Method: Created ACAVCaps from ACAV100M using a multi-expert pipeline that analyzes audio from speech, music, and acoustic perspectives, then synthesizes these analyses into detailed descriptions using a large language model.

Result: Models pre-trained on ACAVCaps show substantially stronger generalization capabilities on various downstream tasks compared to those trained on other leading captioning datasets.

Conclusion: ACAVCaps addresses the data limitations in audio captioning and enables better training of large audio-language models for general audio understanding.

Abstract: General audio understanding is a fundamental goal for large audio-language models, with audio captioning serving as a cornerstone task for their development. However, progress in this domain is hindered by existing datasets, which lack the scale and descriptive granularity required to train truly versatile models. To address this gap, we introduce ACAVCaps, a new large-scale, fine-grained, and multi-faceted audio captioning dataset. Derived from the ACAV100M collection, ACAVCaps is constructed using a multi-expert pipeline that analyzes audio from diverse perspectives-including speech, music, and acoustic properties-which are then synthesized into rich, detailed descriptions by a large language model. Experimental results demonstrate that models pre-trained on ACAVCaps exhibit substantially stronger generalization capabilities on various downstream tasks compared to those trained on other leading captioning datasets. The dataset is available at https://github.com/xiaomi-research/acavcaps.

Method: Uses DiT-based framework with separate LoRA modules for identity and audio timbre control via self-attention layers. Introduces contrastive learning alongside flow matching to enhance preservation of identity/timbre. Trained on large-scale audio-visual human dataset.

Result: Extensive experiments show OmniCustom outperforms existing methods in generating audio-video content with consistent identity and timbre fidelity.

Conclusion: Proposes a novel sync audio-video customization task and presents OmniCustom as an effective solution for joint audio-video generation with identity and timbre control.

Abstract: Existing mainstream video customization methods focus on generating identity-consistent videos based on given reference images and textual prompts. Benefiting from the rapid advancement of joint audio-video generation, this paper proposes a more compelling new task: sync audio-video customization, which aims to synchronously customize both video identity and audio timbre. Specifically, given a reference image $I^{r}$ and a reference audio $A^{r}$, this novel task requires generating videos that maintain the identity of the reference image while imitating the timbre of the reference audio, with spoken content freely specifiable through user-provided textual prompts. To this end, we propose OmniCustom, a powerful DiT-based audio-video customization framework that can synthesize a video following reference image identity, audio timbre, and text prompts all at once in a zero-shot manner. Our framework is built on three key contributions. First, identity and audio timbre control are achieved through separate reference identity and audio LoRA modules that operate through self-attention layers within the base audio-video generation model. Second, we introduce a contrastive learning objective alongside the standard flow matching objective. It uses predicted flows conditioned on reference inputs as positive examples and those without reference conditions as negative examples, thereby enhancing the model ability to preserve identity and timbre. Third, we train OmniCustom on our constructed large-scale, high-quality audio-visual human dataset. Extensive experiments demonstrate that OmniCustom outperforms existing methods in generating audio-video content with consistent identity and timbre fidelity. Project page: https://omnicustom-project.github.io/page/.

Method: Introduces S3-Bench benchmark suite with dual-domain design (simulator + real-world streaming videos) and AMF-VLM model with memory folding (compresses long observations) and active exploration (outputs actions to acquire missing evidence).

Result: AMF-VLM improves by 8.8% on simulated split and 13.3% on real split of S3-Eval compared to models using identical training data, while maintaining competitive transferability to standard spatial benchmarks.

Conclusion: The work addresses crucial gaps in spatial VLMs for embodied agents by introducing streaming spatial QA with active exploration, enabling real-time reasoning and perception in dynamic environments.

Abstract: Spatial understanding is fundamental for embodied agents, yet most spatial VLMs and benchmarks remain offline-evaluating post-hoc QA over pre-recorded inputs and overlooking two crucial deployment-critical requirements: long-horizon streaming inference and active perception when the current view is insufficient. To address this gap, we introduce S3-Bench, a benchmark suite for streaming spatial question answering with active exploration, where queries are temporally grounded to specific timestamps and must be answered using only observations available up to that moment. S3-Bench adopts a dual-domain design, combining a scalable simulator with controllable trajectories and exploration actions, and real-world streaming videos that capture practical sensing artifacts for rigorous generalization evaluation. Overall, it spans 10K+ scenes and 26K+ trajectories, with dedicated training (S3-Train) and evaluation (S3-Eval) splits. We further propose AMF-VLM, which supports streaming spatial reasoning under bounded computing via (i) memory folding, which compresses long-horizon observations into compact structured memory, and (ii) active exploration, which outputs explicit actions (e.g. move/rotate/scan) to acquire missing evidence before answering. Extensive experiments demonstrate that, compared to models using identical training data, our approach yields improvements of 8.8% and 13.3% on the simulated and real splits of S3-Eval, respectively, while maintaining competitive transferability to standard spatial benchmarks.

Method: Two-phase design: Monte Carlo simulation to identify optimal CAT configurations, then empirical evaluation of 38 LLMs using human-calibrated medical item bank with adaptive testing that dynamically selects items based on real-time ability estimates and terminates when reaching reliability threshold.

Result: CAT-derived proficiency estimates achieved near-perfect correlation with full-bank estimates (r = 0.988) while using only 1.3% of items, reducing evaluation time from hours to minutes with substantial reductions in token usage and computational cost while preserving inter-model rankings.

Conclusion: Establishes a psychometric framework for rapid, low-cost benchmarking of medical knowledge in LLMs, serving as a standardized pre-screening and monitoring tool (not a substitute for clinical validation).

Abstract: The rapid proliferation of large language models (LLMs) in healthcare creates an urgent need for scalable and psychometrically sound evaluation methods. Conventional static benchmarks are costly to administer repeatedly, vulnerable to data contamination, and lack calibrated measurement properties for fine-grained performance tracking. We propose and validate a computerized adaptive testing (CAT) framework grounded in item response theory (IRT) for efficient assessment of standardized medical knowledge in LLMs. The study comprises a two-phase design: a Monte Carlo simulation to identify optimal CAT configurations and an empirical evaluation of 38 LLMs using a human-calibrated medical item bank. Each model completed both the full item bank and an adaptive test that dynamically selected items based on real-time ability estimates and terminated upon reaching a predefined reliability threshold (standard error <= 0.3). Results show that CAT-derived proficiency estimates achieved a near-perfect correlation with full-bank estimates (r = 0.988) while using only 1.3 percent of the items. Evaluation time was reduced from several hours to minutes per model, with substantial reductions in token usage and computational cost, while preserving inter-model performance rankings. This work establishes a psychometric framework for rapid, low-cost benchmarking of foundational medical knowledge in LLMs. The proposed adaptive methodology is intended as a standardized pre-screening and continuous monitoring tool and is not a substitute for real-world clinical validation or safety-oriented prospective studies.

Method: Design of a production RAG pipeline component that processes documents up to 32K tokens with adaptive inference strategies to balance response time and verification coverage

Result: Full-context verification substantially improves detection of unsupported responses compared with truncated validation, with practical deployment in real-time systems

Conclusion: Provides practical guidance for building reliable large-scale RAG applications, highlighting when long-context verification is necessary and how latency budgets shape model design

Abstract: Retrieval-augmented generation (RAG) is increasingly deployed in enterprise search and document-centric assistants, where responses must be grounded in long and complex source materials. In practice, verifying that generated answers faithfully reflect retrieved documents is difficult: large language models can check long contexts but are too slow and costly for interactive services, while lightweight classifiers operate within strict context limits and frequently miss evidence outside truncated passages. We present the design of a real-time verification component integrated into a production RAG pipeline that enables full-document grounding under latency constraints. The system processes documents up to 32K tokens and employs adaptive inference strategies to balance response time and verification coverage across workloads. We describe the architectural decisions, operational trade-offs, and evaluation methodology used to deploy the verifier, and show that full-context verification substantially improves detection of unsupported responses compared with truncated validation. Our experience highlights when long-context verification is necessary, why chunk-based checking often fails in real documents, and how latency budgets shape model design. These findings provide practical guidance for practitioners building reliable large-scale retrieval-augmented applications. (Model, benchmark, and code: https://huggingface.co/llm-semantic-router)

Method: Developed TVD (Task, Validator, Data) framework to trigger ISC through domain tasks where harmful content generation is the only valid completion. Created ISC-Bench with 53 scenarios across 8 professional disciplines and evaluated on JailbreakBench.

Result: Three representative scenarios yielded worst-case safety failure rates averaging 95.3% across four frontier LLMs (including GPT-5.2 and Claude Sonnet 4.5), substantially exceeding standard jailbreak attacks. Frontier models were more vulnerable than earlier LLMs.

Conclusion: Frontier LLMs retain inherently unsafe internal capabilities despite alignment efforts; their advanced capabilities become liabilities when tasks involve harmful content. This creates a growing attack surface as new dual-use tools emerge, requiring caution in high-stakes deployments.

Abstract: This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): under certain task conditions, models enter a state in which they continuously generate harmful content while executing otherwise benign tasks. We introduce TVD (Task, Validator, Data), a framework that triggers ISC through domain tasks where generating harmful content is the only valid completion, and construct ISC-Bench containing 53 scenarios across 8 professional disciplines. Evaluated on JailbreakBench, three representative scenarios yield worst-case safety failure rates averaging 95.3% across four frontier LLMs (including GPT-5.2 and Claude Sonnet 4.5), substantially exceeding standard jailbreak attacks. Frontier models are more vulnerable than earlier LLMs: the very capabilities that enable complex task execution become liabilities when tasks intrinsically involve harmful content. This reveals a growing attack surface: almost every professional domain uses tools that process sensitive data, and each new dual-use tool automatically expands this vulnerability–even without any deliberate attack. Despite substantial alignment efforts, frontier LLMs retain inherently unsafe internal capabilities: alignment reshapes observable outputs but does not eliminate the underlying risk profile. These findings underscore the need for caution when deploying LLMs in high-stakes settings. Source code: https://github.com/wuyoscar/ISC-Bench

Method: Adapted two evaluation tasks from human studies: 1) Director Task assessing VPT in referential communication paradigm, and 2) Rotating Figure Task probing perspective-taking across angular disparities.

Result: Across both tasks, MLMs show pronounced deficits in Level 2 VPT, which requires inhibiting one’s own perspective to adopt another’s viewpoint.

Conclusion: Current MLMs have critical limitations in representing and reasoning about alternative perspectives, with important implications for their use in collaborative contexts.

Abstract: As multimodal language models (MLMs) are increasingly used in social and collaborative settings, it is crucial to evaluate their perspective-taking abilities. Existing benchmarks largely rely on text-based vignettes or static scene understanding, leaving visuospatial perspective-taking (VPT) underexplored. We adapt two evaluation tasks from human studies: the Director Task, assessing VPT in a referential communication paradigm, and the Rotating Figure Task, probing perspective-taking across angular disparities. Across tasks, MLMs show pronounced deficits in Level 2 VPT, which requires inhibiting one’s own perspective to adopt another’s. These results expose critical limitations in current MLMs’ ability to represent and reason about alternative perspectives, with implications for their use in collaborative contexts.

Method: Created DISCO evaluation suite to separately test OCR pipelines and vision-language models on parsing and question answering tasks across diverse document types including handwritten text, multilingual scripts, medical forms, infographics, and multi-page documents.

Result: Performance varies substantially across tasks and document characteristics: OCR pipelines better for handwriting and long/multi-page documents, VLMs better for multilingual text and visually rich layouts. Task-aware prompting has mixed effects.

Conclusion: Need complexity-aware approach selection for document processing; empirical guidance provided for selecting strategies based on document structure and reasoning demands.

Abstract: Document intelligence requires accurate text extraction and reliable reasoning over document content. We introduce \textbf{DISCO}, a \emph{Document Intelligence Suite for COmparative Evaluation}, that evaluates optical character recognition (OCR) pipelines and vision-language models (VLMs) separately on parsing and question answering across diverse document types, including handwritten text, multilingual scripts, medical forms, infographics, and multi-page documents. Our evaluation shows that performance varies substantially across tasks and document characteristics, underscoring the need for complexity-aware approach selection. OCR pipelines are generally more reliable for handwriting and for long or multi-page documents, where explicit text grounding supports text-heavy reasoning, while VLMs perform better on multilingual text and visually rich layouts. Task-aware prompting yields mixed effects, improving performance on some document types while degrading it on others. These findings provide empirical guidance for selecting document processing strategies based on document structure and reasoning demands.

Method: Uses hybrid weighted k-shortest, beam, and constrained random-walk strategies to enumerate bounded-length candidate paths. Learns a differentiable path scorer with contrastive path encoder and lightweight verifier. Injects path latents into LLM via cross-attention. Operates within Neural-Socratic Graph Dialogue loop where LLM diagnostic messages trigger graph edits or seed expansions for adaptive retrieval.

Result: Demonstrates consistent improvements in answer accuracy, evidence coverage, and end-to-end efficiency compared to strong graph- and LLM-based baselines on standard multi-hop KGQA benchmarks. Provides analysis of trade-offs between semantic weighting, verifier filtering, and iterative updates.

Conclusion: S-Path-RAG provides a token-efficient, topology-aware retrieval mechanism with interpretable path-level traces, offering practical deployment recommendations for constrained compute and token budgets while improving multi-hop reasoning over knowledge graphs.

Abstract: We present S-Path-RAG, a semantic-aware shortest-path Retrieval-Augmented Generation framework designed to improve multi-hop question answering over large knowledge graphs. S-Path-RAG departs from one-shot, text-heavy retrieval by enumerating bounded-length, semantically weighted candidate paths using a hybrid weighted $k$-shortest, beam, and constrained random-walk strategy, learning a differentiable path scorer together with a contrastive path encoder and lightweight verifier, and injecting a compact soft mixture of selected path latents into a language model via cross-attention. The system runs inside an iterative Neural-Socratic Graph Dialogue loop in which concise diagnostic messages produced by the language model are mapped to targeted graph edits or seed expansions, enabling adaptive retrieval when the model expresses uncertainty. This combination yields a retrieval mechanism that is both token-efficient and topology-aware while preserving interpretable path-level traces for diagnostics and intervention. We validate S-Path-RAG on standard multi-hop KGQA benchmarks and through ablations and diagnostic analyses. The results demonstrate consistent improvements in answer accuracy, evidence coverage, and end-to-end efficiency compared to strong graph- and LLM-based baselines. We further analyze trade-offs between semantic weighting, verifier filtering, and iterative updates, and report practical recommendations for deployment under constrained compute and token budgets.

Method: Developed Berta as open-source modular scribe platform combining automatic speech recognition with large language models, deployed within Alberta Health Services integrated with their Snowflake AI Data Cloud infrastructure while retaining all clinical data within secure environment.

Result: 198 emergency physicians used system over 8 months across 105 facilities, generating 22,148 clinical sessions and 2800+ hours of audio; usage grew from 680 to 5530 monthly sessions; operating costs averaged <$30 per physician per month (70-95% reduction vs commercial); approved for expansion to 850 physicians.

Conclusion: First provincial-scale deployment of AI scribe integrated with existing health system infrastructure; open-source release provides reproducible, cost-effective alternative supporting data sovereignty and informed evaluation of AI documentation technology.

Abstract: Commercial AI scribes cost $99-600 per physician per month, operate as opaque systems, and do not return data to institutional infrastructure, limiting organizational control over data governance, quality improvement, and clinical workflows. We developed Berta, an open-source modular scribe platform for AI-enabled clinical documentation, and deployed a customized implementation within Alberta Health Services (AHS) integrated with their existing Snowflake AI Data Cloud infrastructure. The system combines automatic speech recognition with large language models while retaining all clinical data within the secure AHS environment. During eight months (November 2024 to July 2025), 198 emergency physicians used the system in 105 urban and rural facilities, generating 22148 clinical sessions and more than 2800 hours of audio. The use grew from 680 to 5530 monthly sessions. Operating costs averaged less than $30 per physician per month, a 70-95% reduction compared to commercial alternatives. AHS has since approved expansion to 850 physicians. This is the first provincial-scale deployment of an AI scribe integrated with existing health system infrastructure. By releasing Berta as open source, we provide a reproducible, cost-effective alternative that health systems can adapt to their own secure environments, supporting data sovereignty and informed evaluation of AI documentation technology.

Method: The system creates a directed acyclic graph (DAG) from documents, uses web agents to assign credibility scores to nodes and edges, and provides final scores by interpreting the paper’s argumentative structure.

Result: Implemented system and reported results on a dataset of 104 research papers, demonstrating the approach’s feasibility for research verification.

Conclusion: Plato’s Cave provides a human-centered approach to research verification that can help address the challenges of information overload and credibility assessment in academic publishing.

Abstract: The growing publication rate of research papers has created an urgent need for better ways to fact-check information, assess writing quality, and identify unverifiable claims. We present Plato’s Cave as an open-source, human-centered research verification system that (i) creates a directed acyclic graph (DAG) from a document, (ii) leverages web agents to assign credibility scores to nodes and edges from the DAG, and (iii) gives a final score by interpreting and evaluating the paper’s argumentative structure. We report the system implementation and results on a collected dataset of 104 research papers.

Method: Three innovations: 1) Adaptive probing that generates follow-up questions based on concepts the model actually mentions, 2) On-demand fact verification from authoritative sources, 3) Survival statistics with constant sample sizes at every depth level. Framework can be deployed on any knowledge domain with publicly verifiable facts without requiring pre-constructed test sets.

Result: Empirical validation across four diverse domains (Medicine, Constitutional Law, Ancient Rome, Quantum Computing) with five frontier models reveals depth-dependent performance variation hidden by standard benchmarks. Expected Valid Depth (EVD) ranges from 3.45 to 7.55 across model-domain combinations. Model rankings vary substantially by domain with no single model dominating all areas. Cost-performance analysis shows expensive models don’t always achieve deeper knowledge.

Conclusion: DepthCharge provides comparative evaluation tool for measuring LLM knowledge depth across domains, revealing that domain-specific evaluation is more informative than aggregate benchmarks for model selection in professional applications.

Abstract: Large Language Models appear competent when answering general questions but often fail when pushed into domain-specific details. No existing methodology provides an out-of-the-box solution for measuring how deeply LLMs can sustain accurate responses under adaptive follow-up questioning across arbitrary domains. We present DepthCharge, a domain-agnostic framework that measures knowledge depth through three innovations: adaptive probing that generates follow-up questions based on concepts the model actually mentions, on-demand fact verification from authoritative sources, and survival statistics with constant sample sizes at every depth level. The framework can be deployed on any knowledge domain with publicly verifiable facts, without requiring pre-constructed test sets or domain-specific expertise. DepthCharge results are relative to the evaluator model used for answer checking, making the framework a tool for comparative evaluation rather than absolute accuracy certification. Empirical validation across four diverse domains (Medicine, Constitutional Law, Ancient Rome, and Quantum Computing) with five frontier models demonstrates that DepthCharge reveals depth-dependent performance variation hidden by standard benchmarks. Expected Valid Depth (EVD) ranges from 3.45 to 7.55 across model-domain combinations, and model rankings vary substantially by domain, with no single model dominating all areas. Cost-performance analysis further reveals that expensive models do not always achieve deeper knowledge, suggesting that domain-specific evaluation is more informative than aggregate benchmarks for model selection in professional applications.

Method: Fine-tuned Llama 3-70B on synthetic training data derived from electronic health records using EHR-grounded templates and coding policies. Created privacy-preserving synthetic pairs of clinical notes and gold codes, then evaluated exact-code prediction for both ICD-10-CM and CPT coding systems.

Result: Zero-shot baseline achieved F1 score of 0.18 for exact code match. After fine-tuning on synthetic corpus, exact-match F1 exceeded 0.70, representing large absolute gains. Performance remained high on complex categories requiring multi-step clinical reasoning (Advanced Illness, Frailty classes), and the model retained medical comprehension capabilities.

Conclusion: Synthetic, policy-aware data can efficiently teach general-purpose LLMs to support precise medical coding without exposing protected health information. This offers a practical path for training coding agents safely and iteratively on specific tasks representing real-world populations.

Abstract: Improving the accuracy and reliability of medical coding reduces clinician burnout and supports revenue cycle processes, freeing providers to focus more on patient care. However, automating the assignment of ICD-10-CM and CPT codes from clinical documentation remains a challenge due to heterogeneous records, nuanced coding guidelines, and long-tail distributions. Large language models have been proposed to help or automate specific medical coding tasks. However, foundation models are not explicitly trained for medical coding and zero-shot coding has yielded poor results. We investigate whether a modern open-weight foundation model can be adapted for an expert-level medical coding task using privacy-preserving synthetic training data derived from electronic health records. We fine-tune Llama 3-70B on pairs of clinical notes and gold codes generated from EHR-grounded templates and coding policies, then evaluate exact-code prediction for ICD-10-CM and CPT. A zero-shot baseline with the unadapted model achieved an F1 score of 0.18 for exact code match. After fine-tuning on the synthetic corpus, exact-match F1 exceeded 0.70, representing a large absolute gain across both code systems. Notably, performance remained high on complex categories that often require multi-step clinical reasoning and code composition, including Advanced Illness and Frailty classes, and the model retained its performance on medical comprehension tasks. These results indicate that synthetic, policy-aware data can efficiently teach a general-purpose large language model to support precise medical coding without exposing protected health information. The approach offers a practical path for training coding agents safely and iteratively on specific tasks that represent real-world populations.

Method: Used spontaneous speech from Diachronic Electronic Corpus of Tyneside English (DECTE), evaluated commercial ASR system output, analyzed 3,000+ transcription errors by linguistic domain and social variables (gender, age, socioeconomic status), and conducted acoustic case study of vowel features.

Result: Phonological variation accounts for majority of errors, with dialect-specific features (vowel quality, glottalisation), local vocabulary, and non-standard grammatical forms causing recurrent failures. Error rates vary by social groups - higher for men and speakers at age extremes.

Conclusion: ASR errors are socially patterned, not random. Sociolinguistic expertise should be incorporated into speech technology evaluation/development. More equitable ASR requires explicit attention to dialectal variation and community-based speech data.

Abstract: Automatic Speech Recognition (ASR) systems are widely used in everyday communication, education, healthcare, and industry, yet their performance remains uneven across speakers, particularly when dialectal variation diverges from the mainstream accents represented in training data. This study investigates ASR bias through a sociolinguistic analysis of Newcastle English, a regional variety of North-East England that has been shown to challenge current speech recognition technologies. Using spontaneous speech from the Diachronic Electronic Corpus of Tyneside English (DECTE), we evaluate the output of a state-of-the-art commercial ASR system and conduct a fine-grained analysis of more than 3,000 transcription errors. Errors are classified by linguistic domain and examined in relation to social variables including gender, age, and socioeconomic status. In addition, an acoustic case study of selected vowel features demonstrates how gradient phonetic variation contributes directly to misrecognition. The results show that phonological variation accounts for the majority of errors, with recurrent failures linked to dialect-specific features like vowel quality and glottalisation, as well as local vocabulary and non-standard grammatical forms. Error rates also vary across social groups, with higher error frequencies observed for men and for speakers at the extremes of the age spectrum. These findings indicate that ASR errors are not random but socially patterned and can be explained from a sociolinguistic perspective. Thus, the study demonstrates the importance of incorporating sociolinguistic expertise into the evaluation and development of speech technologies and argues that more equitable ASR systems require explicit attention to dialectal variation and community-based speech data.

Method: Memory Sparse Attention (MSA) uses scalable sparse attention and document-wise RoPE to achieve linear complexity. Includes KV cache compression with Memory Parallel for efficient 100M-token inference on 2xA800 GPUs, and Memory Interleaving for multi-hop reasoning across scattered memory segments.

Result: MSA shows less than 9% degradation when scaling from 16K to 100M tokens, significantly outperforms frontier LLMs, state-of-the-art RAG systems, and leading memory agents in long-context benchmarks, enabling 100M-token inference on limited hardware.

Conclusion: MSA provides a scalable foundation for lifetime-scale memory in general-purpose models by decoupling memory capacity from reasoning, addressing key bottlenecks in long-context processing.

Abstract: Long-term memory is a cornerstone of human intelligence. Enabling AI to process lifetime-scale information remains a long-standing pursuit in the field. Due to the constraints of full-attention architectures, the effective context length of large language models (LLMs) is typically limited to 1M tokens. Existing approaches, such as hybrid linear attention, fixed-size memory states (e.g., RNNs), and external storage methods like RAG or agent systems, attempt to extend this limit. However, they often suffer from severe precision degradation and rapidly increasing latency as context length grows, an inability to dynamically modify memory content, or a lack of end-to-end optimization. These bottlenecks impede complex scenarios like large-corpus summarization, Digital Twins, and long-history agent reasoning, while limiting memory capacity and slowing inference. We present Memory Sparse Attention (MSA), an end-to-end trainable, efficient, and massively scalable memory model framework. Through core innovations including scalable sparse attention and document-wise RoPE, MSA achieves linear complexity in both training and inference while maintaining exceptional stability, exhibiting less than 9% degradation when scaling from 16K to 100M tokens. Furthermore, KV cache compression, combined with Memory Parallel, enables 100M-token inference on 2xA800 GPUs. We also propose Memory Interleaving to facilitate complex multi-hop reasoning across scattered memory segments. MSA significantly surpasses frontier LLMs, state-of-the-art RAG systems, and leading memory agents in long-context benchmarks. These results demonstrate that by decoupling memory capacity from reasoning, MSA provides a scalable foundation to endow general-purpose models with intrinsic, lifetime-scale memory.

Method: Systematic evaluation of 8 frontier reasoning language models across 9 diverse tasks covering competition math, science QA, code generation, and multi-domain reasoning, analyzing actual inference costs versus listed prices.

Result: Found pricing reversal phenomenon in 21.8% of model-pair comparisons, with reversal magnitude up to 28x; identified thinking token consumption heterogeneity as root cause (up to 900% variation); removing thinking token costs reduces reversals by 70%; per-query cost prediction is fundamentally difficult due to up to 9.7x thinking token variation.

Conclusion: Listed API pricing is an unreliable proxy for actual inference costs, calling for cost-aware model selection and transparent per-request cost monitoring rather than relying solely on listed prices.

Abstract: Developers and consumers increasingly choose reasoning language models (RLMs) based on their listed API prices. However, how accurately do these prices reflect actual inference costs? We conduct the first systematic study of this question, evaluating 8 frontier RLMs across 9 diverse tasks covering competition math, science QA, code generation, and multi-domain reasoning. We uncover the pricing reversal phenomenon: in 21.8% of model-pair comparisons, the model with a lower listed price actually incurs a higher total cost, with reversal magnitude reaching up to 28x. For example, Gemini 3 Flash’s listed price is 78% cheaper than GPT-5.2’s, yet its actual cost across all tasks is 22% higher. We trace the root cause to vast heterogeneity in thinking token consumption: on the same query, one model may use 900% more thinking tokens than another. In fact, removing thinking token costs reduces ranking reversals by 70% and raises the rank correlation (Kendall’s $τ$ ) between price and cost rankings from 0.563 to 0.873. We further show that per-query cost prediction is fundamentally difficult: repeated runs of the same query yield thinking token variation up to 9.7x, establishing an irreducible noise floor for any predictor. Our findings demonstrate that listed API pricing is an unreliable proxy for actual cost, calling for cost-aware model selection and transparent per-request cost monitoring.

Method: Reframe instruction-following clustering as generative task, train large reasoning models as autonomous clustering agents using reasoning-driven training pipeline. Models interpret high-level clustering instructions and infer corresponding latent groupings.

Result: Approach consistently outperforms strong embedding-based methods and LRM baselines across diverse datasets and clustering scenarios in ReasonCluster benchmark (28 tasks spanning daily dialogue, legal cases, financial reports).

Conclusion: Explicit reasoning fosters more faithful and interpretable instruction-based clustering, demonstrating superiority of reasoning-driven approach over traditional embedding methods for instruction-following clustering tasks.

Abstract: General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions. In contrast, instruction-tuned embedders can align embeddings with textual instructions yet cannot autonomously infer latent corpus structures, such as determining the optimal number of clusters. To address both limitations, we reframe instruction-following clustering as a generative task and train large reasoning models (LRMs) as autonomous clustering agents. Our reasoning-driven training pipeline enables LRMs to interpret high-level clustering instructions and then infer the corresponding latent groupings. To evaluate this paradigm, we introduce ReasonCluster, a comprehensive benchmark comprising 28 diverse tasks spanning daily dialogue, legal cases, and financial reports. Experiments across diverse datasets and clustering scenarios show that our approach consistently outperforms strong embedding-based methods and LRM baselines, demonstrating that explicit reasoning fosters more faithful and interpretable instruction-based clustering.

Method: Created 400 test cases via scene-by-scene data synthesis with expert editing, averaging 22 rounds per case (max 52). Uses LLM-as-judge evaluation with instance-level rubrics and atomic test points validated against expert annotations.

Result: Tested 17 frontier models, all underperformed with overall accuracy below 60% (best model: 59.75%). Human-LLM agreement reached 91.94% for evaluation protocol.

Conclusion: MedMT-Bench fills a critical gap in medical AI evaluation and can drive research toward safer, more reliable medical LLMs by testing practical requirements missing in current benchmarks.

Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities across various specialist domains and have been integrated into high-stakes areas such as medicine. However, as existing medical-related benchmarks rarely stress-test the long-context memory, interference robustness, and safety defense required in practice. To bridge this gap, we introduce MedMT-Bench, a challenging medical multi-turn instruction following benchmark that simulates the entire diagnosis and treatment process. We construct the benchmark via scene-by-scene data synthesis refined by manual expert editing, yielding 400 test cases that are highly consistent with real-world application scenarios. Each test case has an average of 22 rounds (maximum of 52 rounds), covering 5 types of difficult instruction following issues. For evaluation, we propose an LLM-as-judge protocol with instance-level rubrics and atomic test points, validated against expert annotations with a human-LLM agreement of 91.94%. We test 17 frontier models, all of which underperform on MedMT-Bench (overall accuracy below 60.00%), with the best model reaching 59.75%. MedMT-Bench can be an essential tool for driving future research towards safer and more reliable medical AI. The benchmark is available in https://openreview.net/attachment?id=aKyBCsPOHB&name=supplementary_material

Method: A 5-stage framework built on Tianyi that targets 5 master TCM physicians, curates multi-source materials, and trains a single model to internalize all five knowledge systems across seven clinical tasks including diagnosis, treatment selection, prescription generation, and clinical advice.

Result: Implemented on Qwen2.5-1.5B-Base, Med-Shicheng runs on resource-constrained GPUs while achieving performance comparable to DeepSeek-R1 and GPT-5. The study also reveals that LLM-as-a-judge tracks overall trends but shows bias on fine-grained individualized distinctions.

Conclusion: The framework successfully enables systematic learning and transfer of physicians’ expertise, but highlights the need for physician involvement in evaluation when ground truth is unavailable and for domain-adapted judge models.

Abstract: Medicine is an empirical discipline refined through long-term observation and the messy, high-variance reality of clinical practice. Physicians build diagnostic and therapeutic competence through repeated cycles of application, reflection, and improvement, forming individualized methodologies. Yet outcomes vary widely, and master physicians’ knowledge systems are slow to develop and hard to transmit at scale, contributing to the scarcity of high-quality clinical expertise. To address this, we propose Med-Shicheng, a general framework that enables large language models to systematically learn and transfer distinguished physicians’ diagnostic-and-therapeutic philosophy and case-dependent adaptation rules in a standardized way. Built on Tianyi, Med-Shicheng consists of five stages. We target five National Masters of Chinese Medicine or distinguished TCM physicians, curate multi-source materials, and train a single model to internalize all five knowledge systems across seven tasks, including etiology-pathogenesis analysis, syndrome diagnosis, treatment principle selection, prescription generation, prescription explanation, symptom evolution with regimen adjustment, and clinical advice. Implemented on Qwen2.5-1.5B-Base, Med-Shicheng runs on resource-constrained GPUs while achieving performance comparable to DeepSeek-R1 and GPT-5. We also examine the reliability of LLM-as-a-judge versus physician evaluation: automated judging tracks overall trends but shows bias on fine-grained individualized distinctions, highlighting the need for physician involvement when ground truth is unavailable and for domain-adapted judge models.

Method: Created Chitrakshara dataset series from Common Crawl covering 11 Indian languages: (1) Chitrakshara-IL - large-scale interleaved pretraining dataset with 193M images, 30B text tokens, and 50M multilingual documents; (2) Chitrakshara-Cap - 44M image-text pairs with 733M tokens. Detailed data collection pipeline including curation, filtering, and processing methodologies.

Result: Produced comprehensive multimodal datasets for Indian languages with quality and diversity analysis to assess representativeness across Indic languages. The datasets enable development of more culturally inclusive Vision-Language Models.

Conclusion: Chitrakshara addresses the gap in non-English multimodal datasets and provides resources for developing more representative VLMs for Indian languages, promoting cultural inclusivity in multimodal research.

Abstract: Multimodal research has predominantly focused on single-image reasoning, with limited exploration of multi-image scenarios. Recent models have sought to enhance multi-image understanding through large-scale pretraining on interleaved image-text datasets. However, most Vision-Language Models (VLMs) are trained primarily on English datasets, leading to inadequate representation of Indian languages. To address this gap, we introduce the Chitrakshara dataset series, covering 11 Indian languages sourced from Common Crawl. It comprises (1) Chitrakshara-IL, a large-scale interleaved pretraining dataset with 193M images, 30B text tokens, and 50M multilingual documents, and (2) Chitrakshara-Cap, which includes 44M image-text pairs with 733M tokens. This paper details the data collection pipeline, including curation, filtering, and processing methodologies. Additionally, we present a comprehensive quality and diversity analysis to assess the dataset’s representativeness across Indic languages and its potential for developing more culturally inclusive VLMs.

Method: Qworld generates question-specific evaluation criteria through recursive expansion trees. Given a question, it decomposes it into scenarios, perspectives, and fine-grained binary criteria using structured hierarchical and horizontal expansion to specify what high-quality answers must address.

Result: On HealthBench, Qworld covers 89% of expert-authored criteria and generates 79% novel criteria validated by human experts. Experts rate Qworld criteria higher in insight and granularity than prior methods. Applied to 11 frontier LLMs, Qworld reveals capability differences in dimensions like long-term impact, equity, error handling, and interdisciplinary reasoning that coarse rubrics miss.

Conclusion: By formulating criteria generation as structured coverage of question-implied evaluation axes, Qworld enables adaptive evaluation that responds to each question’s specific requirements rather than relying on fixed task-level criteria.

Abstract: Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question’s context. Binary scores and static rubrics fail to capture these context-dependent requirements. Existing methods define criteria at the dataset level or generate them in a single pass, which limits their ability to explore the evaluation space implied by each question. We introduce One-Question-One-World (Qworld), a method that generates question-specific evaluation criteria using a recursive expansion tree. Given a question, Qworld decomposes it into scenarios, perspectives, and fine-grained binary criteria through structured hierarchical and horizontal expansion. The resulting criteria specify what a high-quality answer must address for that question. On HealthBench, Qworld covers 89% of expert-authored criteria and generates 79% novel criteria validated by human experts. Experts rate Qworld criteria higher in insight and granularity than those produced by prior methods. When applied to 11 frontier LLMs on HealthBench and Humanity’s Last Exam, Qworld reveals capability differences in dimensions such as long-term impact, equity, error handling, and interdisciplinary reasoning that coarse rubrics do not distinguish. By formulating criteria generation as structured coverage of question-implied evaluation axes, Qworld enables evaluation that adapts to each question rather than relying on fixed task-level criteria.

Method: 1) Show that fine-tuning language models on text-only QA pairs can match or surpass 3D-LLMs on SQA3D, 2) Introduce Real-3DQA benchmark with filtered easy questions and structured taxonomy, 3) Propose 3D-reweighted training objective to guide models to rely more on 3D visual clues.

Result: Experiments confirm existing 3D-LLMs struggle with spatial relationships when simple cues are removed. The proposed 3D-reweighted training objective substantially enhances 3D-LLM performance in spatial reasoning tasks on the new benchmark.

Conclusion: The findings highlight the need for robust benchmarks and tailored training strategies to advance genuine 3D vision-language understanding, moving beyond textual shortcut exploitation to true 3D spatial reasoning.

Abstract: Recent 3D Large-Language Models (3D-LLMs) claim to understand 3D worlds, especially spatial relationships among objects. Yet, we find that simply fine-tuning a language model on text-only question-answer pairs can perform comparably or even surpass these methods on the SQA3D benchmark without using any 3D input. This indicates that the SQA3D benchmark may not be able to detect if the model exploits textual shortcuts rather than engages in 3D-aware reasoning. To address this issue, we introduce Real-3DQA, a more rigorous evaluation benchmark that filters out easy-to-guess questions and introduces a structured taxonomy to assess various aspects of 3D reasoning. Experiments on Real-3DQA confirm that existing 3D-LLMs struggle with spatial relationships once simple cues are removed. We further propose a 3D-reweighted training objective that guides model to rely more on 3D visual clues, substantially enhancing 3D-LLMs performance in spatial reasoning tasks. Our findings underscore the need for robust benchmarks and tailored training strategies to advance genuine 3D vision-language understanding. Project page: https://real-3dqa.github.io/.

Method: Constructs a multi-resolution manifold over SAE feature embeddings using hierarchical neighborhood embeddings. Enables progressive navigation from coarse concept clusters to fine-grained neighborhoods, supporting discovery, comparison, and relationship analysis among concepts.

Result: Demonstrated on SAE features from SmolLM2, revealing coherent high-level structure, meaningful subclusters, and distinctive rare concepts that are hard to identify with existing workflows.

Conclusion: Concept Explorer provides a scalable interactive system for post-hoc exploration of SAE features, enabling better discovery and analysis of interpretable concepts in LLMs.

Abstract: Sparse autoencoders (SAEs) trained on large language model activations output thousands of features that enable mapping to human-interpretable concepts. The current practice for analyzing these features primarily relies on inspecting top-activating examples, manually browsing individual features, or performing semantic search on interested concepts, which makes exploratory discovery of concepts difficult at scale. In this paper, we present Concept Explorer, a scalable interactive system for post-hoc exploration of SAE features that organizes concept explanations using hierarchical neighborhood embeddings. Our approach constructs a multi-resolution manifold over SAE feature embeddings and enables progressive navigation from coarse concept clusters to fine-grained neighborhoods, supporting discovery, comparison, and relationship analysis among concepts. We demonstrate the utility of Concept Explorer on SAE features extracted from SmolLM2, where it reveals coherent high-level structure, meaningful subclusters, and distinctive rare concepts that are hard to identify with existing workflows.

Method: Proposes a heterogeneous MAS framework with two key components: (1) Orchestrator Calibration identifies models with superior coordination and synthesis capabilities, and (2) Agent Self-Assessment allows tool agents to profile their own domain-specific strengths. The orchestrator dynamically activates the most compatible agents based on these profiles during inference.

Result: Outperforms individual models and existing MAS baselines across five mathematical reasoning and code generation benchmarks. Achieves 96.00% accuracy on AIME24 and 77.91% on LiveCodeBench, significantly improving over homogeneous role-play baselines (80.00% and 65.93%).

Conclusion: Team-of-Thoughts demonstrates that heterogeneous multi-agent systems with specialized model coordination can significantly outperform homogeneous approaches, effectively leveraging diverse model expertise through systematic orchestration and self-assessment mechanisms.

Abstract: Existing Multi-Agent Systems (MAS) typically rely on homogeneous model configurations, failing to exploit the diverse expertise inherent in different post-trained architectures. We propose Team-of-Thoughts, a heterogeneous MAS framework that treats diverse models as specialized tools within an orchestrator-driven paradigm. Team-of-Thoughts introduces two novel components: (1) Orchestrator Calibration, which identifies models with superior coordination and synthesis capabilities, and (2) Agent Self-Assessment, a protocol where tool agents profile their own domain-specific strengths to guide selection. At inference, the orchestrator dynamically activates the most compatible agents based on these profiles to maximize capability coverage. Across five mathematical reasoning and code generation benchmarks, Team-of-Thoughts consistently outperforms individual models and existing MAS baselines. Notably, on AIME24 and LiveCodeBench, Team-of-Thoughts achieves 96.00% and 77.91% accuracy, respectively, significantly improving over homogeneous role-play baselines (80.00% and 65.93%).

Method: Pre-registered six-arm randomized controlled trial with 358 Claude Sonnet 4.5 runs from 1,199 real orchestration instructions. Compared uncompressed control with three uniform retention rates (80%, 50%, 20%) and two structure-aware strategies (entropy-adaptive, recency-weighted). Measured total inference cost (input+output) and embedding-based response similarity.

Result: Moderate compression (r=0.5) reduced mean total cost by 27.9%. Aggressive compression (r=0.2) increased mean cost by 1.8% despite input reduction, with small output expansion (1.03x vs control). Recency-weighted compression achieved 23.5% savings. Moderate compression and recency-weighted compression formed the cost-similarity Pareto frontier.

Conclusion: “Compress more” is not a reliable production heuristic; output tokens must be considered as a first-class outcome when designing compression policies. Moderate compression and structure-aware strategies offer optimal cost-similarity trade-offs.

Abstract: The economics of prompt compression depend not only on reducing input tokens but on how compression changes output length, which is typically priced several times higher. We evaluate this in a pre-registered six-arm randomized controlled trial of prompt compression on production multi-agent task-orchestration, analyzing 358 successful Claude Sonnet 4.5 runs (59-61 per arm) drawn from a randomized corpus of 1,199 real orchestration instructions. We compare an uncompressed control with three uniform retention rates (r=0.8, 0.5, 0.2) and two structure-aware strategies (entropy-adaptive and recency-weighted), measuring total inference cost (input+output) and embedding-based response similarity. Moderate compression (r=0.5) reduced mean total cost by 27.9%, while aggressive compression (r=0.2) increased mean cost by 1.8% despite substantial input reduction, consistent with small mean output expansion (1.03x vs. control) and heavy-tailed uncertainty. Recency-weighted compression achieved 23.5% savings and, together with moderate compression, occupied the empirical cost-similarity Pareto frontier, whereas aggressive compression was dominated on both cost and similarity. These results show that “compress more” is not a reliable production heuristic and that output tokens must be treated as a first-class outcome when designing compression policies.

Method: Controlled replication and extension study across 5,400 API calls, three benchmarks, and multiple providers. Formalized instruction survival probability (Psi) metric to capture task-critical prompt segments after truncation. Introduced Compression Robustness Index (CRI) for cross-benchmark evaluation. Included direct NVML measurements from rented RunPod GPUs for energy analysis.

Result: Strong benchmark effect observed: DeepSeek shows severe output expansion on MBPP (56x) but lower expansion on HumanEval (5x), while GPT-4o-mini is more stable. Prompt structure, not provider identity alone, moderates compression effects. Single-benchmark assessments can be misleading. Token savings can overstate actual energy savings.

Conclusion: Benchmark-diverse testing and structure-aware compression policies are needed for reliable, energy-conscious LLM deployment. Compression evaluation must consider output dynamics and energy impacts beyond simple token reduction metrics.

Abstract: Prompt compression is often evaluated by input-token reduction, but its real deployment impact depends on how compression changes output length and total inference cost. We present a controlled replication and extension study of benchmark-dependent output dynamics under aggressive compression, covering 5,400 API calls across three benchmarks and multiple providers. To explain conflicting prior observations, we formalize instruction survival probability (Psi), a structural metric that captures whether task-critical prompt segments remain after truncation. Results show a strong benchmark effect: under r=0.3, DeepSeek exhibits severe output expansion on MBPP (56x, Psi approx 0.15) but substantially lower expansion on HumanEval (5x, Psi approx 0.72), while GPT-4o-mini is comparatively stable across benchmarks. This reconciles the apparent discrepancy between previously reported extreme explosion and lower replication effects by identifying prompt structure, not provider identity alone, as the primary moderator. We introduce the Compression Robustness Index (CRI) for cross-benchmark evaluation and show that single-benchmark assessments can produce misleading conclusions about compression safety and efficiency. To contextualize energy claims, we incorporate companion direct NVML measurements from rented RunPod GPUs and show that token savings can overstate joule savings. These findings motivate benchmark-diverse testing and structure-aware compression policies for reliable, energy-conscious LLM deployment.

Method: Conducted 28,421 API trials across three providers (OpenAI GPT-4o-mini, Anthropic Claude-3.5-Sonnet, DeepSeek-Chat), five benchmarks (HumanEval, MBPP, GSM8K, MATH, MMLU), and four compression ratios (1.0, 0.7, 0.5, 0.3). Used token-based energy proxy calibrated against local measurements and tracked quality with benchmark pass rates.

Result: Compression caused substantial quality loss (26.0% pass rate at baseline vs. 1.5% at r=0.7) and strongly provider-dependent energy effects. DeepSeek showed output expansion (21 to 798 tokens at r=0.3) with energy increases up to +2,140%, while GPT-4o-mini had mixed effects including some reduction at r=0.5.

Conclusion: Input-token reduction alone is not a reliable energy optimization strategy for production inference. Model selection and output-length control provide more consistent energy-quality tradeoffs than prompt compression.

Abstract: The rapid proliferation of Large Language Models has created an environmental paradox: the very technology that could help solve climate challenges is itself becoming a significant contributor to global carbon emissions. We test whether prompt compression improves inference energy efficiency in 28,421 successful API trials (28,428 planned) across three providers (OpenAI GPT-4o-mini, Anthropic Claude-3.5-Sonnet, and DeepSeek-Chat), five benchmarks (HumanEval, MBPP, GSM8K, MATH, MMLU), and four compression ratios (r in {1.0, 0.7, 0.5, 0.3}). Energy is estimated with a token-based proxy calibrated against local direct measurements, and quality is tracked with benchmark pass rates. Compression produced substantial quality loss (overall pass rate 26.0% at baseline vs. 1.5% at r=0.7) and strongly provider-dependent energy behavior. DeepSeek exhibited output expansion under compression (21 to 798 tokens at r=0.3), corresponding to energy increases up to +2,140%, while GPT-4o-mini showed mixed effects including a reduction at r=0.5. These results indicate that input-token reduction alone is not a reliable energy optimization strategy in production inference. For the evaluated settings, model selection and output-length control provided more consistent energy-quality tradeoffs than prompt compression.

Method: End-to-end multi-agent LLM system combined with CNN-based tumor segmentation; extractor agent identifies clinical variables from unstructured notes, scorer agent applies BT-RADS decision logic integrating extracted variables with volumetric measurements.

Result: System achieved 76.0% accuracy vs 57.5% for initial clinical assessments (+18.5 percentage points; P<.001); high sensitivity for context-dependent categories (BT-1b 100%, BT-1a 92.7%, BT-3a 87.5%) and high positive predictive value for BT-4 detection (92.9%).

Conclusion: Multi-agent LLM system achieved higher BT-RADS classification agreement with expert reference standard compared to initial clinical scoring, demonstrating potential for automated glioma MRI assessment.

Abstract: The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response assessment in patients with diffuse gliomas but requires complex integration of imaging trends, medication effects, and radiation timing. This study evaluates an end-to-end multi-agent large language model (LLM) and convolutional neural network (CNN) system for automated BT-RADS classification. A multi-agent LLM system combined with automated CNN-based tumor segmentation was retrospectively evaluated on 509 consecutive post-treatment glioma MRI examinations from a single high-volume center. An extractor agent identified clinical variables (steroid status, bevacizumab status, radiation date) from unstructured clinical notes, while a scorer agent applied BT-RADS decision logic integrating extracted variables with volumetric measurements. Expert reference standard classifications were established by an independent board-certified neuroradiologist. Of 509 examinations, 492 met inclusion criteria. The system achieved 374/492 (76.0%; 95% CI, 72.1%-79.6%) accuracy versus 283/492 (57.5%; 95% CI, 53.1%-61.8%) for initial clinical assessments (+18.5 percentage points; P<.001). Context-dependent categories showed high sensitivity (BT-1b 100%, BT-1a 92.7%, BT-3a 87.5%), while threshold-dependent categories showed moderate sensitivity (BT-3c 74.8%, BT-2 69.2%, BT-4 69.3%, BT-3b 57.1%). For BT-4, positive predictive value was 92.9%. The multi-agent LLM system achieved higher BT-RADS classification agreement with expert reference standard compared to initial clinical scoring, with high accuracy for context-dependent scores and high positive predictive value for BT-4 detection.

Method: Created Konkani-Instruct-100k synthetic instruction-tuning dataset using Gemini 3, evaluated leading open-weight architectures (Llama 3.1, Qwen2.5, Gemma 3) and proprietary models, and developed Konkani LLM through fine-tuning for regional nuances.

Result: Konkani LLM delivers consistent gains over base models in machine translation and is competitive with/surpasses proprietary baselines in several settings. Also developing Multi-Script Konkani Benchmark for cross-script evaluation.

Conclusion: The approach successfully addresses LLM performance gaps in low-resource languages through synthetic data generation and targeted fine-tuning, with potential applications for other under-resourced linguistic contexts.

Abstract: Large Language Models (LLMs) consistently under perform in low-resource linguistic contexts such as Konkani. This performance deficit stems from acute training data scarcity compounded by high script diversity across Devanagari, Romi and Kannada orthographies. To address this gap, we introduce Konkani-Instruct-100k, a comprehensive synthetic instruction-tuning dataset generated through Gemini 3. We establish rigorous baseline benchmarks by evaluating leading open-weights architectures including Llama 3.1, Qwen2.5 and Gemma 3 alongside proprietary closed-source models. Our primary contribution involves the development of Konkani LLM, a series of fine-tuned models optimized for regional nuances. Furthermore, we are developing the Multi-Script Konkani Benchmark to facilitate cross-script linguistic evaluation. In machine translation, Konkani LLM delivers consistent gains over the corresponding base models and is competitive with and in several settings surpasses proprietary baselines

Method: Used a controlled paradigm combining verifiable formatting constraints with benchmark tasks of increasing complexity. Tested across three model families with over 8,000 prompts. Examined different constraint types (terminal vs avoidance), implemented salience-enhanced formatting (explicit instruction framing + trailing reminder), and conducted stacking experiments with multiple constraints.

Result: Compliance drops 2-21% under concurrent task load. Terminal constraints degrade most (up to 50% drop), while avoidance constraints remain robust. Salience-enhanced formatting recovers most lost compliance (90-100% restoration). Interference is bidirectional - formatting constraints can reduce task accuracy (GSM8K accuracy dropped from 93% to 27%). Joint compliance declines sharply as constraints accumulate.

Conclusion: LLM formatting compliance is vulnerable to concurrent task demands, especially for terminal constraints. Simple interventions like salience-enhanced formatting can substantially mitigate these issues, but bidirectional interference and constraint accumulation remain challenges for practical applications.

Abstract: Large language models often fail to satisfy formatting instructions when they must simultaneously perform demanding tasks. We study this behaviour through a prospective memory inspired lens from cognitive psychology, using a controlled paradigm that combines verifiable formatting constraints with benchmark tasks of increasing complexity. Across three model families and over 8,000 prompts, compliance drops by 2-21% under concurrent task load. Vulnerability is highly type-dependent: terminal constraints (requiring action at the response boundary) degrade most, with drops up to 50%, while avoidance constraints remain comparatively robust. A salience-enhanced format (explicit instruction framing plus a trailing reminder) recovers much of the lost compliance, restoring performance to 90-100% in many settings. Interference is bidirectional: formatting constraints can also reduce task accuracy, with one model’s GSM8K accuracy dropping from 93% to 27%. In additional stacking experiments, joint compliance declines sharply as constraints accumulate. All results use deterministic programmatic checkers without an LLM-as-judge component on publicly available datasets.

Method: Constructed a dataset of 1,084 Reddit posts from r/NeutralPolitics covering 138 distinct political targets. Evaluated proprietary and open-source LLMs using zero-shot, few-shot, and context-augmented prompting strategies for Target-Stance Extraction (TSE) - combining target identification and stance detection.

Result: Best performing LLMs achieved performance comparable to highly trained human annotators and remained robust on challenging posts with low inter-annotator agreement.

Conclusion: LLMs can extract complex political opinions with minimal supervision, offering a scalable tool for computational social science and political text analysis through Target-Stance Extraction.

Abstract: Political polarization emerges from a complex interplay of beliefs about policies, figures, and issues. However, most computational analyses reduce discourse to coarse partisan labels, overlooking how these beliefs interact. This is especially evident in online political conversations, which are often nuanced and cover a wide range of subjects, making it difficult to automatically identify the target of discussion and the opinion expressed toward them. In this study, we investigate whether Large Language Models (LLMs) can address this challenge through Target-Stance Extraction (TSE), a recent natural language processing task that combines target identification and stance detection, enabling more granular analysis of political opinions. For this, we construct a dataset of 1,084 Reddit posts from r/NeutralPolitics, covering 138 distinct political targets and evaluate a range of proprietary and open-source LLMs using zero-shot, few-shot, and context-augmented prompting strategies. Our results show that the best models perform comparably to highly trained human annotators and remain robust on challenging posts with low inter-annotator agreement. These findings demonstrate that LLMs can extract complex political opinions with minimal supervision, offering a scalable tool for computational social science and political text analysis.

Method: Fine-tune a lightweight LLM using a novel structural loss function to generate hierarchical JSON structures from scientific sentences. Then use a generative model to reconstruct original text from these JSONs. Evaluate using semantic and lexical similarity metrics between original and reconstructed sentences.

Result: Hierarchical JSON formats are shown to be capable of effectively retaining information from scientific texts, as demonstrated by high semantic and lexical similarity between original and reconstructed sentences.

Conclusion: Structured hierarchical representations can preserve the meaning of scientific sentences, suggesting potential applications in scientific text processing, knowledge extraction, and information retrieval systems.

Abstract: This paper investigates whether structured representations can preserve the meaning of scientific sentences. To test this, a lightweight LLM is fine-tuned using a novel structural loss function to generate hierarchical JSON structures from sentences collected from scientific articles. These JSONs are then used by a generative model to reconstruct the original text. Comparing the original and reconstructed sentences using semantic and lexical similarity we show that hierarchical formats are capable of retaining information of scientific texts effectively.

Method: Three-stage pipeline: (1) structure-aware chunking treating headers, code blocks, tables, and lists as atomic units; (2) single LLM call per chunk extracting title, summary, keywords, typed entities, hypothetical questions, and semantic key with rolling key propagation; (3) restructuring chunks by merging those sharing semantic keys via bin-packing.

Result: Evaluation on 30 queries over 18-document Markdown corpus shows Config D (BM25 over structural chunks) achieves Recall@5=1.000 and MRR=0.911, while dense retrieval over full pipeline reaches Recall@5=0.867.

Conclusion: MDKeyChunker provides efficient structure-aware document chunking with single-call metadata extraction, improving retrieval performance while reducing LLM calls compared to traditional fixed-size chunking approaches.

Abstract: RAG pipelines typically rely on fixed-size chunking, which ignores document structure, fragments semantic units across boundaries, and requires multiple LLM calls per chunk for metadata extraction. We present MDKeyChunker, a three-stage pipeline for Markdown documents that (1) performs structure-aware chunking treating headers, code blocks, tables, and lists as atomic units; (2) enriches each chunk via a single LLM call extracting title, summary, keywords, typed entities, hypothetical questions, and a semantic key, while propagating a rolling key dictionary to maintain document-level context; and (3) restructures chunks by merging those sharing the same semantic key via bin-packing, co-locating related content for retrieval. The single-call design extracts all seven metadata fields in one LLM invocation, eliminating the need for separate per-field extraction passes. Rolling key propagation replaces hand-tuned scoring with LLM-native semantic matching. An empirical evaluation on 30 queries over an 18-document Markdown corpus shows Config D (BM25 over structural chunks) achieves Recall@5=1.000 and MRR=0.911, while dense retrieval over the full pipeline (Config C) reaches Recall@5=0.867. MDKeyChunker is implemented in Python with four dependencies and supports any OpenAI-compatible endpoint.

Method: Uses multilingual and African language-specialized Transformer models (mDeBERTa-v3-base, SwahBERT, AfriBERTa-large) with class-weighted loss functions, iterative stratified data splitting, and per-label threshold tuning to handle severe class imbalance across three subtasks.

Result: Best configuration (mDeBERTa-v3-base) achieves 0.8032 macro-F1 on validation for binary polarization detection, with competitive performance on multi-label tasks (up to 0.556 macro-F1).

Conclusion: Transformer-based approaches are effective for polarization detection but face persistent challenges with implicit polarization, code-switching, and distinguishing heated political discourse from genuine polarization.

Abstract: This paper describes my submission to the Polarization Shared Task at SemEval-2025, which addresses polarization detection and classification in social media text. I develop Transformer-based systems for English and Swahili across three subtasks: binary polarization detection, multi-label target type classification, and multi-label manifestation identification. The approach leverages multilingual and African language-specialized models (mDeBERTa-v3-base, SwahBERT, AfriBERTa-large), class-weighted loss functions, iterative stratified data splitting, and per-label threshold tuning to handle severe class imbalance. The best configuration, mDeBERTa-v3-base, achieves 0.8032 macro-F1 on validation for binary detection, with competitive performance on multi-label tasks (up to 0.556 macro-F1). Error analysis reveals persistent challenges with implicit polarization, code-switching, and distinguishing heated political discourse from genuine polarization.

Method: Two experiments on English NP/Z garden-path sentences using Maze and self-paced reading tasks, comparing human behavior with predictions from an ensemble of large language models.

Result: No evidence for real-time digging-in effects. Positive digging-in trends only appear sentence-finally where wrap-up effects confound interpretation. Nonfinal items show reverse trends consistent with neural model predictions.

Conclusion: Digging-in effects are not robust real-time phenomena in human sentence processing but artifacts of wrap-up processes at sentence boundaries.

Abstract: Digging-in effects, where disambiguation difficulty increases with longer ambiguous regions, have been cited as evidence for self-organized sentence processing, in which structural commitments strengthen over time. In contrast, surprisal theory predicts no such effect unless lengthening genuinely shifts statistical expectations, and neural language models appear to show the opposite pattern. Whether digging-in is a robust real-time phenomenon in human sentence processing – or an artifact of wrap-up processes or methodological confounds – remains unclear. We report two experiments on English NP/Z garden-path sentences using Maze and self-paced reading, comparing human behavior with predictions from an ensemble of large language models. We find no evidence for real-time digging-in effects. Critically, items with sentence-final versus nonfinal disambiguation show qualitatively different patterns: positive digging-in trends appear only sentence-finally, where wrap-up effects confound interpretation. Nonfinal items – the cleaner test of real-time processing – show reverse trends consistent with neural model predictions.

Method: Created Swiss-Bench SBP-002 with 395 expert-crafted items across three Swiss regulatory domains (FINMA, Legal-CH, EFK), seven task types, and three languages (German, French, Italian). Evaluated ten frontier models using structured three-dimension scoring with blind three-judge LLM panel (GPT-4o, Claude Sonnet 4, Qwen3-235B) with majority-vote aggregation and weighted kappa = 0.605.

Result: Models clustered into three tiers: Tier A (35-38% correct), Tier B (26-29%), Tier C (13-21%). Top model Qwen 3.5 Plus achieved only 38.2% correct. Task difficulty varied widely: legal translation and case analysis had 69-72% correct, while regulatory Q&A, hallucination detection, and gap analysis remained below 9%. Open-weight models performed competitively against closed-source counterparts.

Conclusion: Swiss regulatory compliance tasks are challenging for frontier models even under zero-retrieval conditions, with open-weight models showing competitive performance. The benchmark provides an empirical reference for assessing model capabilities in this domain.

