long-context, long-horizon, and agentic workloads by leveraging stable attention patterns within semantic spans.

Abstract: Long-context autoregressive decoding remains expensive because each decoding step must repeatedly process a growing history. We observe a consistent pattern during decoding: within a sentence, and more generally within a short semantically coherent span, the dominant attention support often remains largely stable. Motivated by this observation, we propose Slow-Fast Inference (SFI), a training-free decoding framework that decouples generation into frequent low-cost fast steps and occasional dense-attention slow steps. Fast steps reuse a compact sparse memory for efficient decoding. Slow steps are triggered near semantic boundaries. At slow steps, the model revisits the broader context and uses the Selector to refresh the selected memory for subsequent fast steps. Across the evaluated context lengths, SFI delivers approximately $1.6\times$–$14.4\times$ higher decoding throughput while generally maintaining quality on par with the full-KV baseline across long-context and long-CoT settings. Because SFI is training-free and applies directly to existing checkpoints, it offers a practical path to reducing inference cost for contemporary autoregressive reasoning models in long-context, long-horizon, and agentic workloads.

Method: Mathematical proof using deterministic mass-action kinetics formulation of chemical reaction networks, analytical regret bounds, asymptotic behavior analysis, Vapnik-Chervonenkis dimension analysis, and numerical experiments on MNIST digit classification.

Result: The chemical reaction network without hidden layers successfully learns classification tasks previously requiring hidden layers in spiking neural networks, achieving higher accuracy and efficiency on MNIST digit classification.

Conclusion: Chemical reaction networks can exhibit powerful learning capabilities without hidden layers, potentially enabling more efficient machine learning in chemical computers and providing insights into biological learning mechanisms.

Abstract: We mathematically prove that chemical reaction networks without hidden layers can solve tasks for which spiking neural networks require hidden layers. Our proof uses the deterministic mass-action kinetics formulation of chemical reaction networks. Specifically, we prove that a certain reaction network without hidden layers can learn a classification task previously proved to be achievable by a spiking neural network with hidden layers. We provide analytical regret bounds for the global behavior of the network and analyze its asymptotic behavior and Vapnik-Chervonenkis dimension. In a numerical experiment, we confirm the learning capacity of the proposed chemical reaction network for classifying handwritten digits in pixel images, and we show that it solves the task more accurately and efficiently than a spiking neural network with hidden layers. This provides a motivation for machine learning in chemical computers and a mathematical explanation for how biological cells might exhibit more efficient learning behavior within biochemical reaction networks than neuronal networks.

Method: Use Temporal Convolutional Networks (TCNs) for multi-label classification of TF binding sites on DNA sequences. Approach treats TF binding prediction as multi-label problem to capture correlations among TFs and their cooperative regulatory mechanisms.

Result: TCN models achieve reliable predictive performances for multiple TFs, reveal biologically meaningful motifs and co-binding patterns consistent with known TF interactions, and suggest novel relationships and cooperation among TFs.

Conclusion: Multi-label learning with TCNs effectively captures TF interactions and cooperative mechanisms, providing insights into complex regulatory logic beyond individual TF binding predictions.

Abstract: Transcription factors (TFs) regulate gene expression through complex and co-operative mechanisms. While many TFs act together, the logic underlying TFs binding and their interactions is not fully understood yet. Most current approaches for TF binding site prediction focus on individual TFs and binary classification tasks, without a full analysis of the possible interactions among various TFs. In this paper we investigate DNA TF binding site recognition as a multi-label classification problem, achieving reliable predictions for multiple TFs on DNA sequences retrieved in public repositories. Our deep learning models are based on Temporal Convolutional Networks (TCNs), which are able to predict multiple TF binding profiles, capturing correlations among TFs andtheir cooperative regulatory mechanisms. Our results suggest that multi-label learning leading to reliable predictive performances can reveal biologically meaningful motifs and co-binding patterns consistent with known TF interactions, while also suggesting novel relationships and cooperation among TFs.

Method: Introduces cross-domain Bellman consistency measure and QAvatar framework that combines source and target Q-functions with hyperparameter-free adaptive weighting

Result: QAvatar achieves reliable transfer across various RL benchmarks including locomotion and robot arm manipulation tasks

Conclusion: The proposed approach effectively addresses cross-domain RL challenges by leveraging source-domain knowledge while avoiding negative transfer