Abstract: While recent work has benchmarked large language models on Swiss legal translation (Niklaus et al., 2025) and academic legal reasoning from university exams (Fan et al., 2025), no existing benchmark evaluates frontier model performance on applied Swiss regulatory compliance tasks. I introduce Swiss-Bench SBP-002, a trilingual benchmark of 395 expert-crafted items spanning three Swiss regulatory domains (FINMA, Legal-CH, EFK), seven task types, and three languages (German, French, Italian), and evaluate ten frontier models from March 2026 using a structured three-dimension scoring framework assessed via a blind three-judge LLM panel (GPT-4o, Claude Sonnet 4, Qwen3-235B) with majority-vote aggregation and weighted kappa = 0.605, with reference answers validated by an independent human legal expert on a 100-item subset (73% rated Correct, 0% Incorrect, perfect Legal Accuracy). Results reveal three descriptive performance clusters: Tier A (35-38% correct), Tier B (26-29%), and Tier C (13-21%). The benchmark proves difficult: even the top-ranked model (Qwen 3.5 Plus) achieves only 38.2% correct, with 47.3% incorrect and 14.4% partially correct. Task type difficulty varies widely: legal translation and case analysis yield 69-72% correct rates, while regulatory Q&A, hallucination detection, and gap analysis remain below 9%. Within this roster (seven open-weight, three closed-source), an open-weight model leads the ranking, and several open-weight models match or outperform their closed-source counterparts. These findings provide an initial empirical reference point for assessing frontier model capability on Swiss regulatory tasks under zero-retrieval conditions.

Method: Multilingual CTC-based ASR models trained on the WAXAL corpus using pre-trained speech encoders. Joint training across five languages with comprehensive evaluation against OmniASR baselines.

Result: Best model achieves average WER of 30.48% on WAXAL test set, outperforming OmniASR with fewer parameters. Includes analysis of gender bias, vowel length/consonant gemination contributions to errors, and multilingual CTC training dynamics.

Conclusion: Ethio-ASR provides effective multilingual ASR for underrepresented Ethiopian languages, with models and code publicly available to advance speech technology for these languages.

Abstract: We present Ethio-ASR, a suite of multilingual CTC-based automatic speech recognition (ASR) models jointly trained on five Ethiopian languages: Amharic, Tigrinya, Oromo, Sidaama, and Wolaytta. These languages belong to the Semitic, Cushitic, and Omotic branches of the Afroasiatic family, and remain severely underrepresented in speech technology despite being spoken by the vast majority of Ethiopia’s population. We train our models on the recently released WAXAL corpus using several pre-trained speech encoders and evaluate against strong multilingual baselines, including OmniASR. Our best model achieves an average WER of 30.48% on the WAXAL test set, outperforming the best OmniASR model with substantially fewer parameters. We further provide a comprehensive analysis of gender bias, the contribution of vowel length and consonant gemination to ASR errors, and the training dynamics of multilingual CTC models. Our models and codebase are publicly available to the research community.

Method: Probed hidden representations across five ethical frameworks in six LLMs (4B-72B parameters), analyzed differentiated ethical subspaces and asymmetric transfer patterns, and conducted post-hoc validation to examine dependence on surface features.

Result: Models show differentiated ethical subspaces with asymmetric transfer (e.g., deontology probes partially generalize to virtue scenarios while commonsense probes fail catastrophically on justice). Disagreement between deontological and utilitarian probes correlates with higher behavioral entropy, though this may partly reflect shared sensitivity to scenario difficulty.

Conclusion: LLMs do distinguish between different ethical frameworks in their internal representations rather than collapsing ethics into a single dimension, though probes partially depend on surface features of benchmark templates, requiring cautious interpretation of results.

Abstract: When large language models make ethical judgments, do their internal representations distinguish between normative frameworks, or collapse ethics into a single acceptability dimension? We probe hidden representations across five ethical frameworks (deontology, utilitarianism, virtue, justice, commonsense) in six LLMs spanning 4B–72B parameters. Our analysis reveals differentiated ethical subspaces with asymmetric transfer patterns – e.g., deontology probes partially generalize to virtue scenarios while commonsense probes fail catastrophically on justice. Disagreement between deontological and utilitarian probes correlates with higher behavioral entropy across architectures, though this relationship may partly reflect shared sensitivity to scenario difficulty. Post-hoc validation reveals that probes partially depend on surface features of benchmark templates, motivating cautious interpretation. We discuss both the structural insights these methods provide and their epistemological limitations.

Method: Cascaded pipeline with general-purpose local models for acronym detection, routing to domain-specific biomedical models for context-relevant expansions; all models deployed on-device (2B-10B parameters).

Result: General models achieve high detection accuracy (~0.988) but poor expansion (~0.655); cascaded approach with domain-specific models increases expansion accuracy to ~0.81; demonstrates viability of on-device privacy-preserving models.

Conclusion: Privacy-preserving on-device models (2B-10B) can deliver high-fidelity clinical acronym disambiguation, bridging privacy constraints with healthcare LLM applications.

Abstract: Large Language Models (LLMs) offer transformative solutions across many domains, but healthcare integration is hindered by strict data privacy constraints. Clinical narratives are dense with ambiguous acronyms, misinterpretation these abbreviations can precipitate severe outcomes like life-threatening medication errors. While cloud-dependent LLMs excel at Acronym Disambiguation, transmitting Protected Health Information to external servers violates privacy frameworks. To bridge this gap, this study pioneers the evaluation of small-parameter models deployed entirely on-device to ensure privacy preservation. We introduce a privacy-preserving cascaded pipeline leveraging general-purpose local models to detect clinical acronyms, routing them to domain-specific biomedical models for context-relevant expansions. Results reveal that while general instruction-following models achieve high detection accuracy (~0.988), their expansion capabilities plummet (~0.655). Our cascaded approach utilizes domain-specific medical models to increase expansion accuracy to (~0.81). This novel work demonstrates that privacy-preserving, on-device (2B-10B) models deliver high-fidelity clinical acronym disambiguation support.

Method: The authors analyze how intermediate representations evolve during training, introduce a metric to quantify a model’s intrinsic suitability for early-exit, and propose a benchmark for researchers to explore early-exit benefits across different models and workloads.

Result: Results show a diminishing trend in early-exit effectiveness across newer model generations, with dense transformers generally offering greater early-exit potential than Mixture-of-Experts and State Space Models. Larger models (20B+ parameters) and base pretrained models without specialized tuning exhibit higher early-exit potential.

Conclusion: Early-exit inference strategies need re-evaluation for modern LLMs due to architectural improvements that reduce layer redundancy, with certain model types and sizes showing more promise for early-exit approaches than others.

Abstract: In Large Language Model (LLM) inference, early-exit refers to stopping computation at an intermediate layer once the prediction is sufficiently confident, thereby reducing latency and cost. However, recent LLMs adopt improved pretraining recipes and architectures that reduce layer redundancy, potentially limiting early-exit opportunities. We re-evaluate layer-wise early-exit in modern LLMs and analyze how intermediate representations evolve during training. We introduce a metric to quantify a model’s intrinsic suitability for early-exit and propose a benchmark for researchers to explore the potential early-exit benefits on different models and workloads. Our results show a diminishing trend in early-exit effectiveness across newer model generations. We further find that dense transformers generally offer greater early-exit potential than Mixture-of-Experts and State Space Models. In addition, larger models, particularly those with more than 20 billion parameters, and base pretrained models without specialized tuning tend to exhibit higher early-exit potential.

Method: Created IslamicMMLU benchmark with 10,013 multiple-choice questions across three tracks: Quran (2,013 questions), Hadith (4,000 questions), and Fiqh (4,000 questions). Includes madhab bias detection in Fiqh track. Evaluated 26 LLMs on this benchmark.

Result: Model accuracy ranged from 39.8% to 93.8% (Gemini 3 Flash). Quran track showed widest performance span (32.4% to 99.3%). Fiqh track revealed variable madhab preferences across models. Arabic-specific models underperformed frontier models.

Conclusion: IslamicMMLU provides comprehensive evaluation of LLMs on Islamic knowledge, revealing significant performance variations and biases. The benchmark and leaderboard are publicly available for ongoing evaluation.

Abstract: Large language models are increasingly consulted for Islamic knowledge, yet no comprehensive benchmark evaluates their performance across core Islamic disciplines. We introduce IslamicMMLU, a benchmark of 10,013 multiple-choice questions spanning three tracks: Quran (2,013 questions), Hadith (4,000 questions), and Fiqh (jurisprudence, 4,000 questions). Each track is formed of multiple types of questions to examine LLMs capabilities handling different aspects of Islamic knowledge. The benchmark is used to create the IslamicMMLU public leaderboard for evaluating LLMs, and we initially evaluate 26 LLMs, where their averaged accuracy across the three tracks varied between 39.8% to 93.8% (by Gemini 3 Flash). The Quran track shows the widest span (99.3% to 32.4%), while the Fiqh track includes a novel madhab (Islamic school of jurisprudence) bias detection task revealing variable school-of-thought preferences across models. Arabic-specific models show mixed results, but they all underperform compared to frontier models. The evaluation code and leaderboard are made publicly available.

Method: Used longform, infant-centered audio recordings from rural Bolivia and urban U.S. to analyze temporal patterns of speech input and infants’ pre-linguistic vocal behavior.

Result: Child-directed speech in Bolivia, though less frequent, was as temporally clustered as in the U.S. Infants were most likely to produce speech-like vocalizations during directed speech periods, with Bolivian infants responding more to older children’s speech than adult speech.

Conclusion: Language development impact depends not only on quantity of child-directed speech but also on temporal concentration and source, with older children serving as important input sources in communities where adult speech to infants is less frequent.

Abstract: Children in many parts of the world hear relatively little speech directed to them, yet still reach major language development milestones. What differs about the speech input that infants learn from when directed input is rare? Using longform, infant-centered audio recordings taken in rural Bolivia and the urban U.S., we examined temporal patterns of infants’ speech input and their pre-linguistic vocal behavior. We find that child-directed speech in Bolivia, though less frequent, was just as temporally clustered as speech input in the U.S, arriving in concentrated bursts rather than spread across the day. In both communities, infants were most likely to produce speech-like vocalizations during periods of speech directed to them, with the probability of infants’ speech-like vocalizations during target child-directed speech nearly double that during silence. In Bolivia, infants’ speech-like vocalizations were also more likely to occur during bouts of directed speech from older children than from adults. Together, these findings suggest that the developmental impact of child-directed speech may depend not only on quantity, but on temporal concentration and source, with older children serving as an important source of input in some communities, including where adult speech to infants is less frequent.

Method: The method involves perturbing a language model by fine-tuning it on a single adversarial example, then measuring how this perturbation “infects” or transfers to other examples. This approach makes no geometric assumptions and tests whether representations exist by examining how learning perturbations propagate.

Result: Perturbation reveals structured transfer at multiple linguistic grain sizes in trained language models, suggesting that LMs both generalize along representational lines and acquire linguistic abstractions from experience alone. The method doesn’t find representations where it shouldn’t (e.g., in untrained LMs).

Conclusion: By reconceptualizing representations as conduits for learning rather than patterns of activation, the perturbation approach escapes the dilemma of current representation-finding methods and provides evidence that trained LMs develop structured linguistic representations that support generalization.

Abstract: Linguistic representation learning in deep neural language models (LMs) has been studied for decades, for both practical and theoretical reasons. However, finding representations in LMs remains an unsolved problem, in part due to a dilemma between enforcing implausible constraints on representations (e.g., linearity; Arora et al. 2024) and trivializing the notion of representation altogether (Sutter et al., 2025). Here we escape this dilemma by reconceptualizing representations not as patterns of activation but as conduits for learning. Our approach is simple: we perturb an LM by fine-tuning it on a single adversarial example and measure how this perturbation ``infects’’ other examples. Perturbation makes no geometric assumptions, and unlike other methods, it does not find representations where it should not (e.g., in untrained LMs). But in trained LMs, perturbation reveals structured transfer at multiple linguistic grain sizes, suggesting that LMs both generalize along representational lines and acquire linguistic abstractions from experience alone.

Method: Used PoliticsBench framework adapted from EQ-Bench-v3 psychometric benchmark. Tested 8 prominent LLMs through 20 evolving multi-turn roleplay scenarios where models reported stances and determined actions. Scored responses on scale of 10 political values.

Result: 7 of 8 models leaned left (Claude, Deepseek, Gemini, GPT, Llama, Qwen Base, Qwen Instruction-Tuned), while Grok leaned right. Left-leaning LLMs strongly exhibited liberal traits, moderately conservative ones. Slight variations in alignment scores across roleplay stages with no clear pattern. Most models used consequence-based reasoning, Grok used facts/statistics.

Conclusion: First psychometric evaluation of political values in LLMs through multi-stage free-text interactions, revealing systematic political biases that could impact objectivity when LLMs are used as information sources.

Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity. Existing benchmarks of LLM social bias primarily evaluate gender and racial stereotypes. When political bias is included, it is typically measured at a coarse level, neglecting the specific values that shape sociopolitical leanings. This study investigates political bias in eight prominent LLMs (Claude, Deepseek, Gemini, GPT, Grok, Llama, Qwen Base, Qwen Instruction-Tuned) using PoliticsBench: a novel multi-turn roleplay framework adapted from the EQ-Bench-v3 psychometric benchmark. We test whether commercially developed LLMs display a systematic left-leaning bias that becomes more pronounced in later stages of multi-stage roleplay. Through twenty evolving scenarios, each model reported its stance and determined its course of action. Scoring these responses on a scale of ten political values, we explored the values underlying chatbots’ deviations from unbiased standards. Seven of our eight models leaned left, while Grok leaned right. Each left-leaning LLM strongly exhibited liberal traits and moderately exhibited conservative ones. We discovered slight variations in alignment scores across stages of roleplay, with no particular pattern. Though most models used consequence-based reasoning, Grok frequently argued with facts and statistics. Our study presents the first psychometric evaluation of political values in LLMs through multi-stage, free-text interactions.

Method: Systematic evaluation of LLM formalizers vs planners in classic BlocksWorld domain with state spaces up to 10^165; development of divide-and-conquer formalizing technique to improve robustness; introduction of unraveling problems with exponential formal language requirements; and proposal of LLM-as-higher-order-formalizer paradigm where LLMs generate program generators to decouple token output from combinatorial explosion.

Result: LLM formalizers significantly outperform LLM planners, with some maintaining perfect accuracy even in massive state spaces; divide-and-conquer techniques greatly improve robustness of smaller LLM formalizers; the new LLM-as-higher-order-formalizer paradigm effectively addresses combinatorial explosion challenges in unraveling problems.

Conclusion: LLM formalizers scale much better than LLM planners for complex planning problems, and novel techniques like divide-and-conquer formalizing and LLM-as-higher-order-formalizer paradigms can address remaining scalability challenges, particularly for problems with combinatorial explosion in formal language requirements.

Abstract: Recent work shows overwhelming evidence that LLMs, even those trained to scale their reasoning trace, perform unsatisfactorily when solving planning problems too complex. Whether the same conclusion holds for LLM formalizers that generate solver-oriented programs remains unknown. We systematically show that LLM formalizers greatly out-scale LLM planners, some retaining perfect accuracy in the classic BlocksWorld domain with a huge state space of size up to $10^{165}$. While performance of smaller LLM formalizers degrades with problem complexity, we show that a divide-and-conquer formalizing technique can greatly improve its robustness. Finally, we introduce unraveling problems where one line of problem description realistically corresponds to exponentially many lines of formal language such as the Planning Domain Definition Language (PDDL), greatly challenging LLM formalizers. We tackle this challenge by introducing a new paradigm, namely LLM-as-higher-order-formalizer, where an LLM generates a program generator. This decouples token output from the combinatorial explosion of the underlying formalization and search space.

Method: Created a longitudinal benchmark with 2,400 human-annotated multi-session interaction trajectories across health, politics, values, and preferences. Includes three tracks: Temporal Belief Consistency, Contradiction Detection, and Evidence-Driven Revision. Evaluated seven models (GPT-4o, Claude 3.5, Gemini 1.5, LLaMA-3, Mistral-Large) under zero-shot and RAG settings.

Result: Found a trade-off: models that personalize aggressively resist belief drift poorly, while factually grounded models miss legitimate belief updates. Introduced four novel metrics: Belief Revision Accuracy (BRA), Drift Coherence Score (DCS), Contradiction Resolution Rate (CRR), and Evidence Sensitivity Index (ESI).

Conclusion: Belief dynamics in multi-session LLM interactions require specialized evaluation beyond static fact retrieval. The BeliefShift benchmark provides tools to assess how models handle evolving user beliefs, revealing fundamental trade-offs in current architectures.

Abstract: LLMs are increasingly used as long-running conversational agents, yet every major benchmark evaluating their memory treats user information as static facts to be stored and retrieved. That’s the wrong model. People change their minds, and over extended interactions, phenomena like opinion drift, over-alignment, and confirmation bias start to matter a lot. BeliefShift introduces a longitudinal benchmark designed specifically to evaluate belief dynamics in multi-session LLM interactions. It covers three tracks: Temporal Belief Consistency, Contradiction Detection, and Evidence-Driven Revision. The dataset includes 2,400 human-annotated multi-session interaction trajectories spanning health, politics, personal values, and product preferences. We evaluate seven models including GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, LLaMA-3, and Mistral-Large under zero-shot and retrieval-augmented generation (RAG) settings. Results reveal a clear trade-off: models that personalize aggressively resist drift poorly, while factually grounded models miss legitimate belief updates. We further introduce four novel evaluation metrics: Belief Revision Accuracy (BRA), Drift Coherence Score (DCS), Contradiction Resolution Rate (CRR), and Evidence Sensitivity Index (ESI).

Method: Proposes MTP-D, a simple yet effective self-distillation method with minimal additional training cost. Also introduces a looped extension strategy for MTP-D that enables effective and economical MTP head extension. Systematically explores distillation strategies and MTP scalability through extensive experiments.

Result: MTP-D boosts MTP head acceptance rates by +7.5% while preserving main-head performance. The looped extension strategy enables further significant inference speedup to 1-head MTP (+220.4%). Validated through experiments on seven benchmarks.

Conclusion: MTP-D and looped extension strategy effectively enhance MTP-head performance and inference efficiency, facilitating practical usage of MTP in LLMs.

Abstract: As Large Language Models (LLMs) scale up, inference efficiency becomes a critical bottleneck. Multi-Token Prediction (MTP) could accelerate LLM inference by predicting multiple future tokens in parallel. However, existing MTP approaches still face two challenges: limited acceptance rates of MTP heads, and difficulties in jointly training multiple MTP heads. Therefore, we propose MTP-D, a simple yet effective self-distillation method with minimal additional training cost, which boosts MTP head acceptance rates (+7.5%) while maximumly preserving main-head performance. We also introduce a looped extension strategy for MTP-D, enabling effective and economical MTP head extension and further significant inference speedup to 1-head MTP (+220.4%). Moreover, we systematically explore and validate key insights on the distillation strategies and the potential scalability of MTP through extensive experiments on seven benchmarks. These results demonstrate that our MTP-D and looped extension strategy effectively enhance MTP-head performance and inference efficiency, facilitating the practical usage of MTP in LLMs.

Method: Used Gemini 2.5 as backbone LLM with two prompting strategies: zero-shot baseline and multi-stage reasoning variant. Evaluated on 80 de-identified transcripts from real clinical encounters with six experienced physicians providing over 90 hours of structured review.

Result: General-purpose LLMs are not yet fully reliable but can produce clinically meaningful and guideline-relevant questions, showing potential to reduce cognitive burden and make evidence-based medicine more actionable at point of care.

Conclusion: LLMs show significant potential as ambient assistants for generating evidence-based questions during clinical encounters, though further development is needed to improve reliability for real-world implementation.

Abstract: Evidence-based medicine (EBM) is central to high-quality care, but remains difficult to implement in fast-paced primary care settings. Physicians face short consultations, increasing patient loads, and lengthy guideline documents that are impractical to consult in real time. To address this gap, we investigate the feasibility of using large language models (LLMs) as ambient assistants that surface targeted, evidence-based questions during physician-patient encounters. Our study focuses on question generation rather than question answering, with the aim of scaffolding physician reasoning and integrating guideline-based practice into brief consultations. We implemented two prompting strategies, a zero-shot baseline and a multi-stage reasoning variant, using Gemini 2.5 as the backbone model. We evaluated on a benchmark of 80 de-identified transcripts from real clinical encounters, with six experienced physicians contributing over 90 hours of structured review. Results indicate that while general-purpose LLMs are not yet fully reliable, they can produce clinically meaningful and guideline-relevant questions, suggesting significant potential to reduce cognitive burden and make EBM more actionable at the point of care.

Method: Introduces OmniACBench with 3,559 verified instances covering six acoustic features: speech rate, phonation, pronunciation, emotion, global accent, and timbre. Models must read a text script aloud with appropriate tone and manner given a spoken instruction, text script, and image. Evaluates eight models extensively.

Result: Experiments reveal models’ limitations in acoustic control despite strong performance on textual-output evaluations. Main bottleneck is integrating multimodal context for faithful speech generation, not processing individual modalities. Identifies three common failure modes: weak direct control, failed implicit inference, and failed multimodal grounding.

Conclusion: Omni-modal models struggle with acoustic control and verbalizing responses effectively. The benchmark provides insights for developing models that can better integrate multimodal context for speech generation.

Abstract: Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak their answers. To study this, we introduce OmniACBench, a benchmark for evaluating context-grounded acoustic control in omni-modal models. Given a spoken instruction, a text script, and an image, a model must read the script aloud with an appropriate tone and manner. OmniACBench comprises 3,559 verified instances covering six acoustic features: speech rate, phonation, pronunciation, emotion, global accent, and timbre. Extensive experiments on eight models reveal their limitations in the proposed setting, despite their strong performance on prior textual-output evaluations. Our analyses show that the main bottleneck lies not in processing individual modalities, but in integrating multimodal context for faithful speech generation. Moreover, we identify three common failure modes-weak direct control, failed implicit inference, and failed multimodal grounding-providing insights for developing models that can verbalize responses effectively.

Method: Uses pretrained encoder-decoder language model for text-to-text generation that simultaneously outputs argumentatively annotated text for spans, components, and relations.

Result: Achieves state-of-the-art performance on three benchmark datasets: Argument-annotated Essays Corpus (AAEC), AbstRCT, and Cornell eRulemaking Corpus (CDCP).

Conclusion: Proposed method simplifies argument mining by eliminating postprocessing and hyperparameter tuning while maintaining strong performance across different argumentative structures.

Abstract: Argument Mining(AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. Consequently, these methods need rule-based postprocessing to derive argumentative structures from the output of each subtask. This approach adds to the complexity of the model and expands the search space of the hyperparameters. To address this difficulty, we propose a simple yet strong method based on a text-to-text generation approach using a pretrained encoder-decoder language model. Our method simultaneously generates argumentatively annotated text for spans, components, and relations, eliminating the need for task-specific postprocessing and hyperparameter tuning. Furthermore, because it is a straightforward text-to-text generation method, we can easily adapt our approach to various types of argumentative structures. Experimental results demonstrate the effectiveness of our method, as it achieves state-of-the-art performance on three different types of benchmark datasets: the Argument-annotated Essays Corpus(AAEC), AbstRCT, and the Cornell eRulemaking Corpus(CDCP)

Method: POISE is a closed-loop framework that maintains a structured, genealogically linked archive connecting proposals, executable implementations, standardized evaluations, and natural-language reflections to support evidence-driven iteration. It searches over algorithmic mechanisms tightly coupled with training dynamics.

Result: In mathematical reasoning experiments starting from GRPO, POISE evaluated 64 candidate algorithms and discovered improved mechanisms including analytic-variance scaling and validity masking. The best variant improved weighted Overall from 47.8 to 52.5 (+4.6) and increased AIME25 pass@32 from 26.7% to 43.3%.

Conclusion: POISE demonstrates the feasibility of automated policy optimization discovery for language models while supporting interpretable design principles, offering a systematic approach to algorithm improvement.

Abstract: Discovering improved policy optimization algorithms for language models remains a costly manual process requiring repeated mechanism-level modification and validation. Unlike simple combinatorial code search, this problem requires searching over algorithmic mechanisms tightly coupled with training dynamics while reusing empirical evidence across iterations. We propose POISE, a closed-loop framework for automated discovery of policy optimization algorithms for language models. POISE maintains a structured, genealogically linked archive linking proposals, executable implementations, standardized evaluations, and natural-language reflections to support evidence-driven iteration. In mathematical reasoning experiments starting from GRPO, POISE evaluates 64 candidate algorithms and discovers improved mechanisms, including analytic-variance scaling and validity masking. The best variant improves weighted Overall from 47.8 to 52.5 (+4.6) and increases AIME25 pass@32 from 26.7% to 43.3%, demonstrating the feasibility of automated policy optimization discovery while supporting interpretable design principles.

Method: Developed a RAG framework using the Doha Historical Dictionary of Arabic (DHDA) with hybrid retrieval and intent-based routing to provide precise historical lexical information to Arabic LLMs.

Result: Improved accuracy of Arabic-native LLMs (Fanar and ALLaM) to over 85%, reducing performance gap with proprietary models like Gemini, with high human-judge agreement (kappa = 0.87).

Conclusion: Integrating diachronic lexicographic resources into RAG frameworks significantly enhances Arabic language understanding, especially for historical and religious texts.

Abstract: Large language models (LLMs) have achieved remarkable progress in many language tasks, yet they continue to struggle with complex historical and religious Arabic texts such as the Quran and Hadith. To address this limitation, we develop a retrieval-augmented generation (RAG) framework grounded in diachronic lexicographic knowledge. Unlike prior RAG systems that rely on general-purpose corpora, our approach retrieves evidence from the Doha Historical Dictionary of Arabic (DHDA), a large-scale resource documenting the historical development of Arabic vocabulary. The proposed pipeline combines hybrid retrieval with an intent-based routing mechanism to provide LLMs with precise, contextually relevant historical information. Our experiments show that this approach improves the accuracy of Arabic-native LLMs, including Fanar and ALLaM, to over 85%, substantially reducing the performance gap with Gemini, a proprietary large-scale model. Gemini also serves as an LLM-as-a-judge system for automatic evaluation in our experiments. The automated judgments were verified through human evaluation, demonstrating high agreement (kappa = 0.87). An error analysis further highlights key linguistic challenges, including diacritics and compound expressions. These findings demonstrate the value of integrating diachronic lexicographic resources into retrieval-augmented generation frameworks to enhance Arabic language understanding, particularly for historical and religious texts. The code and resources are publicly available at: https://github.com/somayaeltanbouly/Doha-Dictionary-RAG.

Method: Proposes CoCR-RAG framework with concept distillation algorithm that extracts essential concepts from Abstract Meaning Representation (AMR) graphs, then fuses and reconstructs them into unified contexts using LLMs.

Result: Significantly outperforms existing context-reconstruction methods on PopQA and EntityQuestions datasets, shows robustness across various backbone LLMs, and works as flexible plug-and-play component.

Conclusion: CoCR-RAG effectively addresses multi-source information fusion in RAG through linguistically grounded concept-level integration, improving factual consistency in web Q&A systems.

Abstract: Retrieval-augmented generation (RAG) has shown promising results in enhancing Q&A by incorporating information from the web and other external sources. However, the supporting documents retrieved from the heterogeneous web often originate from multiple sources with diverse writing styles, varying formats, and inconsistent granularity. Fusing such multi-source documents into a coherent and knowledge-intensive context remains a significant challenge, as the presence of irrelevant and redundant information can compromise the factual consistency of the inferred answers. This paper proposes the Concept-oriented Context Reconstruction RAG (CoCR-RAG), a framework that addresses the multi-source information fusion problem in RAG through linguistically grounded concept-level integration. Specifically, we introduce a concept distillation algorithm that extracts essential concepts from Abstract Meaning Representation (AMR), a stable semantic representation that structures the meaning of texts as logical graphs. The distilled concepts from multiple retrieved documents are then fused and reconstructed into a unified, information-intensive context by Large Language Models, which supplement only the necessary sentence elements to highlight the core knowledge. Experiments on the PopQA and EntityQuestions datasets demonstrate that CoCR-RAG significantly outperforms existing context-reconstruction methods across these Web Q&A benchmarks. Furthermore, CoCR-RAG shows robustness across various backbone LLMs, establishing itself as a flexible, plug-and-play component adaptable to different RAG frameworks.

Method: SGT introduces a training-time sparse depth allocation framework that progressively extends recurrence from deeper to shallower layers via targeted attention looping on high-entropy informative heads, creating structural sparsity by selectively increasing depth for only a small subset of parameters.

Result: SGT consistently outperforms training-time static block-level looping baselines across multiple parameter scales while reducing additional training FLOPs overhead from approximately 16-20% to only 1-3% relative to standard Transformer backbone.

Conclusion: Progressive depth allocation via sparse attention looping on informative heads is more efficient than static parameter reuse, enabling deeper effective depth with minimal computational overhead.

Abstract: Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution. Under this paradigm, the network structure remains static along the training timeline, and additional computational depth is uniformly assigned to entire blocks at the parameter level. This rigidity across training time and parameter space leads to substantial computational redundancy during training. In contrast, we argue that depth allocation during training should not be a static preset, but rather a progressively growing structural process. Our systematic analysis reveals a deep-to-shallow maturation trajectory across layers, where high-entropy attention heads play a crucial role in semantic integration. Motivated by this observation, we introduce the Sparse Growing Transformer (SGT). SGT is a training-time sparse depth allocation framework that progressively extends recurrence from deeper to shallower layers via targeted attention looping on informative heads. This mechanism induces structural sparsity by selectively increasing depth only for a small subset of parameters as training evolves. Extensive experiments across multiple parameter scales demonstrate that SGT consistently outperforms training-time static block-level looping baselines under comparable settings, while reducing the additional training FLOPs overhead from approximately 16–20% to only 1–3% relative to a standard Transformer backbone.

Method: Proposes Thinking with Tables (TWT) using program-aided code-based neuro-symbolic reasoning that interacts with external environments for operations like information extraction and element modeling.

Result: TWT consistently outperforms existing baselines by an average of 10% in accuracy across eight datasets, achieving performance comparable to or surpassing proprietary commercial SOTA LLMs on TVMU tasks.

Conclusion: TWT effectively addresses core challenges in tabular-vision multimodal understanding through neuro-symbolic reasoning, demonstrating strong performance on diverse tabular-vision tasks.

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities across modalities such as images and text. However, tabular data, despite being a critical real-world modality, remains relatively underexplored in multimodal learning. In this paper, we focus on the task of Tabular-Vision Multi-Modal Understanding (TVMU) and identify three core challenges: (1) high structural variability and data incompleteness in tables, (2) implicit and complex feature dependencies, and (3) significant heterogeneity in problem-solving pipelines across downstream tasks. To address these issues, we propose Thinking with Tables (TWT). TWT employs a program-aided code-based neuro-symbolic reasoning mechanism that facilitates key operations, such as information extraction and element modeling, by interacting with external environments. We evaluate TWT on eight representative datasets. Experimental results demonstrate that TWT consistently outperforms existing baselines by an average of 10% in accuracy, achieving performance comparable to, or even surpassing, proprietary commercial SOTA LLMs on TVMU tasks. Models and codes are available at https://github.com/kunyang-YU/Thinking-with-Tables

Method: Proposes a RAG pipeline combining: 1) rule-grounded synthetic data generation using a symbolic inheritance calculator to create high-quality corpus with full reasoning traces, 2) hybrid retrieval (dense and BM25) with cross-encoder reranking, and 3) schema-constrained output validation for legal and numerical consistency.

Result: Achieves MIR-E score of 0.935 and ranks first on the official QIAS 2026 blind-test leaderboard, demonstrating that retrieval-grounded, schema-aware generation significantly improves reliability in high-precision Arabic legal reasoning tasks.

Conclusion: The proposed system effectively addresses the challenges of Islamic inheritance law reasoning through a combination of synthetic data generation, hybrid retrieval, and schema validation, showing strong performance in Arabic legal reasoning tasks.

Abstract: Islamic inheritance (Ilm al-Mawarith) is a multi-stage legal reasoning task requiring the identification of eligible heirs, resolution of blocking rules (hajb), assignment of fixed and residual shares, handling of adjustments such as awl and radd, and generation of a consistent final distribution. The task is further complicated by variations across legal schools and civil-law codifications, requiring models to operate under explicit legal configurations. We present a retrieval-augmented generation (RAG) pipeline for this setting, combining rule-grounded synthetic data generation, hybrid retrieval (dense and BM25) with cross-encoder reranking, and schema-constrained output validation. A symbolic inheritance calculator is used to generate a large high-quality synthetic corpus with full intermediate reasoning traces, ensuring legal and numerical consistency. The proposed system achieves a MIR-E score of 0.935 and ranks first on the official QIAS 2026 blind-test leaderboard. Results demonstrate that retrieval-grounded, schema-aware generation significantly improves reliability in high-precision Arabic legal reasoning tasks.

Method: Two-phase supervised fine-tuning approach enables an 8B-parameter model to internalize the entire database schema, eliminating long-context prompts and replacing costly API calls with efficient local inference.

Result: Reduced input tokens by over 99% (from 17k to <100), achieved 98.4% execution success and 92.5% semantic accuracy, outperforming prompt-engineered Gemini Flash 2.0 baseline (95.6% execution, 89.4% semantic accuracy).

Conclusion: Demonstrates practical path for high-precision, low-latency text-to-SQL applications using domain-specialized, self-hosted LLMs in large-scale production, solving cost and latency challenges of API-based models.

Abstract: Applying large, proprietary API-based language models to text-to-SQL tasks poses a significant industry challenge: reliance on massive, schema-heavy prompts results in prohibitive per-token API costs and high latency, hindering scalable production deployment. We present a specialized, self-hosted 8B-parameter model designed for a conversational bot in CriQ, a sister app to Dream11, India’s largest fantasy sports platform with over 250 million users, that answers user queries about cricket statistics. Our novel two-phase supervised fine-tuning approach enables the model to internalize the entire database schema, eliminating the need for long-context prompts. This reduces input tokens by over 99%, from a 17k-token baseline to fewer than 100, and replaces costly external API calls with efficient local inference. The resulting system achieves 98.4% execution success and 92.5% semantic accuracy, substantially outperforming a prompt-engineered baseline using Google’s Gemini Flash 2.0 (95.6% execution, 89.4% semantic accuracy). These results demonstrate a practical path toward high-precision, low-latency text-to-SQL applications using domain-specialized, self-hosted language models in large-scale production environments.

Method: Three techniques: (1) Teacher Error Knowledge using Whisper large-v3 hypotheses as training-time history, (2) Context Dropout to regularize over-reliance on history, and (3) Direct Preference Optimization on curated failure cases to improve robustness.

Result: On TED-LIUM 3 and zero-shot LibriSpeech, the approach reduces WER from 5.59% (oracle-history training) to 5.17% with DPO. Under irrelevant-context attacks, DPO shows smallest degradation (5.17% → 5.63%), indicating improved robustness to misleading context.

Conclusion: The proposed unified training framework effectively addresses contextual exposure bias in Speech-LLMs, improving robustness to error-prone and misleading context through teacher error knowledge, regularization, and preference optimization.

Abstract: Contextual automatic speech recognition (ASR) with Speech-LLMs is typically trained with oracle conversation history, but relies on error-prone history at inference, causing a train-test mismatch in the context channel that we term contextual exposure bias. We propose a unified training framework to improve robustness under realistic histories: (i) Teacher Error Knowledge by using Whisper large-v3 hypotheses as training-time history, (ii) Context Dropout to regularize over-reliance on history, and (iii) Direct Preference Optimization (DPO) on curated failure cases. Experiments on TED-LIUM 3 (in-domain) and zero-shot LibriSpeech (out-of-domain) show consistent gains under predicted-history decoding. With a two-utterance history as context, SFT with Whisper hypotheses reduce WER from 5.59% (oracle-history training) to 5.47%, and DPO further improves to 5.17%. Under irrelevant-context attacks, DPO yields the smallest degradation (5.17% -> 5.63%), indicating improved robustness to misleading context. Our code and models are published on https://github.com/XYGuo1996/Contextual_Speech_LLMs.

Method: FinToolSyn uses a forward synthesis framework with three stages: 1) persona instruction, 2) atomic tool synthesis, and 3) dynamic retrieval dialogue generation. It constructs a repository of 43,066 tools and synthesizes over 148k dialogue instances, incorporating dynamic retrieval to emulate noisy candidate sets in massive tool spaces.

Result: Models trained on FinToolSyn achieve a 21.06% improvement in tool-calling capabilities. The framework provides a robust foundation for tool learning in financial scenarios and includes a dedicated benchmark for evaluating tool-calling in realistic financial scenarios.

Conclusion: FinToolSyn addresses key limitations of existing synthesis methods by using forward synthesis with dynamic retrieval, better capturing real-world financial needs and providing substantial improvements in LLM tool-use capabilities for finance.

Abstract: Tool-use capabilities are vital for Large Language Models (LLMs) in finance, a domain characterized by massive investment targets and data-intensive inquiries. However, existing data synthesis methods typically rely on a reverse synthesis paradigm, generating user queries from pre-sampled tools. This approach inevitably introduces artificial explicitness, yielding queries that fail to capture the implicit, event-driven nature of real-world needs. Moreover, its reliance on static tool sets overlooks the dynamic retrieval process required to navigate massive tool spaces. To address these challenges, we introduce \textit{FinToolSyn}, a forward synthesis framework designed to generate high-quality financial dialogues. Progressing from persona instruction and atomic tool synthesis to dynamic retrieval dialogue generation, our pipeline constructs a repository of 43,066 tools and synthesizes over 148k dialogue instances, incorporating dynamic retrieval to emulate the noisy candidate sets typical of massive tool spaces. We also establish a dedicated benchmark to evaluate tool-calling capabilities in realistic financial scenarios. Extensive experiments demonstrate that models trained on FinToolSyn achieve a 21.06% improvement, providing a robust foundation for tool learning in financial scenarios.

Method: Introduces concept-level deficiency prediction task, creates ConceptKT dataset with concept annotations, explores in-context learning with LLMs/LRMs, and investigates strategies for selecting informative historical records based on conceptual alignment and semantic similarity.

Result: Experimental results show that selecting response histories based on conceptual alignment and semantic similarity improves performance on both correctness prediction and concept-level deficiency identification.

Conclusion: Concept-level deficiency prediction enhances knowledge tracing by providing fine-grained diagnostic feedback, and context selection strategies significantly improve LLM/LRM performance on this task.

Abstract: Knowledge Tracing (KT) is a critical technique for modeling student knowledge to support personalized learning. However, most KT systems focus on binary correctness prediction and cannot diagnose the underlying conceptual misunderstandings that lead to errors. Such fine-grained diagnostic feedback is essential for designing targeted instruction and effective remediation. In this work, we introduce the task of concept-level deficiency prediction, which extends traditional KT by identifying the specific concepts a student is likely to struggle with on future problems. We present ConceptKT, a dataset annotated with labels that capture both the concepts required to solve each question and the missing concepts underlying incorrect responses. We investigate in-context learning approaches to KT and evaluate the diagnostic capabilities of various Large Language Models (LLMs) and Large Reasoning Models (LRMs). Different strategies for selecting informative historical records are explored. Experimental results demonstrate that selecting response histories based on conceptual alignment and semantic similarity leads to improved performance on both correctness prediction and concept-level deficiency identification.

Method: LLMpedia generates approximately 1M encyclopedic articles across three model families entirely from parametric memory without retrieval. It evaluates factuality by comparing to Wikipedia for covered subjects and curated web evidence for frontier subjects. The approach includes a capture-trap benchmark inspired by prior Grokipedia analysis.

Result: For gpt-5-mini, the verifiable true rate on Wikipedia-covered subjects is only 74.7% (15+ points below benchmark scores). Frontier subjects verifiable only through curated web evidence fall to 63.2% true rate. Wikipedia covers just 61% of surfaced subjects, and three model families overlap by only 7.3% in subject choice. LLMpedia achieves higher factuality at roughly half the textual similarity to Wikipedia compared to Grokipedia.

Conclusion: Language models’ factuality is significantly overestimated by current benchmarks. LLMpedia provides a more realistic evaluation through parametric encyclopedia generation, revealing substantial gaps in factual accuracy, especially for frontier subjects. The fully open release enables better factuality evaluation and knowledge materialization research.

Abstract: Benchmarks such as MMLU suggest flagship language models approach factuality saturation, with scores above 90%. We show this picture is incomplete. \emph{LLMpedia} generates encyclopedic articles entirely from parametric memory, producing ${\sim}$1M articles across three model families without retrieval. For gpt-5-mini, the verifiable true rate on Wikipedia-covered subjects is only 74.7% – more than 15 percentage points below the benchmark-based picture, consistent with the availability bias of fixed-question evaluation. Beyond Wikipedia, frontier subjects verifiable only through curated web evidence fall further to 63.2% true rate. Wikipedia covers just 61% of surfaced subjects, and three model families overlap by only 7.3% in subject choice. In a capture-trap benchmark inspired by prior analysis of Grokipedia, LLMpedia achieves substantially higher factuality at roughly half the textual similarity to Wikipedia. Unlike Grokipedia, every prompt, artifact, and evaluation verdict is publicly released, making LLMpedia the first fully open parametric encyclopedia – bridging factuality evaluation and knowledge materialization. All data, code, and a browsable interface are at https://llmpedia.net.

Method: Proposes a unified framework using identical neutral prompts to jointly analyze intrinsic gender bias (encoded in internal representations) and extrinsic bias (expressed in generated outputs), enabling direct comparison. Examines alignment effects through supervised fine-tuning for bias reduction.

Result: Finds consistent association between latent gender information and expressed bias under unified protocol (contrary to prior weak correlations). Alignment reduces expressed bias but measurable gender associations remain in internal representations and can be reactivated via adversarial prompting. Debiasing effects on structured benchmarks don’t generalize to realistic scenarios like story generation.

Conclusion: Current alignment approaches may reduce surface-level bias in outputs without fundamentally changing underlying gender representations, highlighting the need for more comprehensive bias mitigation strategies that address both intrinsic and extrinsic aspects.

Abstract: During training, Large Language Models (LLMs) learn social regularities that can lead to gender bias in downstream applications. Most mitigation efforts focus on reducing bias in generated outputs, typically evaluated on structured benchmarks, which raises two concerns: output-level evaluation does not reveal whether alignment modifies the model’s underlying representations, and structured benchmarks may not reflect realistic usage scenarios. We propose a unified framework to jointly analyze intrinsic and extrinsic gender bias in LLMs using identical neutral prompts, enabling direct comparison between gender-related information encoded in internal representations and bias expressed in generated outputs. Contrary to prior work reporting weak or inconsistent correlations, we find a consistent association between latent gender information and expressed bias when measured under the unified protocol. We further examine the effect of alignment through supervised fine-tuning aimed at reducing gender bias. Our results suggest that while the latter indeed reduces expressed bias, measurable gender-related associations are still present in internal representations, and can be reactivated under adversarial prompting. Finally, we consider two realistic settings and show that debiasing effects observed on structured benchmarks do not necessarily generalize, e.g., to the case of story generation.

Method: Created MedAidDialog dataset by extending MDDial corpus with synthetic consultations generated by LLMs, covering 7 languages. Developed MedAidLM using parameter-efficient fine-tuning on quantized small language models with optional patient pre-context information.

Result: System effectively performs symptom elicitation through multi-turn dialogue and generates diagnostic recommendations. Medical expert evaluation confirms plausibility and coherence of generated consultations.

Conclusion: MedAidLM enables realistic multilingual medical consultations without high-end computational infrastructure, addressing accessibility gaps in healthcare-limited settings.

Abstract: Conversational artificial intelligence has the potential to assist users in preliminary medical consultations, particularly in settings where access to healthcare professionals is limited. However, many existing medical dialogue systems operate in a single-turn question–answering paradigm or rely on template-based datasets, limiting conversational realism and multilingual applicability. In this work, we introduce MedAidDialog, a multilingual multi-turn medical dialogue dataset designed to simulate realistic physician–patient consultations. The dataset extends the MDDial corpus by generating synthetic consultations using large language models and further expands them into a parallel multilingual corpus covering seven languages: English, Hindi, Telugu, Tamil, Bengali, Marathi, and Arabic. Building on this dataset, we develop MedAidLM, a conversational medical model trained using parameter-efficient fine-tuning on quantized small language models, enabling deployment without high-end computational infrastructure. Our framework additionally incorporates optional patient pre-context information (e.g., age, gender, allergies) to personalize the consultation process. Experimental results demonstrate that the proposed system can effectively perform symptom elicitation through multi-turn dialogue and generate diagnostic recommendations. We further conduct medical expert evaluation to assess the plausibility and coherence of the generated consultations.

Method: Analyzes embedding vectors of antonymic word pairs across different embedding models, focusing on difference vectors between word pairs. Uses specific projection configurations to examine geometric patterns, discovering a curious “swirl” pattern that appears consistently across models.

Result: Discovers a consistent “swirl” pattern in the geometry of antonymic word pairs across different embedding models when viewed in specific projection configurations, suggesting geometric regularities in how antonyms are encoded.

Conclusion: Antonym relationships appear to have detectable geometric signatures in transformer embeddings, manifested as specific patterns like the discovered “swirl” in difference vectors, which distinguishes them from synonym relationships.

Abstract: Antonyms, or opposites, are sometimes defined as \emph{word pairs that have all of the same contextually relevant properties but one}. Seeing how transformer models seem to encode concepts as directions, this begs the question if one can detect antonymity'' in the geometry of the embedding vectors of word pairs, especially based on their difference vectors. Such geometrical studies are then naturally contrasted by comparing antonymic pairs to their opposites; synonyms. This paper started as an exploratory project on the complexity of the systems needed to detect the geometry of the embedding vectors of antonymic word pairs. What we now report is a curious swirl’’ that appears across embedding models in a somewhat specific projection configuration.

Method: Proposes a framework combining sociolinguistic and technical NLP dimensions, with a case study on Luxembourgish featuring significant orthographic variation. Tests NLP models on data with variation vs. standard orthography, and explores fine-tuning approaches that include variation.

Result: Large performance discrepancies between models tested on varied vs. standard orthographic data. Models are not robust to natural language variation. However, including variation in the fine-tuning process can improve performance on varied data.

Conclusion: Language variation should be actively included in NLP research rather than treated as noise. The proposed framework facilitates this integration while being grounded in sociolinguistic theory, leading to more robust NLP systems.

Abstract: In Natural Language Processing (NLP), variation is typically seen as noise and “normalised away” before processing, even though it is an integral part of language. Conversely, studying language variation in social contexts is central to sociolinguistics. We present a framework to combine the sociolinguistic dimension of language with the technical dimension of NLP. We argue that by embracing sociolinguistics, variation can actively be included in a research setup, in turn informing the NLP side. To illustrate this, we provide a case study on Luxembourgish, an evolving language featuring a large amount of orthographic variation, demonstrating how NLP performance is impacted. The results show large discrepancies in the performance of models tested and fine-tuned on data with a large amount of orthographic variation in comparison to data closer to the (orthographic) standard. Furthermore, we provide a possible solution to improve the performance by including variation in the fine-tuning process. This case study highlights the importance of including variation in the research setup, as models are currently not robust to occurring variation. Our framework facilitates the inclusion of variation in the thought-process while also being grounded in the theoretical framework of sociolinguistics.

Method: The authors analyze the projection problem theoretically and empirically through a pilot study on SemEval-2016 Task 6 data, examining agreement patterns between label-level and dimension-level annotations.

Result: When dimensions align, label agreement exceeds dimensional agreement; when dimensions conflict, the pattern reverses - label agreement collapses while dimensional agreement remains intact, with Policy dimension reaching high agreement.

Conclusion: The projection problem is real and activates precisely where it matters most - in complex cases where multi-dimensional attitudes conflict, suggesting the need for multi-dimensional stance modeling rather than simple three-way classification.

Abstract: Stance detection is nearly always formulated as classifying text into Favor, Against, or Neutral – a convention inherited from debate analysis and applied without modification to social media since SemEval-2016. But attitudes toward complex targets are not unitary: a person can accept climate science while opposing carbon taxes, expressing support on one dimension and opposition on another. When annotators must compress such multi-dimensional attitudes into a single label, different annotators weight different dimensions – producing disagreement that reflects not confusion but different compression choices. We call this the \textbf{projection problem}, and show that its cost is conditional: when a text’s dimensions align, any weighting yields the same label and three-way annotation works well; when dimensions conflict, label agreement collapses while agreement on individual dimensions remains intact. A pilot study on SemEval-2016 Task 6 confirms this crossover: on dimension-consistent texts, label agreement (Krippendorff’s $α= 0.307$) exceeds dimensional agreement ($α= 0.082$); on dimension-conflicting texts, the pattern reverses – label $α$ drops to $0.085$ while dimensional $α$ rises to $0.334$, with Policy reaching $0.572$. The projection problem is real – but it activates precisely where it matters most.

Method: Extended FederatedScope-LLM framework for multilingual instruction-tuning experiments, and introduced Local Dynamic Early Stopping (LDES-FL) mechanism that allows clients to pause/resume training based on client-side validation performance.

Result: Monolingual local fine-tuning most effective for single-language specialization; federated training better for balanced multilingual models. Increasing within-client multilinguality leads to stronger, fairer global models, narrows gap to centralized multilingual fine-tuning, with largest gains for lower-resource languages.

Conclusion: Client language composition is a key design variable in multilingual FL that shapes performance, fairness and efficiency.

Abstract: Federated Learning (FL) of Large Language Models (LLMs) in multilingual environments presents significant challenges stemming from heterogeneous language distributions across clients and disparities in language resource availability. To address these challenges, we extended the FederatedScope-LLM framework to support multilingual instruction-tuning experiments with LLMs. We also introduced a novel client-specific early stopping mechanism, Local Dynamic Early Stopping (LDES-FL), which allows clients to pause and resume local training based on client-side validation performance, enhancing training efficiency and sustainability. Through a series of experiments, we studied how client language composition - from fully monolingual to increasingly multilingual clients - affects multilingual quality, fairness and training cost. Monolingual local fine-tuning remains the most effective for single-language specialization, whereas federated training is better suited to learning a single balanced multilingual model. In FL, increasing within-client multilinguality leads to stronger and fairer global models, narrows the gap to centralized multilingual fine-tuning, and yields the largest gains for lower-resource languages, albeit at the cost of more optimization steps. Overall, our results identify client language composition as a key design variable in multilingual FL, shaping performance, fairness and efficiency

Method: Hybrid framework combining: 1) dense semantic embeddings from pre-trained Sentence-BERT, 2) KB lookup strategy using FAISS for efficient retrieval from training-set cluster centroids, 3) HDBSCAN density-based clustering for unassigned mentions, 4) surface-form normalization and abbreviation resolution, with blocking strategy adaptation for large-scale settings.

Result: Achieved CoNLL F1 scores of 0.98, 0.98, and 0.96 on Subtasks 1, 2, and 3 respectively in the SOMD 2026 Shared Task for Cross-Document Coreference Resolution of software mentions.

Conclusion: The proposed hybrid approach effectively resolves software mention coreference across documents, with the combination of semantic embeddings, knowledge base lookup, and clustering proving successful for both standard and large-scale settings.

Abstract: This paper describes the system submitted to the SOMD 2026 Shared Task for Cross-Document Coreference Resolution (CDCR) of software mentions. Our approach addresses the challenge of identifying and clustering inconsistent software mentions across scientific corpora. We propose a hybrid framework that combines dense semantic embeddings from a pre-trained Sentence-BERT model, Knowledge Base (KB) lookup strategy built from training-set cluster centroids using FAISS for efficient retrieval, and HDBSCAN density-based clustering for mentions that cannot be confidently assigned to existing clusters. Surface-form normalization and abbreviation resolution are applied to improve canonical name matching. The same core pipeline is applied to Subtasks 1 and 2. To address the large scale settings of Subtask 3, the pipeline was adapted by utilising a blocking strategy based on entity types and canonicalized surface forms. Our system achieved CoNLL F1 scores of 0.98, 0.98, and 0.96 on Subtasks 1, 2, and 3 respectively.

Method: Joint training of a compact encoder-decoder model with shared byte-level tokenizer across all four stages. Combines masked language modeling (MLM), translation language modeling (TLM), sequence-to-sequence translation, and part-of-speech tagging with task-aware loss. Adds Latin transliteration and IPA reconstruction as auxiliary views, integrated through KL-based consistency and embedding-level fusion.

Result: Translation yielded the strongest gains for alignment. IPA with KL consistency improved cross-branch alignment, while early fusion showed limited efficacy. Overall alignment remained limited but provided a reproducible baseline and practical guidance for historical language modeling.

Conclusion: The findings provide a reproducible baseline for modeling historical languages under constraints and show how normalization and task design shape what counts as alignment in typologically distant settings. The approach offers practical guidance despite limited overall alignment.

Abstract: We study word-level semantic alignment across four historical stages of Ancient Egyptian. These stages differ in script and orthography, and parallel data are scarce. We jointly train a compact encoder-decoder model with a shared byte-level tokenizer on all four stages, combining masked language modeling (MLM), translation language modeling (TLM), sequence-to-sequence translation, and part-of-speech tagging under a task-aware loss with fixed weights and uncertainty-based scaling. To reduce surface divergence we add Latin transliteration and IPA reconstruction as auxiliary views. We integrate these views through KL-based consistency and through embedding-level fusion. We evaluate alignment quality using pairwise metrics, specifically ROC-AUC and triplet accuracy, on curated Egyptian-English and intra-Egyptian cognate datasets. Translation yields the strongest gains. IPA with KL consistency improves cross-branch alignment, while early fusion demonstrates limited efficacy. Although the overall alignment remains limited, the findings provide a reproducible baseline and practical guidance for modeling historical languages under real constraints. They also show how normalization and task design shape what counts as alignment in typologically distant settings.

Method: Created a large-scale Hindi-Sanskrit parallel corpus (92,196 sentences) from diverse contemporary sources including spoken tutorials, children’s magazines, radio conversations, and instruction materials. Benchmarked the dataset by fine-tuning three models: ByT5, NLLB, and IndicTrans-v2.

Result: Models trained on Samasāmayik achieve significant performance gains on in-domain test data while maintaining comparable performance on other test sets. Comparative analysis shows minimal semantic/lexical overlap with existing corpora, confirming dataset novelty.

Conclusion: Samasāmayik establishes a strong new baseline for contemporary Hindi-Sanskrit translation and serves as a robust resource for low-resource Indic language machine translation research.

Abstract: We release Samasāmayik, a novel, meticulously curated, large-scale Hindi-Sanskrit corpus, comprising 92,196 parallel sentences. Unlike most data available in Sanskrit, which focuses on classical era text and poetry, this corpus aggregates data from diverse sources covering contemporary materials, including spoken tutorials, children’s magazines, radio conversations, and instruction materials. We benchmark this new dataset by fine-tuning three complementary models - ByT5, NLLB and IndicTrans-v2, to demonstrate its utility. Our experiments demonstrate that models trained on the Samasamayik corpus achieve significant performance gains on in-domain test data, while achieving comparable performance on other widely used test sets, establishing a strong new performance baseline for contemporary Hindi-Sanskrit translation. Furthermore, a comparative analysis against existing corpora reveals minimal semantic and lexical overlap, confirming the novelty and non-redundancy of our dataset as a robust new resource for low-resource Indic language MT.

Method: Densely annotates multiplayer 3D gameplay videos at 1.22 labels/second with time-synced concurrent captions structured around Self, Other Agents, and World triadic system. Creates 2.4K diagnostic QA pairs organized into three cognitive complexity levels with structured distractor taxonomy.

Result: Evaluation of frontier MLLMs shows substantial gap from human performance, with common failures in temporal and cross-video grounding, agent-role attribution, and handling game decision density.

Conclusion: GameplayQA provides a framework for evaluating agentic perception and reasoning, revealing current MLLM limitations and stimulating research at intersection of embodied AI, agentic perception, and world modeling.

Abstract: Multimodal LLMs are increasingly deployed as perceptual backbones for autonomous agents in 3D environments, from robotics to virtual worlds. These applications require agents to perceive rapid state changes, attribute actions to the correct entities, and reason about concurrent multi-agent behaviors from a first-person perspective, capabilities that existing benchmarks do not adequately evaluate. We introduce GameplayQA, a framework for evaluating agentic-centric perception and reasoning through video understanding. Specifically, we densely annotate multiplayer 3D gameplay videos at 1.22 labels/second, with time-synced, concurrent captions of states, actions, and events structured around a triadic system of Self, Other Agents, and the World, a natural decomposition for multi-agent environments. From these annotations, we refined 2.4K diagnostic QA pairs organized into three levels of cognitive complexity, accompanied by a structured distractor taxonomy that enables fine-grained analysis of where models hallucinate. Evaluation of frontier MLLMs reveals a substantial gap from human performance, with common failures in temporal and cross-video grounding, agent-role attribution, and handling the decision density of the game. We hope GameplayQA stimulates future research at the intersection of embodied AI, agentic perception, and world modeling.

Method: Fine-tuned Qwen3.5-2B with LoRA on FineLeanCorpus using three regimes: supervised fine-tuning with curriculum learning, SFT without curriculum, and reinforcement learning with GRPO using cycle consistency reward based on NL→Lean4→NL’ loop similarity.

Result: RL substantially outperformed both SFT variants (mean cycle consistency 0.669 vs. 0.513 on FLC; 0.561 vs. 0.422 on PutnamBench) with minimal impact on formalization quality and only +0.011 nats cross-entropy loss. Curriculum ordering provided no measurable benefit.

Conclusion: Reinforcement learning with cycle consistency reward is effective for autoformalization, outperforming supervised approaches while maintaining formalization quality, with curriculum learning offering no advantage.

Abstract: Autoformalization - automatically translating natural language mathematical texts into formal proof language such as Lean4 - can help accelerate AI-assisted mathematical research, be it via proof verification or proof search. I fine-tune Qwen3.5-2B with LoRA for natural language to Lean4 formalization on FineLeanCorpus and consider three training regimes: supervised fine-tuning (SFT) with curriculum learning (difficulty 1 to 10), SFT without curriculum ordering, and reinforcement learning using group relative policy optimization (GRPO) with a cycle consistency reward. Cycle consistency measures how well the meaning of a statement is preserved through a NL to Lean4 to NL’ loop, computed as cosine similarity of off-the-shelf sentence embeddings. On an unseen subset of FineLeanCorpus (FLC) and on PutnamBench, RL substantially outperforms both SFT variants (mean cycle consistency 0.669 vs. 0.513 on FLC; 0.561 vs. 0.422 on PutnamBench), while increasing cross-entropy loss by only 0.011 nats, with minimal impact on formalization quality. Curriculum ordering provides no measurable benefit over shuffled training.

Method: Derived a hierarchy of pedagogical aspects from human pairwise preferences on MRBench, synthesized minimally contrastive response pairs differing along key aspects, and developed Bradley-Terry preference models trained on weighted-sum rankings from MRBench, synthetic pairs, and data combinations.

Result: Using only synthetic data, the best model achieved 0.69 pairwise accuracy on human preference tests; combining weighted-sum data with targeted synthetic groups improved accuracy to 0.74, outperforming larger general-purpose reward models with only a 0.5B-parameter backbone.

Conclusion: The approach enables effective evaluation of AI tutor pedagogical quality through synthetic contrastive data and specialized preference models, offering a scalable solution for assessing educational aspects that standard NLG metrics miss.

Abstract: Evaluating the pedagogical quality of AI tutors remains challenging: standard NLG metrics do not determine whether responses identify mistakes, scaffold reasoning, or avoid revealing the answers. For the task of mistake remediation, we derive a hierarchy of pedagogical aspects from human pairwise preferences on MRBench, and synthesize minimally contrastive response pairs that differ along key aspects (e.g., mistake identification and location, targetedness, scaffolding, actionability, clarity, and coherence). We develop and release Bradley-Terry preference models trained on weighted-sum rankings that we automatically create from MRBench, synthetic pairs, and data combinations. Using only synthetic data, our best model reaches 0.69 pairwise accuracy on a human preference test, and combining weighted-sum data with targeted synthetic groups improves accuracy to 0.74, outperforming larger general-purpose reward models while using only a 0.5B-parameter backbone.

Method: Developed Interaction2Eval, an LLM-based framework with specialized components for child speech recognition, Mandarin homophone disambiguation, and rubric-based reasoning on a large-scale dataset (TEPE-TCI-370h)

Result: Achieved up to 88% agreement with human expert judgments and demonstrated 18x efficiency gain in assessment workflow

Conclusion: AI can enable scalable, continuous quality monitoring in early childhood education, shifting from infrequent audits to monthly AI-assisted assessment with human oversight

Abstract: High-quality teacher-child interaction (TCI) is fundamental to early childhood development, yet traditional expert-based assessment faces a critical scalability challenge. In large systems like China’s-serving 36 million children across 250,000+ kindergartens-the cost and time requirements of manual observation make continuous quality monitoring infeasible, relegating assessment to infrequent episodic audits that limit timely intervention and improvement tracking. In this paper, we investigate whether AI can serve as a scalable assessment teammate by extracting structured quality indicators and validating their alignment with human expert judgments. Our contributions include: (1) TEPE-TCI-370h (Tracing Effective Preschool Education), the first large-scale dataset of naturalistic teacher-child interactions in Chinese preschools (370 hours, 105 classrooms) with standardized ECQRS-EC and SSTEW annotations; (2) We develop Interaction2Eval, a specialized LLM-based framework addressing domain-specific challenges-child speech recognition, Mandarin homophone disambiguation, and rubric-based reasoning-achieving up to 88% agreement; (3) Deployment validation across 43 classrooms demonstrating an 18x efficiency gain in the assessment workflow, highlighting its potential for shifting from annual expert audits to monthly AI-assisted monitoring with targeted human oversight. This work not only demonstrates the technical feasibility of scalable, AI-augmented quality assessment but also lays the foundation for a new paradigm in early childhood education-one where continuous, inclusive, AI-assisted evaluation becomes the engine of systemic improvement and equitable growth.

Method: Proposes PINGALA decoding approach with: 1) Line segmentation instead of monolithic sequence processing, 2) Token selection favoring longer tokens to encourage well-formed words, 3) Phonetically aware SLP1 transliteration scheme, 4) Reference-free evaluation using cross-encoders.

Result: 10% improvement in semantic coherence with comparable metrical adherence; 46% increase in metrical alignment using SLP1 transliteration with comparable semantic similarity; better evaluation alignment with true poetry instances.

Conclusion: Segmenting Sanskrit verses as grouped-lines significantly improves semantic coherence, and phonetic transliteration enhances metrical alignment, making PINGALA an effective approach for Sanskrit poetry generation with instruction fine-tuned LLMs.

Abstract: Poetry generation in Sanskrit typically requires the verse to be semantically coherent and adhere to strict prosodic rules. In Sanskrit prosody, every line of a verse is typically a fixed length sequence of syllables adhering to prescribed binary patterns of syllable weights. We observe that instead of treating a verse as a monolithic sequence, segmenting them as grouped-lines leads to significant improvement in semantic coherence by 10% with comparable metrical adherence. Specifically, PINGALA, our proposed decoding approach is designed to encourage every line to have well-formed words and our token selection biases the model towards it by preferring longer tokens. Writing in Sanskrit follows phonemic orthography, hence using a phonetically aware transliteration scheme, SLP1, increased the metrical alignment by 46% with comparable semantic similarity, for a instruction fine-tuned large language models like Phi-4. We also introduce a new approach for reference-free evaluation using cross-encoders which achieved better alignment with true poetry instances.

Method: Uses sorry-driven formal decomposition leveraging the sorry placeholder in Lean to precisely isolate unresolved subgoals while preserving verified proof structure, extracting each failed subproblem into clean, self-contained contexts for independent resolution.

Result: Experimental results on challenging mathematical competition benchmarks (IMO 2025 and Putnam 2025) demonstrate significant advantages in proving efficiency compared to existing approaches.

Conclusion: Mechanic effectively addresses the dilemma between inefficient full regeneration and context-degrading iterative repairs by isolating subproblems, improving automated theorem proving for complex mathematical reasoning.

Abstract: Recent advances in large language models (LLMs) and LLM-based agents have substantially improved the capabilities of automated theorem proving. However, for problems requiring complex mathematical reasoning, current systems rarely succeed on the first try and must repeatedly modify their proof strategies. Existing approaches for handling failed attempts typically either discard the entire proof and regenerate it from scratch or iteratively fix errors within the proof. The former is inefficient, as it may abandon mostly correct reasoning due to localized errors, while the latter, although preserving prior progress, leads to progressively longer contexts which progressively degrades the model’s ability to attend to the remaining unresolved subproblems. To address this dilemma, we propose Mechanic, a novel agent system that employs a sorry-driven formal decomposition strategy. By leveraging the sorry placeholder in Lean to precisely isolate unresolved subgoals while preserving the surrounding verified proof structure, Mechanic extracts each failed subproblem into a clean, self-contained context and resolves it independently. This avoids both the waste of full regeneration and the excessive context length induced by repeated repairs. Experimental results on challenging mathematical competition benchmarks, including IMO 2025 and Putnam 2025, demonstrate that our agent achieves significant advantages in proving efficiency.

Method: Conducted controlled experiments varying conditioning context richness and task coverage across multiple models (Qwen3-8B, DeepSeek-Distill-Qwen-7B, Olmo3-7B-Instruct). Analyzed how self-distillation affects epistemic verbalization and performance in both in-domain and out-of-distribution mathematical reasoning tasks.

Result: Self-distillation reduces response length but degrades mathematical reasoning performance by up to 40%. Rich conditioning contexts suppress uncertainty expression, enabling rapid in-domain optimization but harming OOD performance where uncertainty expression is beneficial.

Conclusion: Exposing appropriate levels of uncertainty is crucial for robust reasoning. Optimizing reasoning behavior should go beyond reinforcing correct answer traces to preserve epistemic verbalization capabilities.

Abstract: Self-distillation has emerged as an effective post-training paradigm for LLMs, often improving performance while shortening reasoning traces. However, in mathematical reasoning, we find that it can reduce response length while degrading performance. We trace this degradation to the suppression of epistemic verbalization - the model’s expression of uncertainty during reasoning. Through controlled experiments varying conditioning context richness and task coverage, we show that conditioning the teacher on rich information suppresses uncertainty expression, enabling rapid in-domain optimization with limited task coverage but harming OOD performance, where unseen problems benefit from expressing uncertainty and adjusting accordingly. Across Qwen3-8B, DeepSeek-Distill-Qwen-7B, and Olmo3-7B-Instruct, we observe performance drops of up to 40%. Our findings highlight that exposing appropriate levels of uncertainty is crucial for robust reasoning and underscore the importance of optimizing reasoning behavior beyond merely reinforcing correct answer traces.

Method: Embedding-based approach that analyzes scaffolding dynamics by computing cosine similarity between tutor/student contributions and task-relevant content (problem statements and correct solutions), applied to 1,576 real-world math tutoring dialogues.

Result: Revealed systematic differences in task alignment and distinct temporal patterns; tutor contributions showed stronger grounding in problem content early in interactions; student solution alignment was modestly positively associated with progression; role-specific semantic alignment predicts tutorial progression beyond baseline features.

Conclusion: Scaffolding is a continuous, role-sensitive process grounded in task semantics; the approach provides a principled method for analyzing instructional dialogue and evaluating conversational tutoring systems.

Abstract: Adaptive scaffolding enhances learning, yet the field lacks robust methods for measuring it within authentic tutoring dialogue. This gap has become more pressing with the rise of remote human tutoring and large language model-based systems. We introduce an embedding-based approach that analyzes scaffolding dynamics by aligning the semantics of dialogue turns, problem statements, and correct solutions. Specifically, we operationalize alignment by computing cosine similarity between tutor and student contributions and task-relevant content. We apply this framework to 1,576 real-world mathematics tutoring dialogues from the Eedi Question Anchored Tutoring Dialogues dataset. The analysis reveals systematic differences in task alignment and distinct temporal patterns in how participants ground their contributions in problem and solution content. Further, mixed-effects models show that role-specific semantic alignment predicts tutorial progression beyond baseline features such as message order and length. Tutor contributions exhibited stronger grounding in problem content early in interactions. In contrast, student solution alignment was modestly positively associated with progression. These findings support scaffolding as a continuous, role-sensitive process grounded in task semantics. By capturing role-specific alignment over time, this approach provides a principled method for analyzing instructional dialogue and evaluating conversational tutoring systems.

Method: Developed a multilingual AI-powered interface that dynamically identifies key concepts in text and maps them to contextually relevant pictograms. Evaluated across five languages (English, French, Italian, Spanish, Arabic) through multilingual coverage analysis, expert clinical review by speech therapists and special education professionals, and latency assessment.

Result: High pictogram coverage and visual scaffolding density across all five languages. Expert audits showed semantic appropriateness exceeding 95% for European languages and ~90% for Arabic. System latency remained within interactive thresholds suitable for real-time educational use.

Conclusion: The system demonstrates technical viability, semantic safety, and acceptability for automated multimodal scaffolding to improve accessibility for neurodiverse learners across multiple languages.

Abstract: Reading comprehension presents a significant challenge for children with Special Educational Needs and Disabilities (SEND), often requiring intensive one-on-one reading support. To assist therapists in scaling this support, we developed a multilingual, AI-powered interface that automatically enhances text with visual scaffolding. This system dynamically identifies key concepts and maps them to contextually relevant pictograms, supporting learners across languages. We evaluated the system across five typologically diverse languages (English, French, Italian, Spanish, and Arabic), through multilingual coverage analysis, expert clinical review by speech therapists and special education professionals, and latency assessment. Evaluation results indicate high pictogram coverage and visual scaffolding density across the five languages. Expert audits suggested that automatically selected pictograms were semantically appropriate, with combined correct and acceptable ratings exceeding 95% for the four European languages and approximately 90% for Arabic despite reduced pictogram repository coverage. System latency remained within interactive thresholds suitable for real-time educational use. These findings support the technical viability, semantic safety, and acceptability of automated multimodal scaffolding to improve accessibility for neurodiverse learners.

Method: MARCH orchestrates three specialized agents: Solver generates initial RAG response, Proposer decomposes it into atomic propositions, and Checker validates these against retrieved evidence in isolation (information asymmetry). The pipeline is trained with multi-agent reinforcement learning for co-evolution.

Result: Extensive experiments show MARCH substantially reduces hallucination rates. An 8B-parameter LLM with MARCH achieves performance competitive with powerful closed-source models.

Conclusion: MARCH provides a scalable path for factual self-improvement of LLMs through co-evolution, addressing confirmation bias in hallucination detection.

Abstract: Hallucination remains a critical bottleneck for large language models (LLMs), undermining their reliability in real-world applications, especially in Retrieval-Augmented Generation (RAG) systems. While existing hallucination detection methods employ LLM-as-a-judge to verify LLM outputs against retrieved evidence, they suffer from inherent confirmation bias, where the verifier inadvertently reproduces the errors of the original generation. To address this, we introduce Multi-Agent Reinforced Self-Check for Hallucination (MARCH), a framework that enforces rigorous factual alignment by leveraging deliberate information asymmetry. MARCH orchestrates a collaborative pipeline of three specialized agents: a Solver, a Proposer, and a Checker. The Solver generates an initial RAG response, which the Proposer decomposes into claim-level verifiable atomic propositions. Crucially, the Checker validates these propositions against retrieved evidence in isolation, deprived of the Solver’s original output. This well-crafted information asymmetry scheme breaks the cycle of self-confirmation bias. By training this pipeline with multi-agent reinforcement learning (MARL), we enable the agents to co-evolve and optimize factual adherence. Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucination rates. Notably, an 8B-parameter LLM equipped with MARCH achieves performance competitive with powerful closed-source models. MARCH paves a scalable path for factual self-improvement of LLMs through co-evolution. The code is at https://github.com/Qwen-Applications/MARCH.

Method: Combines ColBERT-based retriever fine-tuned with contrastive learning and a generator aligned using Direct Preference Optimization (DPO). Uses synthetic queries and collected pairwise preferences to adapt to the policy domain. Evaluates retrieval quality, answer relevance, and faithfulness.

Result: Domain-specific fine-tuning improves retrieval metrics but doesn’t consistently improve end-to-end QA performance. Stronger retrieval can sometimes lead to more confident hallucinations when relevant documents are absent from the corpus.

Conclusion: Improvements to individual RAG components don’t necessarily translate to more reliable answers in policy domains. Provides practical insights for designing grounded QA systems over dynamic regulatory corpora.

Abstract: Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks. We study the application of RAG to AI governance and policy analysis using the AI Governance and Regulatory Archive (AGORA) corpus, a curated collection of 947 AI policy documents. Our system combines a ColBERT-based retriever fine-tuned with contrastive learning and a generator aligned to human preferences using Direct Preference Optimization (DPO). We construct synthetic queries and collect pairwise preferences to adapt the system to the policy domain. Through experiments evaluating retrieval quality, answer relevance, and faithfulness, we find that domain-specific fine-tuning improves retrieval metrics but does not consistently improve end-to-end question answering performance. In some cases, stronger retrieval counterintuitively leads to more confident hallucinations when relevant documents are absent from the corpus. These results highlight a key concern for those building policy-focused RAG systems: improvements to individual components do not necessarily translate to more reliable answers. Our findings provide practical insights for designing grounded question-answering systems over dynamic regulatory corpora.

Method: Develops a causal model for coupled autoregressive generation that allows different LLMs to sample responses with the same source of randomness. This enables controlled comparisons by synchronizing the random seed across models.

Result: Coupled generation requires up to 75% fewer samples than vanilla generation to reach the same conclusions on benchmark datasets. Surprisingly, coupled and vanilla generation can lead to different model rankings in pairwise comparisons even with infinite samples.

Conclusion: Existing LLM evaluation protocols may produce misleading rankings due to uncontrolled randomness. Coupled autoregressive generation provides a more rigorous evaluation framework that controls for this confounding factor.

Abstract: State of the art large language models rely on randomization to respond to a prompt. As an immediate consequence, a model may respond differently to the same prompt if asked multiple times. In this work, we argue that the evaluation and ranking of large language models should control for the randomization underpinning their functioning. Our starting point is the development of a causal model for coupled autoregressive generation, which allows different large language models to sample responses with the same source of randomness. Building upon our causal model, we first show that, on evaluations based on benchmark datasets, coupled autoregressive generation leads to the same conclusions as vanilla autoregressive generation but using provably fewer samples. However, we further show that, on evaluations based on (human) pairwise comparisons, coupled and vanilla autoregressive generation can surprisingly lead to different rankings when comparing more than two models, even with an infinite amount of samples. This suggests that the apparent advantage of a model over others in existing evaluation protocols may not be genuine but rather confounded by the randomness inherent to the generation process. To illustrate and complement our theoretical results, we conduct experiments with several large language models from the Llama, Mistral and Qwen families. We find that, across multiple benchmark datasets, coupled autoregressive generation requires up to 75% fewer samples to reach the same conclusions as vanilla autoregressive generation. Further, we find that the win-rates derived from pairwise comparisons by a strong large language model to prompts from the LMSYS Chatbot Arena platform differ under coupled and vanilla autoregressive generation.

Method: Two-stage generation framework: first plans section structure, then generates each section with retrieval-based prompts; model-agnostic approach works with both open- and closed-source LLMs

Result: Wrapper substantially improves factual accuracy, coherence, and completeness compared to fine-tuned baselines; comprehensive evaluation shows effectiveness across lexical, semantic, LLM-based, and expert-driven assessments

Conclusion: Establishes new benchmark dataset and generalizable generation framework for AI-assisted legal drafting, paving way for future research in structured legal text generation

Abstract: Automating legal document drafting can improve efficiency and reduce the burden of manual legal work. Yet, the structured generation of private legal documents remains underexplored, particularly in the Indian context, due to the scarcity of public datasets and the complexity of adapting models for long-form legal drafting. To address this gap, we introduce VidhikDastaavej, a large-scale, anonymized dataset of private legal documents curated in collaboration with an Indian law firm. Covering 133 diverse categories, this dataset is the first resource of its kind and provides a foundation for research in structured legal text generation and Legal AI more broadly. We further propose a Model-Agnostic Wrapper (MAW), a two-stage generation framework that first plans the section structure of a legal draft and then generates each section with retrieval-based prompts. MAW is independent of any specific LLM, making it adaptable across both open- and closed-source models. Comprehensive evaluation, including lexical, semantic, LLM-based, and expert-driven assessments with inter-annotator agreement, shows that the wrapper substantially improves factual accuracy, coherence, and completeness compared to fine-tuned baselines. This work establishes both a new benchmark dataset and a generalizable generation framework, paving the way for future research in AI-assisted legal drafting.

Method: DiKE consists of: 1) Knowledge Representation Disentanglement (KRD) module that decomposes subject representation into target-knowledge-related and unrelated components, and 2) Disentanglement-based Knowledge Edit (DKE) module that updates only target-related component while preserving unrelated one. Uses closed-form rank-one parameter update based on matrix theory for efficient editing.

Result: Extensive experiments across multiple LLMs show DiKE substantially improves fine-grained irrelevant knowledge preservation while maintaining competitive general editing performance. Introduces FINE-KED benchmark for evaluation.

Conclusion: DiKE addresses the limitation of existing knowledge editing methods by disentangling knowledge representations, enabling targeted updates while preserving fine-grained irrelevant knowledge through a theoretically grounded, efficient approach.

Abstract: Knowledge Editing has emerged as a promising solution for efficiently updating embedded knowledge in large language models (LLMs). While existing approaches demonstrate effectiveness in integrating new knowledge and preserving the original capabilities of LLMs, they fail to maintain fine-grained irrelevant knowledge, namely facts that share the same subject as edited knowledge but differ in relation and object. This challenge arises because subject representations inherently encode multiple attributes, causing the target and fine-grained irrelevant knowledge to become entangled in the representation space, and thus vulnerable to unintended alterations during editing. To address this, we propose DiKE, a novel approach that Disentangles Knowledge representations for LLM Editing (DiKE). DiKE consists of two key components: a Knowledge Representation Disentanglement (KRD) module that decomposes the subject representation into target-knowledge-related and -unrelated components, and a Disentanglementbased Knowledge Edit (DKE) module that updates only the target-related component while explicitly preserving the unrelated one. We further derive a closedform, rank-one parameter update based on matrix theory to enable efficient and minimally invasive edits. To rigorously evaluate fine-grained irrelevant knowledge preservation, we construct FINE-KED, a new benchmark comprising fine-grained irrelevant knowledge at different levels of relational similarity to the edited knowledge. Extensive experiments across multiple LLMs demonstrate that DiKE substantially improves fine-grained irrelevant knowledge preservation while maintaining competitive general editing performance.

Method: Proposes Text-to-Talk (TtT) framework integrating AR text generation with NAR audio diffusion using absorbing discrete diffusion. Uses modality-aware attention for causal text decoding and bidirectional audio modeling. Implements three training strategies to reduce train-test discrepancies and employs block-wise diffusion for parallel audio synthesis.

Result: Comprehensive experiments on Audio-QA, ASR, AAC and speech-to-speech benchmarks show TtT consistently surpasses strong AR and NAR baselines. Ablation studies confirm the contribution of each component.

Conclusion: TtT provides an effective unified framework for audio-text multimodal systems by combining AR text generation with NAR audio diffusion, addressing the different relational dependencies between modalities while maintaining efficiency and performance.

Abstract: Recent advances in large language models (LLMs) have attracted significant interest in extending their capabilities to multimodal scenarios, particularly for speech-to-speech conversational systems. However, existing multimodal models handling interleaved audio and text rely on autoregressive (AR) methods, overlooking that text depends on target-target relations whereas audio depends mainly on source-target relations. In this work, we propose Text-to-Talk (TtT), a unified audio-text framework that integrates AR text generation with non-autoregressive (NAR) audio diffusion in a single Transformer. By leveraging the any-order AR property of absorbing discrete diffusion, our approach provides a unified training objective for text and audio. To support this hybrid generation paradigm, we design a modality-aware attention mechanism that enforces causal decoding for text while allowing bidirectional modeling within audio spans, and further introduce three training strategies that reduce train-test discrepancies. During inference, TtT employs block-wise diffusion to synthesize audio in parallel while flexibly handling variable-length outputs. Comprehensive experiments on Audio-QA, ASR, AAC and speech-to-speech benchmarks show that TtT consistently surpasses strong AR and NAR baselines, with additional ablation and training-strategy analyses confirming the contribution of each component. We will open-source our models, data and code to facilitate future research in this direction.

Method: Proposes KnowledgeSmith framework that: 1) Formulates editing and unlearning as constrained optimization problems, 2) Creates automatic dataset generator for structured interventions across multiple graph levels and data scales, 3) Enables controlled studies of modification strategy propagation through model knowledge.

Result: Extensive experiments reveal nuanced insights: LLMs don’t update knowledge similarly to humans across different knowledge levels, existence of consistency-capacity trade-off, and detailed understanding of knowledge propagation, plasticity scaling, consistency, and robustness.

Conclusion: The findings provide systematic understanding of LLM knowledge updating mechanisms and offer suggestions for designing more reliable and scalable knowledge updating strategies for large language models.

Abstract: Knowledge editing and machine unlearning are two popular approaches for large language models (LLMs) to stay up-to-date. However, the knowledge updating mechanism of LLMs remains largely unexplored due to insufficient, isolated, and small-scale evaluation. For instance, are LLMs similar to humans in modifying certain knowledge? What differs editing and unlearning as training data increases? This paper proposes KnowledgeSmith, a unified framework to systematically understand the updating mechanism of LLMs. We first cast editing and unlearning as instances of one constrained optimization problem. Then, we propose an automatic dataset generator that provides structured interventions across multiple graph levels and data scales, enabling controlled studies of how different modification strategies propagate through model knowledge. Extensive experiments demonstrate nuanced insights over knowledge propagation, plasticity scaling, consistency, and robustness. For instance, our results show that LLMs do not exhibit similar updating as humans for different levels of knowledge, and there exists consistency-capacity trade-off. We hope our findings can offer suggestions to the design of more reliable and scalable strategies. Code: https://github.com/AIFrontierLab/KnowledgeSmith.git

Method: Proposes FedSRD (Sparsify-Reconstruct-Decompose) framework with importance-aware sparsification to reduce upload parameters, server aggregation in full-rank space to mitigate conflicts, and decomposition into sparse low-rank format for broadcast. Also introduces FedSRD-e variant for reduced computational overhead.

Result: Experiments on 10 benchmarks show the framework reduces communication costs by up to 90% while improving performance on heterogeneous client data.

Conclusion: FedSRD provides an efficient solution for federated LLM fine-tuning that addresses communication bottlenecks while maintaining performance on decentralized data.

Abstract: The current paradigm of training large language models (LLMs) on public available Web data is becoming unsustainable as high-quality data sources in specialized domains near exhaustion. Federated Learning (FL) emerges as a practical solution for the next generation of AI on a decentralized Web, enabling privacy-preserving collaborative fine-tuning on decentralized private data. While Low-Rank Adaptation (LoRA) is standard for efficient fine-tuning, its federated application faces a critical bottleneck: communication overhead under heterogeneous network conditions. Structural redundancy in LoRA parameters increases communication costs and causes aggregation conflicts. To address this, we propose FedSRD, a Sparsify-Reconstruct-Decompose framework for communication-efficient federated LLM fine-tuning. We introduce importance-aware sparsification to reduce the upload parameter count while preserving the structural integrity of LoRA updates. The server aggregates updates in full-rank space to mitigate conflicts, then decomposes the global update into a sparse low-rank format for broadcast, ensuring a symmetrically efficient cycle. We also propose an efficient variant, FedSRD-e, to reduce computational overhead. Experiments on 10 benchmarks show our framework significantly reduces communication costs by up to 90% while improving performance on heterogeneous client data.

Method: Formalizes benchmark construction as finding an optimal diagnostic basis in a binary code-test matrix. Introduces WrongSelect algorithm to select maximally diverse wrong codes, creating TC-Bench - a compact, diverse benchmark from competitive programming submissions.

Result: Even advanced test case generation methods achieve only ~60% exclusion rates on TC-Bench, exposing significant gaps in diagnostic power and highlighting substantial room for improvement.

Conclusion: The framework provides a principled approach to benchmark construction that is inflation-resistant and computationally efficient, revealing limitations in current LLM-based test generation methods.

Abstract: Evaluating test cases automatically generated by Large Language Models (LLMs) is a critical yet challenging task. Existing benchmarks often evaluate the exclusion ratio on large, unstructured collections of wrong codes, suffering from high computational costs and score inflation. Furthermore, they inadvertently reward generators that detect common, trivial bugs, while failing to penalize their inability to identify rare yet critical faults. In this work, we connect two fundamental questions: (1) What is the minimal set of wrong codes sufficient to represent the entire error space? and (2) What is the minimal set of test cases needed to distinguish them? We introduce a novel framework that formalizes benchmark construction as finding an optimal diagnostic basis in a binary code-test matrix, where rows represent wrong codes and columns represent test case results. The rank of this matrix specifies the minimal number of independent error patterns (wrong codes) and provides a tight upper bound on the number of test cases required for complete fault coverage. Our objective is to identify a basis of size equal to the matrix rank that maximizes internal diversity. To tackle this NP-hard problem, we propose WrongSelect, an efficient approximation algorithm to select maximally diverse wrong codes. Applying this framework to millions of competitive programming submissions, we construct TC-Bench, a compact, diverse, and inflation-resistant benchmark. Extensive experiments show that even the most advanced test case generation methods achieve only ~60% exclusion rates on TC-Bench, exposing a significant gap in their diagnostic power and highlighting substantial room for future improvement. Our dataset is available at: https://huggingface.co/datasets/Luoberta/TC-Bench and our code is at: https://github.com/Luowaterbi/TC-Bench.

Method: SyTTA couples two uncertainty signals: input-side perplexity (indicating mismatch with domain-specific terminology/patterns) and output-side predictive entropy (indicating diffuse/unstable token probabilities). Uses these signals for on-the-fly adaptation during inference without labeled examples.

Result: Across diverse model architectures and domain-specific benchmarks, SyTTA delivers consistent gains. On agricultural question answering, improves Rouge-LSum by over 120% on Qwen-2.5-7B with only 4 extra tokens per query.

Conclusion: Effective test-time adaptation for language models is achievable without labeled examples, supporting deployment in label-scarce domains. The framework is efficient and practical for real-world applications.

Abstract: Large language models (LLMs) are increasingly deployed in specialized domains such as finance, medicine, and agriculture, where they face significant distribution shifts from their training data. Domain-specific fine-tuning can mitigate this challenge but relies on high-quality labeled data that is expensive and slow to collect in expertise-limited settings. We study label-free test-time adaptation for language models and present SyTTA, an inference-time framework that adapts models on-the-fly without additional supervision. SyTTA couples two complementary uncertainty signals that arise under distribution shift: input-side perplexity, indicating mismatch with domain-specific terminology and patterns, and output-side predictive entropy, indicating diffuse and unstable token probabilities during generation. Across diverse model architectures and domain-specific benchmarks, SyTTA delivers consistent gains. Notably, on agricultural question answering, SyTTA improves Rouge-LSum by over 120% on Qwen-2.5-7B with only 4 extra tokens per query. These results show that effective test-time adaptation for language models is achievable without labeled examples, supporting deployment in label-scarce domains. The code will be made available upon acceptance.

Method: STEAM uses Bayesian optimization to search among 133 candidate languages for the back-translation that best recovers watermark strength. It’s compatible with any watermarking method, non-invasive, and easily extendable to new languages.

Result: STEAM achieves average gains of +0.23 AUC and +37% TPR@1%, providing scalable and fairer watermarking across diverse languages, including low-resource ones.

Conclusion: STEAM offers a robust, scalable solution for multilingual watermark detection that addresses the limitations of existing methods, particularly for low-resource languages, making watermarking more equitable across linguistic diversity.

Abstract: Multilingual watermarking aims to make large language model (LLM) outputs traceable across languages, yet current methods still fall short. Despite claims of cross-lingual robustness, they are evaluated only on high-resource languages. We show that existing multilingual watermarking methods are not truly multilingual: they fail to remain robust under translation attacks in medium- and low-resource languages. We trace this failure to semantic clustering, which fails when the tokenizer vocabulary contains too few full-word tokens for a given language. To address this, we introduce STEAM, a detection method that uses Bayesian optimisation to search among 133 candidate languages for the back-translation that best recovers the watermark strength. It is compatible with any watermarking method, robust across different tokenizers and languages, non-invasive, and easily extendable to new languages. With average gains of +0.23 AUC and +37% TPR@1%, STEAM provides a scalable approach toward fairer watermarking across the diversity of languages.

Method: Investigates how three key features of human quantification are encoded in LLM architectures, comparing thinking vs. instruct models across different languages. Examines quantifier scales, ranges of use, prototypicality values, and numerical biases to determine differences from human performance.

Result: Thinking models showed high accuracy in numerosity estimation and organizing quantifiers into scales, but key differences remain between humans and LLMs across all model types, particularly in ranges of use and prototypicality values.

Conclusion: This work paves the way for addressing MLLMs as semantic and pragmatic agents, while cross-linguistic analysis can reveal whether their abilities are robust and stable across different languages, despite current gaps in quantification understanding.

Abstract: Quantification has been proven to be a particularly difficult linguistic phenomenon for (Multimodal) Large Language Models (MLLMs). However, given that quantification interfaces with the logic, pragmatic, and numerical domains, the exact reasons for the poor performance are still unclear. This paper looks at three key features of human quantification shared cross-linguistically that have remained so far unexplored in the (M)LLM literature: the ordering of quantifiers into scales, the ranges of use and prototypicality, and the biases inherent in the human approximate number system. The aim is to determine how these features are encoded in the models’ architecture, how they may differ from humans, and whether the results are affected by the type of model (thinking vs. instruct) and the language under investigation. Results show that although thinking models showed a high accuracy in the numerosity estimation task and in the organization of quantifiers into scales, there are still key differences between humans and LLMs across all model types, particularly in terms of ranges of use and prototypicality values. This work, thus, paves the way for addressing the nature of MLLMs as semantic and pragmatic agents, while the cross-linguistic lens can elucidate whether their abilities are robust and stable across different languages.

Method: Curated datasets from Twitter and Reddit, implemented “split-then-balance” pipeline, compared traditional lexical models, hybrid approaches, and fine-tuned transformers. Used domain-adapted MentalBERT and introduced hybrid SHAPLLM explainability framework with prototype dashboard.

Result: End-to-end fine-tuning proved critical, with MentalBERT achieving 0.92 accuracy and 0.76 Macro F1 score, outperforming generic models and zero-shot LLM baselines. Created practical “Social Media Screener” dashboard for moderators.

Conclusion: The framework provides a robust baseline for mental health and cyberbullying detection, emphasizing human-in-the-loop screening rather than diagnosis, with future needs for multi-label clinically-validated datasets.

Abstract: Mental health challenges and cyberbullying are increasingly prevalent in digital spaces, necessitating scalable and interpretable detection systems. This paper introduces a unified multiclass classification framework for detecting ten distinct mental health and cyberbullying categories from social media data. We curate datasets from Twitter and Reddit, implementing a rigorous “split-then-balance” pipeline to train on balanced data while evaluating on a realistic, held-out imbalanced test set. We conducted a comprehensive evaluation comparing traditional lexical models, hybrid approaches, and several end-to-end fine-tuned transformers. Our results demonstrate that end-to-end fine-tuning is critical for performance, with the domain-adapted MentalBERT emerging as the top model, achieving an accuracy of 0.92 and a Macro F1 score of 0.76, surpassing both its generic counterpart and a zero-shot LLM baseline. Grounded in a comprehensive ethical analysis, we frame the system as a human-in-the-loop screening aid, not a diagnostic tool. To support this, we introduce a hybrid SHAPLLM explainability framework and present a prototype dashboard (“Social Media Screener”) designed to integrate model predictions and their explanations into a practical workflow for moderators. Our work provides a robust baseline, highlighting future needs for multi-label, clinically-validated datasets at the critical intersection of online safety and computational mental health.

Method: Proposes Collaborative Causal Sensemaking (CCS) as a research agenda with three components: 1) New training environments that reward collaborative thinking, 2) Representations for shared human-AI mental models, and 3) Evaluation centered on trust and complementarity rather than just answer accuracy.

Result: The paper presents a conceptual framework and research agenda rather than empirical results. It argues for shifting from oracle-like answer engines to AI teammates that co-reason with humans over causal structures of shared decisions.

Conclusion: To advance effective human-AI teams, research should focus on developing agents’ collaborative sensemaking capabilities through CCS, moving beyond answer engines to cultivate AI teammates that can co-reason with human partners about causal structures in decision-making.

Abstract: LLM-based agents are increasingly deployed for expert decision support, yet human-AI teams in high-stakes settings do not yet reliably outperform the best individual. We argue this complementarity gap reflects a fundamental mismatch: current agents are trained as answer engines, not as partners in the collaborative sensemaking through which experts actually make decisions. Sensemaking (the ability to co-construct causal explanations, surface uncertainties, and adapt goals) is the key capability that current training pipelines do not explicitly develop or evaluate. We propose Collaborative Causal Sensemaking (CCS) as a research agenda to develop this capability from the ground up, spanning new training environments that reward collaborative thinking, representations for shared human-AI mental models, and evaluation centred on trust and complementarity. Taken together, these directions shift MAS research from building oracle-like answer engines to cultivating AI teammates that co-reason with their human partners over the causal structure of shared decisions, advancing the design of effective human-AI teams.

Method: ProFit leverages the insight that high-probability tokens carry core logical framework while low-probability tokens are replaceable expressions. It selectively masks low-probability tokens during SFT to prevent surface-level overfitting.

Result: Extensive experiments show ProFit consistently outperforms traditional SFT baselines on general reasoning and mathematical benchmarks.

Conclusion: ProFit offers an effective solution to single-reference overfitting in SFT by strategically masking low-probability tokens, achieving better alignment without costly multiple reference datasets.

Abstract: Supervised fine-tuning (SFT) is a fundamental post-training strategy to align Large Language Models (LLMs) with human intent. However, traditional SFT often ignores the one-to-many nature of language by forcing alignment with a single reference answer, leading to the model overfitting to non-core expressions. Although our empirical analysis suggests that introducing multiple reference answers can mitigate this issue, the prohibitive data and computational costs necessitate a strategic shift: prioritizing the mitigation of single-reference overfitting over the costly pursuit of answer diversity. To achieve this, we reveal the intrinsic connection between token probability and semantic importance: high-probability tokens carry the core logical framework, while low-probability tokens are mostly replaceable expressions. Based on this insight, we propose ProFit, which selectively masks low-probability tokens to prevent surface-level overfitting. Extensive experiments confirm that ProFit consistently outperforms traditional SFT baselines on general reasoning and mathematical benchmarks.

Method: ChartAttack framework injects misleaders into chart designs to induce incorrect interpretations. AttackViz dataset contains chart QA pairs labeled with effective misleaders and their induced incorrect answers for evaluation.

Result: ChartAttack significantly degrades QA performance, reducing MLLM accuracy by 17.2 points in-domain and 11.9 cross-domain. Human results show 20.2-point accuracy drop. AttackViz can be used to fine-tune MLLMs to improve robustness.

Conclusion: There’s an urgent need for robustness and security considerations in MLLM-based chart generation systems. The framework highlights vulnerabilities and provides tools for improving model resilience against misleading visualizations.

Abstract: Multimodal large language models (MLLMs) are increasingly used to automate chart generation from data tables, enabling efficient data analysis and reporting but also introducing new misuse risks. In this work, we introduce ChartAttack, a novel framework for evaluating how MLLMs can be misused to generate misleading charts at scale. ChartAttack injects misleaders into chart designs, aiming to induce incorrect interpretations of the underlying data. Furthermore, we create AttackViz, a chart question-answering (QA) dataset where each (chart specification, QA) pair is labeled with effective misleaders and their induced incorrect answers. ChartAttack significantly degrades QA performance, reducing MLLM accuracy by 17.2 points in-domain and 11.9 cross-domain. Preliminary human results (limited sample size) indicate a 20.2-point accuracy drop. Finally, we demonstrate that AttackViz can be used to fine-tune MLLMs to improve robustness against misleading charts. Our findings highlight an urgent need for robustness and security considerations in the design, evaluation, and deployment of MLLM-based chart generation systems. We make our code and data publicly available.

Method: Proposes collaborative premise governance over a knowledge substrate with discrepancy-driven control loops: detecting conflicts, localizing misalignment via typed discrepancies (teleological, epistemic, procedural), and triggering bounded negotiation through decision slices. Uses commitment gating to block action on uncommitted load-bearing premises and value-gated challenge allocation.

Result: Theoretical framework illustrated with tutoring examples, proposing trust attaches to auditable premises and evidence standards rather than conversational fluency. Includes falsifiable evaluation criteria for implementation.

Conclusion: Reliable human-AI partnership requires fundamental shift from answer generation to collaborative premise governance, with systematic mechanisms to prevent sycophantic agreement and ensure transparent, accountable decision-making in high-stakes scenarios.

Abstract: As LLMs expand from assistance to decision support, a dangerous pattern emerges: fluent agreement without calibrated judgment. Low-friction assistants can become sycophantic, baking in implicit assumptions and pushing verification costs onto experts, while outcomes arrive too late to serve as reward signals. In deep-uncertainty decisions (where objectives are contested and reversals are costly), scaling fluent agreement amplifies poor commitments faster than it builds expertise. We argue reliable human-AI partnership requires a shift from answer generation to collaborative premise governance over a knowledge substrate, negotiating only what is decision-critical. A discrepancy-driven control loop operates over this substrate: detecting conflicts, localizing misalignment via typed discrepancies (teleological, epistemic, procedural), and triggering bounded negotiation through decision slices. Commitment gating blocks action on uncommitted load-bearing premises unless overridden under logged risk; value-gated challenge allocates probing under interaction cost. Trust then attaches to auditable premises and evidence standards, not conversational fluency. We illustrate with tutoring and propose falsifiable evaluation criteria.

Method: The simulator integrates three profile components: (1) medical profiles from All of Us EHR data using risk-ratio gating, (2) linguistic profiles modeling health literacy and condition-specific communication, and (3) behavioral profiles representing cooperative, distracted, and adversarial engagement. Grounded in NIST AI Risk Management Framework, it was evaluated against an AI Decision Aid for antidepressant selection across 500 simulated conversations.

Result: The simulator revealed monotonic degradation in AI Decision Aid performance across health literacy levels: Rank-1 concept retrieval ranged from 47.6% (limited literacy) to 81.9% (proficient literacy). Medical concept fidelity was high (96.6% across 8,210 concepts), validated by human annotators (0.73 kappa) and LLM judge (0.78 kappa). Behavioral profiles were reliably distinguished (0.93 kappa), and linguistic profiles showed moderate agreement (0.61 kappa).

Conclusion: The simulator exposes measurable performance risks in conversational healthcare AI, with health literacy emerging as a primary risk factor with direct implications for equitable AI deployment. The approach enables systematic evaluation of AI systems across diverse patient populations.

Abstract: Objective: This paper introduces a patient simulator for scalable, automated evaluation of healthcare conversational agents, generating realistic, controllable interactions that systematically vary across medical, linguistic, and behavioral dimensions to support risk assessment across populations. Methods: Grounded in the NIST AI Risk Management Framework, the simulator integrates three profile components: (1) medical profiles constructed from All of Us electronic health records using risk-ratio gating; (2) linguistic profiles modeling health literacy and condition-specific communication; and (3) behavioral profiles representing cooperative, distracted, and adversarial engagement. Profiles were evaluated against NIST AI RMF trustworthiness requirements and assessed against an AI Decision Aid for antidepressant selection. Results: Across 500 simulated conversations, the simulator revealed monotonic degradation in AI Decision Aid performance across health literacy levels: Rank-1 concept retrieval ranged from 47.6% (limited) to 81.9% (proficient), with corresponding recommendation degradation. Medical concept fidelity was high (96.6% across 8,210 concepts), validated by human annotators (0.73 kappa) and an LLM judge with comparable agreement (0.78 kappa). Behavioral profiles were reliably distinguished (0.93 kappa), and linguistic profiles showed moderate agreement (0.61 kappa). Conclusions: The simulator exposes measurable performance risks in conversational healthcare AI. Health literacy emerged as a primary risk factor with direct implications for equitable AI deployment.

Method: Proposes text-to-FHIRPath QA paradigm that shifts reasoning from free-text generation to FHIRPath query synthesis, using FHIRPath-QA dataset with 14k+ natural language questions paired with validated FHIRPath queries and answers.

Result: Reduced average token usage by 391x (629,829 vs 1,609 tokens per question) and lowered failure rates from 0.36 to 0.09 on clinician-phrased questions; LLMs achieved at most 42% accuracy but improved to 79% with supervised fine-tuning.

Conclusion: Text-to-FHIRPath synthesis offers practical foundation for safe, efficient, interoperable consumer health applications, with FHIRPath-QA dataset serving as benchmark for future research.

Abstract: Though patients are increasingly granted digital access to their electronic health records (EHRs), existing interfaces may not support precise, trustworthy answers to patient-specific questions. Large language models (LLM) show promise in clinical question answering (QA), but retrieval-based approaches are computationally inefficient, prone to hallucination, and difficult to deploy over real-life EHRs. This work introduces FHIRPath-QA, the first open dataset and benchmark for patient-specific QA that includes open-standard FHIRPath queries over real-world clinical data. A text-to-FHIRPath QA paradigm is proposed that shifts reasoning from free-text generation to FHIRPath query synthesis. For o4-mini, this reduced average token usage by 391x relative to retrieval-first prompting (629,829 vs 1,609 tokens per question) and lowered failure rates from 0.36 to 0.09 on clinician-phrased questions. Built on MIMIC-IV on FHIR Demo, the dataset pairs over 14k natural language questions in patient and clinician phrasing with validated FHIRPath queries and answers. Empirically, the evaluated LLMs achieve at most 42% accuracy, highlighting the challenge of the task, but benefit strongly from supervised fine-tuning, with query synthesis accuracy improving from 27% to 79% for 4o-mini. These results highlight that text-to-FHIRPath synthesis has the potential to serve as a practical foundation for safe, efficient, and interoperable consumer health applications, and the FHIRPath-QA dataset and benchmark serve as a starting point for future research on the topic. The full dataset and generation code can be accessed at: https://github.com/mooshifrew/fhirpath-qa.

Method: Four-component framework: 1) DocSplit benchmark dataset with multimodal classifier using BIO tagging, 2) configurable extraction module using multimodal LLMs, 3) Agentic Analytics Module with secure code execution, 4) Rule Validation Module with LLM-driven compliance checks.

Result: Production deployment at healthcare provider achieved 98% classification accuracy, 80% reduced processing latency, and 77% lower operational costs compared to legacy baselines.

Conclusion: IDP Accelerator enables effective end-to-end document intelligence across industries through multimodal LLMs and agentic AI, with demonstrated real-world success and open-source availability.

Abstract: Understanding and extracting structured insights from unstructured documents remains a foundational challenge in industrial NLP. While Large Language Models (LLMs) enable zero-shot extraction, traditional pipelines often fail to handle multi-document packets, complex reasoning, and strict compliance requirements. We present IDP (Intelligent Document Processing) Accelerator, a framework enabling agentic AI for end-to-end document intelligence with four key components: (1) DocSplit, a novel benchmark dataset and multimodal classifier using BIO tagging to segment complex document packets; (2) configurable Extraction Module leveraging multimodal LLMs to transform unstructured content into structured data; (3) Agentic Analytics Module, compliant with the Model Context Protocol (MCP) providing data access through secure, sandboxed code execution; and (4) Rule Validation Module replacing deterministic engines with LLM-driven logic for complex compliance checks. The interactive demonstration enables users to upload document packets, visualize classification results, and explore extracted data through an intuitive web interface. We demonstrate effectiveness across industries, highlighting a production deployment at a leading healthcare provider achieving 98% classification accuracy, 80% reduced processing latency, and 77% lower operational costs over legacy baselines. IDP Accelerator is open-sourced with a live demonstration available to the community.

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Method: N/A - Paper content unavailable due to API rate limiting error

Result: Failed to obtain paper summary or abstract

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Abstract: Failed to fetch summary for 2411.15087: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2411.15087&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

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Abstract: Failed to fetch summary for 2603.21576: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.21576&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

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Abstract: Failed to fetch summary for 2603.22339: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22339&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Introduce a new dataset with multi-view video data, trajectories, high-level instructions, and detailed reasoning traces in English, Spanish, and Chinese from domain experts with diverse cultural backgrounds.

Result: Created a benchmark for multimodal models (VLMs and VLAs) that evaluates instruction following and semantic coherence beyond traditional safety and comfort metrics, available as a public dataset.

Conclusion: The dataset provides a unique resource for studying how different forms of reasoning affect driving competence and enables in-context learning and few-shot generalization for multimodal models.

Abstract: In real-world domains such as self-driving, generalization to rare scenarios remains a fundamental challenge. To address this, we introduce a new dataset designed for end-to-end driving that focuses on long-tail driving events. We provide multi-view video data, trajectories, high-level instructions, and detailed reasoning traces, facilitating in-context learning and few-shot generalization. The resulting benchmark for multimodal models, such as VLMs and VLAs, goes beyond safety and comfort metrics by evaluating instruction following and semantic coherence between model outputs. The multilingual reasoning traces in English, Spanish, and Chinese are from domain experts with diverse cultural backgrounds. Thus, our dataset is a unique resource for studying how different forms of reasoning affect driving competence. Our dataset is available at: https://hf.co/datasets/kit-mrt/kitscenes-longtail

Method: Propose SMPL-FX which couples FLAME’s rich expression space with SMPL-X body model. Tokenize the resulting representation with modality-specific Finite Scalar Quantization VAEs for body, hands, and face. M3T is an autoregressive transformer trained on this multi-modal motion vocabulary with an auxiliary translation objective that encourages semantically grounded embeddings.

Result: Achieves state-of-the-art sign language production quality across three standard benchmarks (How2Sign, CSL-Daily, Phoenix14T). On NMFs-CSL, where signs are distinguishable only by non-manual features, reaches 58.3% accuracy vs 49.0% for strongest comparable pose baseline.

Conclusion: The proposed SMPL-FX model with multi-modal FSQ-VAE tokenization and M3T transformer effectively addresses limitations in sign language production by incorporating rich non-manual features, achieving superior performance especially in cases where non-manual features are crucial for sign distinction.

Abstract: Sign language production requires more than hand motion generation. Non-manual features, including mouthings, eyebrow raises, gaze, and head movements, are grammatically obligatory and cannot be recovered from manual articulators alone. Existing 3D production systems face two barriers to integrating them: the standard body model provides a facial space too low-dimensional to encode these articulations, and when richer representations are adopted, standard discrete tokenization suffers from codebook collapse, leaving most of the expression space unreachable. We propose SMPL-FX, which couples FLAME’s rich expression space with the SMPL-X body, and tokenize the resulting representation with modality-specific Finite Scalar Quantization VAEs for body, hands, and face. M3T is an autoregressive transformer trained on this multi-modal motion vocabulary, with an auxiliary translation objective that encourages semantically grounded embeddings. Across three standard benchmarks (How2Sign, CSL-Daily, Phoenix14T) M3T achieves state-of-the-art sign language production quality, and on NMFs-CSL, where signs are distinguishable only by non-manual features, reaches 58.3% accuracy against 49.0% for the strongest comparable pose baseline.

Method: Proposes Phase-wise Decomposition and Alignment (PDA) framework with three modules: 1) CoT-Prompting Semantic Decomposition (CSD) uses LLM chain-of-thought reasoning to decompose action labels into phase-level descriptions, 2) Text-infused Foreground Filtering (TIF) adaptively filters action-relevant segments using phase-wise semantic cues, and 3) Adaptive Phase-wise Alignment (APA) performs phase-level visual-textual matching and aggregates results across phases.

Result: Extensive experiments on two OV-TAD benchmarks demonstrate the superiority of the proposed method over previous approaches, showing significant enhancement in generalization to unseen actions.

Conclusion: The PDA framework enables fine-grained action pattern learning through phase-wise decomposition and alignment, facilitating transferable action pattern capture and significantly improving generalization to unseen action categories in temporal action detection.

Abstract: Open-Vocabulary Temporal Action Detection (OV-TAD) aims to classify and localize action segments in untrimmed videos for unseen categories. Previous methods rely solely on global alignment between label-level semantics and visual features, which is insufficient to transfer temporal consistent visual knowledge from seen to unseen classes. To address this, we propose a Phase-wise Decomposition and Alignment (PDA) framework, which enables fine-grained action pattern learning for effective prior knowledge transfer. Specifically, we first introduce the CoT-Prompting Semantic Decomposition (CSD) module, which leverages the chain-of-thought (CoT) reasoning ability of large language models to automatically decompose action labels into coherent phase-level descriptions, emulating human cognitive processes. Then, Text-infused Foreground Filtering (TIF) module is introduced to adaptively filter action-relevant segments for each phase leveraging phase-wise semantic cues, producing semantically aligned visual representations. Furthermore, we propose the Adaptive Phase-wise Alignment (APA) module to perform phase-level visual-textual matching, and adaptively aggregates alignment results across phases for final prediction. This adaptive phase-wise alignment facilitates the capture of transferable action patterns and significantly enhances generalization to unseen actions. Extensive experiments on two OV-TAD benchmarks demonstrated the superiority of the proposed method.

Method: Semi-automated data collection with expert refinement following established Visual-WSD benchmark methodology; evaluation of eight multilingual multimodal LLMs against CLIP baseline.

Result: All tested models performed worse than zero-shot CLIP baseline; significant performance gap observed between Ukrainian and English Visual-WSD tasks.

Conclusion: Current multilingual multimodal models struggle with Ukrainian Visual-WSD, highlighting language-specific challenges and need for improved cross-lingual multimodal understanding.

Abstract: This study presents a benchmark for evaluating the Visual Word Sense Disambiguation (Visual-WSD) task in Ukrainian. The main goal of the Visual-WSD task is to identify, with minimal contextual information, the most appropriate representation of a given ambiguous word from a set of ten images. To construct this benchmark, we followed a methodology similar to that proposed by (CITATION), who previously introduced benchmarks for the Visual-WSD task in English, Italian, and Farsi. This approach allows us to incorporate the Ukrainian benchmark into a broader framework for cross-language model performance comparisons. We collected the benchmark data semi-automatically and refined it with input from domain experts. We then assessed eight multilingual and multimodal large language models using this benchmark. All tested models performed worse than the zero-shot CLIP-based baseline model (CITATION) used by (CITATION) for the English Visual-WSD task. Our analysis revealed a significant performance gap in the Visual-WSD task between Ukrainian and English.

Method: 1) Uses MGT’s cross-attention maps for localization with multi-layer attention consolidation; 2) Introduces region-hold sampling to restrict token flipping to low-attention areas; 3) Adapts pre-trained text-to-image MGT into editing model via attention injection; 4) Trained on CrispEdit-2M dataset spanning 7 editing categories.

Result: Achieves similarity performance with 6x faster editing using <1B parameters. Improves style change by 3.6% and style transfer by 17.6% on standard benchmarks. Delivers comparable or superior editing quality while better preserving non-target regions.

Conclusion: MGTs offer a promising alternative to diffusion models for image editing, with inherent localized decoding capabilities that enable precise, targeted edits while preserving surrounding areas, plus faster inference.

Abstract: Recent advances in diffusion models (DMs) have achieved exceptional visual quality in image editing tasks. However, the global denoising dynamics of DMs inherently conflate local editing targets with the full-image context, leading to unintended modifications in non-target regions. In this paper, we shift our attention beyond DMs and turn to Masked Generative Transformers (MGTs) as an alternative approach to tackle this challenge. By predicting multiple masked tokens rather than holistic refinement, MGTs exhibit a localized decoding paradigm that endows them with the inherent capacity to explicitly preserve non-relevant regions during the editing process. Building upon this insight, we introduce the first MGT-based image editing framework, termed EditMGT. We first demonstrate that MGT’s cross-attention maps provide informative localization signals for localizing edit-relevant regions and devise a multi-layer attention consolidation scheme that refines these maps to achieve fine-grained and precise localization. On top of these adaptive localization results, we introduce region-hold sampling, which restricts token flipping within low-attention areas to suppress spurious edits, thereby confining modifications to the intended target regions and preserving the integrity of surrounding non-target areas. To train EditMGT, we construct CrispEdit-2M, a high-resolution dataset spanning seven diverse editing categories. Without introducing additional parameters, we adapt a pre-trained text-to-image MGT into an image editing model through attention injection. Extensive experiments across four standard benchmarks demonstrate that, with fewer than 1B parameters, our model achieves similarity performance while enabling 6 times faster editing. Moreover, it delivers comparable or superior editing quality, with improvements of 3.6% and 17.6% on style change and style transfer tasks, respectively.

Method: Proposes a differentiable, sorting-free stochastic formulation using an unbiased Monte Carlo estimator for pixel-color gradients that evaluates only a small sampled subset of Gaussians per ray, bypassing the need for sorting. For relightable 3DGS, the same stochastic estimator drives per-Gaussian shading with fully ray-traced shadow rays.

Result: For standard 3DGS, matches reconstruction quality and speed of rasterization-based 3DGS while substantially outperforming sorting-based ray tracing. For relightable 3DGS, delivers notably higher reconstruction fidelity than prior work.

Conclusion: The method provides a practical, efficient ray-tracing framework for 3DGS that eliminates sorting bottlenecks and enables high-quality relightable scene reconstruction with fully ray-traced shadows.

Abstract: Ray-tracing-based 3D Gaussian splatting (3DGS) methods overcome the limitations of rasterization – rigid pinhole camera assumptions, inaccurate shadows, and lack of native reflection or refraction – but remain slower due to the cost of sorting all intersecting Gaussians along every ray. Moreover, existing ray-tracing methods still rely on rasterization-style approximations such as shadow mapping for relightable scenes, undermining the generality that ray tracing promises. We present a differentiable, sorting-free stochastic formulation for ray-traced 3DGS – the first framework that uses stochastic ray tracing to both reconstruct and render standard and relightable 3DGS scenes. At its core is an unbiased Monte Carlo estimator for pixel-color gradients that evaluates only a small sampled subset of Gaussians per ray, bypassing the need for sorting. For standard 3DGS, our method matches the reconstruction quality and speed of rasterization-based 3DGS while substantially outperforming sorting-based ray tracing. For relightable 3DGS, the same stochastic estimator drives per-Gaussian shading with fully ray-traced shadow rays, delivering notably higher reconstruction fidelity than prior work.

Method: Physics-informed deep generative model using hierarchical Variational Autoencoder with differentiable Spectral Mixer that enforces consistency with image formation process while learning structural priors.

Result: State-of-the-art performance on 66 challenging benchmarks across 3 real-world datasets, outperforming 10 baseline methods in high noise regimes, overlapping spectra, and low spectral dimensionality scenarios.

Conclusion: λSplit enables robust spectral unmixing with implicit noise removal, compatible with standard confocal microscopes without hardware modifications, establishing new state-of-the-art for fluorescence microscopy.

Abstract: In fluorescence microscopy, spectral unmixing aims to recover individual fluorophore concentrations from spectral images that capture mixed fluorophore emissions. Since classical methods operate pixel-wise and rely on least-squares fitting, their performance degrades with increasingly overlapping emission spectra and higher levels of noise, suggesting that a data-driven approach that can learn and utilize a structural prior might lead to improved results. Learning-based approaches for spectral imaging do exist, but they are either not optimized for microscopy data or are developed for very specific cases that are not applicable to fluorescence microscopy settings. To address this, we propose λSplit, a physics-informed deep generative model that learns a conditional distribution over concentration maps using a hierarchical Variational Autoencoder. A fully differentiable Spectral Mixer enforces consistency with the image formation process, while the learned structural priors enable state-of-the-art unmixing and implicit noise removal. We demonstrate λSplit on 3 real-world datasets that we synthetically cast into a total of 66 challenging spectral unmixing benchmarks. We compare our results against a total of 10 baseline methods, including classical methods and a range of learning-based methods. Our results consistently show competitive performance and improved robustness in high noise regimes, when spectra overlap considerably, or when the spectral dimensionality is lowered, making λSplit a new state-of-the-art for spectral unmixing of fluorescent microscopy data. Importantly, λSplit is compatible with spectral data produced by standard confocal microscopes, enabling immediate adoption without specialized hardware modifications.

Method: Proposes Verifier on Hidden States (VHS) that analyzes intermediate hidden representations of Diffusion Transformer single-step generators directly, avoiding pixel space conversion.

Result: VHS reduces joint generation-and-verification time by 63.3%, compute FLOPs by 51%, VRAM usage by 14.5% compared to standard MLLM verifiers, while achieving +2.7% improvement on GenEval at same inference budget.

Conclusion: Operating directly on generator hidden states enables efficient inference-time scaling without sacrificing verification quality, making verification more practical for real-world deployment.

Abstract: Inference-time scaling has emerged as an effective way to improve generative models at test time by using a verifier to score and select candidate outputs. A common choice is to employ Multimodal Large Language Models (MLLMs) as verifiers, which can improve performance but introduce substantial inference-time cost. Indeed, diffusion pipelines operate in an autoencoder latent space to reduce computation, yet MLLM verifiers still require decoding candidates to pixel space and re-encoding them into the visual embedding space, leading to redundant and costly operations. In this work, we propose Verifier on Hidden States (VHS), a verifier that operates directly on intermediate hidden representations of Diffusion Transformer (DiT) single-step generators. VHS analyzes generator features without decoding to pixel space, thereby reducing the per-candidate verification cost while improving or matching the performance of MLLM-based competitors. We show that, under tiny inference budgets with only a small number of candidates per prompt, VHS enables more efficient inference-time scaling reducing joint generation-and-verification time by 63.3%, compute FLOPs by 51% and VRAM usage by 14.5% with respect to a standard MLLM verifier, achieving a +2.7% improvement on GenEval at the same inference-time budget.

Method: Combines six encoder families through late probability fusion: S4D-ViTMoE face encoder with soft-label KL training, frozen layer-selective Wav2Vec2 audio features (layers 6-12), finetuned body-language encoders (TimeSformer, VideoMAE), and Gemini Embedding 2.0 for multimodal video embeddings. Uses ensemble weighting where task-adapted encoders receive 62% of weight.

Result: Achieved Score = 0.279 (ACCP = 0.391, ACCS = 0.168) on test set, placing 6th in the challenge. Key findings: selective audio layer freezing outperforms end-to-end finetuning (0.207 vs 0.161), personalized expression thresholds vary widely (0.05-0.43), and Gemini Embedding 2.0 produces competitive accuracy from only 2-second inputs.

Conclusion: The system demonstrates effective multimodal fusion for emotion recognition, with insights about audio model adaptation for non-verbal content and the importance of personalized expression modeling. Task-specific encoders significantly outperform general-purpose baselines in ensemble weighting.