Abstract: Cross-domain reinforcement learning (CDRL) is meant to improve the data efficiency of RL by leveraging the data samples collected from a source domain to facilitate the learning in a similar target domain. Despite its potential, cross-domain transfer in RL is known to have two fundamental and intertwined challenges: (i) The source and target domains can have distinct state space or action space, and this makes direct transfer infeasible and thereby requires more sophisticated inter-domain mappings; (ii) The transferability of a source-domain model in RL is not easily identifiable a priori, and hence CDRL can be prone to negative effect during transfer. In this paper, we propose to jointly tackle these two challenges through the lens of \textit{cross-domain Bellman consistency} and \textit{hybrid critic}. Specifically, we first introduce the notion of cross-domain Bellman consistency as a way to measure transferability of a source-domain model. Then, we propose $Q$Avatar, which combines the Q functions from both the source and target domains with an adaptive hyperparameter-free weight function. Through this design, we characterize the convergence behavior of $Q$Avatar and show that $Q$Avatar achieves reliable transfer in the sense that it effectively leverages a source-domain Q function for knowledge transfer to the target domain. Through experiments, we demonstrate that $Q$Avatar achieves favorable transferability across various RL benchmark tasks, including locomotion and robot arm manipulation. Our code is available at https://rl-bandits-lab.github.io/Cross-Domain-RL/.

Method: Proposes a closed-loop pipeline with historical feedback memory (sliding window of K=5 recent attempts) that treats failures as learning signals. Uses dual-LLM specialization: Code Generator for PyTorch architectures and Prompt Improver for diagnostic reasoning. Evaluates on CIFAR-10, CIFAR-100, and ImageNette using one-epoch proxy accuracy for fast ranking.

Result: Achieves significant accuracy improvements: DeepSeek-Coder-6.7B from 28.2% to 69.2%, Qwen2.5-7B from 50.0% to 71.5%, and GLM-5 from 43.2% to 62.0% on CIFAR-10. A full 2000-iteration search completes in ≈18 GPU hours on a single RTX 4090.

Conclusion: Establishes a low-budget, reproducible, and hardware-aware paradigm for LLM-driven NAS without cloud infrastructure, demonstrating that LLMs can effectively guide architecture search with limited computational resources.

Abstract: Neural Architecture Search (NAS) automates network design, but conventional methods demand substantial computational resources. We propose a closed-loop pipeline leveraging large language models (LLMs) to iteratively generate, evaluate, and refine convolutional neural network architectures for image classification on a single consumer-grade GPU without LLM fine-tuning. Central to our approach is a historical feedback memory inspired by Markov chains: a sliding window of $K{=}5$ recent improvement attempts keeps context size constant while providing sufficient signal for iterative learning. Unlike prior LLM optimizers that discard failure trajectories, each history entry is a structured diagnostic triple – recording the identified problem, suggested modification, and resulting outcome – treating code execution failures as first-class learning signals. A dual-LLM specialization reduces per-call cognitive load: a Code Generator produces executable PyTorch architectures while a Prompt Improver handles diagnostic reasoning. Since both the LLM and architecture training share limited VRAM, the search implicitly favors compact, hardware-efficient models suited to edge deployment. We evaluate three frozen instruction-tuned LLMs (${\leq}7$B parameters) across up to 2000 iterations in an unconstrained open code space, using one-epoch proxy accuracy on CIFAR-10, CIFAR-100, and ImageNette as a fast ranking signal. On CIFAR-10, DeepSeek-Coder-6.7B improves from 28.2% to 69.2%, Qwen2.5-7B from 50.0% to 71.5%, and GLM-5 from 43.2% to 62.0%. A full 2000-iteration search completes in ${\approx}18$ GPU hours on a single RTX~4090, establishing a low-budget, reproducible, and hardware-aware paradigm for LLM-driven NAS without cloud infrastructure.

Method: Proposes minimax deep deterministic policy gradient (MMDDPG) formulated as a minimax optimization between user policy and adversarial disturbance policy, with a fractional objective to balance task performance and disturbance magnitude for stable training.

Result: Experimental evaluations in MuJoCo environments show MMDDPG achieves significantly improved robustness against both external force perturbations and model parameter variations.

Conclusion: MMDDPG provides an effective framework for learning disturbance-resilient policies in continuous control tasks through adversarial training with stabilized minimax optimization.

Abstract: Reinforcement learning (RL) has achieved remarkable success in a wide range of control and decision-making tasks. However, RL agents often exhibit unstable or degraded performance when deployed in environments subject to unexpected external disturbances and model uncertainties. Consequently, ensuring reliable performance under such conditions remains a critical challenge. In this paper, we propose minimax deep deterministic policy gradient (MMDDPG), a framework for learning disturbance-resilient policies in continuous control tasks. The training process is formulated as a minimax optimization problem between a user policy and an adversarial disturbance policy. In this problem, the user learns a robust policy that minimizes the objective function, while the adversary generates disturbances that maximize it. To stabilize this interaction, we introduce a fractional objective that balances task performance and disturbance magnitude. This objective prevents excessively aggressive disturbances and promotes robust learning. Experimental evaluations in MuJoCo environments demonstrate that the proposed MMDDPG achieves significantly improved robustness against both external force perturbations and model parameter variations.