Abstract: We present our system for the BLEMORE Challenge at FG 2026 on blended emotion recognition with relative salience prediction. Our approach combines six encoder families through late probability fusion: an S4D-ViTMoE face encoder adapted with soft-label KL training, frozen layer-selective Wav2Vec2 audio features, finetuned body-language encoders (TimeSformer, VideoMAE), and – for the first time in emotion recognition – Gemini Embedding 2.0, a large multimodal model whose video embeddings produce competitive presence accuracy (ACCP = 0.320) from only 2 seconds of input. Three key findings emerge from our experiments: selecting prosody-encoding layers (6–12) from frozen Wav2Vec2 outperforms end-to-end finetuning (Score 0.207 vs. 0.161), as the non-verbal nature of BLEMORE audio makes phonetic layers irrelevant; the post-processing salience threshold $β$ varies from 0.05 to 0.43 across folds, revealing that personalized expression styles are the primary bottleneck; and task-adapted encoders collectively receive 62% of ensemble weight over general-purpose baselines. Our 12-encoder system achieves Score = 0.279 (ACCP = 0.391, ACCS = 0.168) on the test set, placing 6th.

Method: Systematic benchmarking of six VLMs (four proprietary, two open-weight) against psychophysical data across three image quality scales: contrast, colorfulness, and overall preference, including attribute weighting analysis and intramodel consistency evaluation.

Result: VLMs show strong attribute-dependent variability - high human alignment for colorfulness (ρ up to 0.93) but underperformance on contrast, with most models weighting colorfulness higher than contrast for overall preference similar to human data. Most self-consistent models aren’t most human-aligned, and human-VLM agreement increases with perceptual separability.

Conclusion: VLMs demonstrate promising but attribute-specific capabilities for perceptual IQA, with complex tradeoffs between self-consistency and human alignment, suggesting their reliability depends on stimulus differences and scene-dependent perceptual cues.

Abstract: Psychophysical experiments remain the most reliable approach for perceptual image quality assessment (IQA), yet their cost and limited scalability encourage automated approaches. We investigate whether Vision Language Models (VLMs) can approximate human perceptual judgments across three image quality scales: contrast, colorfulness and overall preference. Six VLMs four proprietary and two openweight models are benchmarked against psychophysical data. This work presents a systematic benchmark of VLMs for perceptual IQA through comparison with human psychophysical data. The results reveal strong attribute dependent variability models with high human alignment for colorfulness (ρup to 0.93) underperform on contrast and vice-versa. Attribute weighting analysis further shows that most VLMs assign higher weights to colorfulness compared to contrast when evaluating overall preference similar to the psychophysical data. Intramodel consistency analysis reveals a counterintuitive tradeoff: the most self consistent models are not necessarily the most human aligned suggesting response variability reflects sensitivity to scene dependent perceptual cues. Furthermore, human-VLM agreement is increased with perceptual separability, indicating VLMs are more reliable when stimulus differences are clearly expressed.

Method: Introduces BIRCH-Trees benchmark spanning three forest datasets (temperate, tropical, boreal). Presents DINOvTree approach using Vision Foundation Model backbone with task-specific heads for simultaneous height regression and species classification.

Result: DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54-58% of parameters compared to second-best approach. Outperforms biological allometric equations and other vision methods.

Conclusion: BIRCH-Trees provides valuable benchmark for tree analysis from UAV imagery. DINOvTree demonstrates effectiveness of Vision Foundation Models for simultaneous tree trait estimation with parameter efficiency, advancing scalable forest monitoring.

Abstract: Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species. Unoccupied Aerial Vehicles (UAVs) capturing high-resolution imagery from a single RGB camera offer a cost-effective and scalable approach for mapping and measuring individual trees. We introduce BIRCH-Trees, the first benchmark for individual tree height and species estimation from tree-centered UAV images, spanning three datasets: temperate forests, tropical forests, and boreal plantations. We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task-specific heads for simultaneous height and species prediction. Through extensive evaluations on BIRCH-Trees, we compare DINOvTree against commonly used vision methods, including VFMs, as well as biological allometric equations. We find that DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54% to 58% of the parameters of the second-best approach.

Method: A model-agnostic approach that aggregates features from successive convolutional blocks, computes class-wise mean embeddings, applies L2 normalization to form compact ID prototypes, and uses cosine similarity between test features and prototypes as OOD score during inference.

Result: Extensive experiments on state-of-the-art OOD benchmarks show robust, architecture-agnostic performance with improvements of up to 4.41% in AUROC and 13.58% reduction in FPR, demonstrating strong generalization for image classification.

Conclusion: Multi-layer feature aggregation is a powerful yet underexplored signal for OOD detection that challenges the dominance of penultimate-layer-based methods, offering improved robustness and generalization across diverse architectures.

Abstract: Deep learning models are increasingly deployed in safety-critical applications, where reliable out-of-distribution (OOD) detection is essential to ensure robustness. Existing methods predominantly rely on the penultimate-layer activations of neural networks, assuming they encapsulate the most informative in-distribution (ID) representations. In this work, we revisit this assumption to show that intermediate layers encode equally rich and discriminative information for OOD detection. Based on this observation, we propose a simple yet effective model-agnostic approach that leverages internal representations across multiple layers. Our scheme aggregates features from successive convolutional blocks, computes class-wise mean embeddings, and applies L_2 normalization to form compact ID prototypes capturing class semantics. During inference, cosine similarity between test features and these prototypes serves as an OOD score–ID samples exhibit strong affinity to at least one prototype, whereas OOD samples remain uniformly distant. Extensive experiments on state-of-the-art OOD benchmarks across diverse architectures demonstrate that our approach delivers robust, architecture-agnostic performance and strong generalization for image classification. Notably, it improves AUROC by up to 4.41% and reduces FPR by 13.58%, highlighting multi-layer feature aggregation as a powerful yet underexplored signal for OOD detection, challenging the dominance of penultimate-layer-based methods. Our code is available at: https://github.com/sgchr273/cosine-layers.git.

Method: Proposes MoCHA, a text canonicalization framework that projects each caption onto its motion-recoverable content before encoding. Presents two learned variants: an LLM-based approach (GPT-5.2) and a distilled FlanT5 model requiring no LLM at inference. Works as a preprocessing step compatible with any retrieval architecture.

Result: Sets new SOTA on HumanML3D (13.9% T2M R@1, +3.1pp) and KIT-ML (24.3% T2M R@1, +10.3pp). Reduces within-motion text-embedding variance by 11-19%. Improves cross-dataset transfer substantially: H to K by 94% and K to H by 52%.

Conclusion: Canonicalization reduces embedding variance and improves motion-language alignment. Standardizing the language space yields more transferable motion-language representations, with learned canonicalizers providing substantial gains over rule-based methods.

Abstract: Text-motion retrieval systems learn shared embedding spaces from motion-caption pairs via contrastive objectives. However, each caption is not a deterministic label but a sample from a distribution of valid descriptions: different annotators produce different text for the same motion, mixing motion-recoverable semantics (action type, body parts, directionality) with annotator-specific style and inferred context that cannot be determined from 3D joint coordinates alone. Standard contrastive training treats each caption as the single positive target, overlooking this distributional structure and inducing within-motion embedding variance that weakens alignment. We propose MoCHA, a text canonicalization framework that reduces this variance by projecting each caption onto its motion-recoverable content prior to encoding, producing tighter positive clusters and better-separated embeddings. Canonicalization is a general principle: even deterministic rule-based methods improve cross-dataset transfer, though learned canonicalizers provide substantially larger gains. We present two learned variants: an LLM-based approach (GPT-5.2) and a distilled FlanT5 model requiring no LLM at inference time. MoCHA operates as a preprocessing step compatible with any retrieval architecture. Applied to MoPa (MotionPatches), MoCHA sets a new state of the art on both HumanML3D (H) and KIT-ML (K): the LLM variant achieves 13.9% T2M R@1 on H (+3.1pp) and 24.3% on K (+10.3pp), while the LLM-free T5 variant achieves gains of +2.5pp and +8.1pp. Canonicalization reduces within-motion text-embedding variance by 11-19% and improves cross-dataset transfer substantially, with H to K improving by 94% and K to H by 52%, demonstrating that standardizing the language space yields more transferable motion-language representations.

Method: First employs white-box attacks to reveal fundamental vulnerabilities, then develops two query-efficient black-box algorithms: one based on gradient estimation and another gradient-free approach, both optimizing pixel-space perturbations via frequency-domain parameterization without requiring model internals access.

Result: Extensive experiments across multiple datasets show AdvSplat can significantly disrupt reconstruction results by injecting imperceptible perturbations into input images, demonstrating the security vulnerabilities of feed-forward 3DGS models.

Conclusion: The study surfaces an urgent security and robustness problem in feed-forward 3DGS, highlighting adversarial vulnerabilities that need community attention for this commercially promising technology.

Abstract: 3D Gaussian Splatting (3DGS) is increasingly recognized as a powerful paradigm for real-time, high-fidelity 3D reconstruction. However, its per-scene optimization pipeline limits scalability and generalization, and prevents efficient inference. Recently emerged feed-forward 3DGS models address these limitations by enabling fast reconstruction from a few input views after large-scale pretraining, without scene-specific optimization. Despite their advantages and strong potential for commercial deployment, the use of neural networks as the backbone also amplifies the risk of adversarial manipulation. In this paper, we introduce AdvSplat, the first systematic study of adversarial attacks on feed-forward 3DGS. We first employ white-box attacks to reveal fundamental vulnerabilities of this model family. We then develop two improved, practically relevant, query-efficient black-box algorithms that optimize pixel-space perturbations via a frequency-domain parameterization: one based on gradient estimation and the other gradient-free, without requiring any access to model internals. Extensive experiments across multiple datasets demonstrate that AdvSplat can significantly disrupt reconstruction results by injecting imperceptible perturbations into the input images. Our findings surface an overlooked yet urgent problem in this domain, and we hope to draw the community’s attention to this emerging security and robustness challenge.

Method: Proposes a framework that integrates equivariant contrastive learning directly into the registration model. Uses contrastive learning to learn robust feature representations invariant to tissue deformations. Jointly optimizes contrastive and registration objectives to ensure learned representations are both informative and suitable for registration tasks.

Result: Evaluated on abdominal and thoracic image registration tasks, including intra-patient and inter-patient scenarios. Experimental results show significant performance improvement over strong baseline methods, demonstrating the effectiveness of integrating contrastive learning directly into the registration framework.

Conclusion: Direct integration of equivariant contrastive learning into medical image registration framework significantly improves performance by learning robust feature representations that are invariant to tissue deformations and suitable for registration tasks.

Abstract: Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time points. However, intensity inconsistencies and nonlinear tissue deformations pose significant challenges to the robustness of registration methods. Recent approaches leveraging self-supervised representation learning show promise by pre-training feature extractors to generate robust anatomical embeddings, that farther used for the registration. In this work, we propose a novel framework that integrates equivariant contrastive learning directly into the registration model. Our approach leverages the power of contrastive learning to learn robust feature representations that are invariant to tissue deformations. By jointly optimizing the contrastive and registration objectives, we ensure that the learned representations are not only informative but also suitable for the registration task. We evaluate our method on abdominal and thoracic image registration tasks, including both intra-patient and inter-patient scenarios. Experimental results demonstrate that the integration of contrastive learning directly into the registration framework significantly improves performance, surpassing strong baseline methods.

Method: dCAP uses a transformer with cross-view and temporal attention to robustly aggregate spatial cues while maintaining temporal consistency for continuous 6-DoF relative pose estimation. Integrated with BEVFormer, it replaces static calibration with dynamically predicted extrinsics for 3D object detection.

Result: Experiments show dCAP achieves stable, accurate perception while addressing limitations of static calibration. The framework is evaluated on STT4AT, a CARLA-based benchmark simulating semi-trailer trucks with synchronized multi-sensor suites and time-varying inter-rig geometry.

Conclusion: dCAP provides a robust vision-based solution for dynamic calibration in articulated vehicles, enabling more reliable perception for autonomous trucking. The dataset, development kit, and source code will be publicly released.

Abstract: Autonomous trucking poses unique challenges due to articulated tractor-trailer geometry, and time-varying sensor poses caused by the fifth-wheel joint and trailer flex. Existing perception and calibration methods assume static baselines or rely on high-parallax and texture-rich scenes, limiting their reliability under real-world settings. We propose dCAP (dynamic Calibration and Articulated Perception), a vision-based framework that continuously estimates the 6-DoF (degree of freedom) relative pose between tractor and trailer cameras. dCAP employs a transformer with cross-view and temporal attention to robustly aggregate spatial cues while maintaining temporal consistency, enabling accurate perception under rapid articulation and occlusion. Integrated with BEVFormer, dCAP improves 3D object detection by replacing static calibration with dynamically predicted extrinsics. To facilitate evaluation, we introduce STT4AT, a CARLA-based benchmark simulating semi-trailer trucks with synchronized multi-sensor suites and time-varying inter-rig geometry across diverse environments. Experiments demonstrate that dCAP achieves stable, accurate perception while addressing the limitations of static calibration in autonomous trucking. The dataset, development kit, and source code will be publicly released.

Method: Proposes Bidirectional Conservative-Radical Complementary Learning (Bi-CRCL), a dual-learner framework inspired by complementary learning systems. It includes: 1) a conservative learner that preserves prior knowledge through stability-oriented updates, 2) a radical learner that rapidly adapts to new categories via plasticity-oriented learning, and 3) a bidirectional interaction mechanism enabling forward transfer and backward consolidation. During inference, outputs from both learners are adaptively fused.

Result: Experiments on five medical imaging datasets demonstrate consistent improvements over state-of-the-art methods under diverse settings, including cross-dataset shifts and varying task configurations.

Conclusion: Bi-CRCL effectively addresses the challenges of class-incremental learning in medical imaging by balancing stability and plasticity through complementary learners, enabling scalable clinical deployment of diagnostic systems that can continuously learn new disease categories.

Abstract: Class-incremental learning (CIL) in medical image-guided diagnosis requires retaining prior diagnostic knowledge while adapting to newly emerging disease categories, which is critical for scalable clinical deployment. This problem is particularly challenging due to heterogeneous data and privacy constraints that prevent memory replay. Although pretrained foundation models (PFMs) have advanced general-domain CIL, their potential in medical imaging remains underexplored, where domain-specific adaptation is essential yet difficult due to anatomical complexity and inter-institutional heterogeneity. To address this gap, we conduct a systematic benchmark of recent PFM-based CIL methods and propose Bidirectional Conservative-Radical Complementary Learning (Bi-CRCL), a dual-learner framework inspired by complementary learning systems. Bi-CRCL integrates a conservative learner that preserves prior knowledge through stability-oriented updates and a radical learner that rapidly adapts to new categories via plasticity-oriented learning. A bidirectional interaction mechanism enables forward transfer and backward consolidation, allowing continual integration of new knowledge while mitigating catastrophic forgetting. During inference, outputs from both learners are adaptively fused for robust predictions. Experiments on five medical imaging datasets demonstrate consistent improvements over state-of-the-art methods under diverse settings, including cross-dataset shifts and varying task configurations.

Method: MCFT selectively fine-tunes portions of pre-trained encoder while enforcing momentum-based consistency constraints to preserve task-agnostic representations. No additional parameters beyond standard task head. Two variants: semi-supervised framework using unlabeled data, and pruning-based variant for computational efficiency.

Result: Outperforms prior methods with 3.30% gain in 5-shot settings and up to 6.13% improvement with semi-supervised learning. Maintains original model parameter count and inference efficiency, suitable for resource-constrained deployment.

Conclusion: MCFT effectively bridges gap between full and parameter-efficient fine-tuning for point cloud models, achieving strong performance in low-data regimes while preserving inference efficiency and pre-trained representations.

Abstract: Point cloud foundation models demonstrate strong generalization, yet adapting them to downstream tasks remains challenging in low-data regimes. Full fine-tuning often leads to overfitting and significant drift from pre-trained representations, while existing parameter-efficient fine-tuning (PEFT) methods mitigate this issue by introducing additional trainable components at the cost of increased inference-time latency. We propose Momentum-Consistency Fine-Tuning (MCFT), an adapter-free approach that bridges the gap between full and parameter-efficient fine-tuning. MCFT selectively fine-tunes a portion of the pre-trained encoder while enforcing a momentum-based consistency constraint to preserve task-agnostic representations. Unlike PEFT methods, MCFT introduces no additional representation learning parameters beyond a standard task head, maintaining the original model’s parameter count and inference efficiency. We further extend MCFT with two variants: a semi-supervised framework that leverages abundant unlabeled data to enhance few-shot performance, and a pruning-based variant that improves computational efficiency through structured layer removal. Extensive experiments on object recognition and part segmentation benchmarks demonstrate that MCFT consistently outperforms prior methods, achieving a 3.30% gain in 5-shot settings and up to a 6.13% improvement with semi-supervised learning, while remaining well-suited for resource-constrained deployment.

Method: Uses YOLOv11m as base architecture with three strategies: loss reweighting, data resampling, and transfer learning. Builds ensemble by combining models trained under each strategy using Weighted Boxes Fusion (WBF) to promote complementary detection behavior.

Result: Ensemble achieves mAP50-95 of 0.201 on preliminary test set and 0.147 on final test set, representing 29% improvement over best individual model on final test set.

Conclusion: Combining complementary imbalance mitigation strategies through ensemble learning effectively improves cervical cell detection performance in challenging Pap smear images with class imbalance and nuclear overlap.

Abstract: Automated detection and classification of cervical cells in conventional Pap smear images can strengthen cervical cancer screening at scale by reducing manual workload, improving triage, and increasing consistency across readers. However, it is challenged by severe class imbalance and frequent nuclear overlap. We present our approach to the RIVA Cervical Cytology Challenge (ISBI 2026), which requires multi-class detection of eight Bethesda cell categories under these conditions. Using YOLOv11m as the base architecture, we systematically evaluate three strategies to improve detection performance: loss reweighting, data resampling and transfer learning. We build an ensemble by combining models trained under each strategy, promoting complementary detection behavior and combining them through Weighted Boxes Fusion (WBF). The ensemble achieves a mAP50-95 of 0.201 on the preliminary test set and 0.147 on the final test set, representing a 29% improvement over the best individual model on the final test set and demonstrating the effectiveness of combining complementary imbalance mitigation strategies.

Method: Dataset creation and publication approach - the dataset is hosted on figshare with a specific DOI and licensed under Creative Commons Attribution 4.0

Result: A publicly available dataset for industrial smoke segmentation accessible via https://doi.org/10.21942/uva.31847188

Conclusion: The dataset provides a valuable resource for researchers working on smoke detection and segmentation in industrial environments

Abstract: This report describes a dataset for industrial smoke segmentation, published on a figshare repository (https://doi.org/10.21942/uva.31847188). The dataset is licensed under CC BY 4.0.

Method: Proposes a joint-centric attention model that detects body joints, extracts joint-centered clips to suppress background, tokenizes them using Video Vision Transformer (ViViT), and learns cross-joint attention to model spatial-temporal interactions between body parts for seizure pattern recognition.

Result: Extensive cross-subject experiments show the method consistently outperforms state-of-the-art CNN-, graph-, and transformer-based approaches on unseen subjects.

Conclusion: Focusing on body dynamics through joint-centric attention improves cross-subject generalization for seizure detection, making automated video-based monitoring more clinically viable.

Abstract: Automated seizure detection from long-term clinical videos can substantially reduce manual review time and enable real-time monitoring. However, existing video-based methods often struggle to generalize to unseen subjects due to background bias and reliance on subject-specific appearance cues. We propose a joint-centric attention model that focuses exclusively on body dynamics to improve cross-subject generalization. For each video segment, body joints are detected and joint-centered clips are extracted, suppressing background context. These joint-centered clips are tokenized using a Video Vision Transformer (ViViT), and cross-joint attention is learned to model spatial and temporal interactions between body parts, capturing coordinated movement patterns characteristic of seizure semiology. Extensive cross-subject experiments show that the proposed method consistently outperforms state-of-the-art CNN-, graph-, and transformer-based approaches on unseen subjects.

Method: Uses pretrained teacher encoder for multi-scale deep features, compact up-then-down decoder with multi-loop iterative refinement to enforce normality priors in feature space. Single model trained by mixing one normal sample from each of nine heterogeneous datasets.

Result: Achieves state-of-the-art on nine medical benchmarks under all four settings (one-shot universal, full-shot universal, one-shot specialized, full-shot specialized), consistently outperforming previous methods.

Conclusion: SIR offers efficient and scalable solution for multi-domain clinical anomaly detection with minimal normal samples and no task-specific retraining.

Abstract: Unsupervised medical anomaly detection is severely limited by the scarcity of normal training samples. Existing methods typically train dedicated models for each dataset or disease, requiring hundreds of normal images per task and lacking cross-modality generalization. We propose Semantic Iterative Reconstruction (SIR), a framework that enables a single universal model to detect anomalies across diverse medical domains using extremely few normal samples. SIR leverages a pretrained teacher encoder to extract multi-scale deep features and employs a compact up-then-down decoder with multi-loop iterative refinement to enforce robust normality priors in deep feature space. The framework adopts a one-shot universal design: a single model is trained by mixing exactly one normal sample from each of nine heterogeneous datasets, enabling effective anomaly detection on all corresponding test sets without task-specific retraining. Extensive experiments on nine medical benchmarks demonstrate that SIR achieves state-of-the-art under all four settings – one-shot universal, full-shot universal, one-shot specialized, and full-shot specialized – consistently outperforming previous methods. SIR offers an efficient and scalable solution for multi-domain clinical anomaly detection. Code is available at https://github.com/jusufzn212427/sir4ad.

Method: Developed reproducible deep learning pipeline for binary retinal disease risk classification. Compared baseline CNN with transfer learning using pretrained VGG16 backbone. Addressed class imbalance with class weighting. Evaluated on held-out data using accuracy, precision, recall, F1-score, confusion matrices, and ROC-AUC.

Result: VGG16 transfer learning model achieved 90.8% test accuracy with weighted F1-score of 0.90, substantially outperforming baseline CNN (83.1% accuracy). Transfer learning improved discrimination but revealed challenges in sensitivity to minority disease cases.

Conclusion: Transfer learning with pretrained models improves retinal disease classification performance over baseline CNNs. However, practical limitations exist related to dataset characteristics, class imbalance, and threshold selection. The paper provides guidance for reproducibility and future improvements for clinically reliable screening.

Abstract: Retinal diseases remain among the leading preventable causes of visual impairment worldwide. Automated screening based on fundus image analysis has the potential to expand access to early detection, particularly in underserved populations. This paper presents a reproducible deep learning pipeline for binary retinal disease risk classification from publicly available fundus photographs. We implement and compare a baseline convolutional neural network with a transfer learning approach using a pretrained VGG16 backbone and evaluate generalization on held-out data. To address class imbalance, we apply class weighting and report standard classification metrics including accuracy, precision, recall, F1-score, confusion matrices, and ROC-AUC. The VGG16 transfer learning model achieves 90.8% test accuracy with a weighted F1-score of 0.90, substantially outperforming the baseline CNN (83.1% accuracy). Results indicate that transfer learning improves discrimination compared to a baseline CNN, while also revealing remaining challenges in sensitivity to minority disease cases. We discuss practical limitations related to dataset characteristics, class imbalance, and threshold selection, and provide guidance for reproducibility and future improvements for clinically reliable screening

Method: Uses SAM3 detector on later frames to find same-category candidates, performs DINOv3-based object-level matching with transformation-aware target feature pool to retrieve reliable anchors, then injects these anchors back into SAM3 tracker with first-frame mask for multi-anchor propagation.

Result: Achieves J&F of 51.17% on MOSEv2 test set, ranking 3rd in the PVUW 2026 Challenge MOSEv2 track.

Conclusion: Automatic re-prompting framework with multi-anchor propagation effectively addresses core challenges in semi-supervised VOS, demonstrating robustness to target disappearance/reappearance and same-category distractors.

Abstract: This technical report explores the MOSEv2 track of the PVUW 2026 Challenge, which targets complex semi-supervised video object segmentation. Built on SAM3, we develop an automatic re-prompting framework to improve robustness under target disappearance and reappearance, severe transformation, and strong same-category distractors. Our method first applies the SAM3 detector to later frames to identify same-category object candidates, and then performs DINOv3-based object-level matching with a transformation-aware target feature pool to retrieve reliable target anchors. These anchors are injected back into the SAM~3 tracker together with the first-frame mask, enabling multi-anchor propagation rather than relying solely on the initial prompt. This simple directly benefits several core challenges of MOSEv2. Our solution achieves a J&F of 51.17% on the test set, ranking 3rd in the MOSEv2 track.

Method: Train Sparse Autoencoders (SAEs) on embeddings from BiomedParse (biomedical) and DINOv3 (general-purpose) vision models using 909,873 CT and MRI 2D slices from TotalSegmentator dataset.

Result: SAEs achieve high reconstruction fidelity (R² up to 0.941), recover 87.8% downstream performance with only 10 features (99.4% dimensionality reduction), preserve semantic fidelity in retrieval, enable LLM-based concept interpretation, and bridge clinical language with latent representations.

Conclusion: SAEs provide a promising pathway toward interpretable, concept-driven medical vision systems by replacing opaque foundation model representations with human-understandable sparse features.

Abstract: Vision foundation models (FMs) achieve state-of-the-art performance in medical imaging. However, they encode information in abstract latent representations that clinicians cannot interrogate or verify. The goal of this study is to investigate Sparse Autoencoders (SAEs) for replacing opaque FM image representations with human-interpretable, sparse features. We train SAEs on embeddings from BiomedParse (biomedical) and DINOv3 (general-purpose) using 909,873 CT and MRI 2D image slices from the TotalSegmentator dataset. We find that learned sparse features: (a) reconstruct original embeddings with high fidelity (R2 up to 0.941) and recover up to 87.8% of downstream performance using only 10 features (99.4% dimensionality reduction), (b) preserve semantic fidelity in image retrieval tasks, (c) correspond to specific concepts that can be expressed in language using large language model (LLM)-based auto-interpretation. (d) bridge clinical language and abstract latent representations in zero-shot language-driven image retrieval. Our work indicates SAEs are a promising pathway towards interpretable, concept-driven medical vision systems. Code repository: https://github.com/pwesp/sail.

Method: Proposes 3D-LLDM, a label-guided 3D latent diffusion model using ControlNet-based architecture. Uses hepatobiliary phase MR images with Gd-EOB-DTPA contrast agent to derive structural masks for liver, portal vein, hepatic vein, and hepatocellular carcinoma, which guide volumetric synthesis.

Result: Achieves FID of 28.31, improving over GANs by 70.9% and over state-of-the-art diffusion baselines by 26.7%. Synthetic volumes improve hepatocellular carcinoma segmentation by up to 11.153% Dice score across five CNN architectures when used for data augmentation.

Conclusion: 3D-LLDM effectively generates high-quality synthetic medical volumes with segmentation masks, demonstrating significant improvements in downstream segmentation tasks through data augmentation.

Abstract: Deep learning and generative models are advancing rapidly, with synthetic data increasingly being integrated into training pipelines for downstream analysis tasks. However, in medical imaging, their adoption remains constrained by the scarcity of reliable annotated datasets. To address this limitation, we propose 3D-LLDM, a label-guided 3D latent diffusion model that generates high-quality synthetic magnetic resonance (MR) volumes with corresponding anatomical segmentation masks. Our approach uses hepatobiliary phase MR images enhanced with the Gd-EOB-DTPA contrast agent to derive structural masks for the liver, portal vein, hepatic vein, and hepatocellular carcinoma, which then guide volumetric synthesis through a ControlNet-based architecture. Trained on 720 real clinical hepatobiliary phase MR scans from Samsung Medical Center, 3D-LLDM achieves a Fréchet Inception Distance (FID) of 28.31, improving over GANs by 70.9% and over state-of-the-art diffusion baselines by 26.7%. When used for data augmentation, the synthetic volumes improve hepatocellular carcinoma segmentation by up to 11.153% Dice score across five CNN architectures.

Method: Introduces S3-Bench benchmark suite with dual-domain design (simulator + real-world streaming videos) and AMF-VLM model with memory folding (compresses long observations) and active exploration (outputs actions to acquire missing evidence).

Result: AMF-VLM improves by 8.8% on simulated split and 13.3% on real split of S3-Eval compared to models using identical training data, while maintaining competitive transferability to standard spatial benchmarks.

Conclusion: The work addresses crucial gaps in spatial VLMs for embodied agents by introducing streaming spatial QA with active exploration, enabling real-time reasoning and perception in dynamic environments.

Abstract: Spatial understanding is fundamental for embodied agents, yet most spatial VLMs and benchmarks remain offline-evaluating post-hoc QA over pre-recorded inputs and overlooking two crucial deployment-critical requirements: long-horizon streaming inference and active perception when the current view is insufficient. To address this gap, we introduce S3-Bench, a benchmark suite for streaming spatial question answering with active exploration, where queries are temporally grounded to specific timestamps and must be answered using only observations available up to that moment. S3-Bench adopts a dual-domain design, combining a scalable simulator with controllable trajectories and exploration actions, and real-world streaming videos that capture practical sensing artifacts for rigorous generalization evaluation. Overall, it spans 10K+ scenes and 26K+ trajectories, with dedicated training (S3-Train) and evaluation (S3-Eval) splits. We further propose AMF-VLM, which supports streaming spatial reasoning under bounded computing via (i) memory folding, which compresses long-horizon observations into compact structured memory, and (ii) active exploration, which outputs explicit actions (e.g. move/rotate/scan) to acquire missing evidence before answering. Extensive experiments demonstrate that, compared to models using identical training data, our approach yields improvements of 8.8% and 13.3% on the simulated and real splits of S3-Eval, respectively, while maintaining competitive transferability to standard spatial benchmarks.

Method: Propose entropy-guided autoencoder combining standard reconstruction loss with novel Minimal Latent Entropy (MLE) loss. MLE minimizes entropy of latent embeddings, encouraging concentration around high-density normal regions, pulling sparse anomalous embeddings into normal cluster for poor anomaly reconstruction.

Result: Extensive experiments on two widely used benchmarks and challenging self-collected driving dataset demonstrate robust and superior performance over baselines.

Conclusion: Dual-loss design produces clear reconstruction gap enabling effective anomaly detection in fully unsupervised setting without requiring labeled data or normal-only videos.

Abstract: In this paper, we address the challenging problem of single-scene, fully unsupervised video anomaly detection (VAD), where raw videos containing both normal and abnormal events are used directly for training and testing without any labels. This differs sharply from prior work that either requires extensive labeling (fully or weakly supervised) or depends on normal-only videos (one-class classification), which are vulnerable to distribution shifts and contamination. We propose an entropy-guided autoencoder that detects anomalies through reconstruction error by reconstructing normal frames well while making anomalies reconstruct poorly. The key idea is to combine the standard reconstruction loss with a novel Minimal Latent Entropy (MLE) loss in the autoencoder. Reconstruction loss alone maps normal and abnormal inputs to distinct latent clusters due to their inherent differences, but also risks reconstructing anomalies too well to detect. Therefore, MLE loss addresses this by minimizing the entropy of latent embeddings, encouraging them to concentrate around high-density regions. Since normal frames dominate the raw video, sparse anomalous embeddings are pulled into the normal cluster, so the decoder emphasizes normal patterns and produces poor reconstructions for anomalies. This dual-loss design produces a clear reconstruction gap that enables effective anomaly detection. Extensive experiments on two widely used benchmarks and a challenging self-collected driving dataset demonstrate that our method achieves robust and superior performance over baselines.

Method: Uses diffusion-based modeling with two key modules: 1) structured environmental conditioning encoding obstacles, objects of interest, and lighting; 2) individual-group interaction module capturing both interpersonal relations and group-level conformity via graph-based design.

Result: Outperforms state-of-the-art methods on multiple benchmark datasets, demonstrating the importance of explicit environmental conditioning and multi-level social interaction.

Conclusion: Explicit environmental conditioning combined with multi-level social interaction modeling is essential for realistic crowd simulation, and EnvSocial-Diff provides an effective framework for this integration.

Abstract: Modeling realistic pedestrian trajectories requires accounting for both social interactions and environmental context, yet most existing approaches largely emphasize social dynamics. We propose \textbf{EnvSocial-Diff}: a diffusion-based crowd simulation model informed by social physics and augmented with environmental conditioning and individual–group interaction. Our structured environmental conditioning module explicitly encodes obstacles, objects of interest, and lighting levels, providing interpretable signals that capture scene constraints and attractors. In parallel, the individual–group interaction module goes beyond individual-level modeling by capturing both fine-grained interpersonal relations and group-level conformity through a graph-based design. Experiments on multiple benchmark datasets demonstrate that EnvSocial-Diff outperforms the latest state-of-the-art methods, underscoring the importance of explicit environmental conditioning and multi-level social interaction for realistic crowd simulation. Code is here: https://github.com/zqyq/EnvSocial-Diff.

Method: 1) Constructed a large-scale training dataset with 1.3M audio clips and 2.3M images covering 14,133 species with ecological trait labels. 2) Built upon BioCLIP2 with a two-stage training framework to align audio representations with visual and textual representations. 3) Developed a cross-modal retrieval benchmark covering all directional retrieval across three modalities at three taxonomic levels.

Result: The model learns a unified representation space that captures species-level semantics beyond taxonomy, advancing multimodal biodiversity understanding. Extensive experiments demonstrate effective cross-modal alignment across visual, textual, and acoustic modalities.

Conclusion: BioVITA successfully integrates audio with visual and textual modalities for biological applications, providing a comprehensive framework for multimodal biodiversity understanding and creating new opportunities for ecological research through unified multimodal representations.

Abstract: Understanding animal species from multimodal data poses an emerging challenge at the intersection of computer vision and ecology. While recent biological models, such as BioCLIP, have demonstrated strong alignment between images and textual taxonomic information for species identification, the integration of the audio modality remains an open problem. We propose BioVITA, a novel visual-textual-acoustic alignment framework for biological applications. BioVITA involves (i) a training dataset, (ii) a representation model, and (iii) a retrieval benchmark. First, we construct a large-scale training dataset comprising 1.3 million audio clips and 2.3 million images, covering 14,133 species annotated with 34 ecological trait labels. Second, building upon BioCLIP2, we introduce a two-stage training framework to effectively align audio representations with visual and textual representations. Third, we develop a cross-modal retrieval benchmark that covers all possible directional retrieval across the three modalities (i.e., image-to-audio, audio-to-text, text-to-image, and their reverse directions), with three taxonomic levels: Family, Genus, and Species. Extensive experiments demonstrate that our model learns a unified representation space that captures species-level semantics beyond taxonomy, advancing multimodal biodiversity understanding. The project page is available at: https://dahlian00.github.io/BioVITA_Page/

Method: Proposes a data-training co-design framework with: 1) Realistic Scene Synthesis strategy to construct large-scale, structurally diverse full-page end-to-end supervision by composing layout templates with document elements, and 2) Document-Aware Training Recipe with progressive learning and structure-token optimization for enhanced structural fidelity and decoding stability.

Result: Integrated into a 1B-parameter MLLM, the method achieves superior accuracy and robustness across both scanned/digital and real-world captured scenarios, with evaluation on the proposed Wild-OmniDocBench benchmark.

Conclusion: The framework effectively addresses data scarcity and structural inconsistency in document parsing, demonstrating strong performance in real-world scenarios, with all models, data synthesis pipelines, and benchmarks to be publicly released.

Abstract: Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Traditional cascaded pipelines depend on precise layout analysis and often fail under casually captured or non-standard conditions. Although end-to-end approaches mitigate this dependency, they still exhibit repetitive, hallucinated, and structurally inconsistent predictions - primarily due to the scarcity of large-scale, high-quality full-page (document-level) end-to-end parsing data and the lack of structure-aware training strategies. To address these challenges, we propose a data-training co-design framework for robust end-to-end document parsing. A Realistic Scene Synthesis strategy constructs large-scale, structurally diverse full-page end-to-end supervision by composing layout templates with rich document elements, while a Document-Aware Training Recipe introduces progressive learning and structure-token optimization to enhance structural fidelity and decoding stability. We further build Wild-OmniDocBench, a benchmark derived from real-world captured documents for robustness evaluation. Integrated into a 1B-parameter MLLM, our method achieves superior accuracy and robustness across both scanned/digital and real-world captured scenarios. All models, data synthesis pipelines, and benchmarks will be publicly released to advance future research in document understanding.

Method: Introduces FilterGS with: 1) Parallel filtering mechanism with two complementary filters that select Gaussian elements efficiently without tree traversal, and 2) Novel GTC metric to quantify Gaussian-tile pair redundancy, plus a scene-adaptive Gaussian shrinking strategy to reduce redundant pairs.

Result: Extensive experiments show FilterGS achieves state-of-the-art rendering speeds while maintaining competitive visual quality across multiple large-scale datasets.

Conclusion: FilterGS successfully addresses the efficiency bottlenecks in large-scale 3D Gaussian Splatting through parallel filtering and redundancy reduction, enabling faster rendering without compromising visual quality.

Abstract: 3D Gaussian Splatting has revolutionized neural rendering with real-time performance. However, scaling this approach to large scenes using Level-of-Detail methods faces critical challenges: inefficient serial traversal consuming over 60% of rendering time, and redundant Gaussian-tile pairs that incur unnecessary processing overhead. To address these limitations, we introduce FilterGS, featuring a parallel filtering mechanism with two complementary filters that select Gaussian elements efficiently without tree traversal. Additionally, we propose a novel GTC metric that quantifies the redundancy of Gaussian-tile key-value pairs. Based on this metric, we introduce a scene-adaptive Gaussian shrinking strategy that effectively reduces redundant pairs. Extensive experiments demonstrate that FilterGS achieves state-of-the-art rendering speeds while maintaining competitive visual quality across multiple large-scale datasets. Project page: https://github.com/xenon-w/FilterGS

Method: 1) Created MMTIT-Bench: human-verified multilingual multi-scenario benchmark with 1,400 images across 14 non-English/non-Chinese languages and diverse settings. 2) Proposed CPR-Trans: Cognition-Perception-Reasoning for Translation data paradigm integrating scene cognition, text perception, and translation reasoning in unified process. 3) Used VLLM-driven data generation pipeline for structured, interpretable supervision aligning perception with reasoning.

Result: Experiments on 3B and 7B models show consistent gains in accuracy and interpretability. The benchmark enables rigorous assessment of end-to-end TIMT across diverse scenarios and languages.

Conclusion: MMTIT-Bench addresses the lack of evaluation resources for multilingual TIMT, while CPR-Trans provides effective reasoning paradigm that integrates visual cognition with translation reasoning, improving model performance and interpretability.

Abstract: End-to-end text-image machine translation (TIMT), which directly translates textual content in images across languages, is crucial for real-world multilingual scene understanding. Despite advances in vision-language large models (VLLMs), robustness across diverse visual scenes and low-resource languages remains underexplored due to limited evaluation resources. We present MMTIT-Bench, a human-verified multilingual and multi-scenario benchmark with 1,400 images spanning fourteen non-English and non-Chinese languages and diverse settings such as documents, scenes, and web images, enabling rigorous assessment of end-to-end TIMT. Beyond benchmarking, we study how reasoning-oriented data design improves translation. Although recent VLLMs have begun to incorporate long Chain-of-Thought (CoT) reasoning, effective thinking paradigms for TIMT are still immature: existing designs either cascade parsing and translation in a sequential manner or focus on language-only reasoning, overlooking the visual cognition central to VLLMs. We propose Cognition-Perception-Reasoning for Translation (CPR-Trans), a data paradigm that integrates scene cognition, text perception, and translation reasoning within a unified reasoning process. Using a VLLM-driven data generation pipeline, CPR-Trans provides structured, interpretable supervision that aligns perception with reasoning. Experiments on 3B and 7B models show consistent gains in accuracy and interpretability. We will release MMTIT-Bench to promote the multilingual and multi-scenario TIMT research upon acceptance.

Method: KDC-Net uses a dual approach: 1) Hierarchical Semantic Aggregation module captures multi-scale phrase cues to enrich query semantics on text side, 2) Dynamic Temporal Attention mechanism with relative positional encoding and adaptive temporal windows highlights key events with local temporal coherence on video side, plus 3) dynamic CLIP-based distillation with temporal-continuity-aware refinement for knowledge transfer.

Result: Experiments on PRVR benchmarks show KDC-Net consistently outperforms state-of-the-art methods, especially under low moment-to-video ratios.

Conclusion: KDC-Net effectively addresses text-video information density mismatch and improves video segment retrieval through dual context-aware modeling and knowledge distillation.

Abstract: Retrieving partially relevant segments from untrimmed videos remains difficult due to two persistent challenges: the mismatch in information density between text and video segments, and limited attention mechanisms that overlook semantic focus and event correlations. We present KDC-Net, a Knowledge-Refined Dual Context-Aware Network that tackles these issues from both textual and visual perspectives. On the text side, a Hierarchical Semantic Aggregation module captures and adaptively fuses multi-scale phrase cues to enrich query semantics. On the video side, a Dynamic Temporal Attention mechanism employs relative positional encoding and adaptive temporal windows to highlight key events with local temporal coherence. Additionally, a dynamic CLIP-based distillation strategy, enhanced with temporal-continuity-aware refinement, ensures segment-aware and objective-aligned knowledge transfer. Experiments on PRVR benchmarks show that KDC-Net consistently outperforms state-of-the-art methods, especially under low moment-to-video ratios.

Method: Introduces Latent Bias Optimization (LBO) to learn bias vectors reducing trajectory misalignment, and Image Latent Boosting (ILB) for joint optimization of diffusion inversion and VQAE reconstruction by adjusting image latent representations

Result: Significantly improves image reconstruction quality and enhances downstream task performance including image editing and rare concept generation

Conclusion: The proposed LBO and ILB techniques effectively address diffusion inversion challenges, enabling better bridging between diffusion models and real-world applications

Abstract: Recent research has shown that text-to-image diffusion models are capable of generating high-quality images guided by text prompts. But can they be used to generate or approximate real-world images from the seed noise? This is known as the diffusion inversion problem, which serves as a fundamental building block for bridging diffusion models and real-world scenarios. However, existing diffusion inversion methods often suffer from low reconstruction quality or weak robustness. Two major challenges need to be carefully addressed: (1) the misalignment between the inversion and generation trajectories during the diffusion process, and (2) the mismatch between the diffusion inversion process and the VQ autoencoder (VQAE) reconstruction. To address these challenges, we introduce a latent bias vector at each inversion step, which is learned to reduce the misalignment between inversion and generation trajectories. We refer to this strategy as Latent Bias Optimization (LBO). Furthermore, we perform an approximate joint optimization of the diffusion inversion and VQAE reconstruction processes by learning to adjust the image latent representation, which serves as the connecting interface between them. We refer to this technique as Image Latent Boosting (ILB). Extensive experimental results demonstrate that the proposed method significantly improves the image reconstruction quality of the diffusion model, as well as the performance of downstream tasks, including image editing and rare concept generation.

Method: Introduces GenMask, a DiT that generates both segmentation masks and RGB images using a timestep sampling strategy that emphasizes extreme noise for masks and moderate noise for images to handle their different latent distributions.

Result: Achieves state-of-the-art performance on referring and reasoning segmentation benchmarks while eliminating feature extraction pipelines.

Conclusion: Direct generative training for segmentation is more effective than indirect feature extraction approaches, enabling unified architecture for both image and mask generation.

Abstract: Recent approaches for segmentation have leveraged pretrained generative models as feature extractors, treating segmentation as a downstream adaptation task via indirect feature retrieval. This implicit use suffers from a fundamental misalignment in representation. It also depends heavily on indirect feature extraction pipelines, which complicate the workflow and limit adaptation. In this paper, we argue that instead of indirect adaptation, segmentation tasks should be trained directly in a generative manner. We identify a key obstacle to this unified formulation: VAE latents of binary masks are sharply distributed, noise robust, and linearly separable, distinct from natural image latents. To bridge this gap, we introduce timesteps sampling strategy for binary masks that emphasizes extreme noise levels for segmentation and moderate noise for image generation, enabling harmonious joint training. We present GenMask, a DiT trains to generate black-and-white segmentation masks as well as colorful images in RGB space under the original generative objective. GenMask preserves the original DiT architecture while removing the need of feature extraction pipelines tailored for segmentation tasks. Empirically, GenMask attains state-of-the-art performance on referring and reasoning segmentation benchmarks and ablations quantify the contribution of each component.

Method: Two key innovations: 1) Multi-head attention compaction exploiting low-rank structure to store KV matrices economically, and 2) Token-specific attention-aware decompression to reduce latency overhead.

Result: Achieves up to 8x memory efficiency improvement, enables higher batch sizes and faster inference while preserving output quality, and shows further gains when combined with eviction, quantization, and kernel fusion.

Conclusion: AttentionPack provides an effective solution for memory-efficient decoding in large VLMs, particularly beneficial for long-context tasks with multiple high-resolution images/videos in resource-limited environments.

Abstract: Large Vision-Language Models (VLMs) have achieved remarkable success in multi-modal reasoning, but their inference time efficiency remains a significant challenge due to the memory overhead during decoding, especially when the query and answer of VLMs consist of long sequences of visual and text tokens. This paper presents AttentionPack, an adaptive and attention-aware optimization framework tailored for large vision-language models with improving memory-efficiency during decoding, focusing on addressing the challenges due to the increased high number of visual inputs and interactions, particularly in long-context tasks with multiple high-resolution images or videos. AttentionPack is novel in two aspects: (i) We introduce a multi-head attention compaction method for economically storing key and value matrices by exploiting the implicit low-rank structure, and (ii) we develop a token-specific attention-aware decompression mechanism to reduce latency overhead. Experimental results on multiple benchmarks demonstrate that AttentionPack improves memory efficiency by up to 8x, enabling higher batch sizes and faster batch inference while preserving the model output quality or longer context lengths for superior retrieval performance. We also report the effectiveness of AttentionPack combined with eviction, quantization and kernel fusion, showing further efficiency gains for resource-limited environments.

Method: Three main contributions: 1) Construct reasoning datasets with structured cue-level descriptions and reasoning chains; 2) Release T4-Deception multicultural dataset (1695 samples from 4 countries); 3) Propose SICS module (synergizes global priors with sample-adaptive residuals) and DMC module (aligns modality-specific predictions via knowledge distillation).

Result: Achieves state-of-the-art performance on three established benchmarks and the novel T4-Deception dataset, with superior transferability across cultural contexts and robust performance in both in-domain and cross-domain scenarios.

Conclusion: The framework enables auditable deception detection with verifiable reasoning, addresses multicultural generalization, and overcomes shortcut learning through structured reasoning datasets and robust multimodal learning modules.

Abstract: Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security. In these high-stakes settings, investigators need verifiable evidence connecting audiovisual cues to final decisions, along with reliable generalization across domains and cultural contexts. However, existing benchmarks provide only binary labels without intermediate reasoning cues. Datasets are also small with limited scenario coverage, leading to shortcut learning. We address these issues through three contributions. First, we construct reasoning datasets by augmenting existing benchmarks with structured cue-level descriptions and reasoning chains, enabling model output auditable reports. Second, we release T4-Deception, a multicultural dataset based on the unified ``To Tell The Truth’’ television format implemented across four countries. With 1695 samples, it is the largest non-laboratory deception detection dataset. Third, we propose two modules for robust learning under small-data conditions. Stabilized Individuality-Commonality Synergy (SICS) refines multimodal representations by synergizing learnable global priors with sample-adaptive residuals, followed by a polarity-aware adjustment that bi-directionally recalibrates representations. Distilled Modality Consistency (DMC) aligns modality-specific predictions with the fused multimodal predictions via knowledge distillation to prevent unimodal shortcut learning. Experiments on three established benchmarks and our novel dataset demonstrate that our method achieves state-of-the-art performance in both in-domain and cross-domain scenarios, while exhibiting superior transferability across diverse cultural contexts. The datasets and codes will be released.

Method: Proposes Conformal Risk Tube Prediction (CRTP) - a unified formulation that captures spatiotemporal risk uncertainty, provides coverage guarantees for true risks, and produces calibrated risk scores with uncertainty estimates. Includes new dataset and metrics for systematic evaluation.

Result: CRTP delivers substantial improvements over prior approaches, enhancing vision-ROI robustness and downstream performance (e.g., reducing nuisance braking alerts). Systematic analysis examines factors affecting uncertainty estimation including scenario variations and perception error propagation.

Conclusion: The proposed framework addresses critical limitations in current vision risk identification by modeling uncertainty jointly across space and time, providing coverage guarantees, and improving safety in complex driving scenarios.

Abstract: We study object importance-based vision risk object identification (Vision-ROI), a key capability for hazard detection in intelligent driving systems. Existing approaches make deterministic decisions and ignore uncertainty, which could lead to safety-critical failures. Specifically, in ambiguous scenarios, fixed decision thresholds may cause premature or delayed risk detection and temporally unstable predictions, especially in complex scenes with multiple interacting risks. Despite these challenges, current methods lack a principled framework to model risk uncertainty jointly across space and time. We propose Conformal Risk Tube Prediction, a unified formulation that captures spatiotemporal risk uncertainty, provides coverage guarantees for true risks, and produces calibrated risk scores with uncertainty estimates. To conduct a systematic evaluation, we present a new dataset and metrics probing diverse scenario configurations with multi-risk coupling effects, which are not supported by existing datasets. We systematically analyze factors affecting uncertainty estimation, including scenario variations, per-risk category behavior, and perception error propagation. Our method delivers substantial improvements over prior approaches, enhancing vision-ROI robustness and downstream performance, such as reducing nuisance braking alerts. For more qualitative results, please visit our project webpage: https://hcis-lab.github.io/CRTP/

Method: DepthArb uses two core mechanisms: 1) Attention Arbitration Modulation (AAM) enforces depth-ordered visibility by suppressing background activations in overlapping regions, and 2) Spatial Compactness Control (SCC) preserves structural integrity by curbing attention divergence. The framework also introduces OcclBench, a benchmark for evaluating occlusion scenarios.

Result: DepthArb consistently outperforms state-of-the-art baselines in both occlusion accuracy and visual fidelity. It serves as a plug-and-play method that enhances compositional capabilities of diffusion backbones.

Conclusion: DepthArb offers a novel perspective on spatial layering in generative models by resolving occlusion ambiguities through attention arbitration, improving multi-object composition without requiring model retraining.

Abstract: Text-to-image diffusion models frequently exhibit deficiencies in synthesizing accurate occlusion relationships of multiple objects, particularly within dense overlapping regions. Existing training-free layout-guided methods predominantly rely on rigid spatial priors that remain agnostic to depth order, often resulting in concept mixing or illogical occlusion. To address these limitations, we propose DepthArb, a training-free framework that resolves occlusion ambiguities by arbitrating attention competition between interacting objects. Specifically, DepthArb employs two core mechanisms: Attention Arbitration Modulation (AAM), which enforces depth-ordered visibility by suppressing background activations in overlapping regions, and Spatial Compactness Control (SCC), which preserves structural integrity by curbing attention divergence. These mechanisms enable robust occlusion generation without model retraining. To systematically evaluate this capability, we propose OcclBench, a comprehensive benchmark designed to evaluate diverse occlusion scenarios. Extensive evaluations demonstrate that DepthArb consistently outperforms state-of-the-art baselines in both occlusion accuracy and visual fidelity. As a plug-and-play method, DepthArb seamlessly enhances the compositional capabilities of diffusion backbones, offering a novel perspective on spatial layering within generative models.

Method: 1) Proposes identity-affiliation learning threat model; 2) Creates first identity-affiliation dataset with 7 typical private photo scenarios; 3) Demonstrates vulnerability of mainstream VLMs (LLaVA, Qwen-VL, MiniGPT-v2); 4) Proposes DP2-VL defense using data poisoning with imperceptible perturbations that shift embeddings to cause overfitting during fine-tuning.

Result: VLMs can recognize facial identities and infer identity-affiliation relationships even with small-scale fine-tuning. DP2-VL achieves strong generalization across models, robustness to post-processing, and consistent effectiveness across protection ratios.

Conclusion: VLMs pose significant privacy risks through identity-affiliation learning, requiring new defense mechanisms like DP2-VL’s data poisoning approach to protect private photos while maintaining model utility.

Abstract: Recent advances in visual-language alignment have endowed vision-language models (VLMs) with fine-grained image understanding capabilities. However, this progress also introduces new privacy risks. This paper first proposes a novel privacy threat model named identity-affiliation learning: an attacker fine-tunes a VLM using only a few private photos of a target individual, thereby embedding associations between the target facial identity and their private property and social relationships into the model’s internal representations. Once deployed via public APIs, this model enables unauthorized exposure of the target user’s private information upon input of their photos. To benchmark VLMs’ susceptibility to such identity-affiliation leakage, we introduce the first identity-affiliation dataset comprising seven typical scenarios appearing in private photos. Each scenario is instantiated with multiple identity-centered photo-description pairs. Experimental results demonstrate that mainstream VLMs like LLaVA, Qwen-VL, and MiniGPT-v2, can recognize facial identities and infer identity-affiliation relationships by fine-tuning on small-scale private photographic dataset, and even on synthetically generated datasets. To mitigate this privacy risk, we propose DP2-VL, the first Dataset Protection framework for private photos that leverages Data Poisoning. Though optimizing imperceptible perturbations by pushing the original representations toward an antithetical region, DP2-VL induces a dataset-level shift in the embedding space of VLMs’encoders. This shift separates protected images from clean inference images, causing fine-tuning on the protected set to overfit. Extensive experiments demonstrate that DP2-VL achieves strong generalization across models, robustness to diverse post-processing operations, and consistent effectiveness across varying protection ratios.

Method: 1) Construct MVH-Bench benchmark with 4.8k question-answer pairs targeting cross-instance and cross-view hallucination types. 2) Propose Reference Shift Contrastive Decoding (RSCD), a training-free decoding technique that suppresses visual interference by generating negative logits through attention masking.

Result: Experiments on MVH-Bench with Qwen2.5-VL and LLaVA-OneVision show RSCD consistently improves performance by up to 21.1 and 34.6 points over existing hallucination mitigation methods. Recent LVLMs struggle to correctly associate visual evidence with corresponding instances/viewpoints.

Conclusion: Multi-view hallucination is a significant problem in LVLMs. RSCD effectively addresses this issue without requiring additional training, demonstrating substantial improvements over existing methods. The MVH-Bench provides a valuable tool for evaluating multi-view understanding capabilities.

Abstract: Large vision-language models (LVLMs) are increasingly being applied to multi-view image inputs captured from diverse viewpoints. However, despite this growing use, current LVLMs often confuse or mismatch visual information originating from different instances or viewpoints, a phenomenon we term multi-view hallucination. To systematically analyze this problem, we construct MVH-Bench, a benchmark comprising 4.8k question-answer pairs targeting two types of hallucination: cross-instance and cross-view. Empirical results show that recent LVLMs struggle to correctly associate visual evidence with its corresponding instance or viewpoint. To overcome this limitation, we propose Reference Shift Contrastive Decoding (RSCD), a training-free decoding technique that suppresses visual interference by generating negative logits through attention masking. Experiments on MVH-Bench with Qwen2.5-VL and LLaVA-OneVision demonstrate that RSCD consistently improves performance by up to 21.1 and 34.6 points over existing hallucination mitigation methods, highlighting the effectiveness of our approach.

Method: VeriFi embeds compact semantic latent watermarks as content-preserving priors, achieves fine-grained localization without explicit localization artifacts by correlating image features with decoded provenance signals, and uses an AIGC attack simulator combining latent-space mixing with seamless blending for robustness.

Result: Extensive experiments on CelebA-HQ and FFHQ show VeriFi consistently outperforms strong baselines in watermark robustness, localization accuracy, and recovery quality.

Conclusion: VeriFi provides a practical and verifiable defense for deepfake forensics by unifying copyright protection, manipulation localization, and content recovery in a single framework.

Abstract: The proliferation of AIGC-driven face manipulation and deepfakes poses severe threats to media provenance, integrity, and copyright protection. Prior versatile watermarking systems typically rely on embedding explicit localization payloads, which introduces a fidelity–functionality trade-off: larger localization signals degrade visual quality and often reduce decoding robustness under strong generative edits. Moreover, existing methods rarely support content recovery, limiting their forensic value when original evidence must be reconstructed. To address these challenges, we present VeriFi, a versatile watermarking framework that unifies copyright protection, pixel-level manipulation localization, and high-fidelity face content recovery. VeriFi makes three key contributions: (1) it embeds a compact semantic latent watermark that serves as an content-preserving prior, enabling faithful restoration even after severe manipulations; (2) it achieves fine-grained localization without embedding localization-specific artifacts by correlating image features with decoded provenance signals; and (3) it introduces an AIGC attack simulator that combines latent-space mixing with seamless blending to improve robustness to realistic deepfake pipelines. Extensive experiments on CelebA-HQ and FFHQ show that VeriFi consistently outperforms strong baselines in watermark robustness, localization accuracy, and recovery quality, providing a practical and verifiable defense for deepfake forensics.

Method: Three-stage framework: 1) Ophthalmology knowledge pretraining on 86,965 image-text pairs from 26,569 articles, 2) Domain task fine-tuning on 26,929 annotated instances for 12 eye conditions, 3) Multi-step clinical reasoning on 913 patient case reports for assessment, planning, and follow-up care.

Result: VOLMO-2B consistently outperformed baselines (InternVL-2B, LLaVA-Med-7B, MedGemma-4B/27B, RETFound) on image description, disease screening/staging (avg F1 87.4% across 12 conditions), and external validation on AMD and diabetic retinopathy cohorts.

Conclusion: VOLMO provides an effective, open framework for developing ophthalmology-specific MLLMs that can assist clinicians in time-consuming diagnostic workflows and improve early detection of vision-threatening conditions.

Abstract: Vision impairment affects millions globally, and early detection is critical to preventing irreversible vision loss. Ophthalmology workflows require clinicians to integrate medical images, structured clinical data, and free-text notes to determine disease severity and management, which is time-consuming and burdensome. Recent multimodal large language models (MLLMs) show promise, but existing general and medical MLLMs perform poorly in ophthalmology, and few ophthalmology-specific MLLMs are openly available. We present VOLMO (Versatile and Open Large Models for Ophthalmology), a model-agnostic, data-open framework for developing ophthalmology-specific MLLMs. VOLMO includes three stages: ophthalmology knowledge pretraining on 86,965 image-text pairs from 26,569 articles across 82 journals; domain task fine-tuning on 26,929 annotated instances spanning 12 eye conditions for disease screening and severity classification; and multi-step clinical reasoning on 913 patient case reports for assessment, planning, and follow-up care. Using this framework, we trained a compact 2B-parameter MLLM and compared it with strong baselines, including InternVL-2B, LLaVA-Med-7B, MedGemma-4B, MedGemma-27B, and RETFound. We evaluated these models on image description generation, disease screening and staging classification, and assessment-and-management generation, with additional manual review by two healthcare professionals and external validation on three independent cohorts for age-related macular degeneration and diabetic retinopathy. Across settings, VOLMO-2B consistently outperformed baselines, achieving stronger image description performance, an average F1 of 87.4% across 12 eye conditions, and higher scores in external validation.

Method: Created a synthetic benchmark with 50 scenes, large crowd numbers (up to 1000), multiple camera views, and many frames. Also proposed strong baseline methods for multi-view crowd localization and counting.

Result: The benchmark enables more practical evaluation and comparison. The proposed baselines outperform existing methods on SynMVCrowd and show better domain transfer to real scenes for both multi-view and single-image counting.

Conclusion: SynMVCrowd advances multi-view and single-image crowd counting research toward more practical applications by providing a comprehensive synthetic benchmark that addresses limitations of existing small datasets.

Abstract: Existing multi-view crowd counting and localization methods are evaluated under relatively small scenes with limited crowd numbers, camera views, and frames. This makes the evaluation and comparison of existing methods impractical, as small datasets are easily overfit by these methods. To avoid these issues, 3DROM proposes a data augmentation method. Instead, in this paper, we propose a large synthetic benchmark, SynMVCrowd, for more practical evaluation and comparison of multi-view crowd counting and localization tasks. The SynMVCrowd benchmark consists of 50 synthetic scenes with a large number of multi-view frames and camera views and a much larger crowd number (up to 1000), which is more suitable for large-scene multi-view crowd vision tasks. Besides, we propose strong multi-view crowd localization and counting baselines that outperform all comparison methods on the new SynMVCrowd benchmark. Moreover, we prove that better domain transferring multi-view and single-image counting performance could be achieved with the aid of the benchmark on novel new real scenes. As a result, the proposed benchmark could advance the research for multi-view and single-image crowd counting and localization to more practical applications. The codes and datasets are here: https://github.com/zqyq/SynMVCrowd.

Method: The authors propose PointRFT, a reinforcement fine-tuning paradigm for point cloud representation learning. They select three prevalent 3D foundation models and design specialized reward functions: accuracy reward and dispersion reward. These functions are engineered to stabilize training and mitigate distribution shifts. The approach is evaluated through comprehensive few-shot classification experiments comparing different training paradigms.

Result: PointRFT consistently outperforms vanilla supervised fine-tuning (SFT) across diverse benchmarks. When integrated into a hybrid Pretraining-SFT-RFT paradigm, the representational capacity of point cloud foundation models is substantially enhanced, achieving state-of-the-art performance, particularly under data-scarce scenarios.

Conclusion: The paper demonstrates that RL-based methods can effectively empower 3D point cloud fine-tuning. PointRFT represents a significant advancement in point cloud representation learning, showing that reinforcement fine-tuning can substantially improve foundation models’ performance, especially in data-limited situations.

Abstract: Understanding spatial dynamics and semantics in point cloud is fundamental for comprehensive 3D comprehension. While reinforcement learning algorithms such as Group Relative Policy Optimization (GRPO) have recently achieved remarkable breakthroughs in large language models by incentivizing reasoning capabilities through strategic reward design, their potential remains largely unexplored in the 3D perception domain. This naturally raises a pivotal question: Can RL-based methods effectively empower 3D point cloud fine-tuning? In this paper, we propose PointRFT, the first reinforcement fine-tuning paradigm tailored specifically for point cloud representation learning. We select three prevalent 3D foundation models and devise specialized accuracy reward and dispersion reward functions to stabilize training and mitigate distribution shifts. Through comprehensive few-shot classification experiments comparing distinct training paradigms, we demonstrate that PointRFT consistently outperforms vanilla supervised fine-tuning (SFT) across diverse benchmarks. Furthermore, when organically integrated into a hybrid Pretraining-SFT-RFT paradigm, the representational capacity of point cloud foundation models is substantially unleashed, achieving state-of-the-art performance particularly under data-scarce scenarios.

Method: HAVIC learns priors of modality-specific structural coherence, inter-modal micro- and macro-coherence by pre-training on authentic videos. It then performs holistic adaptive aggregation to dynamically fuse audio-visual features for deepfake detection. The authors also introduce HiFi-AVDF, a high-fidelity audio-visual deepfake dataset featuring text-to-video and image-to-video forgeries from state-of-the-art commercial generators.

Result: Extensive experiments across several benchmarks demonstrate that HAVIC significantly outperforms existing state-of-the-art methods, achieving improvements of 9.39% AP and 9.37% AUC on the most challenging cross-dataset scenario.

Conclusion: HAVIC provides a robust and generalizable deepfake detection approach by grounding detection in intrinsic audio-visual coherence, with strong performance demonstrated across challenging benchmarks including cross-dataset scenarios.

Abstract: The rapid progress of generative AI has enabled hyper-realistic audio-visual deepfakes, intensifying threats to personal security and social trust. Most existing deepfake detectors rely either on uni-modal artifacts or audio-visual discrepancies, failing to jointly leverage both sources of information. Moreover, detectors that rely on generator-specific artifacts tend to exhibit degraded generalization when confronted with unseen forgeries. We argue that robust and generalizable detection should be grounded in intrinsic audio-visual coherence within and across modalities. Accordingly, we propose HAVIC, a Holistic Audio-Visual Intrinsic Coherence-based deepfake detector. HAVIC first learns priors of modality-specific structural coherence, inter-modal micro- and macro-coherence by pre-training on authentic videos. Based on the learned priors, HAVIC further performs holistic adaptive aggregation to dynamically fuse audio-visual features for deepfake detection. Additionally, we introduce HiFi-AVDF, a high-fidelity audio-visual deepfake dataset featuring both text-to-video and image-to-video forgeries from state-of-the-art commercial generators. Extensive experiments across several benchmarks demonstrate that HAVIC significantly outperforms existing state-of-the-art methods, achieving improvements of 9.39% AP and 9.37% AUC on the most challenging cross-dataset scenario. Our code and dataset are available at https://github.com/tuffy-studio/HAVIC.

Method: Leverages spatially organized latent features from a pretrained 3D asset generation model that encodes geometry and semantic priors. Trains a lightweight neural decoder to estimate Young’s modulus, density, and Poisson’s ratio directly from single RGB images.

Result: Achieves competitive accuracy in predicting continuous material parameters while requiring only 9.9 seconds per object (120x speedup vs prior methods). Avoids reconstruction and voxelization preprocessing.

Conclusion: SLAT-Phys enables fast, accurate material property estimation from single RGB images by leveraging 3D priors without explicit reconstruction, making it practical for real-world applications.

Abstract: Estimating the material property field of 3D assets is critical for physics-based simulation, robotics, and digital twin generation. Existing vision-based approaches are either too expensive and slow or rely on 3D information. We present SLAT-Phys, an end-to-end method that predicts spatially varying material property fields of 3D assets directly from a single RGB image without explicit 3D reconstruction. Our approach leverages spatially organised latent features from a pretrained 3D asset generation model that encodes rich geometry and semantic prior, and trains a lightweight neural decoder to estimate Young’s modulus, density, and Poisson’s ratio. The coarse volumetric layout and semantic cues of the latent representation about object geometry and appearance enable accurate material estimation. Our experiments demonstrate that our method provides competitive accuracy in predicting continuous material parameters when compared against prior approaches, while significantly reducing computation time. In particular, SLAT-Phys requires only 9.9 seconds per object on an NVIDIA RTXA5000 GPU and avoids reconstruction and voxelization preprocessing. This results in 120x speedup compared to prior methods and enables faster material property estimation from a single image.

Method: HyDRA integrates intermediate and late fusion within a domain-aware framework using a lightweight domain classifier to identify heterogeneous agents and assign them to late-fusion branch, plus anchor-guided pose graph optimization to mitigate localization errors.

Result: Extensive experiments show HyDRA achieves performance comparable to state-of-the-art heterogeneity-aware methods without additional training, maintaining performance as number of collaborating agents increases.

Conclusion: HyDRA provides a unified solution for heterogeneous collaborative perception that enables zero-cost scaling without retraining, addressing key challenges in multi-agent systems.

Abstract: In collaborative perception, an agent’s performance can be degraded by heterogeneity arising from differences in model architecture or training data distributions. To address this challenge, we propose HyDRA (Hybrid Domain-Aware Robust Architecture), a unified pipeline that integrates intermediate and late fusion within a domain-aware framework. We introduce a lightweight domain classifier that dynamically identifies heterogeneous agents and assigns them to the late-fusion branch. Furthermore, we propose anchor-guided pose graph optimization to mitigate localization errors inherent in late fusion, leveraging reliable detections from intermediate fusion as fixed spatial anchors. Extensive experiments demonstrate that, despite requiring no additional training, HyDRA achieves performance comparable to state-of-the-art heterogeneity-aware CP methods. Importantly, this performance is maintained as the number of collaborating agents increases, enabling zero-cost scaling without retraining.

Method: Proposes Contour-Velocity Tokenizer to encode binary gait silhouettes while reshaping their distribution to align with text token space. Establishes Silhouette Language Model (SilLang), a dual-branch framework that enhances visual silhouettes by integrating discrete linguistic embeddings from LLMs.

Result: SilLang consistently improves state-of-the-art methods across SUSTech1K, GREW, and Gait3D benchmarks, demonstrating the effectiveness of bridging gait silhouettes with natural language representations.

Conclusion: The paper successfully demonstrates that leveraging LLMs for gait recognition by aligning binary silhouette encoding with natural language token space can significantly improve performance, opening new directions for multimodal learning in motion analysis.

Abstract: Gait silhouettes, which can be encoded into binary gait codes, are widely adopted to representing motion patterns of pedestrian. Recent approaches commonly leverage visual backbones to encode gait silhouettes, achieving successful performance. However, they primarily focus on continuous visual features, overlooking the discrete nature of binary silhouettes that inherently share a discrete encoding space with natural language. Large Language Models (LLMs) have demonstrated exceptional capability in extracting discriminative features from discrete sequences and modeling long-range dependencies, highlighting their potential to capture temporal motion patterns by identifying subtle variations. Motivated by these observations, we explore bridging binary gait silhouettes and natural language within a binary encoding space. However, the encoding spaces of text tokens and binary gait silhouettes remain misaligned, primarily due to differences in token frequency and density. To address this issue, we propose the Contour-Velocity Tokenizer, which encodes binary gait silhouettes while reshaping their distribution to better align with the text token space. We then establish a dual-branch framework termed Silhouette Language Model, which enhances visual silhouettes by integrating discrete linguistic embeddings derived from LLMs. Implemented on mainstream gait backbones, SilLang consistently improves state-of-the-art methods across SUSTech1K, GREW, and Gait3D.

Method: Proposes CAKE framework with Dynamic Motion Adapter (DMA) to suppress static background noise and emphasize pixel changes, approximating optical flow without explicit computation. Uses Floating Contrastive Learning to distinguish informative motion dynamics from temporal background.

Result: Achieves standout mAP compared with state-of-the-art while using the same backbone. Operates at over 72 FPS on a single CPU, making it highly suitable for resource-constrained systems. Validated on TVSeries, THUMOS'14, Kinetics-400 datasets.

Conclusion: CAKE effectively transfers motion knowledge from optical flow to RGB models, achieving high performance with low computational overhead, making it practical for real-time applications on resource-constrained systems.

Abstract: Online Action Detection (OAD) systems face two primary challenges: high computational cost and insufficient modeling of discriminative temporal dynamics against background motion. Adding optical flow could provides strong motion cues but it incurs significant computational overhead. We propose CAKE, a OAD Flow-based distillation framework to transfer motion knowledge into RGB models. We propose Dynamic Motion Adapter (DMA) to suppress static background noise and emphasize pixel changes, effectively approximating optical flow without explicit computation. The framework also integrates a Floating Contrastive Learning strategy to distinguish informative motion dynamics from temporal background. Various experiments conducted on the TVSeries, THUMOS'14, Kinetics-400 datasets show effectiveness of our model. CAKE achieves a standout mAP compared with SOTA while using the same backbone. Our model operates at over 72 FPS on a single CPU, making it highly suitable for resource-constrained systems.

Method: Reformulates hand reconstruction from arbitrary views as a visual-geometry grounded task. Proposes a feed-forward architecture that jointly infers 3D hand meshes and camera poses from uncalibrated views, drawing inspiration from 3D foundation models that learn explicit geometry directly from visual data.

Result: Extensive evaluations show the approach outperforms state-of-the-art benchmarks and demonstrates strong generalization to uncalibrated, in-the-wild scenarios.

Conclusion: The proposed method successfully bridges the gap between single-view and multi-view approaches by enabling accurate 3D hand reconstruction from uncalibrated views, offering both accuracy and deployment flexibility for real-world applications.

Abstract: Recovering high-fidelity 3D hand geometry from images is a critical task in computer vision, holding significant value for domains such as robotics, animation and VR/AR. Crucially, scalable applications demand both accuracy and deployment flexibility, requiring the ability to leverage massive amounts of unstructured image data from the internet or enable deployment on consumer-grade RGB cameras without complex calibration. However, current methods face a dilemma. While single-view approaches are easy to deploy, they suffer from depth ambiguity and occlusion. Conversely, multi-view systems resolve these uncertainties but typically demand fixed, calibrated setups, limiting their real-world utility. To bridge this gap, we draw inspiration from 3D foundation models that learn explicit geometry directly from visual data. By reformulating hand reconstruction from arbitrary views as a visual-geometry grounded task, we propose a feed-forward architecture that, for the first time in literature, jointly infers 3D hand meshes and camera poses from uncalibrated views. Extensive evaluations show that our approach outperforms state-of-the-art benchmarks and demonstrates strong generalization to uncalibrated, in-the-wild scenarios. Here is the link of our project page: https://lym29.github.io/HGGT/.

Method: Proposes Dual-Branch Swin Transformer network (DB SwinT) that combines Swin Transformer’s long-range dependency modeling with U-Net’s multi-scale feature fusion. Uses dual-branch encoder: local branch recovers fine structural details in occluded areas, global branch captures broader semantic context. Includes Attentional Feature Fusion (AFF) module to adaptively fuse features from both branches.

Result: Achieves IoU scores of 79.35% on Massachusetts dataset and 74.84% on DeepGlobe dataset, demonstrating effectiveness for road extraction from optical remote sensing imagery.

Conclusion: DB SwinT effectively addresses road extraction challenges in complex environments by combining local detail recovery with global context modeling, outperforming existing methods on benchmark datasets.

Abstract: With the continuous improvement in the spatial resolution of optical remote sensing imagery, accurate road extraction has become increasingly important for applications such as urban planning, traffic monitoring, and disaster management. However, road extraction in complex urban and rural environments remains challenging, as roads are often occluded by trees, buildings, and other objects, leading to fragmented structures and reduced extraction accuracy. To address this problem, this paper proposes a Dual-Branch Swin Transformer network (DB SwinT) for road extraction. The proposed framework combines the long-range dependency modeling capability of the Swin Transformer with the multi-scale feature fusion strategy of U-Net, and employs a dual-branch encoder to learn complementary local and global representations. Specifically, the local branch focuses on recovering fine structural details in occluded areas, while the global branch captures broader semantic context to preserve the overall continuity of road networks. In addition, an Attentional Feature Fusion (AFF) module is introduced to adaptively fuse features from the two branches, further enhancing the representation of occluded road segments. Experimental results on the Massachusetts and DeepGlobe datasets show that DB SwinT achieves Intersection over Union (IoU) scores of 79.35% and 74.84%, respectively, demonstrating its effectiveness for road extraction from optical remote sensing imagery.

Method: 1) Created UW-VOS benchmark via semi-automatic data engine with human verification. 2) Proposed SAM-U framework that adapts SAM2 to underwater domain by inserting lightweight adapters into image encoder, requiring only ~2% trainable parameters.

Result: Existing methods experience average 13-point J&F drop on UW-VOS, while SAM-U effectively bridges domain gap and achieves state-of-the-art performance. Analysis identifies small targets, camouflage, and exit-re-entry as critical bottlenecks.

Conclusion: UW-VOS benchmark addresses data scarcity in underwater VOS, SAM-U demonstrates efficient domain adaptation, and analysis provides roadmap for future robust underwater perception research.

Abstract: Underwater Video Object Segmentation (VOS) is essential for marine exploration, yet open-air methods suffer significant degradation due to color distortion, low contrast, and prevalent camouflage. A primary hurdle is the lack of high-quality training data. To bridge this gap, we introduce $\textbf{UW-VOS}$, the first large-scale underwater VOS benchmark comprising 1,431 video sequences across 409 categories with 309,295 mask annotations, constructed via a semi-automatic data engine with rigorous human verification. We further propose $\textbf{SAM-U}$, a parameter-efficient framework that adapts SAM2 to the underwater domain. By inserting lightweight adapters into the image encoder, SAM-U achieves state-of-the-art performance with only $\sim$2$%$ trainable parameters. Extensive experiments reveal that existing methods experience an average 13-point $\mathcal{J}&\mathcal{F}$ drop on UW-VOS, while SAM-U effectively bridges this domain gap. Detailed attribute-based analysis further identifies small targets, camouflage, and exit-re-entry as critical bottlenecks, providing a roadmap for future research in robust underwater perception.

Method: Introduces attention imbalance concept to quantify attention disparity, then proposes Attention Imbalance Rectification (AIR) - a lightweight decoding-time intervention method that reallocates attention weights to rectify modality-wise and token-wise imbalances.

Result: Extensive evaluations on four mainstream LVLMs and three benchmarks (CHAIR, POPE, MM-Vet) with seven baselines show AIR consistently reduces object hallucination rates by up to 35.1% compared to baselines, while improving up to 15.9% of LVLMs’ general capability across diverse vision-language tasks.

Conclusion: Attention imbalance is a key factor driving object hallucination in LVLMs, and AIR provides an effective lightweight solution that not only reduces hallucinations but also enhances overall model performance across vision-language tasks.

Abstract: Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driving and medical image analysis. Through systematic empirical investigation, we identify that the imbalanced attention allocation, both across modalities (i.e., vision and language) and within modalities (among individual tokens), exhibits a strong causal correlation with the occurrence of object hallucination. Leveraging this insight, we introduce a novel concept termed attention imbalance, which not only quantifies the degree of attention disparity but also visually delineates the underlying patterns (e.g., over-attentiveness to irrelevant language tokens or under-attentiveness to discriminative visual features) that drive object hallucination. To mitigate object hallucination, we further propose Attention Imbalance Rectification (AIR), a lightweight decoding-time intervention method that reallocates attention weights and adjusts attention distributions to rectify modality-wise and token-wise imbalances. Extensive evaluations on four mainstream LVLMs and three benchmarks (CHAIR, POPE, and MM-Vet) with seven baselines demonstrate that AIR consistently reduces object hallucination rates, achieving up to a 35.1% reduction compared to the baselines, while improving up to 15.9% of LVLMs’ general capability across diverse vision-language tasks.

Method: Two-part approach: (1) C-TAO dataset with 26x denser annotations for smooth motion dynamics; (2) COVTrack++ framework with Multi-Cue Adaptive Fusion (MCF), Multi-Granularity Hierarchical Aggregation (MGA), and Temporal Confidence Propagation (TCP) modules.

Result: State-of-the-art on TAO dataset with novel TETA reaching 35.4% (val) and 30.5% (test), improving novel AssocA by 4.8% and novel LocA by 5.8% over previous methods, with strong zero-shot generalization on BDD100K.

Conclusion: COVTrack++ effectively addresses OVMOT challenges through synergistic detection-association framework and dense annotation dataset, enabling robust tracking of arbitrary categories including novel objects.

Abstract: Multi-Object Tracking (MOT) has traditionally focused on a few specific categories, restricting its applicability to real-world scenarios involving diverse objects. Open-Vocabulary Multi-Object Tracking (OVMOT) addresses this by enabling tracking of arbitrary categories, including novel objects unseen during training. However, current progress is constrained by two challenges: the lack of continuously annotated video data for training, and the lack of a customized OVMOT framework to synergistically handle detection and association. We address the data bottleneck by constructing C-TAO, the first continuously annotated training set for OVMOT, which increases annotation density by 26x over the original TAO and captures smooth motion dynamics and intermediate object states. For the framework bottleneck, we propose COVTrack++, a synergistic framework that achieves a bidirectional reciprocal mechanism between detection and association through three modules: (1) Multi-Cue Adaptive Fusion (MCF) dynamically balances appearance, motion, and semantic cues for association feature learning; (2) Multi-Granularity Hierarchical Aggregation (MGA) exploits hierarchical spatial relationships in dense detections, where visible child nodes (e.g., object parts) assist occluded parent objects (e.g., whole body) for association feature enhancement; (3) Temporal Confidence Propagation (TCP) recovers flickering detections through high-confidence tracked objects boosting low-confidence candidates across frames, stabilizing trajectories. Extensive experiments on TAO demonstrate state-of-the-art performance, with novel TETA reaching 35.4% and 30.5% on validation and test sets, improving novel AssocA by 4.8% and novel LocA by 5.8% over previous methods, and show strong zero-shot generalization on BDD100K. The code and dataset will be publicly available.

Method: Systematic analysis across multiple unsafe generation benchmark datasets, comparing MLLMs and diffusion models on two dimensions: unsafe content generation and fake image synthesis. Testing current fake image detectors and evaluating their effectiveness against MLLM-generated content.

Result: MLLMs generate more unsafe images than diffusion models. Diffusion models often fail to interpret abstract prompts, producing corrupted outputs, while MLLMs comprehend these prompts and generate unsafe content. MLLM-generated images are harder for current detectors to identify, even when retrained with MLLM-specific data. Longer, more descriptive inputs can bypass detectors.

Conclusion: Emerging safety risks of MLLMs have not been sufficiently recognized, posing new challenges to real-world safety. The enhanced semantic understanding capability of MLLMs introduces greater safety risks compared to diffusion models.

Abstract: Recently, multimodal large language models (MLLMs) have emerged as a unified paradigm for language and image generation. Compared with diffusion models, MLLMs possess a much stronger capability for semantic understanding, enabling them to process more complex textual inputs and comprehend richer contextual meanings. However, this enhanced semantic ability may also introduce new and potentially greater safety risks. Taking diffusion models as a reference point, we systematically analyze and compare the safety risks of emerging MLLMs along two dimensions: unsafe content generation and fake image synthesis. Across multiple unsafe generation benchmark datasets, we observe that MLLMs tend to generate more unsafe images than diffusion models. This difference partly arises because diffusion models often fail to interpret abstract prompts, producing corrupted outputs, whereas MLLMs can comprehend these prompts and generate unsafe content. For current advanced fake image detectors, MLLM-generated images are also notably harder to identify. Even when detectors are retrained with MLLMs-specific data, they can still be bypassed by simply providing MLLMs with longer and more descriptive inputs. Our measurements indicate that the emerging safety risks of the cutting-edge generative paradigm, MLLMs, have not been sufficiently recognized, posing new challenges to real-world safety.

Method: Shifts optimization from spatial to frequency domain using global complex sinusoidal features (Spectral Moments), creating a global basin of attraction. Implements principled Frequency Annealing to transition from global convexity to precise spatial alignment without periodic local minima.

Result: SpectralSplats successfully recovers complex deformations from severely misaligned initializations where standard appearance-based tracking fails catastrophically, working across diverse deformation parameterizations.

Conclusion: Frequency-domain supervision provides a robust solution to vanishing gradient problems in 3DGS tracking, enabling reliable optimization even with severe misalignment through global spectral features and principled annealing.

Abstract: 3D Gaussian Splatting (3DGS) enables real-time, photorealistic novel view synthesis, making it a highly attractive representation for model-based video tracking. However, leveraging the differentiability of the 3DGS renderer “in the wild” remains notoriously fragile. A fundamental bottleneck lies in the compact, local support of the Gaussian primitives. Standard photometric objectives implicitly rely on spatial overlap; if severe camera misalignment places the rendered object outside the target’s local footprint, gradients strictly vanish, leaving the optimizer stranded. We introduce SpectralSplats, a robust tracking framework that resolves this “vanishing gradient” problem by shifting the optimization objective from the spatial to the frequency domain. By supervising the rendered image via a set of global complex sinusoidal features (Spectral Moments), we construct a global basin of attraction, ensuring that a valid, directional gradient toward the target exists across the entire image domain, even when pixel overlap is completely nonexistent. To harness this global basin without introducing periodic local minima associated with high frequencies, we derive a principled Frequency Annealing schedule from first principles, gracefully transitioning the optimizer from global convexity to precise spatial alignment. We demonstrate that SpectralSplats acts as a seamless, drop-in replacement for spatial losses across diverse deformation parameterizations (from MLPs to sparse control points), successfully recovering complex deformations even from severely misaligned initializations where standard appearance-based tracking catastrophically fails.

Method: Developed A^3 framework with four components: A^3-Law (three-stage hierarchical paradigm), A^3-Dataset (120K instruction-response pairs from 30K images), A^3-Align (multimodal LLM trained with CoT-guided learning), and A^3-Bench benchmark.

Result: A^3-Align achieves superior alignment with the A^3-Law paradigm compared to existing models, and demonstrates good generalization to quality advertisement selection and prescriptive advertisement critique tasks.

Conclusion: The A^3 framework provides a comprehensive, theory-driven approach to advertising aesthetic assessment with strong performance and generalization capabilities, offering potential for broader deployment in advertising evaluation.

Abstract: Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized criteria, and interpretability. To address these challenges, we present A^3 (Advertising Aesthetic Assessment), a comprehensive framework encompassing four components: a paradigm (A^3-Law), a dataset (A^3-Dataset), a multimodal large language model (A^3-Align), and a benchmark (A^3-Bench). Central to A^3 is a theory-driven paradigm, A^3-Law, comprising three hierarchical stages: (1) Perceptual Attention, evaluating perceptual image signals for their ability to attract attention; (2) Formal Interest, assessing formal composition of image color and spatial layout in evoking interest; and (3) Desire Impact, measuring desire evocation from images and their persuasive impact. Building on A^3-Law, we construct A^3-Dataset with 120K instruction-response pairs from 30K advertising images, each richly annotated with multi-dimensional labels and Chain-of-Thought (CoT) rationales. We further develop A^3-Align, trained under A^3-Law with CoT-guided learning on A^3-Dataset. Extensive experiments on A^3-Bench demonstrate that A^3-Align achieves superior alignment with A^3-Law compared to existing models, and this alignment generalizes well to quality advertisement selection and prescriptive advertisement critique, indicating its potential for broader deployment. Dataset, code, and models can be found at: https://github.com/euleryuan/A3-Align.

Method: 1) Generate chromatically differentiated representation to make semantic components visually separable; 2) Perform semantic completion to reconstruct coherent object-level shapes including occluded regions; 3) Assemble recovered parts into layered vector representation with inferred occlusion relationships.

Result: Extensive qualitative comparisons and quantitative evaluations demonstrate effectiveness. Enables editing workflows previously inapplicable to flattened vector graphics. Establishes semantic layer reconstruction as practical and valuable task.

Conclusion: SemLayer successfully restores editable layered structures from flattened vector art, addressing a significant limitation in current design workflows and enabling new editing capabilities for vector graphics.

Abstract: Graphic icons are a cornerstone of modern design workflows, yet they are often distributed as flattened single-path or compound-path graphics, where the original semantic layering is lost. This absence of semantic decomposition hinders downstream tasks such as editing, restyling, and animation. We formalize this problem as semantic layer construction for flattened vector art and introduce SemLayer, a visual generation empowered pipeline that restores editable layered structures. Given an abstract icon, SemLayer first generates a chromatically differentiated representation in which distinct semantic components become visually separable. To recover the complete geometry of each part, including occluded regions, we then perform a semantic completion step that reconstructs coherent object-level shapes. Finally, the recovered parts are assembled into a layered vector representation with inferred occlusion relationships. Extensive qualitative comparisons and quantitative evaluations demonstrate the effectiveness of SemLayer, enabling editing workflows previously inapplicable to flattened vector graphics and establishing semantic layer reconstruction as a practical and valuable task. Project page: https://xxuhaiyang.github.io/SemLayer/

Method: Proposes HAM (heterogeneous attention modulation) with style noise initialization, Global Attention Regulation (GAR), and Local Attention Transplantation (LAT) to preserve content details while capturing style

Result: Achieves state-of-the-art performance on multiple quantitative metrics through qualitative and quantitative experiments

Conclusion: HAM effectively addresses style-content trade-off in diffusion-based style transfer without requiring training

Abstract: Diffusion models have demonstrated remarkable performance in image generation, particularly within the domain of style transfer. Prevailing style transfer approaches typically leverage pre-trained diffusion models’ robust feature extraction capabilities alongside external modular control pathways to explicitly impose style guidance signals. However, these methods often fail to capture complex style reference or retain the identity of user-provided content images, thus falling into the trap of style-content balance. Thus, we propose a training-free style transfer approach via $\textbf{h}$eterogeneous $\textbf{a}$ttention $\textbf{m}$odulation ($\textbf{HAM}$) to protect identity information during image/text-guided style reference transfer, thereby addressing the style-content trade-off challenge. Specifically, we first introduces style noise initialization to initialize latent noise for diffusion. Then, during the diffusion process, it innovatively employs HAM for different attention mechanisms, including Global Attention Regulation (GAR) and Local Attention Transplantation (LAT), which better preserving the details of the content image while capturing complex style references. Our approach is validated through a series of qualitative and quantitative experiments, achieving state-of-the-art performance on multiple quantitative metrics.

Method: LGEST combines three key innovations: 1) Deep Spatial-Spectral Autoencoder (DSAE) for compact discriminative embeddings, 2) Cross-Interactive Mixed Expert Feature Pyramid (CIEM-FPN) for dynamic multi-scale feature fusion, and 3) Local-Global Expert System (LGES) with sparsely activated expert pairs (convolutional for fine-grained textures, transformer for long-range dependencies).

Result: Extensive experiments on four benchmark datasets demonstrate that LGEST consistently outperforms state-of-the-art methods in hyperspectral image classification.

Conclusion: LGEST effectively addresses the limitations of existing methods by synergistically combining local-global representations through expert systems and cross-attention mechanisms, achieving superior performance in hyperspectral image classification.

Abstract: Deep learning methods, including Convolutional Neural Networks, Transformers and Mamba, have achieved remarkable success in hyperspectral image (HSI) classification. Nevertheless, existing methods exhibit inflexible integration of local-global representations, inadequate handling of spectral-spatial scale disparities across heterogeneous bands, and susceptibility to the Hughes phenomenon under high-dimensional sample heterogeneity. To address these challenges, we propose Local-Global Expert Spatial-Spectral Transformer (LGEST), a novel framework that synergistically combines three key innovations. The LGEST first employs a Deep Spatial-Spectral Autoencoder (DSAE) to generate compact yet discriminative embeddings through hierarchical nonlinear compression, preserving 3D neighborhood coherence while mitigating information loss in high-dimensional spaces. Secondly, a Cross-Interactive Mixed Expert Feature Pyramid (CIEM-FPN) leverages cross-attention mechanisms and residual mixture-of-experts layers to dynamically fuse multi-scale features, adaptively weighting spectral discriminability and spatial saliency through learnable gating functions. Finally, a Local-Global Expert System (LGES) processes decomposed features via sparsely activated expert pairs: convolutional sub-experts capture fine-grained textures, while transformer sub-experts model long-range contextual dependencies, with a routing controller dynamically selecting experts based on real-time feature saliency. Extensive experiments on four benchmark datasets demonstrate that LGEST consistently outperforms state-of-the-art methods.

Method: Proposes Contrastive Regional Injection Transformer (CoRIT) with Contrastive Gradient Proxy and three training-free strategies: Region Refinement Mask, Regional Signal Injection, and Hierarchical Representation Integration to address optimization collapse.

Result: CoRIT mitigates optimization collapse and achieves state-of-the-art generalization performance across cross-domain and universal forgery benchmarks.

Conclusion: The paper identifies optimization collapse as a fundamental issue in deepfake detection with VLMs and provides both theoretical analysis and practical solution through CoRIT to improve generalization on non-semantic forgeries.

Abstract: While Vision-Language Models (VLMs) like CLIP have emerged as a dominant paradigm for generalizable deepfake detection, a representational disconnect remains: their semantic-centric pre-training is ill-suited for capturing non-semantic artifacts inherent to hyper-realistic synthesis. In this work, we identify a failure mode termed Optimization Collapse, where detectors trained with Sharpness-Aware Minimization (SAM) degenerate to random guessing on non-semantic forgeries once the perturbation radius exceeds a narrow threshold. To theoretically formalize this collapse, we propose the Critical Optimization Radius (COR) to quantify the geometric stability of the optimization landscape, and leverage the Gradient Signal-to-Noise Ratio (GSNR) to measure generalization potential. We establish a theorem proving that COR increases monotonically with GSNR, thereby revealing that the geometric instability of SAM optimization originates from degraded intrinsic generalization potential. This result identifies the layer-wise attenuation of GSNR as the root cause of Optimization Collapse in detecting non-semantic forgeries. Although naively reducing perturbation radius yields stable convergence under SAM, it merely treats the symptom without mitigating the intrinsic generalization degradation, necessitating enhanced gradient fidelity. Building on this insight, we propose the Contrastive Regional Injection Transformer (CoRIT), which integrates a computationally efficient Contrastive Gradient Proxy (CGP) with three training-free strategies: Region Refinement Mask to suppress CGP variance, Regional Signal Injection to preserve CGP magnitude, and Hierarchical Representation Integration to attain more generalizable representations. Extensive experiments demonstrate that CoRIT mitigates optimization collapse and achieves state-of-the-art generalization across cross-domain and universal forgery benchmarks.

Method: Proposes Text-guided Multi-view Knowledge Distillation (TMKD) with: 1) Dual-modality teachers (visual teacher and text teacher/CLIP), 2) Enhanced visual teacher with multi-view inputs (edge and high-frequency features), 3) Text teacher generates semantic weights through prior-aware prompts for adaptive feature fusion, 4) Vision-language contrastive regularization to strengthen semantic knowledge in student model.

Result: Extensive experiments on five benchmarks show TMKD consistently improves knowledge distillation performance by up to 4.49%, validating the effectiveness of the dual-teacher multi-view enhancement strategy.

Conclusion: TMKD demonstrates that enhancing teacher knowledge quality through multi-view visual priors and text guidance significantly improves knowledge distillation performance, offering a novel approach to multimodal knowledge transfer.

Abstract: Knowledge distillation transfers knowledge from large teacher models to smaller students for efficient inference. While existing methods primarily focus on distillation strategies, they often overlook the importance of enhancing teacher knowledge quality. In this paper, we propose Text-guided Multi-view Knowledge Distillation (TMKD), which leverages dual-modality teachers, a visual teacher and a text teacher (CLIP), to provide richer supervisory signals. Specifically, we enhance the visual teacher with multi-view inputs incorporating visual priors (edge and high-frequency features), while the text teacher generates semantic weights through prior-aware prompts to guide adaptive feature fusion. Additionally, we introduce vision-language contrastive regularization to strengthen semantic knowledge in the student model. Extensive experiments on five benchmarks demonstrate that TMKD consistently improves knowledge distillation performance by up to 4.49%, validating the effectiveness of our dual-teacher multi-view enhancement strategy. Code is available at https://anonymous.4open.science/r/TMKD-main-44D1.

Method: AD-Reasoning framework uses modality-specific encoders for structural MRI and six clinical modalities, bidirectional cross-attention fusion, reinforcement fine-tuning with verifiable rewards, and a rule-based verifier to ensure NIA-AA guideline consistency.

Result: Achieves state-of-the-art diagnostic accuracy on AD-MultiSense dataset (10,378-visit multimodal QA dataset), produces structured rationales that improve transparency over baselines.

Conclusion: The framework successfully integrates multimodal data with clinical reasoning guidelines to provide transparent, structured diagnoses for Alzheimer’s disease.

Abstract: Alzheimer’s disease (AD) diagnosis requires integrating neuroimaging with heterogeneous clinical evidence and reasoning under established criteria, yet most multimodal models remain opaque and weakly guideline-aligned. We present AD-Reasoning, a multimodal framework that couples structural MRI with six clinical modalities and a rule-based verifier to generate structured, NIA-AA-consistent diagnoses. AD-Reasoning combines modality-specific encoders, bidirectional cross-attention fusion, and reinforcement fine-tuning with verifiable rewards that enforce output format, guideline evidence coverage, and reasoning–decision consistency. We also release AD-MultiSense, a 10,378-visit multimodal QA dataset with guideline-validated rationales built from ADNI/AIBL. On AD-MultiSense, AD-Reasoning achieves state-of-the-art diagnostic accuracy and produces structured rationales that improve transparency over recent baselines, while providing transparent rationales.

Method: Created a dataset of 7,765 image-annotation instances and 822 generation prompts across real, professional, and synthetic posters. Defined tasks including layout parsing, text-image correspondence, typography/readability analysis, font perception, design quality assessment, and controllable composition-aware generation with metaphor.

Result: Evaluation of state-of-the-art MLLMs and diffusion models revealed persistent gaps in visual hierarchy understanding, typographic semantics, saliency control, and intention communication. Commercial models performed well on high-level reasoning but were insensitive automatic raters, while generators rendered text well but struggled with composition-aware synthesis.

Conclusion: PosterIQ serves as both a quantitative benchmark and diagnostic tool for design reasoning, offering reproducible metrics to catalyze models’ creativity and integrate human-centered design principles into vision-language systems.

Abstract: We present PosterIQ, a design-driven benchmark for poster understanding and generation, annotated across composition structure, typographic hierarchy, and semantic intent. It includes 7,765 image-annotation instances and 822 generation prompts spanning real, professional, and synthetic cases. To bridge visual design cognition and generative modeling, we define tasks for layout parsing, text-image correspondence, typography/readability and font perception, design quality assessment, and controllable, composition-aware generation with metaphor. We evaluate state-of-the-art MLLMs and diffusion-based generators, finding persistent gaps in visual hierarchy, typographic semantics, saliency control, and intention communication; commercial models lead on high-level reasoning but act as insensitive automatic raters, while generators render text well yet struggle with composition-aware synthesis. Extensive analyses show PosterIQ is both a quantitative benchmark and a diagnostic tool for design reasoning, offering reproducible, task-specific metrics. We aim to catalyze models’ creativity and integrate human-centred design principles into generative vision-language systems.

Method: Proposes LGTM (Light-Guided Text-to-Image Diffusion Model via Initial Noise Manipulation) that selectively manipulates specific channels in the initial latent noise based on channel-wise analysis of the latent space to control lighting direction.

Result: Method surpasses prompt-based baselines in lighting consistency while preserving image quality and text alignment, and integrates seamlessly with models like ControlNet.

Conclusion: The approach enables fine-grained lighting control without fine-tuning or modifying pre-trained models, introducing new possibilities for dynamic, user-guided light control in text-to-image generation.

Abstract: Diffusion models have demonstrated high-quality performance in conditional text-to-image generation, particularly with structural cues such as edges, layouts, and depth. However, lighting conditions have received limited attention and remain difficult to control within the generative process. Existing methods handle lighting through a two-stage pipeline that relights images after generation, which is inefficient. Moreover, they rely on fine-tuning with large datasets and heavy computation, limiting their adaptability to new models and tasks. To address this, we propose a novel Training-Free Light-Guided Text-to-Image Diffusion Model via Initial Noise Manipulation (LGTM), which manipulates the initial latent noise of the diffusion process to guide image generation with text prompts and user-specified light directions. Through a channel-wise analysis of the latent space, we find that selectively manipulating latent channels enables fine-grained lighting control without fine-tuning or modifying the pre-trained model. Extensive experiments show that our method surpasses prompt-based baselines in lighting consistency, while preserving image quality and text alignment. This approach introduces new possibilities for dynamic, user-guided light control. Furthermore, it integrates seamlessly with models like ControlNet, demonstrating adaptability across diverse scenarios.

Method: Proposes Lagrangian-Dynamic Informed Network (LaDy) that computes generalized coordinates from joint positions, estimates Lagrangian terms under physical constraints to synthesize generalized forces, and uses an Energy Consistency Loss to enforce the work-energy theorem. A Spatio-Temporal Modulation module fuses generalized forces with spatial representations for discriminative semantics and constructs salient dynamic signals for temporal gating.

Result: Experiments on challenging datasets show LaDy achieves state-of-the-art performance, validating the benefits of integrating physical dynamics for action segmentation.

Conclusion: Incorporating Lagrangian dynamics principles significantly improves skeleton-based action segmentation by providing more discriminative semantics and enhanced boundary awareness through physical coherence constraints.

Abstract: Skeleton-based Temporal Action Segmentation (STAS) aims to densely parse untrimmed skeletal sequences into frame-level action categories. However, existing methods, while proficient at capturing spatio-temporal kinematics, neglect the underlying physical dynamics that govern human motion. This oversight limits inter-class discriminability between actions with similar kinematics but distinct dynamic intents, and hinders precise boundary localization where dynamic force profiles shift. To address these, we propose the Lagrangian-Dynamic Informed Network (LaDy), a framework integrating principles of Lagrangian dynamics into the segmentation process. Specifically, LaDy first computes generalized coordinates from joint positions and then estimates Lagrangian terms under physical constraints to explicitly synthesize the generalized forces. To further ensure physical coherence, our Energy Consistency Loss enforces the work-energy theorem, aligning kinetic energy change with the work done by the net force. The learned dynamics then drive a Spatio-Temporal Modulation module: Spatially, generalized forces are fused with spatial representations to provide more discriminative semantics. Temporally, salient dynamic signals are constructed for temporal gating, thereby significantly enhancing boundary awareness. Experiments on challenging datasets show that LaDy achieves state-of-the-art performance, validating the integration of physical dynamics for action segmentation. Code is available at https://github.com/HaoyuJi/LaDy.

Method: 1) Organize samples into compact local granular balls and cluster ball centers as representatives for hierarchical pseudo-domain discovery. 2) Two-branch learning framework: semantic branch with codebook re-encoding for transferable representations, and style branch for domain-specific appearance variations to reduce semantic-style entanglement.

Result: Extensive experiments on ShanghaiTech A/B, UCF_QNRF, and NWPU-Crowd datasets under strict no-adaptation protocol show the method consistently outperforms strong baselines, especially under large domain gaps.

Conclusion: The proposed granular ball guided stable latent domain discovery framework effectively addresses the instability in pseudo-domain assignment for domain-general crowd counting, leading to improved generalization performance under domain shifts.

Abstract: Single-source domain generalization for crowd counting remains highly challenging because a single labeled source domain often contains heterogeneous latent domains, while test data may exhibit severe distribution shifts. A fundamental difficulty lies in stable latent domain discovery: directly performing flat clustering on evolving sample-level latent features is easily affected by feature noise, outliers, and representation drift, leading to unreliable pseudo-domain assignments and weakened domain-structured learning. To address this issue, we propose a granular ball guided stable latent domain discovery framework for domain-general crowd counting. Specifically, the proposed method first organizes samples into compact local granular balls and then clusters granular ball centers as representatives to obtain pseudo-domains, transforming direct sample-level clustering into a hierarchical representative-based clustering process. This design yields more stable and semantically consistent pseudo-domain assignments. Built upon the discovered latent domains, we further develop a two-branch learning framework that enhances transferable semantic representations via semantic codebook re-encoding while modeling domain-specific appearance variations through a style branch, thereby reducing semantic–style entanglement and improving generalization under domain shifts. Extensive experiments on ShanghaiTech A/B, UCF_QNRF, and NWPU-Crowd under a strict no-adaptation protocol demonstrate that the proposed method consistently outperforms strong baselines, especially under large domain gaps.

Method: Proposes a 2.5D segmentation framework with a novel cross-slice feature fusion (CFF) module integrated into a U-Net-like architecture to fuse inter-slice features and capture contextual information.

Result: Achieved 8.56% reduction in mean absolute distance and 13.92% reduction in root mean square error compared to methods without CFF, demonstrating improved segmentation accuracy and robustness on clinical and DUKE DME datasets.

Conclusion: The 2.5D framework effectively balances contextual awareness and computational efficiency, enabling reliable retinal layer delineation for automated glaucoma evaluation and clinical applications.

Abstract: For accurate glaucoma diagnosis and monitoring, reliable retinal layer segmentation in OCT images is essential. However, existing 2D segmentation methods often suffer from slice-to-slice inconsistencies due to the lack of contextual information across adjacent B-scans. 3D segmentation methods are better for capturing slice-to-slice context, but they require expensive computational resources. To address these limitations, we propose a 2.5D segmentation framework that incorporates a novel cross-slice feature fusion (CFF) module into a U-Net-like architecture. The CFF module fuses inter-slice features to effectively capture contextual information, enabling consistent boundary detection across slices and improved robustness in noisy regions. The framework was validated on both a clinical dataset and the publicly available DUKE DME dataset. Compared to other segmentation methods without the CFF module, the proposed method achieved an 8.56% reduction in mean absolute distance and a 13.92% reduction in root mean square error, demonstrating improved segmentation accuracy and robustness. Overall, the proposed 2.5D framework balances contextual awareness and computational efficiency, enabling anatomically reliable retinal layer delineation for automated glaucoma evaluation and potential clinical applications.

Method: Combi-CAM combines gradient-weighted class activation maps (Grad-CAM) from several layers of the CNN architecture rather than using only information from the deepest layer. This multi-layer approach provides a more comprehensive view of feature contributions.

Result: The approach provides more detailed understanding of how different image features contribute to the model’s geolocalization decisions, offering deeper insights than traditional single-layer methods.

Conclusion: Combi-CAM improves explainability in CNN-based geolocalization models by leveraging multi-layer activation maps, enhancing understanding of model decision-making processes.

Abstract: Planet-scale photo geolocalization involves the intricate task of estimating the geographic location depicted in an image purely based on its visual features. While deep learning models, particularly convolutional neural networks (CNNs), have significantly advanced this field, understanding the reasoning behind their predictions remains challenging. In this paper, we present Combi-CAM, a novel method that enhances the explainability of CNN-based geolocalization models by combining gradient-weighted class activation maps obtained from several layers of the network architecture, rather than using only information from the deepest layer as is typically done. This approach provides a more detailed understanding of how different image features contribute to the model’s decisions, offering deeper insights than the traditional approaches.

Method: HetCache assesses contextual relevance and interaction strength among tokens, divides spatial-temporal tokens into context and generative tokens, and selectively caches context tokens with strongest correlation to generative ones to reduce redundant attention operations.

Result: Achieves 2.67× latency speedup and FLOPs reduction over foundation models with negligible degradation in editing quality.

Conclusion: HetCache effectively accelerates diffusion-based video editing by exploiting architectural redundancy in DiT models while maintaining editing consistency and fidelity.

Abstract: Diffusion-based video editing has emerged as an important paradigm for high-quality and flexible content generation. However, despite their generality and strong modeling capacity, Diffusion Transformers (DiT) remain computationally expensive due to the iterative denoising process, posing challenges for practical deployment. Existing video diffusion acceleration methods primarily exploit denoising timestep-level feature reuse, which mitigates the redundancy in denoising process, but overlooks the architectural redundancy within the DiT that many attention operations over spatio-temporal tokens are redundantly executed, offering little to no incremental contribution to the model output. This work introduces HetCache, a training-free diffusion acceleration framework designed to exploit the inherent heterogeneity in diffusion-based masked video-to-video (MV2V) generation and editing. Instead of uniformly reuse or randomly sampling tokens, HetCache assesses the contextual relevance and interaction strength among various types of tokens in designated computing steps. Guided by spatial priors, it divides the spatial-temporal tokens in DiT model into context and generative tokens, and selectively caches the context tokens that exhibit the strongest correlation and most representative semantics with generative ones. This strategy reduces redundant attention operations while maintaining editing consistency and fidelity. Experiments show that HetCache achieves a noticeable acceleration, including a 2.67$\times$ latency speedup and FLOPs reduction over commonly used foundation models, with negligible degradation in editing quality.

Method: Proposes Spectral Scalpel framework with adaptive multi-scale spectral filters to edit frequency spectra, coupled with discrepancy loss between adjacent actions. Also introduces frequency-aware channel mixer to strengthen channel evolution by aggregating spectra across channels.

Result: Extensive experiments on five public datasets demonstrate state-of-the-art performance for skeleton-based temporal action segmentation.

Conclusion: Presents a novel paradigm for STAS that extends conventional spatio-temporal modeling by incorporating frequency-domain analysis to enhance inter-action discrepancies and sharpen transition boundaries.

Abstract: Skeleton-based Temporal Action Segmentation (STAS) seeks to densely segment and classify diverse actions within long, untrimmed skeletal motion sequences. However, existing STAS methodologies face challenges of limited inter-class discriminability and blurred segmentation boundaries, primarily due to insufficient distinction of spatio-temporal patterns between adjacent actions. To address these limitations, we propose Spectral Scalpel, a frequency-selective filtering framework aimed at suppressing shared frequency components between adjacent distinct actions while amplifying their action-specific frequencies, thereby enhancing inter-action discrepancies and sharpening transition boundaries. Specifically, Spectral Scalpel employs adaptive multi-scale spectral filters as scalpels to edit frequency spectra, coupled with a discrepancy loss between adjacent actions serving as the surgical objective. This design amplifies representational disparities between neighboring actions, effectively mitigating boundary localization ambiguities and inter-class confusion. Furthermore, complementing long-term temporal modeling, we introduce a frequency-aware channel mixer to strengthen channel evolution by aggregating spectra across channels. This work presents a novel paradigm for STAS that extends conventional spatio-temporal modeling by incorporating frequency-domain analysis. Extensive experiments on five public datasets demonstrate that Spectral Scalpel achieves state-of-the-art performance. Code is available at https://github.com/HaoyuJi/SpecScalpel.

Method: Tutor-Student Reinforcement Learning (TSRL) models training as Markov Decision Process. Tutor (PPO agent) observes rich state representation (visual features + learning dynamics) and assigns continuous weight (0-1) to sample loss. Rewarded based on Student’s immediate performance change.

Result: Adaptive curriculum improves Student’s generalization capabilities against unseen manipulation techniques compared to traditional training methods.

Conclusion: TSRL framework enables more efficient and effective training for deepfake detection by dynamically optimizing training curriculum through reinforcement learning.

Abstract: Standard supervised training for deepfake detection treats all samples with uniform importance, which can be suboptimal for learning robust and generalizable features. In this work, we propose a novel Tutor-Student Reinforcement Learning (TSRL) framework to dynamically optimize the training curriculum. Our method models the training process as a Markov Decision Process where a Tutor'' agent learns to guide a Student’’ (the deepfake detector). The Tutor, implemented as a Proximal Policy Optimization (PPO) agent, observes a rich state representation for each training sample, encapsulating not only its visual features but also its historical learning dynamics, such as EMA loss and forgetting counts. Based on this state, the Tutor takes an action by assigning a continuous weight (0-1) to the sample’s loss, thereby dynamically re-weighting the training batch. The Tutor is rewarded based on the Student’s immediate performance change, specifically rewarding transitions from incorrect to correct predictions. This strategy encourages the Tutor to learn a curriculum that prioritizes high-value samples, such as hard-but-learnable examples, leading to a more efficient and effective training process. We demonstrate that this adaptive curriculum improves the Student’s generalization capabilities against unseen manipulation techniques compared to traditional training methods. Code is available at https://github.com/wannac1/TSRL.

Method: LightSplat injects compact 2-byte semantic indices into 3D representations from multi-view images, assigns semantic indices only to salient regions, uses lightweight index-feature mapping to eliminate costly feature optimization, and employs single-step clustering for semantic consistency and efficient inference.

Result: Achieves state-of-the-art performance on LERF-OVS, ScanNet, and DL3DV-OVS across complex indoor-outdoor scenes with up to 50-400x speedup and 64x lower memory usage compared to existing methods.

Conclusion: LightSplat enables scalable language-driven 3D understanding through its efficient training-free framework that dramatically reduces computational overhead while maintaining high performance.

Abstract: Open-vocabulary 3D scene understanding enables users to segment novel objects in complex 3D environments through natural language. However, existing approaches remain slow, memory-intensive, and overly complex due to iterative optimization and dense per-Gaussian feature assignments. To address this, we propose LightSplat, a fast and memory-efficient training-free framework that injects compact 2-byte semantic indices into 3D representations from multi-view images. By assigning semantic indices only to salient regions and managing them with a lightweight index-feature mapping, LightSplat eliminates costly feature optimization and storage overhead. We further ensure semantic consistency and efficient inference via single-step clustering that links geometrically and semantically related masks in 3D. We evaluate our method on LERF-OVS, ScanNet, and DL3DV-OVS across complex indoor-outdoor scenes. As a result, LightSplat achieves state-of-the-art performance with up to 50-400x speedup and 64x lower memory, enabling scalable language-driven 3D understanding. For more details, visit our project page https://vision3d-lab.github.io/lightsplat/.

Method: Variational plug-and-play approach minimizing explicit functional: KL data fidelity term + regularization via pre-trained neural network. Combines classical likelihood maximization with gradient-based denoisers. Formulated in majorization-minimization framework for convergence guarantees.

Result: State-of-the-art performance in deconvolution and tomography under moderate noise, clear superiority in high-noise conditions. Particularly valuable for nuclear medicine applications where Poisson noise is prevalent.

Conclusion: Method successfully integrates pre-trained Gaussian denoisers into Poisson inverse problem solving with theoretical convergence guarantees, offering robust performance especially in challenging high-noise scenarios.

Abstract: In this paper, we present a novel variational plug-and-play algorithm for Poisson inverse problems. Our approach minimizes an explicit functional which is the sum of a Kullback-Leibler data fidelity term and a regularization term based on a pre-trained neural network. By combining classical likelihood maximization methods with recent advances in gradient-based denoisers, we allow the use of pre-trained Gaussian denoisers without sacrificing convergence guarantees. The algorithm is formulated in the majorization-minimization framework, which guarantees convergence to a stationary point. Numerical experiments confirm state-of-the-art performance in deconvolution and tomography under moderate noise, and demonstrate clear superiority in high-noise conditions, making this method particularly valuable for nuclear medicine applications.

Method: Proposes PaddleOCR-VL with: 1) Lightweight Valid Region Focus Module (VRFM) that identifies valid vision tokens using localization and contextual relationship prediction; 2) Compact 0.9B vision-language model trained for detailed recognition guided by VRFM outputs, avoiding direct processing of entire large images.

Result: Achieves state-of-the-art performance in both page-level parsing and element-level recognition. Outperforms existing solutions, competitive with top-tier VLMs, with fast inference using substantially fewer vision tokens and parameters.

Conclusion: Targeted coarse-to-fine parsing is effective for accurate and efficient document understanding. The approach balances performance and efficiency by focusing on semantically relevant regions while suppressing redundant ones.

Abstract: Document parsing is a fine-grained task where image resolution significantly impacts performance. While advanced research leveraging vision-language models benefits from high-resolution input to boost model performance, this often leads to a quadratic increase in the number of vision tokens and significantly raises computational costs. We attribute this inefficiency to substantial visual regions redundancy in document images, like background. To tackle this, we propose PaddleOCR-VL, a novel coarse-to-fine architecture that focuses on semantically relevant regions while suppressing redundant ones, thereby improving both efficiency and performance. Specifically, we introduce a lightweight Valid Region Focus Module (VRFM) which leverages localization and contextual relationship prediction capabilities to identify valid vision tokens. Subsequently, we design and train a compact yet powerful 0.9B vision-language model (PaddleOCR-VL-0.9B) to perform detailed recognition, guided by VRFM outputs to avoid direct processing of the entire large image. Extensive experiments demonstrate that PaddleOCR-VL achieves state-of-the-art performance in both page-level parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference while utilizing substantially fewer vision tokens and parameters, highlighting the effectiveness of targeted coarse-to-fine parsing for accurate and efficient document understanding. The source code and models are publicly available at https://github.com/PaddlePaddle/PaddleOCR.

Method: Proposes CarePilot, a multi-agent framework based on actor-critic paradigm. Actor integrates tool grounding with dual-memory mechanisms (long-term and short-term experience) to predict next semantic actions. Critic evaluates actions, updates memory based on effects, and provides corrective feedback. Uses iterative agentic simulation for training.

Result: CarePilot achieves state-of-the-art performance, outperforming strong closed-source multimodal baselines by ~15.26% and open-source baselines by ~3.38% on the CareFlow benchmark and out-of-distribution datasets.

Conclusion: The CarePilot framework effectively addresses long-horizon automation challenges in healthcare software systems, demonstrating superior performance over existing vision-language models through its multi-agent architecture and memory mechanisms.

Abstract: Multimodal agentic pipelines are transforming human-computer interaction by enabling efficient and accessible automation of complex, real-world tasks. However, recent efforts have focused on short-horizon or general-purpose applications (e.g., mobile or desktop interfaces), leaving long-horizon automation for domain-specific systems, particularly in healthcare, largely unexplored. To address this, we introduce CareFlow, a high-quality human-annotated benchmark comprising complex, long-horizon software workflows across medical annotation tools, DICOM viewers, EHR systems, and laboratory information systems. On this benchmark, existing vision-language models (VLMs) perform poorly, struggling with long-horizon reasoning and multi-step interactions in medical contexts. To overcome this, we propose CarePilot, a multi-agent framework based on the actor-critic paradigm. The Actor integrates tool grounding with dual-memory mechanisms (long-term and short-term experience) to predict the next semantic action from the visual interface and system state. The Critic evaluates each action, updates memory based on observed effects, and either executes or provides corrective feedback to refine the workflow. Through iterative agentic simulation, the Actor learns to perform more robust and reasoning-aware predictions during inference. Our experiments show that CarePilot achieves state-of-the-art performance, outperforming strong closed-source and open-source multimodal baselines by approximately 15.26% and 3.38%, respectively, on our benchmark and out-of-distribution dataset.

Method: Proposes HeROD framework that injects heuristic-inspired spatial and semantic reasoning priors into 3 stages of DETR-style pipeline: proposal ranking, prediction fusion, and Hungarian matching. These interpretable priors bias training and inference toward plausible candidates.

Result: HeROD consistently outperforms strong grounding baselines on RefCOCO, RefCOCO+, and RefCOCOg benchmarks in scarce-label regimes, demonstrating improved label efficiency and convergence performance.

Conclusion: Integrating simple, interpretable reasoning priors provides a practical and extensible path toward better data-efficient vision-language understanding, suggesting explicit reasoning can enhance multimodal learning in low-data conditions.

Abstract: Most referring object detection (ROD) models, especially the modern grounding detectors, are designed for data-rich conditions, yet many practical deployments, such as robotics, augmented reality, and other specialized domains, would face severe label scarcity. In such regimes, end-to-end grounding detectors need to learn spatial and semantic structure from scratch, wasting precious samples. We ask a simple question: Can explicit reasoning priors help models learn more efficiently when data is scarce? To explore this, we first introduce a Data-efficient Referring Object Detection (De-ROD) task, which is a benchmark protocol for measuring ROD performance in low-data and few-shot settings. We then propose the HeROD (Heuristic-inspired ROD), a lightweight, model-agnostic framework that injects explicit, heuristic-inspired spatial and semantic reasoning priors, which are interpretable signals derived based on the referring phrase, into 3 stages of a modern DETR-style pipeline: proposal ranking, prediction fusion, and Hungarian matching. By biasing both training and inference toward plausible candidates, these priors promise to improve label efficiency and convergence performance. On RefCOCO, RefCOCO+, and RefCOCOg, HeROD consistently outperforms strong grounding baselines in scarce-label regimes. More broadly, our results suggest that integrating simple, interpretable reasoning priors provides a practical and extensible path toward better data-efficient vision-language understanding.

Method: Proposes LGSAN with four key components: 1) CLIP-based text-image mask generation to guide PVT-v2 features, 2) Fourier Edge Enhancement Module for frequency-domain high-frequency information, 3) Structure-Aware Attention Module for better boundary perception, and 4) Coarse-Guided Local Refinement Module for fine-grained reconstruction.

Result: Extensive experiments show highly competitive performance across multiple COD datasets, validating effectiveness and robustness of the approach.

Conclusion: Language guidance combined with structure-aware attention and frequency-domain edge enhancement significantly improves camouflaged object detection performance by better focusing on target regions and enhancing boundary perception.