Method: Cornserve provides a flexible task abstraction for expressing Any-to-Any model computation graphs, enabling component disaggregation and independent scaling. It uses an efficient record-and-replay execution model that tracks data dependencies and forwards tensor data directly between components.

Result: Cornserve delivers up to 3.81× higher throughput and 5.79× lower tail latency compared to existing approaches, supporting diverse Any-to-Any models with approximately 23K lines of Python code built on Kubernetes.

Conclusion: Cornserve is an effective distributed serving system for Any-to-Any multimodal models that addresses the unique challenges of serving diverse multimodal requests through flexible computation graph abstraction and efficient distributed execution.

Abstract: Any-to-Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input and generate them as output. Serving these models are challenging; different requests with different input and output modalities traverse different paths through the model computation graph, and each component of the model have different scaling characteristics. We present Cornserve, a distributed serving system for generic Any-to-Any models. Cornserve provides a flexible task abstraction for expressing Any-to-Any model computation graphs, enabling component disaggregation and independent scaling. The distributed runtime dispatches compute to the data plane via an efficient record-and-replay execution model that keeps track of data dependencies, and forwards tensor data between components directly from the producer to the consumer. Built on Kubernetes with approximately 23K new lines of Python, Cornserve supports diverse Any-to-Any models and delivers up to 3.81$\times$ higher throughput and 5.79$\times$ lower tail latency. Cornserve is open-source, and the demo video is available on YouTube.

Method: Three-step approach: 1) Generic prompt template for environment translation, 2) Hierarchical verification (property, interaction, rollout tests), 3) Iterative agent-assisted repair. Applied across three workflows: direct translation, verified translation against existing implementations, and new environment creation.

Result: Achieved significant speedups: EmuRust (1.5x PPO), PokeJAX (22,320x over TypeScript reference), parity with MJX (1.04x), 5x over Brax, 42x PPO for Puffer Pong, TCGJax (6.6x over Python reference). Environment overhead drops below 4% of training time at 200M parameters.

Conclusion: The recipe enables automated, low-cost production of high-performance RL environments with verified semantic equivalence, addressing engineering bottlenecks and contamination concerns in agent pretraining.

Abstract: Translating complex reinforcement learning (RL) environments into high-performance implementations has traditionally required months of specialized engineering. We present a reusable recipe - a generic prompt template, hierarchical verification, and iterative agent-assisted repair - that produces semantically equivalent high-performance environments for <$10 in compute cost. We demonstrate three distinct workflows across five environments. Direct translation (no prior performance implementation exists): EmuRust (1.5x PPO speedup via Rust parallelism for a Game Boy emulator) and PokeJAX, the first GPU-parallel Pokemon battle simulator (500M SPS random action, 15.2M SPS PPO; 22,320x over the TypeScript reference). Translation verified against existing performance implementations: throughput parity with MJX (1.04x) and 5x over Brax at matched GPU batch sizes (HalfCheetah JAX); 42x PPO (Puffer Pong). New environment creation: TCGJax, the first deployable JAX Pokemon TCG engine (717K SPS random action, 153K SPS PPO; 6.6x over the Python reference), synthesized from a web-extracted specification. At 200M parameters, the environment overhead drops below 4% of training time. Hierarchical verification (property, interaction, and rollout tests) confirms semantic equivalence for all five environments; cross-backend policy transfer confirms zero sim-to-sim gap for all five environments. TCGJax, synthesized from a private reference absent from public repositories, serves as a contamination control for agent pretraining data concerns. The paper contains sufficient detail - including representative prompts, verification methodology, and complete results - that a coding agent could reproduce the translations directly from the manuscript.

Method: Frames scaling as compute-constrained optimization over three resources: parallel rollouts per problem, number of problems per batch, and number of update steps. Studies compute-optimal allocation across different base models and data distributions.

Result: Compute-optimal number of parallel rollouts per problem increases predictably with compute budget then saturates. On easy problems, driven by solution sharpening; on hard problems, by coverage expansion. Increasing parallel rollouts mitigates interference across problems.

Conclusion: Provides practical guidance for compute-efficient LLM RL post-training by recasting RL scaling laws as prescriptive allocation rules, validated across models and distributions.