Abstract: Camouflaged Object Detection (COD) aims to segment objects that are highly integrated with the background in terms of color, texture, and structure, making it a highly challenging task in computer vision. Although existing methods introduce multi-scale fusion and attention mechanisms to alleviate the above issues, they generally lack the guidance of textual semantic priors, which limits the model’s ability to focus on camouflaged regions in complex scenes. To address this issue, this paper proposes a Language-Guided Structure-Aware Network (LGSAN). Specifically, based on the visual backbone PVT-v2, we introduce CLIP to generate masks from text prompts and RGB images, thereby guiding the multi-scale features extracted by PVT-v2 to focus on potential target regions. On this foundation, we further design a Fourier Edge Enhancement Module (FEEM), which integrates multi-scale features with high-frequency information in the frequency domain to extract edge enhancement features. Furthermore, we propose a Structure-Aware Attention Module (SAAM) to effectively enhance the model’s perception of object structures and boundaries. Finally, we introduce a Coarse-Guided Local Refinement Module (CGLRM) to enhance fine-grained reconstruction and boundary integrity of camouflaged object regions. Extensive experiments demonstrate that our method consistently achieves highly competitive performance across multiple COD datasets, validating its effectiveness and robustness.

Method: The paper introduces Head Ensemble Classifiers (HEC), inspired by Gaussian Discriminant Analysis. HEC ranks the most discriminative vision and text attention heads from LVLMs and combines them into a training-free classifier. It leverages LVLMs’ internal representations, particularly attention heads, which can outperform the model itself at classification tasks.

Result: HEC achieves state-of-the-art performance in few-shot and zero-shot classification across 12 datasets, bridging the performance gap between CLIP-based and LVLM-based classification methods.

Conclusion: Despite poor raw performance, LVLMs contain valuable internal representations (especially attention heads) that can be effectively leveraged for classification tasks. HEC demonstrates that LVLMs can match or exceed CLIP-based methods for classification when their internal representations are properly utilized.

Abstract: Current Large Vision Language Models (LVLMs) excel at many zero-shot tasks like image captioning, visual question answering and OCR. However, these same models suffer from poor performance at image classification tasks, underperforming against CLIP-based methods. Notably, this gap is surprising because many LVLMs use CLIP-pretrained vision encoders. Yet LVLMs are not inherently limited by CLIP’s architecture with independent vision and text encoders. In CLIP, this separation biases classification toward class-name matching rather than joint visual-text reasoning. In this paper we show that, despite their poor raw performance, LVLMs can improve visual feature class separability at inference using prompt conditioning, and LVLMs’ internal representations, especially attention heads, can outperform the model itself at zero-shot and few-shot classification. We introduce Head Ensemble Classifiers (HEC) to bridge the performance gap between CLIP-based and LVLM-based classification methods. Inspired by Gaussian Discriminant Analysis, HEC ranks the most discriminative vision and text heads and combines them into a training-free classifier. We show that HEC achieves state-of-the-art performance in few-shot and zero-shot classification across 12 datasets.

Method: Proposes RefReward-SR, an LR reference-aware reward model that assesses HR reconstructions conditioned on LR inputs, treating LR as semantic anchor. Uses MLLM visual-linguistic priors to evaluate semantic consistency and plausibility. Creates RefSR-18K dataset with LR-conditioned preference rankings. Fine-tunes MLLM with Group Relative Policy Optimization using LR-conditioned ranking rewards, and integrates GRPO into SR model training with RefReward-SR as reward signal.

Result: The framework achieves substantially better alignment with human judgments, producing reconstructions that preserve semantic consistency while enhancing perceptual plausibility and visual naturalness.

Conclusion: RefReward-SR provides a novel approach to preference-aligned super-resolution by leveraging MLLMs for LR-conditioned evaluation, addressing the misalignment between traditional metrics and human perception.

Abstract: Recent advances in generative super-resolution (SR) have greatly improved visual realism, yet existing evaluation and optimization frameworks remain misaligned with human perception. Full-Reference and No-Reference metrics often fail to reflect perceptual preference, either penalizing semantically plausible details due to pixel misalignment or favoring visually sharp but inconsistent artifacts. Moreover, most SR methods rely on ground-truth (GT)-dependent distribution matching, which does not necessarily correspond to human judgments. In this work, we propose RefReward-SR, a low-resolution (LR) reference-aware reward model for preference-aligned SR. Instead of relying on GT supervision or NR evaluation, RefReward-SR assesses high-resolution (HR) reconstructions conditioned on their LR inputs, treating the LR image as a semantic anchor. Leveraging the visual-linguistic priors of a Multimodal Large Language Models (MLLM), it evaluates semantic consistency and plausibility in a reasoning-aware manner. To support this paradigm, we construct RefSR-18K, the first large-scale LR-conditioned preference dataset for SR, providing pairwise rankings based on LR-HR consistency and HR naturalness. We fine-tune the MLLM with Group Relative Policy Optimization (GRPO) using LR-conditioned ranking rewards, and further integrate GRPO into SR model training with RefReward-SR as the core reward signal for preference-aligned generation. Extensive experiments show that our framework achieves substantially better alignment with human judgments, producing reconstructions that preserve semantic consistency while enhancing perceptual plausibility and visual naturalness. Code, models, and datasets will be released upon paper acceptance.

Method: Dual-sided framework with: (1) Hierarchical Directional Alignment (HDA) using cosine similarity in early stages and MSE matching in deep stages, and (2) Adversarial Knowledge Transfer (AKT) with symmetric KL distillation on clean and adversarial proxy data. Uses lightweight adapters with only 1.46M parameters.

Result: Achieves state-of-the-art personalized accuracy on CIFAR-100 (63.42%), Flowers-102 (84.23%), and Caltech-101 (95.67%) under Dirichlet non-IID partitions. Remains robust to out-of-domain proxy data.

Conclusion: HEART-PFL simultaneously enhances personalization and global stability, offering a strong and scalable solution for PFL. HDA and AKT provide complementary gains in alignment, robustness, and optimization stability.

Abstract: Personalized Federated Learning (PFL) aims to deliver effective client-specific models under heterogeneous distributions, yet existing methods suffer from shallow prototype alignment and brittle server-side distillation. We propose HEART-PFL, a dual-sided framework that (i) performs depth-aware Hierarchical Directional Alignment (HDA) using cosine similarity in the early stage and MSE matching in the deep stage to preserve client specificity, and (ii) stabilizes global updates through Adversarial Knowledge Transfer (AKT) with symmetric KL distillation on clean and adversarial proxy data. Using lightweight adapters with only 1.46M trainable parameters, HEART-PFL achieves state-of-the-art personalized accuracy on CIFAR-100, Flowers-102, and Caltech-101 (63.42%, 84.23%, and 95.67%, respectively) under Dirichlet non-IID partitions, and remains robust to out-of-domain proxy data. Ablation studies further confirm that HDA and AKT provide complementary gains in alignment, robustness, and optimization stability, offering insights into how the two components mutually reinforce effective personalization. Overall, these results demonstrate that HEART-PFL simultaneously enhances personalization and global stability, highlighting its potential as a strong and scalable solution for PFL(code available at https://github.com/danny0628/HEART-PFL).

Method: RVLM uses an iterative generate-execute loop where at each step the model writes Python code, invokes vision sub-agents, manipulates images, and accumulates evidence. RRouter is a lightweight controller that predicts optimal iteration budget from task-complexity features and monitors progress for early termination.

Result: Evaluated on BraTS 2023 Meningioma (brain MRI) and MIMIC-CXR (chest X-ray) using Gemini 2.5 Flash without fine-tuning. Shows high consistency on salient findings, can detect cross-modal discrepancies, generates structured reports, and recognizes view-specific artifacts.

Conclusion: The framework provides auditable, code-grounded medical AI predictions with adaptive computational efficiency, addressing key limitations in clinical AI governance and practical deployment.

Abstract: Medical AI systems face two fundamental limitations. First, conventional vision-language models (VLMs) perform single-pass inference, yielding black-box predictions that cannot be audited or explained in clinical terms. Second, iterative reasoning systems that expose intermediate steps rely on fixed iteration budgets wasting compute on simple cases while providing insufficient depth for complex ones. We address both limitations with a unified framework. RVLM replaces single-pass inference with an iterative generate-execute loop: at each step, the model writes Python code, invokes vision sub-agents, manipulates images, and accumulates evidence. Every diagnostic claim is grounded in executable code, satisfying auditability requirements of clinical AI governance frameworks. RRouter makes iteration depth adaptive: a lightweight controller predicts the optimal budget from task-complexity features, then monitors progress and terminates early when reasoning stalls. We evaluate on BraTS 2023 Meningioma (brain MRI) and MIMIC-CXR (chest X-ray) using Gemini 2.5 Flash without fine-tuning. Across repeated runs, RVLM shows high consistency on salient findings (e.g., mass presence and enhancement) and can detect cross-modal discrepancies between Fluid-Attenuated Inversion Recovery (FLAIR) signal characteristics and segmentation boundaries. On MIMIC-CXR, it generates structured reports and correctly recognises view-specific artefacts. Code: https://github.com/nican2018/rvlm.

Method: Species-adaptive architecture that processes vocalizations across frequency ranges (10Hz elephant rumbles to 4kHz puppy whines) and pairs them with probabilistic visual matching that tolerates stress-induced appearance changes. Grounded in 50 years of cognitive science showing animals perceive quantity approximately and communicate identity acoustically.

Result: First multimodal reunification system integrating visual and acoustic biometrics, demonstrating AI can serve vulnerable populations lacking human language by being grounded in biological communication principles.

Conclusion: Multimodal AI systems incorporating both visual and acoustic biometrics can significantly improve pet reunification success by addressing the fundamental way animals recognize each other, moving beyond appearance-only approaches.

Abstract: Every year, 10 million pets enter shelters, separated from their families. Despite desperate searches by both guardians and lost animals, 70% never reunite, not because matches do not exist, but because current systems look only at appearance, while animals recognize each other through sound. We ask, why does computer vision treat vocalizing species as silent visual objects? Drawing on five decades of cognitive science showing that animals perceive quantity approximately and communicate identity acoustically, we present the first multimodal reunification system integrating visual and acoustic biometrics. Our species-adaptive architecture processes vocalizations from 10Hz elephant rumbles to 4kHz puppy whines, paired with probabilistic visual matching that tolerates stress-induced appearance changes. This work demonstrates that AI grounded in biological communication principles can serve vulnerable populations that lack human language.

Method: Proposes InstanceRSR framework that: 1) uses LR images as global consistency guidance while jointly modeling image data and semantic segmentation maps for semantic relevance, 2) designs instance representation learning module to align diffusion latent space with instance latent space for instance-aware feature alignment, 3) incorporates scale alignment mechanism for fine-grained perception.

Result: Extensive experiments on multiple real-world benchmarks show InstanceRSR significantly outperforms existing methods in both quantitative metrics and visual quality, achieving new SOTA performance.

Conclusion: InstanceRSR effectively addresses the limitation of existing RSR methods by incorporating instance-level feature alignment and semantic modeling, enabling both photorealistic detail generation and semantic consistency at the instance level.

Abstract: Existing real-world super-resolution (RSR) methods based on generative priors have achieved remarkable progress in producing high-quality and globally consistent reconstructions. However, they often struggle to recover fine-grained details of diverse object instances in complex real-world scenes. This limitation primarily arises because commonly adopted denoising losses (e.g., MSE) inherently favor global consistency while neglecting instance-level perception and restoration. To address this issue, we propose InstanceRSR, a novel RSR framework that jointly models semantic information and introduces instance-level feature alignment. Specifically, we employ low-resolution (LR) images as global consistency guidance while jointly modeling image data and semantic segmentation maps to enforce semantic relevance during sampling. Moreover, we design an instance representation learning module to align the diffusion latent space with the instance latent space, enabling instance-aware feature alignment, and further incorporate a scale alignment mechanism to enhance fine-grained perception and detail recovery. Benefiting from these designs, our approach not only generates photorealistic details but also preserves semantic consistency at the instance level. Extensive experiments on multiple real-world benchmarks demonstrate that InstanceRSR significantly outperforms existing methods in both quantitative metrics and visual quality, achieving new state-of-the-art (SOTA) performance.

Method: B-MoE uses a Body-part-aware Mixture-of-Experts framework with specialized experts for distinct body regions (head, body, upper limbs, lower limbs). Each expert uses a lightweight Macro-Micro Motion Encoder (M3E) to capture long-range context and fine-grained local motion. A cross-attention routing mechanism learns inter-region relationships and dynamically selects informative regions. A dual-stream encoder fuses region-specific semantic cues with global motion features.

Result: Experiments on three challenging benchmarks (MA-52, SocialGesture, and MPII-GroupInteraction) show consistent state-of-the-art gains, with improvements in ambiguous, underrepresented, and low amplitude classes.

Conclusion: B-MoE effectively addresses the challenges of micro-action recognition by explicitly modeling the structured nature of human motion through body-part specialization and fusion of local/global features, achieving superior performance on difficult micro-action recognition tasks.

Abstract: Micro-actions, fleeting and low-amplitude motions, such as glances, nods, or minor posture shifts, carry rich social meaning but remain difficult for current action recognition models to recognize due to their subtlety, short duration, and high inter-class ambiguity. In this paper, we introduce B-MoE, a Body-part-aware Mixture-of-Experts framework designed to explicitly model the structured nature of human motion. In B-MoE, each expert specializes in a distinct body region (head, body, upper limbs, lower limbs), and is based on the lightweight Macro-Micro Motion Encoder (M3E) that captures long-range contextual structure and fine-grained local motion. A cross-attention routing mechanism learns inter-region relationships and dynamically selects the most informative regions for each micro-action. B-MoE uses a dual-stream encoder that fuses these region-specific semantic cues with global motion features to jointly capture spatially localized cues and temporally subtle variations that characterize micro-actions. Experiments on three challenging benchmarks (MA-52, SocialGesture, and MPII-GroupInteraction) show consistent state-of-theart gains, with improvements in ambiguous, underrepresented, and low amplitude classes.

Method: Introduces a unified, memory-augmented Vision-Language agent that simultaneously handles data association, object captioning, and exploration policy within a single autoregressive framework. The model processes current RGB observation, top-down explored map, and object-level episodic memory serialized into object-level tokens to ensure persistent object identity and semantic consistency across extended sequences.

Result: Extensive evaluation on manually annotated object-level test set shows improvements of up to +11.86% in standard captioning scores and +7.39% in caption self-similarity over baseline models, while enabling scalable performance through compact scene representation.

Conclusion: The proposed unified framework successfully addresses the inconsistency problem in VLMs for embodied agents by maintaining persistent object identity and semantic consistency across viewpoints through memory-augmented autoregressive processing.

Abstract: Vision-Language Models (VLMs) often yield inconsistent descriptions of the same object across viewpoints, hindering the ability of embodied agents to construct consistent semantic representations over time. Previous methods resolved inconsistencies using offline multi-view aggregation or multi-stage pipelines that decouple exploration, data association, and caption learning, with limited capacity to reason over previously observed objects. In this paper, we introduce a unified, memory-augmented Vision-Language agent that simultaneously handles data association, object captioning, and exploration policy within a single autoregressive framework. The model processes the current RGB observation, a top-down explored map, and an object-level episodic memory serialized into object-level tokens, ensuring persistent object identity and semantic consistency across extended sequences. To train the model in a self-supervised manner, we collect a dataset in photorealistic 3D environments using a disagreement-based policy and a pseudo-captioning model that enforces consistency across multi-view caption histories. Extensive evaluation on a manually annotated object-level test set, demonstrate improvements of up to +11.86% in standard captioning scores and +7.39% in caption self-similarity over baseline models, while enabling scalable performance through a compact scene representation. Code, model weights, and data are available at https://github.com/hsp-iit/epos-vlm

Method: ScrollScape maps spatial expansion of large canvases to temporal evolution of video frames, leveraging video model temporal consistency as global constraint. Uses Scanning Positional Encoding (ScanPE) as flexible moving camera and Scrolling Super-Resolution (ScrollSR) to handle memory bottlenecks. Fine-tuned on curated 3K multi-ratio image dataset.

Result: Achieves unprecedented 32K resolution with exceptional global coherence and visual fidelity, significantly outperforming existing image-diffusion baselines by eliminating severe localized artifacts across diverse domains at extreme scales.

Conclusion: ScrollScape successfully overcomes structural bottlenecks in extreme aspect ratio image generation by reformulating the problem as video generation, leveraging temporal consistency priors from video models to ensure long-range structural integrity.

Abstract: While diffusion models excel at generating images with conventional dimensions, pushing them to synthesize ultra-high-resolution imagery at extreme aspect ratios (EAR) often triggers catastrophic structural failures, such as object repetition and spatial fragmentation.This limitation fundamentally stems from a lack of robust spatial priors, as static text-to-image models are primarily trained on image distributions with conventional dimensions.To overcome this bottleneck, we present ScrollScape, a novel framework that reformulates EAR image synthesis into a continuous video generation process through two core innovations.By mapping the spatial expansion of a massive canvas to the temporal evolution of video frames, ScrollScape leverages the inherent temporal consistency of video models as a powerful global constraint to ensure long-range structural integrity.Specifically, Scanning Positional Encoding (ScanPE) distributes global coordinates across frames to act as a flexible moving camera, while Scrolling Super-Resolution (ScrollSR) leverages video super-resolution priors to circumvent memory bottlenecks, efficiently scaling outputs to an unprecedented 32K resolution. Fine-tuned on a curated 3K multi-ratio image dataset, ScrollScape effectively aligns pre-trained video priors with the EAR generation task. Extensive evaluations demonstrate that it significantly outperforms existing image-diffusion baselines by eliminating severe localized artifacts. Consequently, our method overcomes inherent structural bottlenecks to ensure exceptional global coherence and visual fidelity across diverse domains at extreme scales.

Method: Introduces TopoMesh, a sparse voxel-based VAE that unifies both GT and predicted meshes under a shared Dual Marching Cubes topological framework. Converts input meshes into DMC-compliant representations via remeshing with L∞ distance metric to preserve sharp edges. Decoder outputs meshes in same DMC format, enabling explicit vertex/face-level correspondences for direct mesh supervision.

Result: TopoMesh significantly outperforms existing VAEs in reconstruction fidelity, achieving superior preservation of sharp features and geometric details compared to prior methods.

Conclusion: By establishing explicit mesh-level correspondences through a unified DMC topological framework, TopoMesh overcomes the representation mismatch problem in 3D VAEs, enabling higher-fidelity reconstruction and better preservation of geometric details.

Abstract: The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE’s reconstruction capability sets a firm upper bound on generation quality. A fundamental challenge limiting existing VAEs is the representation mismatch between ground-truth meshes and network predictions: GT meshes have arbitrary, variable topology, while VAEs typically predict fixed-structure implicit fields (\eg, SDF on regular grids). This inherent misalignment prevents establishing explicit mesh-level correspondences, forcing prior work to rely on indirect supervision signals such as SDF or rendering losses. Consequently, fine geometric details, particularly sharp features, are poorly preserved during reconstruction. To address this, we introduce TopoMesh, a sparse voxel-based VAE that unifies both GT and predicted meshes under a shared Dual Marching Cubes (DMC) topological framework. Specifically, we convert arbitrary input meshes into DMC-compliant representations via a remeshing algorithm that preserves sharp edges using an L$\infty$ distance metric. Our decoder outputs meshes in the same DMC format, ensuring that both predicted and target meshes share identical topological structures. This establishes explicit correspondences at the vertex and face level, allowing us to derive explicit mesh-level supervision signals for topology, vertex positions, and face orientations with clear gradients. Our sparse VAE architecture employs this unified framework and is trained with Teacher Forcing and progressive resolution training for stable and efficient convergence. Extensive experiments demonstrate that TopoMesh significantly outperforms existing VAEs in reconstruction fidelity, achieving superior preservation of sharp features and geometric details.

Method: VERIA uses off-the-shelf foundation models to synthesize synchronized RGB-LiDAR instances, then applies sequential semantic and geometric verification to curate instances that match real LiDAR statistics while spanning wider intra-class variation. Includes stage-wise yield decomposition for pipeline diagnostics.

Result: On nuScenes and Lyft datasets, VERIA improves rare-class 3D object detection in both LiDAR-only and multimodal settings.

Conclusion: VERIA’s verification-centric approach effectively addresses long-tail distribution challenges in 3D perception by generating diverse, contextually appropriate synthetic instances that improve rare-class detection performance.

Abstract: Long-tail distributions in driving datasets pose a fundamental challenge for 3D perception, as rare classes exhibit substantial intra-class diversity yet available samples cover this variation space only sparsely. Existing instance augmentation methods based on copy-paste or asset libraries improve rare-class exposure but are often limited in fine-grained diversity and scene-context placement. We propose VERIA, an image-first multimodal augmentation framework that synthesizes synchronized RGB–LiDAR instances using off-the-shelf foundation models and curates them with sequential semantic and geometric verification. This verification-centric design tends to select instances that better match real LiDAR statistics while spanning a wider range of intra-class variation. Stage-wise yield decomposition provides a log-based diagnostic of pipeline reliability. On nuScenes and Lyft, VERIA improves rare-class 3D object detection in both LiDAR-only and multimodal settings. Our code is available at https://sgvr.kaist.ac.kr/VERIA/.

Method: Proposes Contextual Video-Text Contrastive Learning (VTC_CTX), Clip Order Prediction (COP), Cycle-Consistency Alignment, and Frame-Text Matching (FTM) to enhance multimodal alignment and temporal understanding in surgical videos.

Result: Achieves state-of-the-art performance across multiple surgical benchmarks for zero-shot recognition of phases, steps, instruments, and triplets.

Conclusion: CliPPER demonstrates effective video-language pretraining for surgical domain with novel temporal alignment strategies, enabling strong zero-shot performance on complex surgical tasks.

Abstract: Video-language foundation models have proven to be highly effective in zero-shot applications across a wide range of tasks. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and precise temporal understanding is often required for complex downstream tasks. To address this challenge, we introduce CliPPER (Contextual Video-Language Pretraining on Long-form Intraoperative Surgical Procedures for Event Recognition), a novel video-language pretraining framework trained on surgical lecture videos. Our method is designed for fine-grained temporal video-text recognition and introduces several novel pretraining strategies to improve multimodal alignment in long-form surgical videos. Specifically, we propose Contextual Video-Text Contrastive Learning (VTC_CTX) and Clip Order Prediction (COP) pretraining objectives, both of which leverage temporal and contextual dependencies to enhance local video understanding. In addition, we incorporate a Cycle-Consistency Alignment over video-text matches within the same surgical video to enforce bidirectional consistency and improve overall representation coherence. Moreover, we introduce a more refined alignment loss, Frame-Text Matching (FTM), to improve the alignment between video frames and text. As a result, our model establishes a new state-of-the-art across multiple public surgical benchmarks, including zero-shot recognition of phases, steps, instruments, and triplets. The source code and pretraining captions can be found at https://github.com/CAMMA-public/CliPPER.

Method: Proposes RS-SSM with Channel-wise Amplitude Perceptron (CwAP) to extract specific information distribution and Forgetting Gate Information Refiner (FGIR) to invert and refine the forgetting gate matrix for complementary refinement.

Result: Achieves state-of-the-art performance on four VSS benchmarks while maintaining high computational efficiency.

Conclusion: RS-SSM effectively enhances spatiotemporal pixel-level segmentation by refining forgotten specific information in state space compression.

Abstract: Recently, state space models have demonstrated efficient video segmentation through linear-complexity state space compression. However, Video Semantic Segmentation (VSS) requires pixel-level spatiotemporal modeling capabilities to maintain temporal consistency in segmentation of semantic objects. While state space models can preserve common semantic information during state space compression, the fixed-size state space inevitably forgets specific information, which limits the models’ capability for pixel-level segmentation. To tackle the above issue, we proposed a Refining Specifics State Space Model approach (RS-SSM) for video semantic segmentation, which performs complementary refining of forgotten spatiotemporal specifics. Specifically, a Channel-wise Amplitude Perceptron (CwAP) is designed to extract and align the distribution characteristics of specific information in the state space. Besides, a Forgetting Gate Information Refiner (FGIR) is proposed to adaptively invert and refine the forgetting gate matrix in the state space model based on the specific information distribution. Consequently, our RS-SSM leverages the inverted forgetting gate to complementarily refine the specific information forgotten during state space compression, thereby enhancing the model’s capability for spatiotemporal pixel-level segmentation. Extensive experiments on four VSS benchmarks demonstrate that our RS-SSM achieves state-of-the-art performance while maintaining high computational efficiency. The code is available at https://github.com/zhoujiahuan1991/CVPR2026-RS-SSM.

Method: Combines a diffusion-based world model that generates augmented future frames with region-specific edits, followed by a selective correction stage that aligns safety-critical regions with real-world observations while preserving intended augmentations elsewhere.

Result: Demonstrated in driving scenarios where semantic region structure is well-defined and real-world feedback is available, showing the pipeline can generate, cache, and selectively correct augmented future frames.

Conclusion: SEGAR represents an early step toward using generative world models as practical AR infrastructure, enabling pre-computation of augmented frames with selective real-world alignment for safety-critical applications.

Abstract: Generative world models offer a compelling foundation for augmented-reality (AR) applications: by predicting future image sequences that incorporate deliberate visual edits, they enable temporally coherent, augmented future frames that can be computed ahead of time and cached, avoiding per-frame rendering from scratch in real time. In this work, we present SEGAR, a preliminary framework that combines a diffusion-based world model with a selective correction stage to support this vision. The world model generates augmented future frames with region-specific edits while preserving others, and the correction stage subsequently aligns safety-critical regions with real-world observations while preserving intended augmentations elsewhere. We demonstrate this pipeline in driving scenarios as a representative setting where semantic region structure is well defined and real-world feedback is readily available. We view this as an early step toward generative world models as practical AR infrastructure, where future frames can be generated, cached, and selectively corrected on demand.

Method: Proposes AMIF model with integrated authorization access control into the image fusion objective, embedding explicit visible copyright identifiers for unauthorized usage and providing high-quality fusion results only upon successful key-based authentication.

Result: First authorizable medical image fusion model that protects intellectual property rights while maintaining clinical utility for authorized users.

Conclusion: AMIF addresses the critical need for intellectual property protection in medical image fusion models through built-in authentication mechanisms, balancing security with clinical utility.

Abstract: Multimodal image fusion enables precise lesion localization and characterization for accurate diagnosis, thereby strengthening clinical decision-making and driving its growing prominence in medical imaging research. A powerful multimodal image fusion model relies on high-quality, clinically representative multimodal training data and a rigorously engineered model architecture. Therefore, the development of such professional radiomics models represents a collaborative achievement grounded in standardized acquisition, clinical-specific expertise, and algorithmic design proficiency, which necessitates protection of associated intellectual property rights. However, current multimodal image fusion models generate fused outputs without built-in mechanisms to safeguard intellectual property rights, inadvertently exposing proprietary model knowledge and sensitive training data through inference leakage. For example, malicious users can exploit fusion outputs and model distillation or other inference-based reverse engineering techniques to approximate the fusion performance of proprietary models. To address this issue, we propose AMIF, the first Authorizable Medical Image Fusion model with built-in authentication, which integrates authorization access control into the image fusion objective. For unauthorized usage, AMIF embeds explicit and visible copyright identifiers into fusion results. In contrast, high-quality fusion results are accessible upon successful key-based authentication.

Method: Introduces neighborhood embedding concept, adaptively identifies neighborhoods similar to target cells, and fits low-to-high resolution mapping within these neighborhoods using neighborhood-adaptive linear regression.

Result: Achieves improvements of 9.16%, 8.16%, 1.86%, 3.89%, and 5.83% in MAE, MAPE, CMJS, SSIM, and GMSD metrics respectively compared to benchmarks, with strong generalization in cross-day and cross-scenario validations.

Conclusion: The method provides low-cost, fine-grained refinement of low-sampling-rate traffic data with minimal paired training data and concise formulation.

Abstract: The time-space (TS) traffic diagram serves as a crucial tool for characterizing the dynamic evolution of traffic flow, with its resolution directly influencing the effectiveness of traffic theory research and engineering applications. However, constrained by monitoring precision and sampling frequency, existing TS traffic diagrams commonly suffer from low resolution. To address this issue, this paper proposes a refinement method for TS traffic diagrams based on neighborhood-adaptive linear regression. Introducing the concept of neighborhood embedding into TS diagram refinement, the method leverages local pattern similarity in TS diagrams, adaptively identifies neighborhoods similar to target cells, and fits the low-to-high resolution mapping within these neighborhoods for refinement. It avoids the over-smoothing tendency of the traditional global linear model, allows the capture of unique traffic wave propagation and congestion evolution characteristics, and outperforms the traditional neighborhood embedding method in terms of local information utilization to achieve target cell refinement. Validation on two real datasets across multiple scales and upscaling factors shows that, compared to benchmark methods, the proposed method achieves improvements of 9.16%, 8.16%, 1.86%, 3.89%, and 5.83% in metrics including MAE, MAPE, CMJS, SSIM, and GMSD, respectively. Furthermore, the proposed method exhibits strong generalization and robustness in cross-day and cross-scenario validations. In summary, requiring only a minimal amount of paired high- and low-resolution training data, the proposed method features a concise formulation, providing a foundation for the low-cost, fine-grained refinement of low-sampling-rate traffic data.

Method: Framework where LLM reasoner controls visual observation through dynamic specification of temporal scope and sampling density. Uses VLM-based tools for broad scans, focused fact extraction, and evidence stitching in a reason-plan-observe loop.

Result: Substantial plug-and-play performance gains without fine-tuning, boosting accuracy by over 5% on challenging long-video benchmarks like LVBench and Video-MME.

Conclusion: Enabling agents to control how they see video is key to unlocking more accurate, robust, and interpretable video reasoning.

Abstract: The dense, temporal nature of video presents a profound challenge for automated analysis. Despite the use of powerful Vision-Language Models, prevailing methods for video understanding are limited by the inherent disconnect between reasoning and perception: they rely on static, pre-processed information and cannot actively seek raw evidence from video as their understanding evolves. To address this, we introduce LensWalk, a flexible agentic framework that empowers a Large Language Model reasoner to control its own visual observation actively. LensWalk establishes a tight reason-plan-observe loop where the agent dynamically specifies, at each step, the temporal scope and sampling density of the video it observes. Using a suite of versatile, Vision-Language Model based tools parameterized by these specifications, the agent can perform broad scans for cues, focus on specific segments for fact extraction, and stitch evidence from multiple moments for holistic verification. This design allows for progressive, on-demand evidence gathering that directly serves the agent’s evolving chain of thought. Without requiring any model fine-tuning, LensWalk delivers substantial, plug-and-play performance gains on multiple model recipes, boosting their accuracy by over 5% on challenging long-video benchmarks like LVBench and Video-MME. Our analysis reveals that enabling an agent to control how it sees is key to unlocking more accurate, robust, and interpretable video reasoning.

Method: Formulates curriculum learning as a sequential decision problem using Reinforcement Learning. Proposes an autonomous class scheduler with: (1) high-dimensional state encoder that maps model training status to latent space, and (2) category-fair policy-gradient objective ensuring balanced improvement across classes. Uses mixed source-target supervision.

Result: Achieves state-of-the-art performance on three widely used benchmarks (ACDC, Dark Zurich, Nighttime Driving) and shows generalization ability in synthetic-to-real semantic segmentation.

Conclusion: The proposed autonomous class scheduler enables more adaptive and dynamic learning by directing network focus to the most informative classes at each training stage, overcoming limitations of static curriculum schedules.

Abstract: The learning order of semantic classes significantly impacts unsupervised domain adaptation for semantic segmentation, especially under adverse weather conditions. Most existing curricula rely on handcrafted heuristics (e.g., fixed uncertainty metrics) and follow a static schedule, which fails to adapt to a model’s evolving, high-dimensional training dynamics, leading to category bias. Inspired by Reinforcement Learning, we cast curriculum learning as a sequential decision problem and propose an autonomous class scheduler. This scheduler consists of two components: (i) a high-dimensional state encoder that maps the model’s training status into a latent space and distills key features indicative of progress, and (ii) a category-fair policy-gradient objective that ensures balanced improvement across classes. Coupled with mixed source-target supervision, the learned class rankings direct the network’s focus to the most informative classes at each stage, enabling more adaptive and dynamic learning. It is worth noting that our method achieves state-of-the-art performance on three widely used benchmarks (e.g., ACDC, Dark Zurich, and Nighttime Driving) and shows generalization ability in synthetic-to-real semantic segmentation.

Method: Anti-I2V operates in both Lab* and frequency domains instead of just RGB space, improving robustness and focusing on salient pixels. It identifies network layers that capture distinct semantic features during denoising to design training objectives that maximize degradation of temporal coherence and generation fidelity.

Result: Through extensive validation, Anti-I2V demonstrates state-of-the-art defense performance against diverse video diffusion models, offering effective protection against malicious human image-to-video generation.

Conclusion: Anti-I2V provides an effective solution to protect against misuse of video diffusion models, particularly addressing the gap in defense against newer DiT architectures while maintaining robustness across diverse diffusion backbones.

Abstract: Advances in diffusion-based video generation models, while significantly improving human animation, poses threats of misuse through the creation of fake videos from a specific person’s photo and text prompts. Recent efforts have focused on adversarial attacks that introduce crafted perturbations to protect images from diffusion-based models. However, most existing approaches target image generation, while relatively few explicitly address image-to-video diffusion models (VDMs), and most primarily focus on UNet-based architectures. Hence, their effectiveness against Diffusion Transformer (DiT) models remains largely under-explored, as these models demonstrate improved feature retention, and stronger temporal consistency due to larger capacity and advanced attention mechanisms. In this work, we introduce Anti-I2V, a novel defense against malicious human image-to-video generation, applicable across diverse diffusion backbones. Instead of restricting noise updates to the RGB space, Anti-I2V operates in both the $L$$a$$b$* and frequency domains, improving robustness and concentrating on salient pixels. We then identify the network layers that capture the most distinct semantic features during the denoising process to design appropriate training objectives that maximize degradation of temporal coherence and generation fidelity. Through extensive validation, Anti-I2V demonstrates state-of-the-art defense performance against diverse video diffusion models, offering an effective solution to the problem.

Method: Extends LeJEPA to multimodal setting with fusion tokens that act as latent bottleneck between modality-specific patch stems in shared transformer. Uses pruned fusion strategy: after initial cross-modal attention, modality-specific tokens are dropped, forcing cross-modal information into shared fusion-token grid before applying Sketched Isotropic Gaussian Regularization (SIGReg) to joint multimodal CLS embedding.

Result: Strongest performance-efficiency trade-off on downstream patch probes among from-scratch multimodal baselines on Waymo; improves CenterNet detection and dense depth while competitive on segmentation. Best results on FLIR thermal benchmark, especially after Waymo-initialized fine-tuning. Best overall accuracy-efficiency balance at substantially lower compute, memory, and training time.

Conclusion: Le MuMo JEPA effectively learns unified multimodal representations through efficient fusion token bottleneck, demonstrating strong performance across various vision tasks while maintaining computational efficiency, making it suitable for real-world multimodal applications.

Abstract: Self-supervised learning has emerged as a powerful paradigm for learning visual representations without manual annotations, yet most methods still operate on a single modality and therefore miss the complementary structure available from heterogeneous sensors. We present Le MuMo JEPA, a self-supervised framework that learns unified representations from RGB images and aligned companion modalities. In our driving experiments, the second modality is camera-aligned LiDAR depth; we also evaluate RGB-thermal training and transfer on the Teledyne FLIR ADAS benchmark. Our approach extends LeJEPA to the multi-modal setting by learning fusion tokens that act as a latent bottleneck between modality-specific patch stems inside a shared transformer. Our default model employs a pruned fusion strategy: after an initial cross-modal attention layer, modality-specific tokens are dropped, forcing cross-modal information into the shared fusion-token grid as an efficient latent bottleneck before Sketched Isotropic Gaussian Regularization (SIGReg) is applied to the joint multimodal CLS embedding. On Waymo, Le MuMo JEPA gives the strongest performance-efficiency trade-off on downstream patch probes among the from-scratch multimodal baselines, improving CenterNet detection and dense depth while remaining competitive on segmentation. Under from-scratch training on nuScenes, Le MuMo JEPA remains the strongest model, and it also gives the best FLIR results, especially after Waymo-initialized fine-tuning. It also retains the best overall accuracy-efficiency balance in our study at substantially lower compute, memory, and estimated training time.

Method: Introduces VFIG-DATA dataset (66K figure-SVG pairs), uses coarse-to-fine training curriculum: supervised fine-tuning to learn atomic primitives, then reinforcement learning refinement to optimize global diagram fidelity, layout consistency, and topological edge cases.

Result: State-of-the-art performance among open-source models, performs on par with GPT-5.2, achieves VLM-Judge score of 0.829 on VFIG-BENCH evaluation suite.

Conclusion: VFIG successfully bridges the gap between raster figures and editable vector graphics through a data-driven approach with specialized training curriculum and comprehensive evaluation metrics.

Abstract: Scalable Vector Graphics (SVG) are an essential format for technical illustration and digital design, offering precise resolution independence and flexible semantic editability. In practice, however, original vector source files are frequently lost or inaccessible, leaving only “flat” rasterized versions (e.g., PNG or JPEG) that are difficult to modify or scale. Manually reconstructing these figures is a prohibitively labor-intensive process, requiring specialized expertise to recover the original geometric intent. To bridge this gap, we propose VFIG, a family of Vision-Language Models trained for complex and high-fidelity figure-to-SVG conversion. While this task is inherently data-driven, existing datasets are typically small-scale and lack the complexity of professional diagrams. We address this by introducing VFIG-DATA, a large-scale dataset of 66K high-quality figure-SVG pairs, curated from a diverse mix of real-world paper figures and procedurally generated diagrams. Recognizing that SVGs are composed of recurring primitives and hierarchical local structures, we introduce a coarse-to-fine training curriculum that begins with supervised fine-tuning (SFT) to learn atomic primitives and transitions to reinforcement learning (RL) refinement to optimize global diagram fidelity, layout consistency, and topological edge cases. Finally, we introduce VFIG-BENCH, a comprehensive evaluation suite with novel metrics designed to measure the structural integrity of complex figures. VFIG achieves state-of-the-art performance among open-source models and performs on par with GPT-5.2, achieving a VLM-Judge score of 0.829 on VFIG-BENCH.

Method: Developed PP-OCRv5, a lightweight two-stage OCR pipeline with 5M parameters. Focused on data-centric investigation by systematically analyzing three dimensions: data difficulty (complexity of samples), data accuracy (label quality), and data diversity (variety of scenarios). Used extensive experiments to show that high-quality, diverse training data can push the performance limits of efficient traditional architectures.

Result: PP-OCRv5 achieves performance competitive with billion-parameter VLMs on standard OCR benchmarks while offering superior localization precision and reduced hallucinations. Demonstrates that traditional two-stage OCR pipelines have higher performance ceilings than commonly assumed when trained with sufficient high-quality, diverse data.

Conclusion: Lightweight specialized models remain viable in the large-model era through data-centric optimization. The work provides practical insights into data curation for OCR and challenges the prevailing emphasis on architectural scaling alone.

Abstract: The advent of “OCR 2.0” and large-scale vision-language models (VLMs) has set new benchmarks in text recognition. However, these unified architectures often come with significant computational demands, challenges in precise text localization within complex layouts, and a propensity for textual hallucinations. Revisiting the prevailing notion that model scale is the sole path to high accuracy, this paper introduces PP-OCRv5, a meticulously optimized, lightweight OCR system with merely 5 million parameters. We demonstrate that PP-OCRv5 achieves performance competitive with many billion-parameter VLMs on standard OCR benchmarks, while offering superior localization precision and reduced hallucinations. The cornerstone of our success lies not in architectural expansion but in a data-centric investigation. We systematically dissect the role of training data by quantifying three critical dimensions: data difficulty, data accuracy, and data diversity. Our extensive experiments reveal that with a sufficient volume of high-quality, accurately labeled, and diverse data, the performance ceiling for traditional, efficient two-stage OCR pipelines is far higher than commonly assumed. This work provides compelling evidence for the viability of lightweight, specialized models in the large-model era and offers practical insights into data curation for OCR. The source code and models are publicly available at https://github.com/PaddlePaddle/PaddleOCR.

Method: Proposes GeoRouter, a dynamic routing framework using an LVLM backbone to analyze visual content and decide whether to use retrieval-based or generation-based geolocalization for each query. Introduces a distance-aware preference objective that converts performance differences between paradigms into continuous supervision signals, and creates GeoRouting dataset for training routing policies.

Result: Extensive experiments on IM2GPS3k and YFCC4k datasets demonstrate that GeoRouter significantly outperforms state-of-the-art baselines in worldwide image geolocalization.

Conclusion: Dynamic routing between complementary geolocalization paradigms (retrieval and generation) using LVLMs effectively leverages their respective strengths, achieving superior performance over single-paradigm approaches.

Abstract: Worldwide image geolocalization aims to predict precise GPS coordinates for images captured anywhere on Earth, which is challenging due to the large visual and geographic diversity. Recent methods mainly follow two paradigms: retrieval-based approaches that match queries against a reference database, and generation-based approaches that directly predict coordinates using Large Vision-Language Models (LVLMs). However, we observe distinct error profiles between them: retrieval excels at fine-grained instance matching, while generation offers robust semantic reasoning. This complementary heterogeneity suggests that no single paradigm is universally superior. To harness this potential, we propose GeoRouter, a dynamic routing framework that adaptively assigns each query to the optimal paradigm. GeoRouter leverages an LVLM backbone to analyze visual content and provide routing decisions. To optimize GeoRouter, we introduce a distance-aware preference objective that converts the distance gap between paradigms into a continuous supervision signal, explicitly reflecting relative performance differences. Furthermore, we construct GeoRouting, the first large-scale dataset tailored for training routing policies with independent paradigm predictions. Extensive experiments on IM2GPS3k and YFCC4k demonstrate that GeoRouter significantly outperforms state-of-the-art baselines.

Method: Proposes EndoVGGT framework with Deformation-aware Graph Attention (DeGAT) module that dynamically constructs feature-space semantic graphs to capture long-range correlations among coherent tissue regions, enabling robust propagation of structural cues across occlusions.

Result: Extensive experiments on SCARED show significant improvements: 24.6% increase in PSNR and 9.1% increase in SSIM over prior state-of-the-art. Strong zero-shot cross-dataset generalization to unseen SCARED and EndoNeRF domains.

Conclusion: EndoVGGT demonstrates the efficacy of dynamic feature-space modeling for consistent surgical 3D reconstruction, with DeGAT learning domain-agnostic geometric priors that enable robust performance across different surgical domains.

Abstract: Accurate 3D reconstruction of deformable soft tissues is essential for surgical robotic perception. However, low-texture surfaces, specular highlights, and instrument occlusions often fragment geometric continuity, posing a challenge for existing fixed-topology approaches. To address this, we propose EndoVGGT, a geometry-centric framework equipped with a Deformation-aware Graph Attention (DeGAT) module. Rather than using static spatial neighborhoods, DeGAT dynamically constructs feature-space semantic graphs to capture long-range correlations among coherent tissue regions. This enables robust propagation of structural cues across occlusions, enforcing global consistency and improving non-rigid deformation recovery. Extensive experiments on SCARED show that our method significantly improves fidelity, increasing PSNR by 24.6% and SSIM by 9.1% over prior state-of-the-art. Crucially, EndoVGGT exhibits strong zero-shot cross-dataset generalization to the unseen SCARED and EndoNeRF domains, confirming that DeGAT learns domain-agnostic geometric priors. These results highlight the efficacy of dynamic feature-space modeling for consistent surgical 3D reconstruction.

Method: Uses a large Vision-Language Model as a prior-extraction engine with layer-decoupled strategy for visual/textual priors. Implements Q-Former-based adapter to compress high-dimensional features into compact prior tokens for diffusion model training. Trains on motion-rendered images for semantic alignment, and uses text-to-image generated reference images during inference for generalization.

Result: Achieves state-of-the-art performance, outperforming existing methods across multiple benchmarks and demonstrating superior generalization to unseen objects and interaction categories.

Conclusion: The ViHOI framework successfully leverages 2D image priors through vision-language models to enhance 3D human-object interaction generation, addressing the limitation of text-only descriptions for physical constraints.

Abstract: Generating realistic and physically plausible 3D Human-Object Interactions (HOI) remains a key challenge in motion generation. One primary reason is that describing these physical constraints with words alone is difficult. To address this limitation, we propose a new paradigm: extracting rich interaction priors from easily accessible 2D images. Specifically, we introduce ViHOI, a novel framework that enables diffusion-based generative models to leverage rich, task-specific priors from 2D images to enhance generation quality. We utilize a large Vision-Language Model (VLM) as a powerful prior-extraction engine and adopt a layer-decoupled strategy to obtain visual and textual priors. Concurrently, we design a Q-Former-based adapter that compresses the VLM’s high-dimensional features into compact prior tokens, which significantly facilitates the conditional training of our diffusion model. Our framework is trained on motion-rendered images from the dataset to ensure strict semantic alignment between visual inputs and motion sequences. During inference, it leverages reference images synthesized by a text-to-image generation model to improve generalization to unseen objects and interaction categories. Experimental results demonstrate that ViHOI achieves state-of-the-art performance, outperforming existing methods across multiple benchmarks and demonstrating superior generalization.

Method: The paper presents a systematic survey of Causal Transfer Learning (CTL) for medical image analysis. It frames domain shift as a causal problem and analyzes how structural causal models, invariant risk minimization, and counterfactual reasoning can be embedded within transfer learning pipelines. The authors organize approaches by task, shift type, and causal assumption, proposing a unified taxonomy connecting causal frameworks and transfer mechanisms.

Result: The survey summarizes datasets, benchmarks, and empirical gains, highlighting when and why causal transfer outperforms correlation-based domain adaptation. It demonstrates how CTL supports fairness, robustness, and trustworthy deployment in multi-institutional and federated settings.

Conclusion: CTL provides a promising paradigm for clinically reliable medical imaging AI by integrating causal reasoning with transfer learning. The paper outlines open challenges and research directions for developing robust, generalizable models that can handle domain shifts across clinical settings.

Abstract: Medical imaging models frequently fail when deployed across hospitals, scanners, populations, or imaging protocols due to domain shift, limiting their clinical reliability. While transfer learning and domain adaptation address such shifts statistically, they often rely on spurious correlations that break under changing conditions. On the other hand, causal inference provides a principled way to identify invariant mechanisms that remain stable across environments. This survey introduces and systematises Causal Transfer Learning (CTL) for medical image analysis. This paradigm integrates causal reasoning with cross-domain representation learning to enable robust and generalisable clinical AI. We frame domain shift as a causal problem and analyse how structural causal models, invariant risk minimisation, and counterfactual reasoning can be embedded within transfer learning pipelines. We studied spanning classification, segmentation, reconstruction, anomaly detection, and multimodal imaging, and organised them by task, shift type, and causal assumption. A unified taxonomy is proposed that connects causal frameworks and transfer mechanisms. We further summarise datasets, benchmarks, and empirical gains, highlighting when and why causal transfer outperforms correlation-based domain adaptation. Finally, we discuss how CTL supports fairness, robustness, and trustworthy deployment in multi-institutional and federated settings, and outline open challenges and research directions for clinically reliable medical imaging AI.

Method: Proposes TSHaMo, a model-agnostic teacher-student diffusion framework where the student learns to synthesize motions from text alone, while the teacher leverages auxiliary signals (e.g., MANO parameters) to provide structured guidance during training via co-training strategy.

Result: Evaluated on GRAB and H2O datasets using two diffusion backbones, TSHaMo consistently improves motion quality and diversity. Ablations confirm robustness and flexibility in using diverse auxiliary inputs without requiring 3D objects at test time.

Conclusion: TSHaMo enables realistic 3D hand motion generation from natural language without needing 3D objects at inference, making it more practical for VR, robotics, and human-computer interaction applications.

Abstract: Generating realistic 3D hand motion from natural language is vital for VR, robotics, and human-computer interaction. Existing methods either focus on full-body motion, overlooking detailed hand gestures, or require explicit 3D object meshes, limiting generality. We propose TSHaMo, a model-agnostic teacher-student diffusion framework for text-driven hand motion generation. The student model learns to synthesize motions from text alone, while the teacher leverages auxiliary signals (e.g., MANO parameters) to provide structured guidance during training. A co-training strategy enables the student to benefit from the teacher’s intermediate predictions while remaining text-only at inference. Evaluated using two diffusion backbones on GRAB and H2O, TSHaMo consistently improves motion quality and diversity. Ablations confirm its robustness and flexibility in using diverse auxiliary inputs without requiring 3D objects at test time.

Method: Created a publicly available silhouette-based frailty gait dataset collected in clinically realistic settings. Evaluated how pretrained gait recognition models (convolutional and hybrid attention-based architectures) can be adapted for frailty classification under limited data conditions, studying selective freezing strategies and handling of class imbalance.

Result: Selectively freezing low-level gait representations while allowing higher-level features to adapt yields more stable and generalizable performance than full fine-tuning or rigid freezing. Conservative handling of class imbalance improves training stability, and combining complementary learning objectives enhances discrimination between clinically adjacent frailty states.

Conclusion: Gait-based representation learning provides a scalable, non-invasive, and interpretable framework for frailty assessment, supporting integration of modern biometric modeling approaches into aging research and clinical practice.

Abstract: Frailty is a condition in aging medicine characterized by diminished physiological reserve and increased vulnerability to stressors. However, frailty assessment remains subjective, heterogeneous, and difficult to scale in clinical practice. Gait is a sensitive marker of biological aging, capturing multisystem decline before overt disability. Yet the application of modern computer vision to gait-based frailty assessment has been limited by small, imbalanced datasets and a lack of clinically representative benchmarks. In this work, we introduce a publicly available silhouette-based frailty gait dataset collected in a clinically realistic setting, spanning the full frailty spectrum and including older adults who use walking aids. Using this dataset, we evaluate how pretrained gait recognition models can be adapted for frailty classification under limited data conditions. We study both convolutional and hybrid attention-based architectures and show that predictive performance depends primarily on how pretrained representations are transferred rather than architectural complexity alone. Across models, selectively freezing low-level gait representations while allowing higher-level features to adapt yields more stable and generalizable performance than either full fine-tuning or rigid freezing. Conservative handling of class imbalance further improves training stability, and combining complementary learning objectives enhances discrimination between clinically adjacent frailty states. Interpretability analyses reveal consistent model attention to lower-limb and pelvic regions, aligning with established biomechanical correlates of frailty. Together, these findings establish gait-based representation learning as a scalable, non-invasive, and interpretable framework for frailty assessment and support the integration of modern biometric modeling approaches into aging research and clinical practice.

Method: Proposes VLAForge with two key components: 1) ForgePerceiver module that enhances visual perception to capture subtle forgery cues while preserving pretrained vision-language alignment, and 2) Identity-Aware VLA score that uses identity-informed text prompting to generate discriminative cross-modal semantics tailored to each identity.

Result: Substantially outperforms state-of-the-art methods on video deepfake detection benchmarks, including both classical face-swapping and recent full-face generation forgeries, at both frame and video levels.

Conclusion: Leveraging vision-language semantics with identity-aware prompting significantly improves deepfake detection performance, demonstrating the untapped potential of cross-modal understanding in forgery detection tasks.

Abstract: Recent Deepfake Video Detection (DFD) studies have demonstrated that pre-trained Vision-Language Models (VLMs) such as CLIP exhibit strong generalization capabilities in detecting artifacts across different identities. However, existing approaches focus on leveraging visual features only, overlooking their most distinctive strength – the rich vision-language semantics embedded in the latent space. We propose VLAForge, a novel DFD framework that unleashes the potential of such cross-modal semantics to enhance model’s discriminability in deepfake detection. This work i) enhances the visual perception of VLM through a ForgePerceiver, which acts as an independent learner to capture diverse, subtle forgery cues both granularly and holistically, while preserving the pretrained Vision-Language Alignment (VLA) knowledge, and ii) provides a complementary discriminative cue – Identity-Aware VLA score, derived by coupling cross-modal semantics with the forgery cues learned by ForgePerceiver. Notably, the VLA score is augmented by an identity prior-informed text prompting to capture authenticity cues tailored to each identity, thereby enabling more discriminative cross-modal semantics. Comprehensive experiments on video DFD benchmarks, including classical face-swapping forgeries and recent full-face generation forgeries, demonstrate that our VLAForge substantially outperforms state-of-the-art methods at both frame and video levels. Code is available at https://github.com/mala-lab/VLAForge.

Method: OmniWeaving leverages massive-scale pretraining on diverse compositional and reasoning-augmented scenarios. It learns to temporally bind interleaved text, multi-image, and video inputs while acting as an intelligent agent to infer complex user intentions. The authors also introduce IntelligentVBench, a comprehensive benchmark for evaluating intelligent unified video generation.

Result: OmniWeaving achieves state-of-the-art performance among open-source unified video generation models. Extensive experiments demonstrate its effectiveness in handling diverse multimodal inputs and complex reasoning tasks for video creation.

Conclusion: OmniWeaving successfully bridges the gap between proprietary and open-source video generation systems by providing a unified framework with multimodal composition and reasoning capabilities. The model and benchmark will be publicly available to advance research in this area.

Abstract: While proprietary systems such as Seedance-2.0 have achieved remarkable success in omni-capable video generation, open-source alternatives significantly lag behind. Most academic models remain heavily fragmented, and the few existing efforts toward unified video generation still struggle to seamlessly integrate diverse tasks within a single framework. To bridge this gap, we propose OmniWeaving, an omni-level video generation model featuring powerful multimodal composition and reasoning-informed capabilities. By leveraging a massive-scale pretraining dataset that encompasses diverse compositional and reasoning-augmented scenarios, OmniWeaving learns to temporally bind interleaved text, multi-image, and video inputs while acting as an intelligent agent to infer complex user intentions for sophisticated video creation. Furthermore, we introduce IntelligentVBench, the first comprehensive benchmark designed to rigorously assess next-level intelligent unified video generation. Extensive experiments demonstrate that OmniWeaving achieves SoTA performance among open-source unified models. The codes and model will be made publicly available soon. Project Page: https://omniweaving.github.io.

Method: Introduces Positive-First Most Ambiguous (PF-MA), an active learning criterion that explicitly addresses class imbalance asymmetry by prioritizing near-boundary samples while favoring likely positives. Also proposes a class coverage metric to measure how well selected positives span the visual variability of the target class.

Result: Experiments on long-tailed datasets including fine-grained botanical data demonstrate that PF-MA consistently outperforms strong baselines in both coverage and classifier performance across varying class sizes and descriptors.

Conclusion: Aligning active learning with asymmetric and user-centric objectives of interactive fine-grained retrieval enables simple yet powerful solutions for retrieving rare and visually subtle categories in realistic human-in-the-loop settings.

Abstract: Real-world fine-grained visual retrieval often requires discovering a rare concept from large unlabeled collections with minimal supervision. This is especially critical in biodiversity monitoring, ecological studies, and long-tailed visual domains, where the target may represent only a tiny fraction of the data, creating highly imbalanced binary problems. Interactive retrieval with relevance feedback offers a practical solution: starting from a small query, the system selects candidates for binary user annotation and iteratively refines a lightweight classifier. While Active Learning (AL) is commonly used to guide selection, conventional AL assumes symmetric class priors and large annotation budgets, limiting effectiveness in imbalanced, low-budget, low-latency settings. We introduce Positive-First Most Ambiguous (PF-MA), a simple yet effective AL criterion that explicitly addresses the class imbalance asymmetry: it prioritizes near-boundary samples while favoring likely positives, enabling rapid discovery of subtle visual categories while maintaining informativeness. Unlike standard methods that oversample negatives, PF-MA consistently returns small batches with a high proportion of relevant samples, improving early retrieval and user satisfaction. To capture retrieval diversity, we also propose a class coverage metric that measures how well selected positives span the visual variability of the target class. Experiments on long-tailed datasets, including fine-grained botanical data, demonstrate that PF-MA consistently outperforms strong baselines in both coverage and classifier performance, across varying class sizes and descriptors. Our results highlight that aligning AL with the asymmetric and user-centric objectives of interactive fine-grained retrieval enables simple yet powerful solutions for retrieving rare and visually subtle categories in realistic human-in-the-loop settings.

Method: VisionToM computes intervention vectors that align visual representations with correct semantic targets, steering the model’s attention through different layers of visual features. This reduces reliance on spurious linguistic priors and improves multimodal reasoning.

Result: Experiments on the EgoToM benchmark (egocentric real-world video dataset for ToM with three multiple-choice QA settings) show substantial improvements in MLLMs’ ToM abilities. Additional open-ended generation tasks demonstrate that VisionToM enables MLLMs to produce more accurate free-form explanations of agents’ mental states.

Conclusion: VisionToM effectively enhances multimodal language models’ Theory of Mind capabilities by addressing visual understanding gaps and improving attention mechanisms, pushing machine-human collaboration toward greater alignment through better mental state inference.

Abstract: As large language models (LLMs) continue to advance, there is increasing interest in their ability to infer human mental states and demonstrate a human-like Theory of Mind (ToM). Most existing ToM evaluations, however, are centered on text-based inputs, while scenarios relying solely on visual information receive far less attention. This leaves a gap, since real-world human-AI interaction typically requires multimodal understanding. In addition, many current methods regard the model as a black box and rarely probe how its internal attention behaves in multiple-choice question answering (QA). The impact of LLM hallucinations on such tasks is also underexplored from an interpretability perspective. To address these issues, we introduce VisionToM, a vision-oriented intervention framework designed to strengthen task-aware reasoning. The core idea is to compute intervention vectors that align visual representations with the correct semantic targets, thereby steering the model’s attention through different layers of visual features. This guidance reduces the model’s reliance on spurious linguistic priors, leading to more reliable multimodal language model (MLLM) outputs and better QA performance. Experiments on the EgoToM benchmark-an egocentric, real-world video dataset for ToM with three multiple-choice QA settings-demonstrate that our method substantially improves the ToM abilities of MLLMs. Furthermore, results on an additional open-ended generation task show that VisionToM enables MLLMs to produce free-form explanations that more accurately capture agents’ mental states, pushing machine-human collaboration toward greater alignment.

Method: Two-component framework: (1) physical condition generator that transforms invalid trajectories into physically plausible conditions, and (2) physics-enhanced video generator for high-fidelity multi-view driving videos. Trained on large-scale heterogeneous dataset combining real-world videos with diverse challenging scenarios from CARLA simulator.

Result: PhyGenesis consistently outperforms state-of-the-art methods, especially on challenging trajectories, demonstrating strong physical consistency and visual fidelity in generated driving videos.

Conclusion: The proposed challenging-trajectory learning strategy enables trajectory correction and promotes physically consistent video generation, making PhyGenesis a robust world model for autonomous driving simulation.

Abstract: Video generation models have shown strong potential as world models for autonomous driving simulation. However, existing approaches are primarily trained on real-world driving datasets, which mostly contain natural and safe driving scenarios. As a result, current models often fail when conditioned on challenging or counterfactual trajectories-such as imperfect trajectories generated by simulators or planning systems-producing videos with severe physical inconsistencies and artifacts. To address this limitation, we propose PhyGenesis, a world model designed to generate driving videos with high visual fidelity and strong physical consistency. Our framework consists of two key components: (1) a physical condition generator that transforms potentially invalid trajectory inputs into physically plausible conditions, and (2) a physics-enhanced video generator that produces high-fidelity multi-view driving videos under these conditions. To effectively train these components, we construct a large-scale, physics-rich heterogeneous dataset. Specifically, in addition to real-world driving videos, we generate diverse challenging driving scenarios using the CARLA simulator, from which we derive supervision signals that guide the model to learn physically grounded dynamics under extreme conditions. This challenging-trajectory learning strategy enables trajectory correction and promotes physically consistent video generation. Extensive experiments demonstrate that PhyGenesis consistently outperforms state-of-the-art methods, especially on challenging trajectories. Our project page is available at: https://wm-research.github.io/PhyGenesis/.

Method: 1) Analyze mixing prototypes as shrinkage estimator; 2) Project image prototypes onto principal directions of semantic text embedding space to obtain text-aligned semantic image subspace; 3) For datasets with poor cross-modal alignment, model anisotropy using class covariances; 4) Combine text-aligned mixed prototype classifier with image-specific LDA classifier.

Result: Mixed prototypes improve classification performance but image prototypes add noise. Text-aligned image prototypes further improve classification when mixed with text embeddings. Combined text-aligned mixed prototype and image-specific LDA classifier outperforms existing methods across few-shot classification benchmarks.

Conclusion: The proposed approach of text-aligned image subspace projection and combined classifier framework effectively leverages both text and image information for improved few-shot classification, addressing limitations of direct prototype mixing and poor cross-modal alignment scenarios.

Abstract: Vision-language models (VLMs) like CLIP are trained with the objective of aligning text and image pairs. To improve CLIP-based few-shot image classification, recent works have observed that, along with text embeddings, image embeddings from the training set are an important source of information. In this work we investigate the impact of directly mixing image and text prototypes for few-shot classification and analyze this from a bias-variance perspective. We show that mixing prototypes acts like a shrinkage estimator. Although mixed prototypes improve classification performance, the image prototypes still add some noise in the form of instance-specific background or context information. In order to capture only information from the image space relevant to the given classification task, we propose projecting image prototypes onto the principal directions of the semantic text embedding space to obtain a text-aligned semantic image subspace. These text-aligned image prototypes, when mixed with text embeddings, further improve classification. However, for downstream datasets with poor cross-modal alignment in CLIP, semantic alignment might be suboptimal. We show that the image subspace can still be leveraged by modeling the anisotropy using class covariances. We demonstrate that combining a text-aligned mixed prototype classifier and an image-specific LDA classifier outperforms existing methods across few-shot classification benchmarks.

Method: Used Vision Transformer (TSViT) architecture with Sentinel-2 time series data for classification. Extended model for multitask learning of both farming system and crop type. Varied patch sizes to test spatial context influence on classification accuracy.

Result: Discrimination between organic and conventional farming is feasible but varies by crop type. Winter rye, wheat, and oat achieved F1 scores ≥0.8, while grassland, orchards, grapevines, and hops scored ≤0.4. Joint learning provided limited benefits, but spatial context improved both tasks.

Conclusion: Agricultural farming system classification is possible in diverse regions using multispectral remote sensing, with spatial context being more important than multitask learning for improving performance.

Abstract: Organic farming is a key element in achieving more sustainable agriculture. For a better understanding of the development and impact of organic farming, comprehensive, spatially explicit information is needed. This study presents an approach for the discrimination of organic and conventional farming systems using intra-annual Sentinel-2 time series. In addition, it examines two factors influencing this discrimination: the joint learning of crop type information in a concurrent task and the role of spatial context. A Vision Transformer model based on the Temporo-Spatial Vision Transformer (TSViT) architecture was used to construct a classification model for the two farming systems. The model was extended for simultaneous learning of the crop type, creating a multitask learning setting. By varying the patch size presented to the model, we tested the influence of spatial context on the classification accuracy of both tasks. We show that discrimination between organic and conventional farming systems using multispectral remote sensing data is feasible. However, classification performance varies substantially across crop types. For several crops, such as winter rye, winter wheat, and winter oat, F1 scores of 0.8 or higher can be achieved. In contrast, other agricultural land use classes, such as permanent grassland, orchards, grapevines, and hops, cannot be reliably distinguished, with F1 scores for the organic management class of 0.4 or lower. Joint learning of farming system and crop type provides only limited additional benefits over single-task learning. In contrast, incorporating wider spatial context improves the performance of both farming system and crop type classification. Overall, we demonstrate that a classification of agricultural farming systems is possible in a diverse agricultural region using multispectral remote sensing data.

Method: The paper presents a standardized benchmark challenge with dataset, task formulation, evaluation protocol, and baseline model for multimodal speaker identification under missing-modality and cross-lingual conditions.

Result: The challenge design provides a framework for evaluating robust multimodal speaker identification methods, though specific performance results are not reported as this is a challenge announcement paper.

Conclusion: The POLY-SIM Grand Challenge 2026 aims to advance research in practical multimodal speaker identification by addressing real-world limitations through standardized evaluation of robustness to missing modalities and linguistic variability.

Abstract: Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing. However, in real-world applications, such assumptions often do not hold. Visual information may be missing due to occlusions, camera failures, or privacy constraints, while multilingual speakers introduce additional complexity due to linguistic variability across languages. These challenges significantly affect the robustness and generalization of multimodal speaker identification systems. The POLY-SIM Grand Challenge 2026 aims to advance research in multimodal speaker identification under missing-modality and cross-lingual conditions. Specifically, the Grand Challenge encourages the development of robust methods that can effectively leverage incomplete multimodal inputs while maintaining strong performance across different languages. This report presents the design and organization of the POLY-SIM Grand Challenge 2026, including the dataset, task formulation, evaluation protocol, and baseline model. By providing a standardized benchmark and evaluation framework, the challenge aims to foster progress toward more robust and practical multimodal speaker identification systems.

Method: Proposes TextFlow framework with two complementary modules: Flow Manifold Steering (FMS) preserves structural and style consistency by modeling visual flow of characters and background regions, and Attention Boost (AttnBoost) enhances textual content rendering through attention-based guidance. The approach performs end-to-end text editing through semantic alignment and spatial refinement in a plug-and-play manner.

Result: Extensive experiments show the framework achieves visual quality and text accuracy comparable to or superior to training-based counterparts, generalizing well across diverse scenes and languages.

Conclusion: TextFlow advances scene text editing toward a more efficient, generalizable, and training-free paradigm, making it more accessible and scalable for practical applications.

Abstract: Scene text editing seeks to modify textual content in natural images while maintaining visual realism and semantic consistency. Existing methods often require task-specific training or paired data, limiting their scalability and adaptability. In this paper, we propose TextFlow, a training-free scene text editing framework that integrates the strengths of Attention Boost (AttnBoost) and Flow Manifold Steering (FMS) to enable flexible, high-fidelity text manipulation without additional training. Specifically, FMS preserves the structural and style consistency by modeling the visual flow of characters and background regions, while AttnBoost enhances the rendering of textual content through attention-based guidance. By jointly leveraging these complementary modules, our approach performs end-to-end text editing through semantic alignment and spatial refinement in a plug-and-play manner. Extensive experiments demonstrate that our framework achieves visual quality and text accuracy comparable to or superior to those of training-based counterparts, generalizing well across diverse scenes and languages. This study advances scene text editing toward a more efficient, generalizable, and training-free paradigm. Code is available at https://github.com/lyb18758/TextFlow

Method: Two core modules: 1) Spatial-Aware Compressive World Encoder (SCWE) that distills geometric knowledge from a foundation model and compresses multi-view images into compact scene tokens via learnable queries, and 2) Dynamic Latent World Model (DLWM) that uses a causal Transformer to autoregressively predict future world status conditioned on historical visual and motion representations.

Result: Achieves new state-of-the-art results: 89.3 EPDMS on NAVSIM v2 and 28.9 HD-Score on HUGSIM, surpassing the best prior perception-free method by 3.2 EPDMS with significantly less training data and a compact 104M-parameter model.

Conclusion: Latent-WAM demonstrates that efficient world modeling with spatial awareness and temporal dynamics can achieve strong autonomous driving performance with minimal computational resources.

Abstract: We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dynamics-informed latent world representations. Existing world-model-based planners suffer from inadequately compressed representations, limited spatial understanding, and underutilized temporal dynamics, resulting in sub-optimal planning under constrained data and compute budgets. Latent-WAM addresses these limitations with two core modules: a Spatial-Aware Compressive World Encoder (SCWE) that distills geometric knowledge from a foundation model and compresses multi-view images into compact scene tokens via learnable queries, and a Dynamic Latent World Model (DLWM) that employs a causal Transformer to autoregressively predict future world status conditioned on historical visual and motion representations. Extensive experiments on NAVSIM v2 and HUGSIM demonstrate new state-of-the-art results: 89.3 EPDMS on NAVSIM v2 and 28.9 HD-Score on HUGSIM, surpassing the best prior perception-free method by 3.2 EPDMS with significantly less training data and a compact 104M-parameter model.

Method: TAG uses classifier-free guidance (CFG) inspired approach: contrasts policy predictions under original observation and object-erased observation, using their difference as residual steering signal to strengthen object evidence influence. No architecture changes needed, minimal training/inference modifications.

Result: Evaluated on LIBERO, LIBERO-Plus, and VLABench benchmarks, TAG consistently improves robustness under clutter, reduces near-miss and wrong-object executions across standard manipulation tasks.

Conclusion: TAG effectively addresses instance-level grounding failures in VLA policies through simple inference-time guidance, enhancing reliability in cluttered environments without requiring architectural changes.

Abstract: Vision–Language–Action (VLA) policies have shown strong progress in mapping language instructions and visual observations to robotic actions, yet their reliability degrades in cluttered scenes with distractors. By analyzing failure cases, we find that many errors do not arise from infeasible motions, but from instance-level grounding failures: the policy often produces a plausible grasp trajectory that lands slightly off-target or even on the wrong object instance. To address this issue, we propose TAG (Target-Agnostic Guidance), a simple inference-time guidance mechanism that explicitly reduces distractor- and appearance-induced bias in VLA policies. Inspired by classifier-free guidance (CFG), TAG contrasts policy predictions under the original observation and an object-erased observation, and uses their difference as a residual steering signal that strengthens the influence of object evidence in the decision process. TAG does not require modifying the policy architecture and can be integrated with existing VLA policies with minimal training and inference changes. We evaluate TAG on standard manipulation benchmarks, including LIBERO, LIBERO-Plus, and VLABench, where it consistently improves robustness under clutter and reduces near-miss and wrong-object executions.

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Abstract: Failed to fetch summary for 2512.23374: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.23374&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 determine conclusion due to failed paper fetch

Abstract: Failed to fetch summary for 2512.15829: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.15829&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 2601.02441: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.02441&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 2511.20592: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.20592&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 2601.10305: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.10305&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to missing paper content

Result: Cannot determine results due to missing paper content

Conclusion: Cannot determine conclusion due to missing paper content

Abstract: Failed to fetch summary for 2601.12527: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.12527&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to data fetch failure

Result: Unable to determine results due to data fetch failure

Conclusion: Unable to draw conclusions due to data fetch failure

Abstract: Failed to fetch summary for 2602.00381: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.00381&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 2602.07058: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.07058&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 2601.20732: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.20732&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to HTTP 429 error preventing access to paper content

Result: Unable to determine results due to HTTP 429 error preventing access to paper content

Conclusion: Unable to determine conclusion due to HTTP 429 error preventing access to paper content

Abstract: Failed to fetch summary for 2602.06037: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.06037&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 determine conclusion due to technical error in fetching paper content

Abstract: Failed to fetch summary for 2602.07047: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.07047&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed paper retrieval.

Result: Unable to determine results due to failed paper retrieval.

Conclusion: Unable to determine conclusion due to failed paper retrieval.

Abstract: Failed to fetch summary for 2602.17665: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.17665&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 2602.18996: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.18996&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 without access to paper content

Abstract: Failed to fetch summary for 2602.19083: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.19083&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 2603.13334: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.13334&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 2602.19900: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.19900&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 2602.21591: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.21591&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 2602.21917: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.21917&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 2603.13119: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.13119&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 2602.22212: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.22212&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 2603.13904: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.13904&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 unavailability of content

Abstract: Failed to fetch summary for 2603.01601: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.01601&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to missing abstract

Result: Unable to determine results due to missing abstract

Conclusion: Unable to draw conclusions due to missing abstract

Abstract: Failed to fetch summary for 2603.07659: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.07659&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed paper retrieval

Result: Unable to determine results due to failed paper retrieval

Conclusion: Unable to determine conclusion due to failed paper retrieval

Abstract: Failed to fetch summary for 2603.18719: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.18719&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

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 about paper content due to technical error in fetching abstract

Abstract: Failed to fetch summary for 2603.11380: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.11380&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: No method information available. The paper content could not be fetched due to technical limitations with the arXiv API.

Result: No results available. The analysis cannot proceed without access to the paper’s abstract or content.

Conclusion: Unable to provide analysis or relevance assessment due to technical limitations preventing access to the paper content.

Abstract: Failed to fetch summary for 2603.11804: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.11804&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as abstract is unavailable due to rate limiting from arXiv API.

Result: Cannot determine results as abstract is unavailable due to rate limiting from arXiv API.

Conclusion: Cannot determine conclusion as abstract is unavailable due to rate limiting from arXiv API.

Abstract: Failed to fetch summary for 2603.12703: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.12703&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 2603.17889: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.17889&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 determine conclusion due to failed paper fetch

Abstract: Failed to fetch summary for 2603.18067: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.18067&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 2603.18739: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.18739&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to content unavailability.

Result: Cannot determine results due to content unavailability.

Conclusion: Cannot draw conclusions due to content unavailability.

Abstract: Failed to fetch summary for 2603.22593: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22593&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 2603.19775: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.19775&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method as the paper content could not be retrieved due to API rate limiting.

Result: Unable to determine results as the paper content could not be retrieved due to API rate limiting.

Conclusion: Unable to draw conclusions about the paper due to technical limitations in accessing the content.

Abstract: Failed to fetch summary for 2603.20012: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.20012&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 2603.21304: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.21304&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to technical error fetching paper content

Result: Unable to determine results due to technical error fetching paper content

Conclusion: Unable to draw conclusions due to technical error fetching paper content

Abstract: Failed to fetch summary for 2603.21387: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.21387&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to missing paper content

Result: Cannot determine results due to missing paper content

Conclusion: Cannot determine conclusion due to missing paper content

Abstract: Failed to fetch summary for 2603.22070: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22070&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 2603.22094: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22094&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to abstract fetch failure

Result: Unable to determine results due to abstract fetch failure

Conclusion: Unable to draw conclusions due to abstract fetch failure

Abstract: Failed to fetch summary for 2603.22193: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22193&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 2603.22883: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22883&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method as paper content could not be retrieved

Result: Cannot determine results as paper content could not be retrieved

Conclusion: Cannot draw conclusions as paper content could not be retrieved

Abstract: Failed to fetch summary for 2603.22893: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22893&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 determine conclusion due to technical error in fetching paper content

Abstract: Failed to fetch summary for 2603.22908: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22908&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed paper retrieval

Result: Unable to determine results due to failed paper retrieval

Conclusion: Unable to analyze paper content due to technical retrieval error

Abstract: Failed to fetch summary for 2603.22972: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22972&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 2603.23067: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.23067&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to technical error in fetching paper information

Result: Unable to determine results due to technical error in fetching paper information

Conclusion: Unable to draw conclusions due to technical error in fetching paper information

Abstract: Failed to fetch summary for 2603.23215: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.23215&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 2603.23286: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.23286&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 2603.23383: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.23383&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to the paper abstract

Result: Cannot determine results without access to the paper abstract

Conclusion: Cannot draw conclusions without access to the paper abstract

Abstract: Failed to fetch summary for 2504.05296: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2504.05296&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 2511.01718: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.01718&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 2512.14187: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.14187&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Train PLDR-LLMs at self-organized criticality, analyze deductive outputs using statistical physics concepts (correlation length, scaling functions, universality classes), define order parameter from global statistics of deductive output parameters, and correlate with benchmark performance.

Result: Models trained near criticality show better reasoning capabilities when order parameter is close to zero, with deductive outputs exhibiting metastable steady states and characteristics of second-order phase transitions.

Conclusion: Reasoning in LLMs can be explained through statistical physics principles and quantified using global model parameters without benchmark evaluations, providing a self-contained theoretical framework.

Abstract: We show that PLDR-LLMs pretrained at self-organized criticality exhibit reasoning at inference time. The characteristics of PLDR-LLM deductive outputs at criticality is similar to second-order phase transitions. At criticality, the correlation length diverges, and the deductive outputs attain a metastable steady state. The steady state behaviour suggests that deductive outputs learn representations equivalent to scaling functions, universality classes and renormalization groups from the training dataset, leading to generalization and reasoning capabilities in the process. We can then define an order parameter from the global statistics of the model’s deductive output parameters at inference. The reasoning capabilities of a PLDR-LLM is better when its order parameter is close to zero at criticality. This observation is supported by the benchmark scores of the models trained at near-criticality and sub-criticality. Our results provide a self-contained explanation on how reasoning manifests in large language models, and the ability to reason can be quantified solely from global model parameter values of the deductive outputs at steady state, without any need for evaluation of curated benchmark datasets through inductive output for reasoning and comprehension.

Method: Introduces Environment Maps with four core components: Contexts (abstracted locations), Actions (parameterized affordances), Workflows (observed trajectories), and Tacit Knowledge (domain definitions and reusable procedures). This structured graph representation consolidates heterogeneous evidence like screen recordings and execution traces.

Result: On WebArena benchmark across five domains, agents with environment maps achieve 28.2% success rate, nearly doubling baseline performance (14.2%) and outperforming agents with raw trajectory data (23.3%).

Conclusion: Environment Maps provide a structured interface between models and environments, establishing a persistent foundation for long-horizon planning that is human-interpretable, editable, and incrementally refinable.

Abstract: Although large language models (LLMs) have advanced rapidly, robust automation of complex software workflows remains an open problem. In long-horizon settings, agents frequently suffer from cascading errors and environmental stochasticity; a single misstep in a dynamic interface can lead to task failure, resulting in hallucinations or trial-and-error. This paper introduces $\textit{Environment Maps}$: a persistent, agent-agnostic representation that mitigates these failures by consolidating heterogeneous evidence, such as screen recordings and execution traces, into a structured graph. The representation consists of four core components: (1) Contexts (abstracted locations), (2) Actions (parameterized affordances), (3) Workflows (observed trajectories), and (4) Tacit Knowledge (domain definitions and reusable procedures). We evaluate this framework on the WebArena benchmark across five domains. Agents equipped with environment maps achieve a 28.2% success rate, nearly doubling the performance of baselines limited to session-bound context (14.2%) and outperforming agents that have access to the raw trajectory data used to generate the environment maps (23.3%). By providing a structured interface between the model and the environment, Environment Maps establish a persistent foundation for long-horizon planning that is human-interpretable, editable, and incrementally refinable.

Method: Combines Whisper-based speech recognition with retrieval-augmented generation (RAG) approaches (hybrid, sparse, dense). Uses supervised care-home trials and controlled testing with 330 spoken transcripts across 11 care categories, evaluating resident identification, care category matching, reminder recognition/extraction, and end-to-end scheduling correctness with safety mechanisms like confidence scoring and human oversight.

Result: Best configuration (GPT-5.2) achieved 100% resident ID and care category matching, 89.09% reminder recognition with 100% recall (zero missed reminders), and 84.65% exact reminder-count agreement for end-to-end scheduling via calendar integration.

Conclusion: Voice-enabled systems with careful evaluation and appropriate safeguards can support accurate documentation, effective task management, and trustworthy AI use in care home settings, though edge cases remain in converting informal spoken instructions to actionable events.

Abstract: Artificial intelligence (AI) is increasingly being explored in health and social care to reduce administrative workload and allow staff to spend more time on patient care. This paper evaluates a voice-enabled Care Home Smart Speaker designed to support everyday activities in residential care homes, including spoken access to resident records, reminders, and scheduling tasks. A safety-focused evaluation framework is presented that examines the system end-to-end, combining Whisper-based speech recognition with retrieval-augmented generation (RAG) approaches (hybrid, sparse, and dense). Using supervised care-home trials and controlled testing, we evaluated 330 spoken transcripts across 11 care categories, including 184 reminder-containing interactions. These evaluations focus on (i) correct identification of residents and care categories, (ii) reminder recognition and extraction, and (iii) end-to-end scheduling correctness under uncertainty (including safe deferral/clarification). Given the safety-critical nature of care homes, particular attention is also paid to reliability in noisy environments and across diverse accents, supported by confidence scoring, clarification prompts, and human-in-the-loop oversight. In the best-performing configuration (GPT-5.2), resident ID and care category matching reached 100% (95% CI: 98.86-100), while reminder recognition reached 89.09% (95% CI: 83.81-92.80) with zero missed reminders (100% recall) but some false positives. End-to-end scheduling via calendar integration achieved 84.65% exact reminder-count agreement (95% CI: 78.00-89.56), indicating remaining edge cases in converting informal spoken instructions into actionable events. The findings suggest that voice-enabled systems, when carefully evaluated and appropriately safeguarded, can support accurate documentation, effective task management, and trustworthy use of AI in care home settings.

Method: Introduces EnterpriseArena, a 132-month enterprise simulator combining financial data, business documents, macroeconomic signals, and expert-validated operating rules. The environment is partially observable, forcing agents to trade off information acquisition against resource conservation.

Result: Experiments with eleven advanced LLMs show high difficulty: only 16% of runs survive the full horizon, and larger models don’t reliably outperform smaller ones, indicating a distinct capability gap.

Conclusion: Long-horizon resource allocation under uncertainty represents a significant challenge for current LLM agents, requiring new approaches beyond current capabilities.

Abstract: Large language models (LLMs) have enabled agentic systems that can reason, plan, and act across complex tasks, but it remains unclear whether they can allocate resources effectively under uncertainty. Unlike short-horizon reactive decisions, allocation requires committing scarce resources over time while balancing competing objectives and preserving flexibility for future needs. We introduce EnterpriseArena, the first benchmark for evaluating agents on long-horizon enterprise resource allocation. It instantiates CFO-style decision-making in a 132-month enterprise simulator combining firm-level financial data, anonymized business documents, macroeconomic and industry signals, and expert-validated operating rules. The environment is partially observable and reveals the state only through budgeted organizational tools, forcing agents to trade off information acquisition against conserving scarce resources. Experiments on eleven advanced LLMs show that this setting remains highly challenging: only 16% of runs survive the full horizon, and larger models do not reliably outperform smaller ones. These results identify long-horizon resource allocation under uncertainty as a distinct capability gap for current LLM agents.

Method: Developed GTO Wizard Benchmark with a public API that evaluates agents against GTO Wizard AI (a superhuman poker agent approximating Nash Equilibria). Integrated AIVAT, a provably unbiased variance reduction technique that achieves equivalent statistical significance with ten times fewer hands than naive Monte Carlo evaluation. Conducted comprehensive benchmarking of state-of-the-art LLMs under zero-shot conditions.

Result: GTO Wizard AI defeated the previous strongest benchmark (Slumbot) by $19.4 ± 4.1 bb/100. Initial LLM benchmarking revealed dramatic progress in reasoning over recent years, but all models (including GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, Grok 4) remain far below the benchmark baseline. Qualitative analysis identified opportunities for improvement in representation and reasoning over hidden states.

Conclusion: The benchmark provides researchers with a precise, quantifiable setting for evaluating advances in planning and reasoning in multi-agent systems with partial observability, with LLMs showing significant progress but still needing substantial improvement for complex reasoning tasks like poker.

Abstract: We introduce GTO Wizard Benchmark, a public API and standardized evaluation framework for benchmarking algorithms in Heads-Up No-Limit Texas Hold’em (HUNL). The benchmark evaluates agents against GTO Wizard AI, a state-of-the-art superhuman poker agent that approximates Nash Equilibria, and defeated Slumbot, the 2018 Annual Computer Poker Competition champion and previous strongest publicly accessible HUNL benchmark, by $19.4$ $\pm$ $4.1$ bb/100. Variance is a fundamental challenge in poker evaluation; we address this by integrating AIVAT, a provably unbiased variance reduction technique that achieves equivalent statistical significance with ten times fewer hands than naive Monte Carlo evaluation. We conduct a comprehensive benchmarking study of state-of-the-art large language models under zero-shot conditions, including GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, Grok 4, and others. Initial results and analysis reveal dramatic progress in LLM reasoning over recent years, yet all models remain far below the baseline established by our benchmark. Qualitative analysis reveals clear opportunities for improvement, including representation and the ability to reason over hidden states. This benchmark provides researchers with a precise and quantifiable setting to evaluate advances in planning and reasoning in multi-agent systems with partial observability.

Method: Extends 3D grounding models to propose RAMP-3D, which formulates planning as sequential reactive prediction of paired 3D masks: a “which-object” mask for picking and a “which-target-region” mask for placement. Processes RGB-D observations and natural language tasks to generate pick-and-place actions.

Result: Achieves 79.5% success rate on long-horizon rearrangement tasks across 11 task variants with 1-30 boxes and diverse natural-language constraints. Significantly outperforms 2D VLM-based baselines.

Conclusion: Mask-based reactive policies are a promising alternative to symbolic pipelines for long-horizon planning in 3D environments, demonstrating strong performance on complex rearrangement tasks with natural language specifications.

Abstract: We study long-horizon planning in 3D environments from under-specified natural-language goals using only visual observations, focusing on multi-step 3D box rearrangement tasks. Existing approaches typically rely on symbolic planners with brittle relational grounding of states and goals, or on direct action-sequence generation from 2D vision-language models (VLMs). Both approaches struggle with reasoning over many objects, rich 3D geometry, and implicit semantic constraints. Recent advances in 3D VLMs demonstrate strong grounding of natural-language referents to 3D segmentation masks, suggesting the potential for more general planning capabilities. We extend existing 3D grounding models and propose Reactive Action Mask Planner (RAMP-3D), which formulates long-horizon planning as sequential reactive prediction of paired 3D masks: a “which-object” mask indicating what to pick and a “which-target-region” mask specifying where to place it. The resulting system processes RGB-D observations and natural-language task specifications to reactively generate multi-step pick-and-place actions for 3D box rearrangement. We conduct experiments across 11 task variants in warehouse-style environments with 1-30 boxes and diverse natural-language constraints. RAMP-3D achieves 79.5% success rate on long-horizon rearrangement tasks and significantly outperforms 2D VLM-based baselines, establishing mask-based reactive policies as a promising alternative to symbolic pipelines for long-horizon planning.

Method: SCoOP uses uncertainty-weighted linear opinion pooling to measure collective, system-level uncertainty across multiple VLMs. It’s a training-free framework that explicitly quantifies uncertainty in multi-VLM systems for hallucination detection and abstention.

Result: On ScienceQA, SCoOP achieves AUROC of 0.866 for hallucination detection (outperforming baselines by 10-13%) and AURAC of 0.907 for abstention (exceeding baselines by 7-9%). It adds only microsecond-level overhead to VLM inference.

Conclusion: SCoOP provides an efficient and principled mechanism for uncertainty-aware aggregation in multi-VLM systems, advancing the reliability of multimodal AI systems with minimal computational overhead.

Abstract: Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models’ outputs amplifies uncertainty and increases the risk of hallucinations. We propose SCoOP (Semantic-Consistent Opinion Pooling), a training-free uncertainty quantification (UQ) framework multi-VLM systems through uncertainty-weighted linear opinion pooling. Unlike prior UQ methods designed for single models, SCoOP explicitly measures collective, system-level uncertainty across multiple VLMs, enabling effective hallucination detection and abstention for highly uncertain samples. On ScienceQA, SCoOP achieves an AUROC of 0.866 for hallucination detection, outperforming baselines (0.732-0.757) by approximately 10-13%. For abstention, it attains an AURAC of 0.907, exceeding baselines (0.818-0.840) by 7-9%. Despite these gains, SCoOP introduces only microsecond-level aggregation overhead relative to the baselines, which is trivial compared to typical VLM inference time (on the order of seconds). These results demonstrate that SCoOP provides an efficient and principled mechanism for uncertainty-aware aggregation, advancing the reliability of multimodal AI systems.

Method: Evaluated several GPT and Llama family models in out-of-the-box settings, comparing LLM-generated scores with human grades, analyzing agreement patterns, and examining relationships between scores and feedback.

Result: LLM-human agreement remains relatively weak and varies with essay characteristics. LLMs tend to assign higher scores to short/underdeveloped essays and lower scores to longer essays with minor errors. LLM-generated feedback is consistent with their scoring patterns.

Conclusion: LLMs follow coherent but different grading patterns than humans, resulting in limited alignment with human grading practices. However, they produce internally consistent feedback and can be used to support essay scoring.

Abstract: Large language models have recently been proposed as tools for automated essay scoring, but their agreement with human grading remains unclear. In this work, we evaluate how LLM-generated scores compare with human grades and analyze the grading behavior of several models from the GPT and Llama families in an out-of-the-box setting, without task-specific training. Our results show that agreement between LLM and human scores remains relatively weak and varies with essay characteristics. In particular, compared to human raters, LLMs tend to assign higher scores to short or underdeveloped essays, while assigning lower scores to longer essays that contain minor grammatical or spelling errors. We also find that the scores generated by LLMs are generally consistent with the feedback they generate: essays receiving more praise tend to receive higher scores, while essays receiving more criticism tend to receive lower scores. These results suggest that LLM-generated scores and feedback follow coherent patterns but rely on signals that differ from those used by human raters, resulting in limited alignment with human grading practices. Nevertheless, our work shows that LLMs produce feedback that is consistent with their grading and that they can be reliably used in supporting essay scoring.

Method: The study analyzes eight benchmarks, 33 agent scaffolds, and 70+ model configurations. It finds that absolute score prediction degrades under scaffold-driven distribution shift, while rank-order prediction remains stable. The paper proposes an optimization-free protocol: evaluate new agents only on tasks with intermediate historical pass rates (30-70%), motivated by Item Response Theory.

Result: The mid-range difficulty filter reduces the number of evaluation tasks by 44-70% while maintaining high rank fidelity under scaffold and temporal shifts. It provides more reliable rankings than random sampling (which exhibits high variance) and outperforms greedy task selection under distribution shift.

Conclusion: Reliable leaderboard ranking for AI agents does not require full-benchmark evaluation. Small task subsets with mid-range difficulty filtering can preserve rankings at substantially lower cost, addressing the expense of agent evaluation while maintaining reliability.

Abstract: Evaluating AI agents on comprehensive benchmarks is expensive because each evaluation requires interactive rollouts with tool use and multi-step reasoning. We study whether small task subsets can preserve agent rankings at substantially lower cost. Unlike static language model benchmarks, agent evaluation is subject to scaffold-driven distribution shift, since performance depends on the framework wrapping the underlying model. Across eight benchmarks, 33 agent scaffolds, and 70+ model configurations, we find that absolute score prediction degrades under this shift, while rank-order prediction remains stable. Exploiting this asymmetry, we propose a simple optimization-free protocol: evaluate new agents only on tasks with intermediate historical pass rates (30-70%). This mid-range difficulty filter, motivated by Item Response Theory, reduces the number of evaluation tasks by 44-70% while maintaining high rank fidelity under scaffold and temporal shifts. It provides more reliable rankings than random sampling, which exhibits high variance across seeds, and outperforms greedy task selection under distribution shift. These results suggest that reliable leaderboard ranking does not require full-benchmark evaluation.

Method: RL-RH-PP integrates reinforcement learning with classical Prioritized Planning (PP) as a backbone. Dynamic priority assignment is formulated as a Partially Observable Markov Decision Process (POMDP). An attention-based neural network autoregressively decodes priority orders on-the-fly, enabling efficient sequential single-agent planning by the PP planner. The framework exploits sequential decision-making nature while delegating complex spatial-temporal interactions to reinforcement learning.

Result: Evaluations in realistic warehouse simulations show RL-RH-PP achieves the highest total throughput among baselines. It generalizes effectively across agent densities, planning horizons, and warehouse layouts. Interpretive analyses reveal that RL-RH-PP proactively prioritizes congested agents and strategically redirects agents from congestion, easing traffic flow and boosting throughput.

Conclusion: The paper demonstrates that learning-guided approaches can augment traditional heuristics in modern warehouse automation. RL-RH-PP shows the potential of integrating reinforcement learning with classical search-based planning for lifelong MAPF problems, achieving superior performance through learned priority assignment policies.

Abstract: Lifelong Multi-Agent Path Finding (MAPF) is critical for modern warehouse automation, which requires multiple robots to continuously navigate conflict-free paths to optimize the overall system throughput. However, the complexity of warehouse environments and the long-term dynamics of lifelong MAPF often demand costly adaptations to classical search-based solvers. While machine learning methods have been explored, their superiority over search-based methods remains inconclusive. In this paper, we introduce Reinforcement Learning (RL) guided Rolling Horizon Prioritized Planning (RL-RH-PP), the first framework integrating RL with search-based planning for lifelong MAPF. Specifically, we leverage classical Prioritized Planning (PP) as a backbone for its simplicity and flexibility in integrating with a learning-based priority assignment policy. By formulating dynamic priority assignment as a Partially Observable Markov Decision Process (POMDP), RL-RH-PP exploits the sequential decision-making nature of lifelong planning while delegating complex spatial-temporal interactions among agents to reinforcement learning. An attention-based neural network autoregressively decodes priority orders on-the-fly, enabling efficient sequential single-agent planning by the PP planner. Evaluations in realistic warehouse simulations show that RL-RH-PP achieves the highest total throughput among baselines and generalizes effectively across agent densities, planning horizons, and warehouse layouts. Our interpretive analyses reveal that RL-RH-PP proactively prioritizes congested agents and strategically redirects agents from congestion, easing traffic flow and boosting throughput. These findings highlight the potential of learning-guided approaches to augment traditional heuristics in modern warehouse automation.

Method: Introduces VehicleMemBench, a multi-user long-context memory benchmark built on an executable in-vehicle simulation environment. The benchmark evaluates tool use and memory by comparing post-action environment state with predefined target states, enabling objective and reproducible evaluation without LLM-based or human scoring. Includes 23 tool modules and each sample contains over 80 historical memory events.

Result: Experiments show that powerful models perform well on direct instruction tasks but struggle in scenarios involving memory evolution, particularly when user preferences change dynamically. Even advanced memory systems struggle to handle domain-specific memory requirements in this environment.

Conclusion: Highlights the need for more robust and specialized memory management mechanisms to support long-term adaptive decision-making in real-world in-vehicle systems. The findings demonstrate limitations of current models in handling dynamic multi-user preference scenarios with memory evolution.

Abstract: With the growing demand for intelligent in-vehicle experiences, vehicle-based agents are evolving from simple assistants to long-term companions. This evolution requires agents to continuously model multi-user preferences and make reliable decisions in the face of inter-user preference conflicts and changing habits over time. However, existing benchmarks are largely limited to single-user, static question-answer settings, failing to capture the temporal evolution of preferences and the multi-user, tool-interactive nature of real vehicle environments. To address this gap, we introduce VehicleMemBench, a multi-user long-context memory benchmark built on an executable in-vehicle simulation environment. The benchmark evaluates tool use and memory by comparing the post-action environment state with a predefined target state, enabling objective and reproducible evaluation without LLM-based or human scoring. VehicleMemBench includes 23 tool modules, and each sample contains over 80 historical memory events. Experiments show that powerful models perform well on direct instruction tasks but struggle in scenarios involving memory evolution, particularly when user preferences change dynamically. Even advanced memory systems struggle to handle domain-specific memory requirements in this environment. These findings highlight the need for more robust and specialized memory management mechanisms to support long-term adaptive decision-making in real-world in-vehicle systems. To facilitate future research, we release the data and code.

Method: The paper applies physics-based analysis of Transformer mechanisms to reveal deterministic components in AI failures. It presents this science in a legal-industry context through a simulated brief-drafting scenario, analyzing how internal states cross calculable thresholds leading to fabrication.

Result: Analysis shows fabrication risk is not an anomalous glitch but a foreseeable consequence of Transformer design. The paper demonstrates that AI’s internal state can cross calculable thresholds causing output to flip from reliable legal reasoning to authoritative-sounding fabrication.

Conclusion: Legal professionals, courts, and regulators should replace outdated “black box” mental models with verification protocols based on understanding how these systems actually fail deterministically. This has direct implications for the evolving duty of technological competence in legal practice.

Abstract: The adoption of generative AI across commercial and legal professions offers dramatic efficiency gains – yet for law in particular, it introduces a perilous failure mode in which the AI fabricates fictitious case law, statutes, and judicial holdings that appear entirely authentic. Attorneys who unknowingly file such fabrications face professional sanctions, malpractice exposure, and reputational harm, while courts confront a novel threat to the integrity of the adversarial process. This failure mode is commonly dismissed as random hallucination', but recent physics-based analysis of the Transformer's core mechanism reveals a deterministic component: the AI's internal state can cross a calculable threshold, causing its output to flip from reliable legal reasoning to authoritative-sounding fabrication. Here we present this science in a legal-industry setting, walking through a simulated brief-drafting scenario. Our analysis suggests that fabrication risk is not an anomalous glitch but a foreseeable consequence of the technology's design, with direct implications for the evolving duty of technological competence. We propose that legal professionals, courts, and regulators replace the outdated black box’ mental model with verification protocols based on how these systems actually fail.

Method: Combines deep reinforcement learning (limited-horizon Bellman-based learning, hindsight experience replay), heuristic search algorithms (batch weighted A*, Q* search, beam search), and formal logic (answer-set programming for goal specification). Uses automatic parallelization across CPUs for data generation and GPUs for reinforcement learning updates.

Result: Developed a comprehensive open-source tool with multiple inheritance structure for domain definition, efficient training through parallelization, and various search algorithms that leverage GPU parallelism and DNN architectures.

Conclusion: DeepXube provides an automated, efficient framework for solving pathfinding problems using learned heuristics, with visualization, profiling, and monitoring features, publicly available as open-source software.

Abstract: DeepXube is a free and open-source Python package and command-line tool that seeks to automate the solution of pathfinding problems by using machine learning to learn heuristic functions that guide heuristic search algorithms tailored to deep neural networks (DNNs). DeepXube is comprised of the latest advances in deep reinforcement learning, heuristic search, and formal logic for solving pathfinding problems. This includes limited-horizon Bellman-based learning, hindsight experience replay, batched heuristic search, and specifying goals with answer-set programming. A robust multiple-inheritance structure simplifies the definition of pathfinding domains and the generation of training data. Training heuristic functions is made efficient through the automatic parallelization of the generation of training data across central processing units (CPUs) and reinforcement learning updates across graphics processing units (GPUs). Pathfinding algorithms that take advantage of the parallelism of GPUs and DNN architectures, such as batch weighted A* and Q* search and beam search are easily employed to solve pathfinding problems through command-line arguments. Finally, several convenient features for visualization, code profiling, and progress monitoring during training and solving are available. The GitHub repository is publicly available at https://github.com/forestagostinelli/deepxube.

Method: DUPLEX uses a dual-system approach: 1) Fast System with lightweight LLM extracts entities/relations from natural language and deterministically maps to PDDL problem files for classical symbolic planners; 2) Slow System activates only upon planning failure, using solver diagnostics to drive high-capacity LLM in iterative reflection and repair.

Result: Extensive evaluations across 12 classical and household planning domains show DUPLEX significantly outperforms existing end-to-end and hybrid LLM baselines in both success rate and reliability.

Conclusion: The key insight is to restrict LLMs to structured semantic grounding (what they’re good at) and leave logical plan synthesis to symbolic planners, rather than trying to make LLMs plan better.

Abstract: While Large Language Models (LLMs) provide semantic flexibility for robotic task planning, their susceptibility to hallucination and logical inconsistency limits their reliability in long-horizon domains. To bridge the gap between unstructured environments and rigorous plan synthesis, we propose DUPLEX, an agentic dual-system neuro-symbolic architecture that strictly confines the LLM to schema-guided information extraction rather than end-to-end planning or code generation. In our framework, a feed-forward Fast System utilizes a lightweight LLM to extract entities, relations etc. from natural language, deterministically mapping them into a Planning Domain Definition Language (PDDL) problem file for a classical symbolic planner. To resolve complex or underspecified scenarios, a Slow System is activated exclusively upon planning failure, leveraging solver diagnostics to drive a high-capacity LLM in iterative reflection and repair. Extensive evaluations across 12 classical and household planning domains demonstrate that DUPLEX significantly outperforms existing end-to-end and hybrid LLM baselines in both success rate and reliability. These results confirm that The key is not to make the LLM plan better, but to restrict the LLM to the part it is good at - structured semantic grounding - and leave logical plan synthesis to a symbolic planner.

Method: Proposes AnalogAgent framework integrating LLM-based multi-agent system with self-evolving memory, coordinating Code Generator, Design Optimizer, and Knowledge Curator to distill execution feedback into adaptive playbook.

Result: Achieves 92% Pass@1 with Gemini and 97.4% Pass@1 with GPT-5; with compact models (Qwen-8B), yields +48.8% average Pass@1 gain and reaches 72.1% Pass@1 overall.

Conclusion: AnalogAgent substantially strengthens open-weight models for high-quality analog circuit design automation and enables cross-task transfer without additional expert feedback, databases, or libraries.

Abstract: Recent advances in large language models (LLMs) suggest strong potential for automating analog circuit design. Yet most LLM-based approaches rely on a single-model loop of generation, diagnosis, and correction, which favors succinct summaries over domain-specific insight and suffers from context attrition that erases critical technical details. To address these limitations, we propose AnalogAgent, a training-free agentic framework that integrates an LLM-based multi-agent system (MAS) with self-evolving memory (SEM) for analog circuit design automation. AnalogAgent coordinates a Code Generator, Design Optimizer, and Knowledge Curator to distill execution feedback into an adaptive playbook in SEM and retrieve targeted guidance for subsequent generation, enabling cross-task transfer without additional expert feedback, databases, or libraries. Across established benchmarks, AnalogAgent achieves 92% Pass@1 with Gemini and 97.4% Pass@1 with GPT-5. Moreover, with compact models (e.g., Qwen-8B), it yields a +48.8% average Pass@1 gain across tasks and reaches 72.1% Pass@1 overall, indicating that AnalogAgent substantially strengthens open-weight models for high-quality analog circuit design automation.

Method: Programmatic processing of massive academic literature corpus, rigorous distillation of 2,000+ publications, quantitative methodology with multi-dimensional taxonomy, automated semantic and statistical analysis to identify cognitive fingerprints and ecosystem patterns.

Result: Revealed a profound paradigm shift: bifurcation into “Semantic Prior” ecosystem dominated by LLMs and “Domain-Specific Generalization” ecosystem, with characterization of cognitive fingerprints showing mechanisms of cross-task synergy, multi-domain interference, and zero-shot generalization.

Conclusion: Provides rigorous quantitative roadmap for designing next-generation Embodied Semantic Simulators that bridge continuous physical control with high-level logical reasoning, offering data-verified insights into RL environment evolution.

Abstract: The remarkable progress of reinforcement learning (RL) is intrinsically tied to the environments used to train and evaluate artificial agents. Moving beyond traditional qualitative reviews, this work presents a large-scale, data-driven empirical investigation into the evolution of RL environments. By programmatically processing a massive corpus of academic literature and rigorously distilling over 2,000 core publications, we propose a quantitative methodology to map the transition from isolated physical simulations to generalist, language-driven foundation agents. Implementing a novel, multi-dimensional taxonomy, we systematically analyze benchmarks against diverse application domains and requisite cognitive capabilities. Our automated semantic and statistical analysis reveals a profound, data-verified paradigm shift: the bifurcation of the field into a “Semantic Prior” ecosystem dominated by Large Language Models (LLMs) and a “Domain-Specific Generalization” ecosystem. Furthermore, we characterize the “cognitive fingerprints” of these distinct domains to uncover the underlying mechanisms of cross-task synergy, multi-domain interference, and zero-shot generalization. Ultimately, this study offers a rigorous, quantitative roadmap for designing the next generation of Embodied Semantic Simulators, bridging the gap between continuous physical control and high-level logical reasoning.

Method: Proposes MAPUS: an LLM-based multi-agent framework where participants are modeled as autonomous agents with individual profiles and schedules, and a coordinator agent performs fairness-aware selection and refines sensing routes through language-based negotiation.

Result: Experiments on real-world datasets show that MAPUS achieves competitive sensing coverage while substantially improving participant satisfaction and fairness compared to existing methods.

Conclusion: MAPUS promotes more human-centric and sustainable urban sensing systems by addressing personal preferences and fairness through LLM-based multi-agent negotiation.

Abstract: Participatory urban sensing leverages human mobility for large-scale urban data collection, yet existing methods typically rely on centralized optimization and assume homogeneous participants, resulting in rigid assignments that overlook personal preferences and heterogeneous urban contexts. We propose MAPUS, an LLM-based multi-agent framework for personalized and fair participatory urban sensing. In our framework, participants are modeled as autonomous agents with individual profiles and schedules, while a coordinator agent performs fairness-aware selection and refines sensing routes through language-based negotiation. Experiments on real-world datasets show that MAPUS achieves competitive sensing coverage while substantially improving participant satisfaction and fairness, promoting more human-centric and sustainable urban sensing systems.

Method: ELITE uses two synergistic mechanisms: 1) Self-reflective knowledge construction that extracts reusable strategies from execution trajectories and maintains an evolving strategy pool through structured refinement operations, and 2) Intent-aware retrieval that identifies relevant strategies from the pool and applies them to current tasks.

Result: On EB-ALFRED and EB-Habitat benchmarks, ELITE achieves 9% and 5% performance improvement over base VLMs in online settings without supervision. In supervised settings, it generalizes effectively to unseen task categories, outperforming state-of-the-art training-based methods.

Conclusion: ELITE effectively bridges the gap between semantic understanding and reliable action execution for embodied agents, demonstrating that experiential learning and intent-aware knowledge transfer can significantly improve VLM-based embodied task performance.

Abstract: Vision-language models (VLMs) have shown remarkable general capabilities, yet embodied agents built on them fail at complex tasks, often skipping critical steps, proposing invalid actions, and repeating mistakes. These failures arise from a fundamental gap between the static training data of VLMs and the physical interaction for embodied tasks. VLMs can learn rich semantic knowledge from static data but lack the ability to interact with the world. To address this issue, we introduce ELITE, an embodied agent framework with {E}xperiential {L}earning and {I}ntent-aware {T}ransfer that enables agents to continuously learn from their own environment interaction experiences, and transfer acquired knowledge to procedurally similar tasks. ELITE operates through two synergistic mechanisms, \textit{i.e.,} self-reflective knowledge construction and intent-aware retrieval. Specifically, self-reflective knowledge construction extracts reusable strategies from execution trajectories and maintains an evolving strategy pool through structured refinement operations. Then, intent-aware retrieval identifies relevant strategies from the pool and applies them to current tasks. Experiments on the EB-ALFRED and EB-Habitat benchmarks show that ELITE achieves 9% and 5% performance improvement over base VLMs in the online setting without any supervision. In the supervised setting, ELITE generalizes effectively to unseen task categories, achieving better performance compared to state-of-the-art training-based methods. These results demonstrate the effectiveness of ELITE for bridging the gap between semantic understanding and reliable action execution.

Method: Four-level hierarchical architecture (Micro, Meso, Macro, Meta) with strategic dormancy/reactivation of reasoning nodes and Memory Palace with five mnemonic encoding styles.

Result: EMoT achieved near-parity with CoT (4.20 vs 4.33/5.0) with higher stability, outperformed CoT on Cross-Domain Synthesis (4.8 vs 4.4), but substantially underperformed on simple problems (27% vs baselines). Strategic dormancy was essential (quality collapsed from 4.2 to 1.0 when disabled).

Conclusion: EMoT shows promise for complex multi-domain reasoning but suffers from systematic overthinking on simple problems and has 33x computational overhead. It’s a specialized framework, not a general-purpose enhancement.

Abstract: Current prompting paradigms for large language models (LLMs), including Chain-of-Thought (CoT) and Tree-of-Thoughts (ToT), follow linear or tree-structured reasoning paths that lack persistent memory, strategic dormancy, and cross-domain synthesis. We present the Enhanced Mycelium of Thought (EMoT) framework, a bio-inspired reasoning architecture that organises cognitive processing into a four-level hierarchy (Micro, Meso, Macro, Meta), implements strategic dormancy and reactivation of reasoning nodes, and integrates a Memory Palace with five mnemonic encoding styles. EMoT is a research prototype for complex, multi-domain problems, not a general-purpose prompting enhancement. Two complementary evaluations reveal a characteristic trade-off. In a blind LLM-as-Judge evaluation across three domains, EMoT achieved near-parity with CoT (4.20 vs. 4.33/5.0) with higher stability, and outperformed CoT on Cross-Domain Synthesis (4.8 vs. 4.4). Ablation studies show that strategic dormancy is architecturally essential (quality collapsed from 4.2 to 1.0 when disabled). On a 15-item short-answer benchmark, EMoT (27%) substantially underperformed simpler baselines, confirming systematic overthinking on simple problems. These results are subject to important limitations: small sample sizes (n=3 complex cases, n=15 short-answer items), LLM-as-Judge evaluation with potential self-preference bias, and approximately 33-fold computational cost overhead. To our knowledge, EMoT is the first reasoning framework to combine hierarchical topology, strategic thought dormancy with reactivation, and mnemonic memory encoding in a single architecture.

Method: Created a standardized benchmark suite spanning four structurally diverse domains: real-world road networks, structured synthetic graphs, game-based grid environments, and high-dimensional robotic motion-planning roadmaps. Provides fixed graph instances, standardized start-goal queries, and both exact and approximate reference Pareto-optimal solution sets.

Result: The benchmark captures a full spectrum of objective interactions from strongly correlated to strictly independent, providing a common foundation for future MOS evaluations.

Conclusion: This standardized benchmark suite enables robust, reproducible, and structurally comprehensive evaluations in multi-objective search research.

Abstract: Empirical evaluation in multi-objective search (MOS) has historically suffered from fragmentation, relying on heterogeneous problem instances with incompatible objective definitions that make cross-study comparisons difficult. This standardization gap is further exacerbated by the realization that DIMACS road networks, a historical default benchmark for the field, exhibit highly correlated objectives that fail to capture diverse Pareto-front structures. To address this, we introduce the first comprehensive, standardized benchmark suite for exact and approximate MOS. Our suite spans four structurally diverse domains: real-world road networks, structured synthetic graphs, game-based grid environments, and high-dimensional robotic motion-planning roadmaps. By providing fixed graph instances, standardized start-goal queries, and both exact and approximate reference Pareto-optimal solution sets, this suite captures a full spectrum of objective interactions: from strongly correlated to strictly independent. Ultimately, this benchmark provides a common foundation to ensure future MOS evaluations are robust, reproducible, and structurally comprehensive.

Method: Multi-agent orchestration framework with specialized agents, continuously evolving Research World Model (Knowledge Graph), structured gap discovery decomposing methods into modules, self-correcting discovery loops analyzing module performance, self-improving development loops using cross-domain mechanism search, and consensus-based validation.

Result: Framework enables end-to-end AI research supervision from literature review through paper writing, with autonomous exploration and self-correcting updates. It’s model-agnostic, supports mainstream LLMs, and scales elastically with token budget.

Conclusion: AutoProf provides a novel approach to automated research with persistent knowledge representation and self-correcting mechanisms, overcoming limitations of stateless linear pipelines.

Abstract: Existing automated research systems operate as stateless, linear pipelines, generating outputs without maintaining a persistent understanding of the research landscape. They process papers sequentially, propose ideas without structured gap analysis, and lack mechanisms for agents to verify or refine each other’s findings. We present AutoProf (Autonomous Professor), a multi-agent orchestration framework where specialized agents provide end-to-end AI research supervision driven by human interests, from literature review through gap discovery, method development, evaluation, and paper writing, via autonomous exploration and self-correcting updates. Unlike sequential pipelines, AutoProf maintains a continuously evolving Research World Model implemented as a Knowledge Graph, capturing methods, benchmarks, limitations, and unexplored gaps as shared memory across agents. The framework introduces three contributions: first, structured gap discovery that decomposes methods into modules, evaluates them across benchmarks, and identifies module-level gaps; second, self-correcting discovery loops that analyze why modules succeed or fail, detect benchmark biases, and assess evaluation adequacy; third, self-improving development loops using cross-domain mechanism search to iteratively address failing components. All agents operate under a consensus mechanism where findings are validated before being committed to the shared model. The framework is model-agnostic, supports mainstream large language models, and scales elastically with token budget from lightweight exploration to full-scale investigation.

Method: Multi-agent framework with four domain-specific specialist agents (respiratory, cardiology, neurology, gastroenterology) using Qwen2.5-7B-Instruct, followed by two-phase self-verification process that measures internal consistency to produce Specialist Confidence Scores (S-scores), which drive weighted fusion for final answer selection and confidence calibration.

Result: ECE reduced by 49-74% across all four experimental settings (MedQA-USMLE and MedMCQA subsets), with full system achieving ECE = 0.091 (74.4% reduction) and AUROC = 0.630 (+0.056) at 59.2% accuracy on MedQA-250. Two-phase verification identified as primary calibration driver, multi-agent reasoning as primary accuracy driver.

Conclusion: Consistency-based verification produces more reliable uncertainty estimates across diverse medical question types, providing practical confidence signals for deferral in safety-critical clinical AI applications.

Abstract: Miscalibrated confidence scores are a practical obstacle to deploying AI in clinical settings. A model that is always overconfident offers no useful signal for deferral. We present a multi-agent framework that combines domain-specific specialist agents with Two-Phase Verification and S-Score Weighted Fusion to improve both calibration and discrimination in medical multiple-choice question answering. Four specialist agents (respiratory, cardiology, neurology, gastroenterology) generate independent diagnoses using Qwen2.5-7B-Instruct. Each diagnosis is then subjected to a two-phase self-verification process that measures internal consistency and produces a Specialist Confidence Score (S-score). The S-scores drive a weighted fusion strategy that selects the final answer and calibrates the reported confidence. We evaluate across four experimental settings, covering 100-question and 250-question high-disagreement subsets of both MedQA-USMLE and MedMCQA. Calibration improvement is the central finding, with ECE reduced by 49-74% across all four settings, including the harder MedMCQA benchmark where these gains persist even when absolute accuracy is constrained by knowledge-intensive recall demands. On MedQA-250, the full system achieves ECE = 0.091 (74.4% reduction over the single-specialist baseline) and AUROC = 0.630 (+0.056) at 59.2% accuracy. Ablation analysis identifies Two-Phase Verification as the primary calibration driver and multi-agent reasoning as the primary accuracy driver. These results establish that consistency-based verification produces more reliable uncertainty estimates across diverse medical question types, providing a practical confidence signal for deferral in safety-critical clinical AI applications.

Method: Introduces incongruent normal form (INF) that replaces self-referential sentences with finite families of non-self-referential sentences. Uses model-theoretic analysis of informativeness, studies incompleteness through incompatible complete extensions, and develops quantitative semantic framework with Fourier-analytic notion of semantic energy based on total influence.

Result: Shows that semantic completeness precludes informativeness while incongruence preserves it, demonstrates that any consistent incomplete first-order theory admits finite incongruent families, and establishes quantitative bounds relating semantic determinacy, informativeness, and spectral simplicity with matrix inequality bounding aggregate semantic variance by total semantic energy.

Conclusion: Incongruence is a fundamental structural and quantitative feature of semantic representation that cannot collapse into a single determinate state without unbounded energy cost, providing a minimal formal basis for semantic knowledge.

Abstract: We introduce incongruent normal form (INF), a structural representation for self-referential semantic sentences. An INF replaces a self-referential sentence with a finite family of non-self-referential sentences that are individually satisfiable but not jointly satisfiable. This transformation isolates the semantic obstruction created by self-reference while preserving classical semantics locally and is accompanied by correctness theorems characterizing when global inconsistency arises from locally compatible commitments. We then study the role of incongruence as a structural source of semantic informativeness. Using a minimal model-theoretic notion of informativeness-understood as the ability of sentences to distinguish among admissible models-we show that semantic completeness precludes informativeness, while incongruence preserves it. Moreover, incongruence is not confined to paradoxical constructions: any consistent incomplete first-order theory admits finite incongruent families arising from incompatible complete extensions. In this sense, incompleteness manifests structurally as locally realizable but globally incompatible semantic commitments, providing a minimal formal basis for semantic knowledge. Finally, we introduce a quantitative semantic framework. In a canonical finite semantic-state setting, we model semantic commitments as Boolean functions and define a Fourier-analytic notion of semantic energy based on total influence. We derive uncertainty-style bounds relating semantic determinacy, informativeness, and spectral simplicity, and establish a matrix inequality bounding aggregate semantic variance by total semantic energy. These results show quantitatively that semantic informativeness cannot collapse into a single determinate state without unbounded energy cost, identifying incongruence as a fundamental structural and quantitative feature of semantic representation.

Method: The authors generalize the two algorithms (their versions using the completion technique) and analyze the class of algorithms they belong to. They provide theoretical analysis to determine whether algorithms in this class can compute optimal strategies.

Result: The paper shows that any algorithm in this generalized class computes the best strategy. Additionally, experimental results demonstrate that the completion technique improves winning performance in practice.

Conclusion: The completion technique is sufficient to enable algorithms like Unbounded Best-First Minimax and Descent Minimax to always determine winning strategies, resolving the open question. The generalized class of algorithms with completion techniques can compute optimal strategies for two-player perfect information games.

Abstract: In this article, we focus on search algorithms for two-player perfect information games, whose objective is to determine the best possible strategy, and ideally a winning strategy. Unfortunately, some search algorithms for games in the literature are not able to always determine a winning strategy, even with an infinite search time. This is the case, for example, of the following algorithms: Unbounded Best-First Minimax and Descent Minimax, which are core algorithms in state-of-the-art knowledge-free reinforcement learning. They were then improved with the so-called completion technique. However, whether this technique sufficiently improves these algorithms to allow them to always determine a winning strategy remained an open question until now. To answer this question, we generalize the two algorithms (their versions using the completion technique), and we show that any algorithm of this class of algorithms computes the best strategy. Finally, we experimentally show that the completion technique improves winning performance.

Method: Develops a measure-theoretic Markov framework with core quantities: state blind-spot mass, state-action blind mass, entropy-based human-in-the-loop escalation gate, and expected oversight-cost identity over workflow visitation measure. Applied to Business Process Intelligence Challenge 2019 purchase-to-pay log with 251,734 cases and 1,595,923 events.

Result: Large workflows can appear well-supported at state level while retaining substantial blind mass over next-step decisions. Refining operational state to include context, economic magnitude, and actor class expanded state space from 42 to 668 and raised state-action blind mass from 0.0165 to 0.1253. On held-out data, m(s) = max_a π̂(a|s) tracked realized autonomous step accuracy within 3.4 percentage points.

Conclusion: The same quantities that delimit statistically credible autonomy also determine expected oversight burden. The framework is applicable to engineering processes with operational event logs and provides tools for evaluating AI agent reliability in organizational settings.

Abstract: Agentic artificial intelligence (AI) in organizations is a sequential decision problem constrained by reliability and oversight cost. When deterministic workflows are replaced by stochastic policies over actions and tool calls, the key question is not whether a next step appears plausible, but whether the resulting trajectory remains statistically supported, locally unambiguous, and economically governable. We develop a measure-theoretic Markov framework for this setting. The core quantities are state blind-spot mass B_n(tau), state-action blind mass B^SA_{pi,n}(tau), an entropy-based human-in-the-loop escalation gate, and an expected oversight-cost identity over the workflow visitation measure. We instantiate the framework on the Business Process Intelligence Challenge 2019 purchase-to-pay log (251,734 cases, 1,595,923 events, 42 distinct workflow actions) and construct a log-driven simulated agent from a chronological 80/20 split of the same process. The main empirical finding is that a large workflow can appear well supported at the state level while retaining substantial blind mass over next-step decisions: refining the operational state to include case context, economic magnitude, and actor class expands the state space from 42 to 668 and raises state-action blind mass from 0.0165 at tau=50 to 0.1253 at tau=1000. On the held-out split, m(s) = max_a pi-hat(a|s) tracks realized autonomous step accuracy within 3.4 percentage points on average. The same quantities that delimit statistically credible autonomy also determine expected oversight burden. The framework is demonstrated on a large-scale enterprise procurement workflow and is designed for direct application to engineering processes for which operational event logs are available.