Abstract: While scaling laws guide compute allocation for LLM pre-training, analogous prescriptions for reinforcement learning (RL) post-training of large language models (LLMs) remain poorly understood. We study the compute-optimal allocation of sampling compute for on-policy RL methods in LLMs, framing scaling as a compute-constrained optimization over three resources: parallel rollouts per problem, number of problems per batch, and number of update steps. We find that the compute-optimal number of parallel rollouts per problem increases predictably with compute budget and then saturates. This trend holds across both easy and hard problems, though driven by different mechanisms: solution sharpening on easy problems and coverage expansion on hard problems. We further show that increasing the number of parallel rollouts mitigates interference across problems, while the number of problems per batch primarily affects training stability and can be chosen within a broad range. Validated across base models and data distributions, our results recast RL scaling laws as prescriptive allocation rules and provide practical guidance for compute-efficient LLM RL post-training.

Method: Develops theoretical analysis under a two-mode mixture abstraction representing old and new tasks. Analyzes forgetting through two forms: mass forgetting (old mixture weight collapses) and old-component drift (old component shifts). Examines forward-KL vs reverse-KL objectives, replay strategies, and analyzes three recent post-training methods (SDFT, TTT-Discover, OAPL) through this theoretical lens.

Result: Forward-KL objectives trained on new data drive old weight to zero (mass forgetting), while reverse-KL objectives converge to true target avoiding mass forgetting. Old-component drift decays exponentially with mode separation. Replay interacts differently with objectives: for forward-KL it must modify training distribution, for reverse-KL it prevents old-mode starvation through bounded importance weighting. Analysis provides explicit conditions under which recent methods retain old mass and exhibit overlap-controlled drift.

Conclusion: Forgetting can be precisely quantified based on interaction between divergence direction, geometric behavioral overlap, sampling regime, and visibility of past behavior during training. The theoretical framework provides principled understanding of forgetting mechanisms in continual learning.

Abstract: Continual post-training of generative models is widely used, yet a principled understanding of when and why forgetting occurs remains limited. We develop theoretical results under a two-mode mixture abstraction (representing old and new tasks), proposed by Chen et al. (2025) (arXiv:2510.18874), and formalize forgetting in two forms: (i) mass forgetting, where the old mixture weight collapses to zero, and (ii) old-component drift, where an already-correct old component shifts during training. For equal-covariance Gaussian modes, we prove that forward-KL objectives trained on data from the new distribution drive the old weight to zero, while reverse-KL objectives converge to the true target (thereby avoiding mass forgetting) and perturb the old mean only through overlap-gated misassignment probabilities controlled by the Bhattacharyya coefficient, yielding drift that decays exponentially with mode separation and a locally well-conditioned geometry with exponential convergence. We further quantify how replay interacts with these objectives. For forward-KL, replay must modify the training distribution to change the population optimum; for reverse-KL, replay leaves the population objective unchanged but prevents finite-batch old-mode starvation through bounded importance weighting. Finally, we analyze three recently proposed near-on-policy post-training methods, SDFT (arxiv:2601.19897), TTT-Discover (arxiv:2601.16175), and OAPL (arxiv:2602.19362), via the same lens and derive explicit conditions under which each retains old mass and exhibits overlap-controlled drift. Overall, our results show that forgetting can by precisely quantified based on the interaction between divergence direction, geometric behavioral overlap, sampling regime, and the visibility of past behavior during training.

Method: Simple post-training method: sample N random parameter perturbations, select top K performing ones, and ensemble predictions via majority vote.

Result: Competitive with standard post-training methods like PPO, GRPO, and ES for contemporary large-scale models despite its simplicity.

Conclusion: Large pretrained models inherently contain diverse task experts, enabling effective post-training through simple random sampling and ensembling approaches.

Abstract: Pretraining produces a learned parameter vector that is typically treated as a starting point for further iterative adaptation. In this work, we instead view the outcome of pretraining as a distribution over parameter vectors, whose support already contains task-specific experts. We show that in small models such expert solutions occupy a negligible fraction of the volume of this distribution, making their discovery reliant on structured optimization methods such as gradient descent. In contrast, in large, well-pretrained models the density of task-experts increases dramatically, so that diverse, task-improving specialists populate a substantial fraction of the neighborhood around the pretrained weights. Motivated by this perspective, we explore a simple, fully parallel post-training method that samples $N$ parameter perturbations at random, selects the top $K$, and ensembles predictions via majority vote. Despite its simplicity, this approach is competitive with standard post-training methods such as PPO, GRPO, and ES for contemporary large-scale models.

Method: The approach involves mapping principal attack surfaces across tools, connectors, hosting boundaries, and multi-agent coordination, with emphasis on indirect prompt injection, confused-deputy behavior, and cascading failures. It assesses current defenses as a layered stack including input-level/model-level mitigations, sandboxed execution, and deterministic policy enforcement.

Result: The analysis identifies key security challenges in AI agents and proposes a framework for understanding attack surfaces and defense mechanisms, while highlighting standards and research gaps in adaptive security benchmarks and secure multi-agent system design.

Conclusion: AI agents introduce novel security challenges requiring new approaches to security design, with recommendations for standards, research gaps, and alignment with NIST risk management principles for secure agentic systems.

Abstract: This article, a lightly adapted version of Perplexity’s response to NIST/CAISI Request for Information 2025-0035, details our observations and recommendations concerning the security of frontier AI agents. These insights are informed by Perplexity’s experience operating general-purpose agentic systems used by millions of users and thousands of enterprises in both controlled and open-world environments. Agent architectures change core assumptions around code-data separation, authority boundaries, and execution predictability, creating new confidentiality, integrity, and availability failure modes. We map principal attack surfaces across tools, connectors, hosting boundaries, and multi-agent coordination, with particular emphasis on indirect prompt injection, confused-deputy behavior, and cascading failures in long-running workflows. We then assess current defenses as a layered stack: input-level and model-level mitigations, sandboxed execution, and deterministic policy enforcement for high-consequence actions. Finally, we identify standards and research gaps, including adaptive security benchmarks, policy models for delegation and privilege control, and guidance for secure multi-agent system design aligned with NIST risk management principles.

Method: Introduces temporal straightening using a curvature regularizer that encourages locally straightened latent trajectories. Jointly learns an encoder and predictor by reducing curvature to make Euclidean distance in latent space a better proxy for geodesic distance.

Result: Temporal straightening makes gradient-based planning more stable and yields significantly higher success rates across a suite of goal-reaching tasks.

Conclusion: Temporal straightening is an effective approach for improving representation learning specifically for latent planning tasks by aligning latent space geometry with planning objectives.

Abstract: Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant – or even detrimental – to planning. Inspired by the perceptual straightening hypothesis in human visual processing, we introduce temporal straightening to improve representation learning for latent planning. Using a curvature regularizer that encourages locally straightened latent trajectories, we jointly learn an encoder and a predictor. We show that reducing curvature this way makes the Euclidean distance in latent space a better proxy for the geodesic distance and improves the conditioning of the planning objective. We demonstrate empirically that temporal straightening makes gradient-based planning more stable and yields significantly higher success rates across a suite of goal-reaching tasks.

Method: STAMP jointly considers token importance to downstream tasks and privacy sensitivity to allocate privacy budgets. It uses a polar mechanism that perturbs only the direction of token embeddings on the unit sphere while preserving magnitude, with decoding via cosine nearest-neighbor search.

Result: Experiments on SQuAD, Yelp, and AG News datasets show STAMP with normalized polar mechanism achieves superior privacy-utility trade-offs across varying per-token privacy budgets compared to isotropic noise mechanisms.

Conclusion: STAMP provides fine-grained, task-aware text privatization with improved privacy-utility balance by selectively allocating privacy budgets and using directional perturbation that preserves semantic neighborhoods in embedding space.

Abstract: We present STAMP (Selective Task-Aware Mechanism for Text Privacy), a new framework for task-aware text privatization that achieves an improved privacy-utility trade-off. STAMP selectively allocates privacy budgets across tokens by jointly considering (i) each token’s importance to the downstream task (as measured via a task- or query-specific representation), and (ii) its privacy sensitivity (e.g., names, dates, identifiers). This token-level partitioning enables fine-grained, group-wise control over the level of noise applied to different parts of the input, balancing privacy protection with task relevance. To privatize individual token embeddings, we introduce the polar mechanism, which perturbs only the direction of embeddings on the unit sphere while preserving their magnitude. Decoding is performed via cosine nearest-neighbor search, aligning the perturbation geometry with the decoding geometry. Unlike isotropic noise mechanisms, the polar mechanism maintains semantic neighborhoods in the embedding space and better preserves downstream utility. Experimental evaluations on SQuAD, Yelp, and AG News datasets demonstrate that STAMP, when combined with the normalized polar mechanism, consistently achieves superior privacy-utility trade-offs across varying per-token privacy budgets.

Method: Proposes Separable Neural Architectures (SNAs) that formalize a representational class unifying additive, quadratic and tensor-decomposed neural models. SNAs constrain interaction order and tensor rank to impose structural inductive bias, factorizing high-dimensional mappings into low-arity components. The approach treats continuous physical states as smooth, separable embeddings and enables distributional modeling of chaotic systems.

Result: Demonstrates compositional versatility across four domains: autonomous waypoint navigation via reinforcement learning, inverse generation of multifunctional microstructures, distributional modeling of turbulent flow, and neural language modeling. Shows that SNAs can mitigate nonphysical drift in deterministic operators while remaining applicable to discrete sequences.

Conclusion: Establishes separable neural architecture as a domain-agnostic primitive for predictive and generative intelligence, capable of unifying both deterministic and distributional representations across diverse domains including physics, language, and perception.

Abstract: Intelligent systems across physics, language and perception often exhibit factorisable structure, yet are typically modelled by monolithic neural architectures that do not explicitly exploit this structure. The separable neural architecture (SNA) addresses this by formalising a representational class that unifies additive, quadratic and tensor-decomposed neural models. By constraining interaction order and tensor rank, SNAs impose a structural inductive bias that factorises high-dimensional mappings into low-arity components. Separability need not be a property of the system itself: it often emerges in the coordinates or representations through which the system is expressed. Crucially, this coordinate-aware formulation reveals a structural analogy between chaotic spatiotemporal dynamics and linguistic autoregression. By treating continuous physical states as smooth, separable embeddings, SNAs enable distributional modelling of chaotic systems. This approach mitigates the nonphysical drift characteristics of deterministic operators whilst remaining applicable to discrete sequences. The compositional versatility of this approach is demonstrated across four domains: autonomous waypoint navigation via reinforcement learning, inverse generation of multifunctional microstructures, distributional modelling of turbulent flow and neural language modelling. These results establish the separable neural architecture as a domain-agnostic primitive for predictive and generative intelligence, capable of unifying both deterministic and distributional representations.

Method: Energy-based fine-tuning (EBFT) uses strided block-parallel sampling to generate multiple rollouts from nested prefixes concurrently, batches feature extraction over these rollouts, and performs on-policy policy-gradient updates using the resulting embeddings to match sequence-level statistics.

Result: EBFT matches RLVR and outperforms SFT on downstream accuracy across Q&A coding, unstructured coding, and translation tasks, while achieving lower validation cross-entropy than both methods.

Conclusion: EBFT provides an effective alternative to cross-entropy training by targeting sequence-level statistics, offering dense semantic feedback without task-specific components, and demonstrating strong empirical performance across multiple domains.

Abstract: Cross-entropy (CE) training provides dense and scalable supervision for language models, but it optimizes next-token prediction under teacher forcing rather than sequence-level behavior under model rollouts. We introduce a feature-matching objective for language-model fine-tuning that targets sequence-level statistics of the completion distribution, providing dense semantic feedback without requiring a task-specific verifier or preference model. To optimize this objective efficiently, we propose energy-based fine-tuning (EBFT), which uses strided block-parallel sampling to generate multiple rollouts from nested prefixes concurrently, batches feature extraction over these rollouts, and uses the resulting embeddings to perform an on-policy policy-gradient update. We present a theoretical perspective connecting EBFT to KL-regularized feature-matching and energy-based modeling. Empirically, across Q&A coding, unstructured coding, and translation, EBFT matches RLVR and outperforms SFT on downstream accuracy while achieving a lower validation cross-entropy than both methods.

Method: Developed interpretation of color representation in FLUX.1 VAE latent space, identified Latent Color Subspace (LCS) reflecting HSL structure, created training-free method using closed-form latent-space manipulation.

Result: LCS interpretation can predict and explicitly control color in FLUX.1, enabling precise color manipulation without additional training.

Conclusion: Understanding latent space structure enables fine-grained control over text-to-image generation, with color as a case study demonstrating the value of interpretability.

Abstract: Text-to-image generation models have advanced rapidly, yet achieving fine-grained control over generated images remains difficult, largely due to limited understanding of how semantic information is encoded. We develop an interpretation of the color representation in the Variational Autoencoder latent space of FLUX.1 [Dev], revealing a structure reflecting Hue, Saturation, and Lightness. We verify our Latent Color Subspace (LCS) interpretation by demonstrating that it can both predict and explicitly control color, introducing a fully training-free method in FLUX based solely on closed-form latent-space manipulation. Code is available at https://github.com/ExplainableML/LCS.

Method: Proposes Structured Agent Distillation that segments agent trajectories into {[REASON]} and {[ACT]} spans, applying segment-specific losses to align each component with the teacher’s behavior. This structure-aware supervision enables better replication of the teacher’s decision process in compact student models.

Result: Experiments on ALFWorld, HotPotQA-ReAct, and WebShop show the approach consistently outperforms token-level and imitation learning baselines, achieving significant compression with minimal performance drop. Scaling and ablation results highlight the importance of span-level alignment.

Conclusion: Structured Agent Distillation enables efficient compression of LLM-based agents while preserving reasoning fidelity and action consistency, making them more deployable in practical applications.

Abstract: Large language models (LLMs) exhibit strong capabilities as decision-making agents by interleaving reasoning and actions, as seen in ReAct-style frameworks. Yet, their practical deployment is constrained by high inference costs and large model sizes. We propose Structured Agent Distillation, a framework that compresses large LLM-based agents into smaller student models while preserving both reasoning fidelity and action consistency. Unlike standard token-level distillation, our method segments trajectories into {[REASON]} and {[ACT]} spans, applying segment-specific losses to align each component with the teacher’s behavior. This structure-aware supervision enables compact agents to better replicate the teacher’s decision process. Experiments on ALFWorld, HotPotQA-ReAct, and WebShop show that our approach consistently outperforms token-level and imitation learning baselines, achieving significant compression with minimal performance drop. Scaling and ablation results further highlight the importance of span-level alignment for efficient and deployable agents.

Method: Develops a Bayesian model where both interventions affect LLM behavior by altering beliefs in latent concepts: activation steering changes concept priors, while in-context learning accumulates evidence. Creates a closed-form Bayesian model that predicts LLM behavior across both intervention types.

Result: The model successfully predicts LLM behavior across context- and activation-based interventions, explains prior empirical phenomena (e.g., sigmoidal learning curves), and predicts novel phenomena (e.g., additivity of interventions in log-belief space leading to sudden behavioral shifts).

Conclusion: Provides a unified Bayesian account of prompt-based and activation-based control of LLMs, offering a methodology for empirically predicting intervention effects and explaining how subtle control changes can induce dramatic behavioral shifts.

Abstract: Large language models (LLMs) can be controlled at inference time through prompts (in-context learning) and internal activations (activation steering). Different accounts have been proposed to explain these methods, yet their common goal of controlling model behavior raises the question of whether these seemingly disparate methodologies can be seen as specific instances of a broader framework. Motivated by this, we develop a unifying, predictive account of LLM control from a Bayesian perspective. Specifically, we posit that both context- and activation-based interventions impact model behavior by altering its belief in latent concepts: steering operates by changing concept priors, while in-context learning leads to an accumulation of evidence. This results in a closed-form Bayesian model that is highly predictive of LLM behavior across context- and activation-based interventions in a set of domains inspired by prior work on many-shot in-context learning. This model helps us explain prior empirical phenomena - e.g., sigmoidal learning curves as in-context evidence accumulates - while predicting novel ones - e.g., additivity of both interventions in log-belief space, which results in distinct phases such that sudden and dramatic behavioral shifts can be induced by slightly changing intervention controls. Taken together, this work offers a unified account of prompt-based and activation-based control of LLM behavior, and a methodology for empirically predicting the effects of these interventions.

Method: Cannot determine method as abstract is unavailable due to rate limiting error

Result: Cannot determine results as abstract is unavailable due to rate limiting error

Conclusion: Cannot draw conclusions as abstract is unavailable due to rate limiting error

Abstract: Failed to fetch summary for 2405.08979: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2405.08979&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed paper fetch

Result: Unable to determine results due to failed paper fetch

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Abstract: Failed to fetch summary for 2409.07412: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2409.07412&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without paper content

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Abstract: Failed to fetch summary for 2411.03387: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2411.03387&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

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Abstract: Failed to fetch summary for 2412.12213: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2412.12213&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

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Abstract: Failed to fetch summary for 2502.01226: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2502.01226&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content. The error prevents retrieval of any technical details.

Result: Cannot determine results without access to paper content. The paper summary was not successfully fetched.

Conclusion: Cannot draw conclusions about the paper’s content due to technical limitations in accessing the summary.

Abstract: Failed to fetch summary for 2502.03264: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2502.03264&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content

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Abstract: Failed to fetch summary for 2502.12981: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2502.12981&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: N/A - No paper content available for analysis

Result: HTTP 429 error from arXiv API prevents paper analysis

Conclusion: Cannot provide paper analysis due to access limitations; need alternative method to obtain paper content

Abstract: Failed to fetch summary for 2505.18017: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2505.18017&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access restrictions

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Abstract: Failed to fetch summary for 2509.00639: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2509.00639&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable due to API rate limiting.

Result: Cannot determine results as paper content is unavailable due to API rate limiting.

Conclusion: Cannot draw conclusions as paper content is unavailable due to API rate limiting.

Abstract: Failed to fetch summary for 2509.06322: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2509.06322&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access restrictions

Result: Unable to determine results due to access restrictions

Conclusion: Unable to draw conclusions due to access restrictions

Abstract: Failed to fetch summary for 2510.04579: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.04579&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: N/A - Paper content unavailable due to HTTP 429 error when attempting to fetch from arXiv

Result: N/A - No results available due to failed API request

Conclusion: Unable to analyze paper due to technical limitations in accessing the content

Abstract: Failed to fetch summary for 2510.08724: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.08724&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as abstract retrieval failed

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Abstract: Failed to fetch summary for 2512.04034: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.04034&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable due to API rate limiting

Result: Cannot determine results as paper content is unavailable due to API rate limiting

Conclusion: Cannot determine conclusion as paper content is unavailable due to API rate limiting

Abstract: Failed to fetch summary for 2602.18196: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.18196&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access error

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Abstract: Failed to fetch summary for 2602.19912: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.19912&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content

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Abstract: Failed to fetch summary for 2602.21550: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.21550&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed paper fetch

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Abstract: Failed to fetch summary for 2603.01204: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.01204&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access error

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Abstract: Failed to fetch summary for 2603.01470: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.01470&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: No method information available - paper content inaccessible

Result: No results available - paper summary fetch failed

Conclusion: Cannot analyze paper due to technical limitations in accessing content

Abstract: Failed to fetch summary for 2603.06605: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.06605&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to the paper content.

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Abstract: Failed to fetch summary for 2603.09427: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.09427&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot analyze method without access to paper content

Result: No results available due to technical access issues

Conclusion: Cannot provide analysis due to API rate limiting preventing paper retrieval

Abstract: Failed to fetch summary for 2408.02839: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2408.02839&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed API request

Result: Unable to determine results due to failed API request

Conclusion: Unable to analyze paper due to technical error in fetching content

Abstract: Failed to fetch summary for 2504.11258: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2504.11258&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable due to HTTP 429 error from arXiv API.

Result: Cannot determine results as paper content is unavailable due to HTTP 429 error from arXiv API.

Conclusion: Cannot draw conclusions as paper content is unavailable due to HTTP 429 error from arXiv API.

Abstract: Failed to fetch summary for 2505.03649: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2505.03649&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content.

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Abstract: Failed to fetch summary for 2509.02337: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2509.02337&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

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Abstract: Failed to fetch summary for 2510.05440: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.05440&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to the paper content.

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Abstract: Failed to fetch summary for 2512.06210: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.06210&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as abstract is unavailable

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Conclusion: Cannot draw conclusions about paper content due to data unavailability

Abstract: Failed to fetch summary for 2512.20058: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.20058&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

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Abstract: Failed to fetch summary for 2601.03946: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.03946&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

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Abstract: Failed to fetch summary for 2602.14677: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.14677&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to access error

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Abstract: Failed to fetch summary for 2602.14928: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.14928&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

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Conclusion: Cannot draw conclusions about paper content due to access limitations

Abstract: Failed to fetch summary for 2602.21468: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.21468&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

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Abstract: Failed to fetch summary for 2602.23665: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.23665&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to paper content

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Abstract: Failed to fetch summary for 2603.06506: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.06506&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed paper fetch

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Abstract: Failed to fetch summary for 2603.07705: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.07705&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

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Abstract: Failed to fetch summary for 2603.07977: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.07977&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content is unavailable

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Abstract: Failed to fetch summary for 2603.08771: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.08771&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to fetch failure

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Abstract: Failed to fetch summary for 2603.10457: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.10457&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Proposes VAS-CFA framework with multiple moral agents fine-tuned to represent distinct normative perspectives, fusing their outputs using combinatorial fusion analysis with rank- and score-based aggregation.

Result: Empirical evaluation shows VAS-CFA outperforms single-agent baselines and prior aggregation approaches on standard metrics, demonstrating multi-agent fusion provides robust value alignment.

Conclusion: Multi-agent fusion with combinatorial analysis offers an effective mechanism for advancing value alignment in LLMs by leveraging cognitive diversity across moral perspectives.

Abstract: Aligning large language models (LLMs) with human values is a central challenge for ensuring trustworthy and safe deployment. While existing methods such as Reinforcement Learning from Human Feedback (RLHF) and its variants have improved alignment, they often rely on a single evaluator or narrowly defined reward signals, limiting their ability to capture ethical pluralism. In this work, we propose the Value Alignment System using Combinatorial Fusion Analysis (VAS-CFA), a framework that operationalizes multi-agent fusion alignment. It instantiates multiple moral agents, each fine-tuned to represent a distinct normative perspective, and fuses their outputs using CFA with both rank- and score-based aggregation. This design leverages cognitive diversity, between agents, to mitigate conflicts and redundancies across multiple agents, producing responses that better reflect human values. Empirical evaluation demonstrates that VAS-CFA outperforms both single agent baselines and prior aggregation approaches on standard metrics, showing that multi-agent fusion provides a robust and effective mechanism for advancing value alignment in LLMs.