Method: Proposes Learning to Guide (LTG) framework where AI provides interpretable guidance rather than making decisions. Develops SLOG (Specific Learning to Guide) approach that transforms any vision-language model into a capable generator of textual guidance using minimal human feedback, ensuring guidance is interpretable and task-specific.

Result: Empirical evaluation shows promise on both synthetic datasets and challenging real-world medical diagnosis tasks, demonstrating the framework’s effectiveness in providing useful guidance while maintaining human decision-making responsibility.

Conclusion: SLOG offers a viable alternative to traditional AI decision-making systems by providing interpretable guidance that assists human experts without removing their decision-making responsibility, addressing regulatory requirements for human oversight in high-stakes domains.

Abstract: There is growing interest in AI systems that support human decision-making in high-stakes domains (e.g., medical diagnosis) to improve decision quality and reduce cognitive load. Mainstream approaches pair human experts with a machine-learning model, offloading low-risk decisions to the model so that experts can focus on cases that require their judgment. This separation of responsibilities setup, however, is inadequate for high-stakes scenarios. The expert may end up over-relying on the machine’s decisions due to anchoring bias, thus losing the human oversight that is increasingly being required by regulatory agencies to ensure trustworthy AI. On the other hand, the expert is left entirely unassisted on the (typically hardest) decisions on which the model abstained. As a remedy, we introduce learning to guide (LTG), an alternative framework in which – rather than taking control from the human expert – the machine provides guidance useful for decision making, and the human is entirely responsible for coming up with a decision. In order to ensure guidance is interpretable and task-specific, we develop SLOG, an approach for turning any vision-language model into a capable generator of textual guidance by leveraging a modicum of human feedback. Our empirical evaluation highlights the promise of SLOG on both on a synthetic dataset and a challenging, real-world medical diagnosis task.

Method: Computational experiments with LLM-powered generative AI agents in controlled supply chain simulations, testing collaborative agents designed with Vendor-Managed Inventory principles against non-AI baselines.

Result: Discovered “collaboration paradox” - collaborative AI agents perform worse than non-AI baselines due to inventory hoarding. Resilience requires high-level AI-driven policy-setting combined with low-level collaborative execution with proactive replenishment.

Conclusion: The work provides insights into emergent behaviors of collaborative AI agents and offers a blueprint for designing stable, effective AI-driven systems for business analytics through a two-layer framework.

Abstract: The rise of autonomous, AI-driven agents in economic settings raises critical questions about their emergent strategic behavior. This paper investigates these dynamics in the cooperative context of a multi-echelon supply chain, a system famously prone to instabilities like the bullwhip effect. We conduct computational experiments with generative AI agents, powered by Large Language Models (LLMs), within a controlled supply chain simulation designed to isolate their behavioral tendencies. Our central finding is the “collaboration paradox”: a novel, catastrophic failure mode where theoretically superior collaborative AI agents, designed with Vendor-Managed Inventory (VMI) principles, perform even worse than non-AI baselines. We demonstrate that this paradox arises from an operational flaw where agents hoard inventory, starving the system. We then show that resilience is only achieved through a synthesis of two distinct layers: high-level, AI-driven proactive policy-setting to establish robust operational targets, and a low-level, collaborative execution protocol with proactive downstream replenishment to maintain stability. Our final framework, which implements this synthesis, can autonomously generate, evaluate, and quantify a portfolio of viable strategic choices. The work provides a crucial insight into the emergent behaviors of collaborative AI agents and offers a blueprint for designing stable, effective AI-driven systems for business analytics.

Method: Transform structured clinical guidelines into queryable knowledge graphs, then dynamically instantiate evaluation queries via graph traversal with combinatorial variation to resist surface-form contamination.

Result: Applied to WHO IMCI guidelines, generated clinically grounded multiple-choice questions. Evaluation of five language models revealed systematic gaps: good symptom recognition but lower accuracy on treatment protocols and clinical management decisions.

Conclusion: The framework provides scalable, contamination-resistant evaluation infrastructure that supports continuous regeneration as guidelines evolve and generalizes to domains with structured decision logic.

Abstract: Rigorous evaluation of domain-specific language models requires benchmarks that are comprehensive, contamination-resistant, and maintainable. Static, manually curated datasets do not satisfy these properties. We present a graph-based evaluation harness that transforms structured clinical guidelines into a queryable knowledge graph and dynamically instantiates evaluation queries via graph traversal. The framework provides three guarantees: (1) complete coverage of guideline relationships; (2) surface-form contamination resistance through combinatorial variation; and (3) validity inherited from expert-authored graph structure. Applied to the WHO IMCI guidelines, the harness generates clinically grounded multiple-choice questions spanning symptom recognition, treatment, severity classification, and follow-up care. Evaluation across five language models reveals systematic capability gaps. Models perform well on symptom recognition but show lower accuracy on treatment protocols and clinical management decisions. The framework supports continuous regeneration of evaluation data as guidelines evolve and generalizes to domains with structured decision logic. This provides a scalable foundation for evaluation infrastructure.

Method: Three-module framework: (1) Perception module abstracts diagrams into structured logic forms, (2) Symbolic Reasoning module applies geometric theorems to decide next deductive step, (3) Sketch Action module executes operations like drawing auxiliary lines or transformations. Trained with two-stage pipeline: supervised fine-tuning on 2,000 symbolic-curated trajectories followed by reinforcement learning with dense symbolic rewards.

Result: GeoSketch significantly improves stepwise reasoning accuracy and problem-solving success over static perception methods on the GeoSketch Benchmark (390 geometry problems requiring auxiliary construction or affine transformations).

Conclusion: GeoSketch advances multimodal reasoning from static interpretation to dynamic, verifiable interaction, establishing a new foundation for solving complex visuospatial problems through hierarchical decision-making, executable visual actions, and symbolic verification.

Abstract: Geometric Problem Solving (GPS) poses a unique challenge for Multimodal Large Language Models (MLLMs), requiring not only the joint interpretation of text and diagrams but also iterative visuospatial reasoning. While existing approaches process diagrams as static images, they lack the capacity for dynamic manipulation - a core aspect of human geometric reasoning involving auxiliary line construction and affine transformations. We present GeoSketch, a neural-symbolic framework that recasts geometric reasoning as an interactive perception-reasoning-action loop. GeoSketch integrates: (1) a Perception module that abstracts diagrams into structured logic forms, (2) a Symbolic Reasoning module that applies geometric theorems to decide the next deductive step, and (3) a Sketch Action module that executes operations such as drawing auxiliary lines or applying transformations, thereby updating the diagram in a closed loop. To train this agent, we develop a two-stage pipeline: supervised fine-tuning on 2,000 symbolic-curated trajectories followed by reinforcement learning with dense, symbolic rewards to enhance robustness and strategic exploration. To evaluate this paradigm, we introduce the GeoSketch Benchmark, a high-quality set of 390 geometry problems requiring auxiliary construction or affine transformations. Experiments on strong MLLM baselines demonstrate that GeoSketch significantly improves stepwise reasoning accuracy and problem-solving success over static perception methods. By unifying hierarchical decision-making, executable visual actions, and symbolic verification, GeoSketch advances multimodal reasoning from static interpretation to dynamic, verifiable interaction, establishing a new foundation for solving complex visuospatial problems.

Method: Proposes SAG-Agent framework that structures raw pixel-level interactions into a persistent State-Action Graph (SAG). Mitigates inefficient exploration by topologically linking functionally similar but visually distinct GUI states. Designs hybrid intrinsic reward mechanism based on graph topology combining state-value reward for exploiting known high-value pathways with novelty reward for targeted exploration.

Result: Evaluated in two complex, open-ended GUI-based decision-making environments (Civilization V and Slay the Spire), demonstrating significant improvements in exploration efficiency and strategic depth over state-of-the-art methods.

Conclusion: SAG-Agent effectively addresses efficiency bottlenecks in API-free GUI interaction by structuring experience into graphs, enabling better generalization and decoupling strategic planning from pure discovery for improved long-horizon reasoning.

Abstract: Most commodity software lacks accessible Application Programming Interfaces (APIs), requiring autonomous agents to interact solely through pixel-based Graphical User Interfaces (GUIs). In this API-free setting, large language model (LLM)-based agents face severe efficiency bottlenecks: limited to local visual experiences, they make myopic decisions and rely on inefficient trial-and-error, hindering both skill acquisition and long-horizon planning. To overcome these limitations, we propose SAG-Agent, an experience-driven learning framework that structures an agent’s raw pixel-level interactions into a persistent State-Action Graph (SAG). SAG-Agent mitigates inefficient exploration by topologically linking functionally similar but visually distinct GUI states, constructing a rich neighborhood of experience that enables the agent to generalize from a diverse set of historical strategies. To facilitate long-horizon reasoning, we design a novel hybrid intrinsic reward mechanism based on the graph topology, combining a state-value reward for exploiting known high-value pathways with a novelty reward that encourages targeted exploration. This approach decouples strategic planning from pure discovery, allowing the agent to effectively value setup actions with delayed gratification. We evaluate SAG-Agent in two complex, open-ended GUI-based decision-making environments (Civilization V and Slay the Spire), demonstrating significant improvements in exploration efficiency and strategic depth over the state-of-the-art methods.

Method: Reformulates forecasting as expert-like multi-stage workflow: context preparation, reasoning-based generation, and reflective evaluation. Uses lightweight toolkit with feature set, knowledge base, case library, and contextual pool to support LLM-based reasoning.

Result: Extensive experiments across multiple benchmarks show CastMind generally outperforms representative baselines.

Conclusion: CastMind transforms forecasting from single-pass output to multi-turn autonomous interaction, enabling accurate time series forecasting with training-free LLMs.

Abstract: Time series forecasting plays a crucial role in decision-making across many real-world applications. Despite substantial progress, most existing methods still treat forecasting as a static, single-pass regression problem. In contrast, human experts form predictions through iterative reasoning that integrates temporal features, domain knowledge, case-based references, and supplementary context, with continuous refinement. In this work, we propose CastMind, an interaction-driven agentic reasoning framework that enables accurate time series forecasting with training-free large language models. CastMind reformulates forecasting as an expert-like process and organizes it into a multi-stage workflow involving context preparation, reasoning-based generation, and reflective evaluation, transforming forecasting from a single-pass output into a multi-turn, autonomous interaction process. To support diverse perspectives commonly considered by human experts, we develop a lightweight toolkit comprising a feature set, a knowledge base, a case library, and a contextual pool that provides external support for LLM-based reasoning. Extensive experiments across multiple benchmarks show that CastMind generally outperforms representative baselines. Code is available at this repository: https://github.com/SkyeGT/CastMind .

Method: Unified framework with reading-order modeling based on layout flow, hierarchy-aware segmentation using table-of-contents anchors, multimodal aggregation pipeline converting visual elements to natural language, and enrichment with ESG, GRI, and sentiment labels.

Result: Outperforms dedicated document parsing systems and general-purpose multimodal models on annotated benchmarks, and releases Aurora-ESG dataset with structured multimodal content and fine-grained annotations.

Conclusion: Pharos-ESG effectively addresses ESG report parsing challenges and supports ESG integration in financial governance through structured multimodal representations.

Abstract: Environmental, Social, and Governance (ESG) principles are reshaping the foundations of global financial gover- nance, transforming capital allocation architectures, regu- latory frameworks, and systemic risk coordination mecha- nisms. However, as the core medium for assessing corpo- rate ESG performance, the ESG reports present significant challenges for large-scale understanding, due to chaotic read- ing order from slide-like irregular layouts and implicit hier- archies arising from lengthy, weakly structured content. To address these challenges, we propose Pharos-ESG, a uni- fied framework that transforms ESG reports into structured representations through multimodal parsing, contextual nar- ration, and hierarchical labeling. It integrates a reading-order modeling module based on layout flow, hierarchy-aware seg- mentation guided by table-of-contents anchors, and a multi- modal aggregation pipeline that contextually transforms vi- sual elements into coherent natural language. The framework further enriches its outputs with ESG, GRI, and sentiment labels, yielding annotations aligned with the analytical de- mands of financial research. Extensive experiments on anno- tated benchmarks demonstrate that Pharos-ESG consistently outperforms both dedicated document parsing systems and general-purpose multimodal models. In addition, we release Aurora-ESG, the first large-scale public dataset of ESG re- ports, spanning Mainland China, Hong Kong, and U.S. mar- kets, featuring unified structured representations of multi- modal content, enriched with fine-grained layout and seman- tic annotations to better support ESG integration in financial governance and decision-making.

Method: Cannot determine method as paper content is unavailable

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Abstract: Failed to fetch summary for 2601.10402: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.10402&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 2601.12410: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.12410&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 2601.19178: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.19178&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 2602.24055: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.24055&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 2603.02788: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.02788&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to retrieval error

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Abstract: Failed to fetch summary for 2603.14185: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.14185&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 2603.21430: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.21430&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed API request

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Abstract: Failed to fetch summary for 2603.22844: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.22844&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 2104.14744: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2104.14744&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to API fetch failure

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Abstract: Failed to fetch summary for 2210.11039: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2210.11039&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 2211.14997: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2211.14997&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 2402.08151: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2402.08151&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 2402.14212: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2402.14212&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 2404.09622: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2404.09622&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 2405.17573: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2405.17573&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 2407.01111: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2407.01111&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 2410.06819: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2410.06819&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 2502.01521: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2502.01521&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 2503.11488: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2503.11488&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 2505.16950: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2505.16950&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 2505.20714: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2505.20714&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 2507.22171: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2507.22171&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 2509.14181: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2509.14181&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 2510.01169: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.01169&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 2510.04607: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.04607&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 2510.11269: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.11269&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 2510.14751: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.14751&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 2510.15495: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2510.15495&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method due to missing abstract

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Abstract: Failed to fetch summary for 2511.06767: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.06767&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 2511.11743: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.11743&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 2511.18000: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2511.18000&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to data fetch failure

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Abstract: Failed to fetch summary for 2512.01035: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.01035&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Cannot determine method without access to the paper content. The system attempted to fetch the paper from arXiv but encountered rate limiting restrictions.

Result: No results can be analyzed since the paper content could not be retrieved. The only available information is the paper ID (2512.09427) which suggests it’s a recent arXiv submission from December 2024.

Conclusion: Cannot provide conclusions about the paper without access to its content. The analysis is limited by the inability to fetch the paper details from arXiv due to rate limiting.

Abstract: Failed to fetch summary for 2512.09427: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2512.09427&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 2601.00473: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.00473&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: No method information available due to fetch failure

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Abstract: Failed to fetch summary for 2601.11702: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.11702&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to abstract fetch failure

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Abstract: Failed to fetch summary for 2601.19072: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2601.19072&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 2602.07150: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.07150&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 2602.07906: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.07906&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 2602.07303: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.07303&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 2602.19345: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.19345&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 as paper content is unavailable.

Abstract: Failed to fetch summary for 2602.23811: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2602.23811&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to data fetch failure

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Abstract: Failed to fetch summary for 2603.08566: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.08566&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 2603.09643: Page request resulted in HTTP 429 (https://export.arxiv.org/api/query?search_query=&id_list=2603.09643&sortBy=relevance&sortOrder=descending&start=0&max_results=100)

Method: Unable to determine method due to failed paper fetch

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Method: Created Echoes dataset with 3,577 tracks (110 hours) spanning multiple genres, generated by 10 AI music systems, with semantic-level alignment between spoofed audio and bona fide references achieved by conditioning generated samples on original waveforms or song descriptors.

Result: Echoes is the hardest in-domain dataset; detectors trained on existing datasets transfer poorly to Echoes; training on Echoes yields strongest generalization performance across datasets.

Conclusion: Provider diversity and semantic alignment help learn more transferable detection cues for music deepfake detection, making Echoes a valuable benchmark for robust detection systems.

Abstract: We introduce Echoes, a new dataset for music deepfake detection designed for training and benchmarking detectors under realistic and provider-diverse conditions. Echoes comprises 3,577 tracks (110 hours of audio) spanning multiple genres (pop, rock, electronic), and includes content generated by ten popular AI music generation systems. To prevent shortcut learning and promote robust generalization, the dataset is deliberately constructed to be challenging, enforcing semantic-level alignment between spoofed audio and bona fide references. This alignment is achieved by conditioning generated audio samples directly on bona-fide waveforms or song descriptors. We evaluate Echoes in a cross-dataset setting against three existing AI-generated music datasets using state-of-the-art Wav2Vec2 XLS-R 2B representations. Results show that (i) Echoes is the hardest in-domain dataset; (ii) detectors trained on existing datasets transfer poorly to Echoes; (iii) training on Echoes yields the strongest generalization performance. These findings suggest that provider diversity and semantic alignment help learn more transferable detection cues.

Method: Proposes Variable-Length Audio FingerPrinting (VLAFP), a deep learning model that supports variable-length audio processing for both training and testing, enabling more flexible and dynamic audio representation.

Result: VLAFP outperforms state-of-the-art methods in live audio identification and audio retrieval across three real-world datasets, demonstrating superior performance with variable-length inputs.

Conclusion: VLAFP represents a significant advancement in audio fingerprinting by enabling variable-length processing, addressing practical limitations of existing methods and improving real-world audio identification performance.

Abstract: Audio fingerprinting converts audio to much lower-dimensional representations, allowing distorted recordings to still be recognized as their originals through similar fingerprints. Existing deep learning approaches rigidly fingerprint fixed-length audio segments, thereby neglecting temporal dynamics during segmentation. To address limitations due to this rigidity, we propose Variable-Length Audio FingerPrinting (VLAFP), a novel method that supports variable-length fingerprinting. To the best of our knowledge, VLAFP is the first deep audio fingerprinting model capable of processing audio of variable length, for both training and testing. Our experiments show that VLAFP outperforms existing state-of-the-arts in live audio identification and audio retrieval across three real-world datasets.

Method: Introduces SID-Bench (first real-world benchmark for semantic-aware interruption detection), proposes Average Penalty Time (APT) metric to assess responsiveness-robustness trade-off, and designs LLM-based detection model optimized through novel training paradigm to capture semantic intent cues.

Result: Model significantly outperforms mainstream baselines, achieving nearly threefold reduction in APT. Successfully resolves tension between speed and stability, establishing new state-of-the-art for intelligent interruption handling.

Conclusion: Presents comprehensive framework for interruption detection in spoken dialogue systems, balancing responsiveness and robustness through semantic understanding. Provides benchmark and code for future research.

Abstract: Achieving natural full-duplex interaction in spoken dialogue systems (SDS) remains a challenge due to the difficulty of accurately detecting user interruptions. Current solutions are polarized between “trigger-happy” VAD-based methods that misinterpret backchannels and robust end-to-end models that exhibit unacceptable response delays. Moreover, the absence of real-world benchmarks and holistic metrics hinders progress in the field. This paper presents a comprehensive frame-work to overcome these limitations. We first introduce SID-Bench, the first benchmark for semantic-aware interruption detection built entirely from real-world human dialogues. To provide a rigorous assessment of the responsiveness-robustness trade-off, we propose the Average Penalty Time (APT) metric, which assigns a temporal cost to both false alarms and late responses. Building on this framework, we design an LLM-based detection model optimized through a novel training paradigm to capture subtle semantic cues of intent. Experimental results show that our model significantly outperforms mainstream baselines, achieving a nearly threefold reduction in APT. By successfully resolving the long-standing tension between speed and stability, our work establishes a new state-of-the-art for intelligent interruption handling in SDS. To facilitate future research, SID-Bench and the associated code are available at: https://github.com/xkx-hub/SID-bench.

Method: Proposes using reservoir computing to streamline MFCC extraction by replacing conventional frequency-domain conversions with convolution operations, eliminating complex transformations while maintaining feature discriminability. Develops an end-to-end audio processing framework integrating this approach.

Result: Demonstrates potential for efficient and real-time speech analysis, contributing to energy-efficient audio processing technologies that can be deployed in embedded systems and voice-driven applications.

Conclusion: Bridges biologically inspired feature extraction with neuromorphic computing, offering a scalable solution for next-generation speech recognition systems with improved efficiency and real-time capabilities.

Abstract: Despite the advancements in cutting-edge technologies, audio signal processing continues to pose challenges and lacks the precision of a human speech processing system. To address these challenges, we propose a novel approach to simplify audio signal processing by leveraging time-domain techniques and reservoir computing. Through our research, we have developed a real-time audio signal processing system by simplifying audio signal processing through the utilization of reservoir computers, which are significantly easier to train. Feature extraction is a fundamental step in speech signal processing, with Mel Frequency Cepstral Coefficients (MFCCs) being a dominant choice due to their perceptual relevance to human hearing. However, conventional MFCC extraction relies on computationally intensive time-frequency transformations, limiting efficiency in real-time applications. To address this, we propose a novel approach that leverages reservoir computing to streamline MFCC extraction. By replacing traditional frequency-domain conversions with convolution operations, we eliminate the need for complex transformations while maintaining feature discriminability. We present an end-to-end audio processing framework that integrates this method, demonstrating its potential for efficient and real-time speech analysis. Our results contribute to the advancement of energy-efficient audio processing technologies, enabling seamless deployment in embedded systems and voice-driven applications. This work bridges the gap between biologically inspired feature extraction and modern neuromorphic computing, offering a scalable solution for next-generation speech recognition systems.

Method: Proposed dropin and plasticity algorithms that dynamically adjust the number of neurons in certain layers to flexibly modulate model parameters. Evaluated on multiple architectures including ResNet, Gated Recurrent Neural Networks (GRU), and Wav2Vec for audio deepfake detection.

Result: Experimental results on ASVSpoof2019 LA, PA, and FakeorReal datasets show consistent computational efficiency improvements with dropin, and maximum relative reductions of ~39% and ~66% in Equal Error Rate with dropin and plasticity approaches respectively.

Conclusion: The proposed plasticity-inspired algorithms provide effective parameter modulation for audio deepfake detection across diverse architectures, offering computational efficiency and significant error rate reductions without full model retraining.

Abstract: Current audio deepfake detection has achieved remarkable performance using diverse deep learning architectures such as ResNet, and has seen further improvements with the introduction of large models (LMs) like Wav2Vec. The success of large language models (LLMs) further demonstrates the benefits of scaling model parameters, but also highlights one bottleneck where performance gains are constrained by parameter counts. Simply stacking additional layers, as done in current LLMs, is computationally expensive and requires full retraining. Furthermore, existing low-rank adaptation methods are primarily applied to attention-based architectures, which limits their scope. Inspired by the neuronal plasticity observed in mammalian brains, we propose novel algorithms, dropin and further plasticity, that dynamically adjust the number of neurons in certain layers to flexibly modulate model parameters. We evaluate these algorithms on multiple architectures, including ResNet, Gated Recurrent Neural Networks, and Wav2Vec. Experimental results using the widely recognised ASVSpoof2019 LA, PA, and FakeorReal dataset demonstrate consistent improvements in computational efficiency with the dropin approach and a maximum of around 39% and 66% relative reduction in Equal Error Rate with the dropin and plasticity approach among these dataset, respectively. The code and supplementary material are available at Github link.

Method: Uses DiT-based framework with separate LoRA modules for identity and audio timbre control via self-attention layers. Introduces contrastive learning alongside flow matching to enhance preservation of identity/timbre. Trained on large-scale audio-visual human dataset.

Result: Extensive experiments show OmniCustom outperforms existing methods in generating audio-video content with consistent identity and timbre fidelity.

Conclusion: Proposes a novel sync audio-video customization task and presents OmniCustom as an effective solution for joint audio-video generation with identity and timbre control.

Abstract: Existing mainstream video customization methods focus on generating identity-consistent videos based on given reference images and textual prompts. Benefiting from the rapid advancement of joint audio-video generation, this paper proposes a more compelling new task: sync audio-video customization, which aims to synchronously customize both video identity and audio timbre. Specifically, given a reference image $I^{r}$ and a reference audio $A^{r}$, this novel task requires generating videos that maintain the identity of the reference image while imitating the timbre of the reference audio, with spoken content freely specifiable through user-provided textual prompts. To this end, we propose OmniCustom, a powerful DiT-based audio-video customization framework that can synthesize a video following reference image identity, audio timbre, and text prompts all at once in a zero-shot manner. Our framework is built on three key contributions. First, identity and audio timbre control are achieved through separate reference identity and audio LoRA modules that operate through self-attention layers within the base audio-video generation model. Second, we introduce a contrastive learning objective alongside the standard flow matching objective. It uses predicted flows conditioned on reference inputs as positive examples and those without reference conditions as negative examples, thereby enhancing the model ability to preserve identity and timbre. Third, we train OmniCustom on our constructed large-scale, high-quality audio-visual human dataset. Extensive experiments demonstrate that OmniCustom outperforms existing methods in generating audio-video content with consistent identity and timbre fidelity. Project page: https://omnicustom-project.github.io/page/.

Method: I2D recursively synthesizes speech using the model’s own outputs as references, creating a distributional shift. Higher-quality models degrade more slowly across iterations, and this differential degradation is measured to amplify performance gaps.

Result: I2D significantly improves discriminability and alignment with human judgments, increasing system-level SRCC from 0.118 to 0.464 for UTMOSv2. Experiments on 11 models across Chinese, English, and emotion datasets demonstrate reliable automated evaluation.

Conclusion: I2D provides a more reliable automated evaluation framework for zero-shot TTS by exploiting iterative synthesis to reveal model robustness and amplify performance differences.

Abstract: Reliable evaluation of modern zero-shot text-to-speech (TTS) models remains challenging. Subjective tests are costly and hard to reproduce, while objective metrics often saturate, failing to distinguish SOTA systems. To address this, we propose Iterate to Differentiate (I2D), an evaluation framework that recursively synthesizes speech using the model’s own outputs as references. Higher-quality models exhibit greater resilience to the distributional shift induced by iterative synthesis, resulting in slower performance degradation. I2D exploits this differential degradation to amplify performance gaps and reveal robustness. By aggregating objective metrics across iterations, I2D improves discriminability and alignment with human judgments, increasing system-level SRCC from 0.118 to 0.464 for UTMOSv2. Experiments on 11 models across Chinese, English, and emotion datasets demonstrate that I2D enables more reliable automated evaluation for zero-shot TTS.

Method: Uses Sub-center ArcFace to estimate sample difficulty online via dominant sub-center cosine similarity. Samples are ranked into easy, medium, and hard tiers using running batch statistics without auxiliary annotations. Learnable weights guide the model through three learning stages.

Result: Achieves 86.8% and 60.0% EER reduction over Sub-center ArcFace baseline on VoxCeleb1-O and SITW datasets. Represents the largest-scale speaker verification system trained to date.

Conclusion: Curry establishes a new paradigm for robust speaker verification on imperfect large-scale data by adaptively handling sample difficulty and reducing the impact of noisy gradients.

Abstract: Speaker verification at large scale remains an open challenge as fixed-margin losses treat all samples equally regardless of quality. We hypothesize that mislabeled or degraded samples introduce noisy gradients that disrupt compact speaker manifolds. We propose Curry (CURriculum Ranking), an adaptive loss that estimates sample difficulty online via Sub-center ArcFace: confidence scores from dominant sub-center cosine similarity rank samples into easy, medium, and hard tiers using running batch statistics, without auxiliary annotations. Learnable weights guide the model from stable identity foundations through manifold refinement to boundary sharpening. To our knowledge, this is the largest-scale speaker verification system trained to date. Evaluated on VoxCeleb1-O, and SITW, Curry reduces EER by 86.8% and 60.0% over the Sub-center ArcFace baseline, establishing a new paradigm for robust speaker verification on imperfect large-scale data.

Method: Speech-level Audio Spectrogram Transformer (AST) using long-duration speech instead of short segments, with novel interpretation method to reveal prediction-relevant acoustic features

Result: Proposed model outperforms segment-level AST, identifies reduced loudness and F0 as relevant depression signals aligning with clinical findings

Conclusion: Interpretable speech foundation model enhances clinical applicability of depression detection tools through responsible AI approach

Abstract: Speech-based depression detection tools could aid early screening. Here, we propose an interpretable speech foundation model approach to enhance the clinical applicability of such tools. We introduce a speech-level Audio Spectrogram Transformer (AST) to detect depression using long-duration speech instead of short segments, along with a novel interpretation method that reveals prediction-relevant acoustic features for clinician interpretation. Our experiments show the proposed model outperforms a segment-level AST, highlighting the impact of segment-level labelling noise and the advantage of leveraging longer speech duration for more reliable depression detection. Through interpretation, we observe our model identifies reduced loudness and F0 as relevant depression signals, aligning with documented clinical findings. This interpretability supports a responsible AI approach for speech-based depression detection, rendering such tools more clinically applicable.

Method: DELULU uses speaker-informed structure in pseudo-label generation by leveraging frame-level embeddings from ReDimNet (a state-of-the-art speaker verification model) to guide k-means clustering during pre-training. This introduces speaker-discriminative inductive bias that aligns representation learning with speaker identity while maintaining self-supervised learning principles.

Result: DELULU significantly outperforms prior SSL models across speaker-centric tasks, achieving up to 62% relative improvement in EER for speaker verification and consistent gains on zero-shot profiling tasks (gender, age, accent, speaker counting). Notably, it surpasses even its teacher model on zero-shot evaluations.

Conclusion: DELULU serves as a strong universal encoder for speaker-aware speech processing, enabling superior performance without task-specific fine-tuning. It successfully bridges the gap between content-driven and speaker-discriminative speech representation learning.

Abstract: Self-supervised speech models have achieved remarkable success on content-driven tasks, yet they remain limited in capturing speaker-discriminative features critical for verification, diarization, and profiling applications. We introduce \textsc{DELULU}, a speaker-aware self-trained foundational model that addresses this limitation by incorporating speaker-informed structure into pseudo-label generation. DELULU leverages frame-level embeddings from ReDimNet, a state-of-the-art speaker verification model, to guide k-means clustering during pre-training, introducing a speaker-discriminative inductive bias that aligns representation learning with speaker identity. DELULU significantly outperforms prior SSL models across a range of speaker-centric tasks, achieving up to \textbf{62% relative improvement} in equal error rate (EER) for speaker verification and consistent gains on zero-shot profiling tasks including gender, age, accent, and speaker counting; notably surpassing even its teacher model on zero-shot evaluations. Our findings demonstrate that \textbf{DELULU is a strong universal encoder for speaker-aware speech processing}, enabling superior performance without task-specific fine-tuning.

Method: Combines task-relevant symbolic rules with mechanistic interpretability to create algorithmic pass/fail scores that reveal exactly where models generalize versus exploit patterns. Demonstrated on NL-to-SQL by training identical architectures with/without schema information.

Result: Standard evaluation shows memorization model achieves 94% field-name accuracy on unseen data, falsely suggesting competence. Symbolic-mechanistic evaluation reveals this model violates core schema generalization rules, a failure invisible to accuracy metrics.

Conclusion: Mechanism-aware evaluation is necessary to properly assess model generalization capabilities beyond superficial accuracy metrics, revealing whether models truly understand tasks or exploit shortcuts.

Abstract: Accuracy-based evaluation cannot reliably distinguish genuine generalization from shortcuts like memorization, leakage, or brittle heuristics, especially in small-data regimes. In this position paper, we argue for mechanism-aware evaluation that combines task-relevant symbolic rules with mechanistic interpretability, yielding algorithmic pass/fail scores that show exactly where models generalize versus exploit patterns. We demonstrate this on NL-to-SQL by training two identical architectures under different conditions: one without schema information (forcing memorization), one with schema (enabling grounding). Standard evaluation shows the memorization model achieves 94% field-name accuracy on unseen data, falsely suggesting competence. Our symbolic-mechanistic evaluation reveals this model violates core schema generalization rules, a failure invisible to accuracy metrics.

Method: ITPO leverages an implicit process reward model to derive fine-grained, turn-wise process rewards from sparse outcome signals. These turn-level signals are more robust than volatile token-level rewards and use normalization for training stability.

Result: ITPO combined with PPO, GRPO, or RLOO consistently achieves improved convergence over existing baselines across three multi-turn collaborative tasks: math tutoring, document writing, and medical recommendation.

Conclusion: ITPO effectively addresses reward sparsity in multi-turn collaboration by inferring semantically aligned turn-wise preferences, enabling more stable and efficient RL optimization for interactive AI services.

Abstract: Multi-turn human-AI collaboration is fundamental to deploying interactive services such as adaptive tutoring, conversational recommendation, and professional consultation. However, optimizing these interactions via reinforcement learning is hindered by the sparsity of verifiable intermediate rewards and the high stochasticity of user responses. To address these challenges, we introduce Implicit Turn-wise Policy Optimization (ITPO). ITPO leverages an implicit process reward model to derive fine-grained, turn-wise process rewards from sparse outcome signals. Unlike volatile token-level rewards, these turn-level signals exhibit superior robustness and may utilize a normalization mechanism to further enhance training stability. We evaluate ITPO across three representative multi-turn collaborative tasks: math tutoring, document writing, and medical recommendation. Empirical results demonstrate that ITPO, when combined with PPO, GRPO, or RLOO, consistently achieves improved convergence than existing baselines. Elaborate trajectory analysis confirms that ITPO infers turn-wise preferences that are semantically aligned with human judgment. Code is publicly available at https://github.com/Graph-COM/ITPO.

Method: The paper conducts exhaustive algorithmic and complexity analysis of upper entropy computation problems. It shows the problem has a strongly polynomial solution and proposes significant improvements over past algorithms, particularly for 2-monotone lower probabilities and their specific cases.

Result: The research demonstrates that upper entropy computation has a strongly polynomial solution and provides improved algorithms that outperform previous approaches for 2-monotone lower probabilities and related cases.

Conclusion: The paper advances the computational understanding of upper entropies in uncertainty quantification, providing efficient algorithmic solutions that can enhance practical applications in model selection, active learning, and OOD detection.

Abstract: Uncertainty quantification is a key aspect in many tasks such as model selection/regularization, or quantifying prediction uncertainties to perform active learning or OOD detection. Within credal approaches that consider modeling uncertainty as probability sets, upper entropy plays a central role as an uncertainty measure. This paper is devoted to the computational aspect of upper entropies, providing an exhaustive algorithmic and complexity analysis of the problem. In particular, we show that the problem has a strongly polynomial solution, and propose many significant improvements over past algorithms proposed for 2-monotone lower probabilities and their specific cases.

Method: Introduces Synthetic Mixed Training that combines synthetic QAs and synthetic documents to leverage complementary training signals. Also proposes Focal Rewriting for synthetic document generation that conditions document generation on specific questions to improve diversity.

Result: Achieves 2.6% relative gain over RAG on QuaLITY benchmark, with final recipe achieving 4.4% relative gain. Outperforms RAG in 5 of 6 settings across models and benchmarks (QuaLITY, LongHealth, FinanceBench), with 9.1% gain when combined with RAG.

Conclusion: Synthetic Mixed Training with Focal Rewriting enables log-linear improvements with increasing synthetic data volume and generator strength, allowing models to surpass RAG performance in data-constrained domains.

Abstract: Synthetic data augmentation helps language models learn new knowledge in data-constrained domains. However, naively scaling existing synthetic data methods by training on more synthetic tokens or using stronger generators yields diminishing returns below the performance of RAG. To break the RAG ceiling, we introduce Synthetic Mixed Training, which combines synthetic QAs and synthetic documents. This leverages their complementary training signals, and enables log-linear improvements as both synthetic data volume and generator strength increase. This allows the model to outperform RAG by a 2.6% relative gain on QuaLITY, a long-document reading comprehension benchmark. In addition, we introduce Focal Rewriting, a simple technique for synthetic document generation that explicitly conditions document generation on specific questions, improving the diversity of synthetic documents and yielding a steeper log-linear scaling curve. On QuaLITY, our final recipe trains a Llama 8B model that outperforms RAG by 4.4% relatively. Across models and benchmarks (QuaLITY, LongHealth, FinanceBench), our training enables models to beat RAG in five of six settings, outperforms by 2.6%, and achieves a 9.1% gain when combined with RAG.

Method: Proposes Preference-based Constrained RL (PbCRL) with: 1) Dead zone mechanism in preference modeling to encourage heavy-tailed cost distributions, 2) Signal-to-Noise Ratio (SNR) loss to encourage exploration via cost variances, 3) Two-stage training strategy to reduce online labeling burden while adaptively enhancing constraint satisfaction.

Result: Empirical results show PbCRL achieves superior alignment with true safety requirements and outperforms state-of-the-art baselines in terms of both safety and reward performance.

Conclusion: PbCRL provides an effective approach for constraint inference in Safe RL, exploring a promising direction for safety-critical applications by better capturing safety preferences through improved preference modeling.

Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to explicitly specify. Existing works on constraint inference rely on restrictive assumptions or extensive expert demonstrations, which is not realistic in many real-world applications. How to cheaply and reliably learn these constraints is the major challenge we focus on in this study. While inferring constraints from human preferences offers a data-efficient alternative, we identify the popular Bradley-Terry (BT) models fail to capture the asymmetric, heavy-tailed nature of safety costs, resulting in risk underestimation. It is still rare in the literature to understand the impacts of BT models on the downstream policy learning. To address the above knowledge gaps, we propose a novel approach namely Preference-based Constrained Reinforcement Learning (PbCRL). We introduce a novel dead zone mechanism into preference modeling and theoretically prove that it encourages heavy-tailed cost distributions, thereby achieving better constraint alignment. Additionally, we incorporate a Signal-to-Noise Ratio (SNR) loss to encourage exploration by cost variances, which is found to benefit policy learning. Further, two-stage training strategy are deployed to lower online labeling burdens while adaptively enhancing constraint satisfaction. Empirical results demonstrate that PbCRL achieves superior alignment with true safety requirements and outperforms the state-of-the-art baselines in terms of safety and reward. Our work explores a promising and effective way for constraint inference in Safe RL, which has great potential in a range of safety-critical applications.

Method: Two-part approach: 1) Host-side: profiling-in-the-loop evolutionary search to discover optimal tiling and data-movement configurations from hardware feedback. 2) Kernel-side: mining optimization motifs by systematically de-optimizing optimized kernels to create “bad-to-good” trajectories, distilling these into retrievable experience bank for guided rewriting. Alternates host tuning and kernel rewriting in closed loop.

Result: On 127 real AscendC operators benchmark, achieves 1.19x geometric-mean speedup over open-source baseline, with 49.61% of operators outperforming their references, beating strong agent and search baselines.

Conclusion: AscendOptimizer successfully bootstraps missing expertise for AscendC optimization through execution-as-experience approach, combining evolutionary search and optimization pattern mining to overcome knowledge bottlenecks in Huawei NPU ecosystem.

Abstract: AscendC (Ascend C) operator optimization on Huawei Ascend neural processing units (NPUs) faces a two-fold knowledge bottleneck: unlike the CUDA ecosystem, there are few public reference implementations to learn from, and performance hinges on a coupled two-part artifact - a host-side tiling program that orchestrates data movement and a kernel program that schedules and pipelines instructions. We present AscendOptimizer, an episodic agent that bootstraps this missing expertise by turning execution into experience. On the host side, AscendOptimizer performs profiling-in-the-loop evolutionary search to discover valid and high-performing tiling and data-movement configurations directly from hardware feedback. On the kernel side, it mines transferable optimization motifs by rewinding optimized kernels - systematically de-optimizing them to synthesize instructive “bad-to-good” trajectories - and distills these motifs into a retrievable experience bank for guided rewriting. By alternating host tuning and kernel rewriting in a closed loop, AscendOptimizer steadily expands feasibility and pushes latency down. On a benchmark of 127 real AscendC operators, AscendOptimizer achieves a 1.19x geometric-mean speedup over the open-source baseline, with 49.61% of operators outperforming their references, outperforming strong agent and search baselines.

Method: Three-stage modular pipeline: (1) aggregates article-level scores with uncertainty-aware weights, (2) fills gaps with causal projection rules, (3) applies causal smoothing. Uses label-free evaluation framework with signal stability diagnostics and counterfactual tests.

Result: Key finding: consistent three-week lead-lag pattern between reconstructed sentiment and stock prices across all pipeline configurations and aggregation regimes, more informative than single correlation coefficients.

Conclusion: Stable, deployable sentiment indicators require careful reconstruction pipelines, not just better classifiers, with causal signal reconstruction providing robust temporal series from sparse news data.

Abstract: Sentiment signals derived from sparse news are commonly used in financial analysis and technology monitoring, yet transforming raw article-level observations into reliable temporal series remains a largely unsolved engineering problem. Rather than treating this as a classification challenge, we propose to frame it as a causal signal reconstruction problem: given probabilistic sentiment outputs from a fixed classifier, recover a stable latent sentiment series that is robust to the structural pathologies of news data such as sparsity, redundancy, and classifier uncertainty. We present a modular three-stage pipeline that (i) aggregates article-level scores onto a regular temporal grid with uncertainty-aware and redundancy-aware weights, (ii) fills coverage gaps through strictly causal projection rules, and (iii) applies causal smoothing to reduce residual noise. Because ground-truth longitudinal sentiment labels are typically unavailable, we introduce a label-free evaluation framework based on signal stability diagnostics, information preservation lag proxies, and counterfactual tests for causality compliance and redundancy robustness. As a secondary external check, we evaluate the consistency of reconstructed signals against stock-price data for a multi-firm dataset of AI-related news titles (November 2024 to February 2026). The key empirical finding is a three-week lead lag pattern between reconstructed sentiment and price that persists across all tested pipeline configurations and aggregation regimes, a structural regularity more informative than any single correlation coefficient. Overall, the results support the view that stable, deployable sentiment indicators require careful reconstruction, not only better classifiers.

Method: StateLinFormer uses linear-attention with a stateful memory mechanism that preserves recurrent memory states across consecutive training segments instead of reinitializing them at each batch boundary. This training paradigm approximates learning on infinitely long sequences.

Result: Experiments in MAZE and ProcTHOR environments show StateLinFormer significantly outperforms stateless linear-attention counterparts and standard Transformer baselines with fixed context windows. As interaction length increases, persistent stateful training substantially improves context-dependent adaptation.

Conclusion: StateLinFormer’s stateful memory mechanism enhances long-horizon memory retention and improves In-Context Learning capabilities for navigation tasks, addressing limitations of both modular systems and fixed-context Transformer models.

Abstract: Effective navigation intelligence relies on long-term memory to support both immediate generalization and sustained adaptation. However, existing approaches face a dilemma: modular systems rely on explicit mapping but lack flexibility, while Transformer-based end-to-end models are constrained by fixed context windows, limiting persistent memory across extended interactions. We introduce StateLinFormer, a linear-attention navigation model trained with a stateful memory mechanism that preserves recurrent memory states across consecutive training segments instead of reinitializing them at each batch boundary. This training paradigm effectively approximates learning on infinitely long sequences, enabling the model to achieve long-horizon memory retention. Experiments across both MAZE and ProcTHOR environments demonstrate that StateLinFormer significantly outperforms its stateless linear-attention counterpart and standard Transformer baselines with fixed context windows. Notably, as interaction length increases, persistent stateful training substantially improves context-dependent adaptation, suggesting an enhancement in the model’s In-Context Learning (ICL) capabilities for navigation tasks.

Method: Proposes Dual-Criterion Curriculum Learning (DCCL) that combines: 1) loss-based criterion (training-based evidence), and 2) density-based criterion learned in data representation space (considers data sparseness amplifies learning difficulty). Evaluated on multivariate time-series forecasting benchmarks using One-Pass and Baby-Steps training schedules.

Result: Empirical results show density-based and hybrid dual-criterion curricula outperform loss-only baselines and standard non-CL training in time-series forecasting settings.

Conclusion: DCCL framework effectively combines complementary difficulty assessment criteria for curriculum learning, demonstrating improved performance in time-series forecasting tasks.

Abstract: Curriculum Learning (CL) is a meta-learning paradigm that trains a model by feeding the data instances incrementally according to a schedule, which is based on difficulty progression. Defining meaningful difficulty assessment measures is crucial and most usually the main bottleneck for effective learning, while also in many cases the employed heuristics are only application-specific. In this work, we propose the Dual-Criterion Curriculum Learning (DCCL) framework that combines two views of assessing instance-wise difficulty: a loss-based criterion is complemented by a density-based criterion learned in the data representation space. Essentially, DCCL calibrates training-based evidence (loss) under the consideration that data sparseness amplifies the learning difficulty. As a testbed, we choose the time-series forecasting task. We evaluate our framework on multivariate time-series benchmarks under standard One-Pass and Baby-Steps training schedules. Empirical results show the interest of density-based and hybrid dual-criterion curricula over loss-only baselines and standard non-CL training in this setting.

Method: Proposes PoiCGAN, a targeted poisoning attack based on feature-label collaborative perturbation. Modifies inputs of discriminator and generator in Conditional GAN to influence training and generate an ideal poison generator that produces specific poisoned samples with automatic label flipping.

Result: Achieves 83.97% higher attack success rate than baselines with less than 8.87% reduction in main task accuracy. Poisoned samples and malicious models show high stealthiness across various datasets.

Conclusion: PoiCGAN effectively bypasses model performance tests and anomaly detection defenses while maintaining attack effectiveness, making it a practical threat to federated learning systems in industrial image classification.

Abstract: Federated Learning (FL), as a popular distributed learning paradigm, has shown outstanding performance in improving computational efficiency and protecting data privacy, and is widely applied in industrial image classification. However, due to its distributed nature, FL is vulnerable to threats from malicious clients, with poisoning attacks being a common threat. A major limitation of existing poisoning attack methods is their difficulty in bypassing model performance tests and defense mechanisms based on model anomaly detection. This often results in the detection and removal of poisoned models, which undermines their practical utility. To ensure both the performance of industrial image classification and attacks, we propose a targeted poisoning attack, PoiCGAN, based on feature-label collaborative perturbation. Our method modifies the inputs of the discriminator and generator in the Conditional Generative Adversarial Network (CGAN) to influence the training process, generating an ideal poison generator. This generator not only produces specific poisoned samples but also automatically performs label flipping. Experiments across various datasets show that our method achieves an attack success rate 83.97% higher than baseline methods, with a less than 8.87% reduction in the main task’s accuracy. Moreover, the poisoned samples and malicious models exhibit high stealthiness.

Method: Proposes adaptive mixed precision quantization that analyzes layer-wise contribution and infers how different quantization types behave across target hardware platforms to assign the most suitable quantization type to each layer.

Result: The approach unlocks new configuration designs that uniform quantization cannot achieve, expanding the solution space for efficient LLM deployment on resource-constrained devices while balancing memory, latency, and accuracy.

Conclusion: The adaptive mixed precision quantization mechanism enables more efficient deployment of LLMs on edge devices by jointly respecting layer importance and overall performance trade-offs, addressing the limitations of uniform quantization approaches.

Abstract: Today, large language models have demonstrated their strengths in various tasks ranging from reasoning, code generation, and complex problem solving. However, this advancement comes with a high computational cost and memory requirements, making it challenging to deploy these models on edge devices to ensure real-time responses and data privacy. Quantization is one common approach to reducing memory use, but most methods apply it uniformly across all layers. This does not account for the fact that different layers may respond differently to reduced precision. Importantly, memory consumption and computational throughput are not necessarily aligned, further complicating deployment decisions. This paper proposes an adaptive mixed precision quantization mechanism that balances memory, latency, and accuracy in edge deployment under user-defined priorities. This is achieved by analyzing the layer-wise contribution and by inferring how different quantization types behave across the target hardware platform in order to assign the most suitable quantization type to each layer. This integration ensures that layer importance and the overall performance trade-offs are jointly respected in this design. Our work unlocks new configuration designs that uniform quantization cannot achieve, expanding the solution space to efficiently deploy the LLMs on resource-constrained devices.

Method: Applied Gram-Schmidt decomposition to residual-stream activations to reveal dual-modulation mechanism. Used targeted vector ablation to establish causal relationships and analyzed geometric evolution across tasks from simple mapping to complex primality testing.

Result: Found that erasing divergence component collapses parity classification from 100% to chance levels (38.57%). Uncovered three-phase layer-wise geometric dynamics and showed social pressure prompts cause insufficient divergence leading to manifold entanglement explaining sycophancy and hallucination.

Conclusion: Discrete logic emergence in LLMs requires irreducible topological deformation, revising linear-isometric presumption. The cost of logical reasoning is topological distortion through dual-modulation mechanism.

Abstract: Large language models (LLMs) generalize smoothly across continuous semantic spaces, yet strict logical reasoning demands the formation of discrete decision boundaries. Prevailing theories relying on linear isometric projections fail to resolve this fundamental tension. In this work, we argue that task context operates as a non-isometric dynamical operator that enforces a necessary “topological distortion.” By applying Gram-Schmidt decomposition to residual-stream activations , we reveal a dual-modulation mechanism driving this process: a class-agnostic topological preservation that anchors global structure to prevent semantic collapse, and a specific algebraic divergence that directionally tears apart cross-class concepts to forge logical boundaries. We validate this geometric evolution across a gradient of tasks, from simple mapping to complex primality testing. Crucially, targeted specific vector ablation establishes a strict causal binding between this topology and model function: algebraically erasing the divergence component collapses parity classification accuracy from 100% to chance levels (38.57%). Furthermore, we uncover a three-phase layer-wise geometric dynamic and demonstrate that under social pressure prompts, models fail to generate sufficient divergence. This results in a “manifold entanglement” that geometrically explains sycophancy and hallucination. Ultimately, our findings revise the linear-isometric presumption, demonstrating that the emergence of discrete logic in LLMs is purchased at an irreducible cost of topological deformation.

Method: Proposes a Residual Attention Physics-Informed Neural Network (RA-PINN) framework integrating unified five-field operator formulation with residual-connected feature propagation and attention-guided channel modulation to capture localized coupling structures and steep gradients.

Result: RA-PINN achieves superior accuracy with lowest MSE, RMSE, and relative L2 errors across four energy-relevant benchmarks compared to Pure-MLP, LSTM-PINN, and pLSTM-PINN, maintaining high structural fidelity in interface-dominated and variable-coefficient settings.

Conclusion: RA-PINN establishes as a robust and accurate computational framework for high-fidelity modeling and optimization of complex electrothermal multiphysics in sustainable energy applications.

Abstract: Efficient thermal management and precise field prediction are critical for the design of advanced energy systems, including electrohydrodynamic transport, microfluidic energy harvesters, and electrically driven thermal regulators. However, the steady-state simulation of these electrothermal coupled multiphysics systems remains challenging for physics-informed neural computation due to strong nonlinear field coupling, temperature-dependent coefficient variability, and complex interface dynamics. This study proposes a Residual Attention Physics-Informed Neural Network (RA-PINN) framework for the unified solution of coupled velocity, pressure, electric-potential, and temperature fields. By integrating a unified five-field operator formulation with residual-connected feature propagation and attention-guided channel modulation, the proposed architecture effectively captures localized coupling structures and steep gradients. We evaluate RA-PINN across four representative energy-relevant benchmarks: constant-coefficient coupling, indirect pressure-gauge constraints, temperature-dependent transport, and oblique-interface consistency. Comparative analysis against Pure-MLP, LSTM-PINN, and pLSTM-PINN demonstrates that RA-PINN achieves superior accuracy, yielding the lowest MSE, RMSE, and relative $L_2$ errors across all scenarios. Notably, RA-PINN maintains high structural fidelity in interface-dominated and variable-coefficient settings where conventional PINN backbones often fail. These results establish RA-PINN as a robust and accurate computational framework for the high-fidelity modeling and optimization of complex electrothermal multiphysics in sustainable energy applications.

Method: Three synergistic innovations: (1) Episodic Pattern Memory Network (EPMN) for abstracting diagnostic patterns from historical resolutions with confidence-calibrated retrieval, (2) meta-cognitive controller for dynamic routing between intuitive and analytical pathways based on problem familiarity, and (3) KubeLLM, a locally-deployable 8B model enhanced through domain-specific post-training on a 7,000-sample Kubernetes Fault Resolution Dataset.

Result: Evaluation on 1,873 real-world scenarios shows MetaKube transforms Qwen3-8B from 50.9 to 90.5 points, approaching GPT-4.1 performance while ensuring data privacy. EPMN contributes 15.3% improvement through experiential learning, with continuous learning experiments showing progressive gains as the system accumulates operational knowledge.

Conclusion: MetaKube demonstrates that experience-aware LLM frameworks can significantly improve diagnostic performance in specialized domains like Kubernetes by learning from operational history, with implications for other domain-specific AI applications requiring privacy and continuous improvement.

Abstract: Existing LLM-based Kubernetes diagnostic systems cannot learn from operational experience, operating on static knowledge bases without improving from past resolutions. We present MetaKube, an experience-aware LLM framework through three synergistic innovations: (1) an Episodic Pattern Memory Network (EPMN) that abstracts diagnostic patterns from historical resolutions and provides confidence-calibrated retrieval for both rapid pattern matching and guided causal exploration, (2) a meta-cognitive controller that dynamically routes between intuitive and analytical pathways based on problem familiarity, optimizing the trade-off between speed and depth, and (3) KubeLLM, a locally-deployable 8B model enhanced through domain-specific post-training on our 7,000-sample Kubernetes Fault Resolution Dataset. Evaluation on 1,873 real-world scenarios demonstrates MetaKube transforms Qwen3-8B from 50.9 to 90.5 points, approaching GPT-4.1 performance while ensuring complete data privacy. EPMN contributes 15.3% improvement through experiential learning, with continuous learning experiments showing progressive gains as the system accumulates operational knowledge. The source code and related resources are available at https://github.com/MetaKube-LLM-for-Kubernetes-Diagnosis/MetaKube.

Method: The authors introduce iSLEEPS dataset (publicly released), use SE-ResNet plus bidirectional LSTM for single-channel EEG sleep staging, employ Grad-CAM for attention visualization, and conduct statistical/computational analyses comparing healthy vs. ischemic stroke cohorts.

Result: Cross-domain performance between healthy and diseased subjects is poor; attention visualizations show models focus on physiologically uninformative EEG regions in patient data; significant sleep architecture differences exist between healthy and stroke cohorts.

Conclusion: Automated sleep staging models need subject-aware or disease-specific approaches with clinical validation before deployment, as models trained on healthy data don’t generalize to clinical populations with disrupted sleep architecture.

Abstract: Accurate sleep staging is essential for diagnosing OSA and hypopnea in stroke patients. Although PSG is reliable, it is costly, labor-intensive, and manually scored. While deep learning enables automated EEG-based sleep staging in healthy subjects, our analysis shows poor generalization to clinical populations with disrupted sleep. Using Grad-CAM interpretations, we systematically demonstrate this limitation. We introduce iSLEEPS, a newly clinically annotated ischemic stroke dataset (to be publicly released), and evaluate a SE-ResNet plus bidirectional LSTM model for single-channel EEG sleep staging. As expected, cross-domain performance between healthy and diseased subjects is poor. Attention visualizations, supported by clinical expert feedback, show the model focuses on physiologically uninformative EEG regions in patient data. Statistical and computational analyses further confirm significant sleep architecture differences between healthy and ischemic stroke cohorts, highlighting the need for subject-aware or disease-specific models with clinical validation before deployment. A summary of the paper and the code is available at https://himalayansaswatabose.github.io/iSLEEPS_Explainability.github.io/

Method: Derives hypergradient of leader’s objective using Boltzmann covariance trick to enable efficient hypergradient estimation solely from interaction samples, even with high-dimensional leader decision space; first method enabling hypergradient-based optimization for 2-player Markov games in decentralized settings

Result: Experiments demonstrate effectiveness in both discrete and continuous state tasks, highlighting impact of hypergradient updates

Conclusion: Proposed method enables efficient hypergradient estimation for decentralized bi-level RL problems, overcoming limitations of prior methods that require extensive data or have complexity scaling issues with high-dimensional decision spaces

Abstract: Many strategic decision-making problems, such as environment design for warehouse robots, can be naturally formulated as bi-level reinforcement learning (RL), where a leader agent optimizes its objective while a follower solves a Markov decision process (MDP) conditioned on the leader’s decisions. In many situations, a fundamental challenge arises when the leader cannot intervene in the follower’s optimization process; it can only observe the optimization outcome. We address this decentralized setting by deriving the hypergradient of the leader’s objective, i.e., the gradient of the leader’s strategy that accounts for changes in the follower’s optimal policy. Unlike prior hypergradient-based methods that require extensive data for repeated state visits or rely on gradient estimators whose complexity can increase substantially with the high-dimensional leader’s decision space, we leverage the Boltzmann covariance trick to derive an alternative hypergradient formulation. This enables efficient hypergradient estimation solely from interaction samples, even when the leader’s decision space is high-dimensional. Additionally, to our knowledge, this is the first method that enables hypergradient-based optimization for 2-player Markov games in decentralized settings. Experiments highlight the impact of hypergradient updates and demonstrate our method’s effectiveness in both discrete and continuous state tasks.

Method: Proposes LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a spatial method that considers both payment and receipt transactions. It extends a lightweight MVGNN module for two-way message passing between nodes in transaction graphs, and incorporates a line graph view of the original transaction graph to enhance transaction information propagation.

Result: Experiments on two real-world datasets (Ethereum phishing transaction network and financial payment transaction dataset from industry partner) show LineMVGNN outperforms state-of-the-art methods for money laundering detection.

Conclusion: LineMVGNN effectively detects money laundering through line-graph-assisted multi-view graph learning, with discussions on scalability, adversarial robustness, and regulatory considerations.

Abstract: Anti-money laundering (AML) systems are important for protecting the global economy. However, conventional rule-based methods rely on domain knowledge, leading to suboptimal accuracy and a lack of scalability. Graph neural networks (GNNs) for digraphs (directed graphs) can be applied to transaction graphs and capture suspicious transactions or accounts. However, most spectral GNNs do not naturally support multi-dimensional edge features, lack interpretability due to edge modifications, and have limited scalability owing to their spectral nature. Conversely, most spatial methods may not capture the money flow well. Therefore, in this work, we propose LineMVGNN (Line-Graph-Assisted Multi-View Graph Neural Network), a novel spatial method that considers payment and receipt transactions. Specifically, the LineMVGNN model extends a lightweight MVGNN module, which performs two-way message passing between nodes in a transaction graph. Additionally, LineMVGNN incorporates a line graph view of the original transaction graph to enhance the propagation of transaction information. We conduct experiments on two real-world account-based transaction datasets: the Ethereum phishing transaction network dataset and a financial payment transaction dataset from one of our industry partners. The results show that our proposed method outperforms state-of-the-art methods, reflecting the effectiveness of money laundering detection with line-graph-assisted multi-view graph learning. We also discuss scalability, adversarial robustness, and regulatory considerations of our proposed method.

Method: Proposes a theory of LLM information susceptibility with a multi-variable utility-function framework, validates empirically across diverse domains and model scales, and applies statistical physics tools.

Result: Fixed LLM intervention doesn’t increase performance susceptibility with sufficient computational resources, but nested co-scaling architectures can exceed susceptibility bounds and open new response channels.

Conclusion: The theory clarifies when LLM intervention helps, shows statistical physics tools can predict AI system design constraints, and suggests nested architectures may be necessary for open-ended agentic self-improvement.

Abstract: Large language models (LLMs) are increasingly deployed as optimization modules in agentic systems, yet the fundamental limits of such LLM-mediated improvement remain poorly understood. Here we propose a theory of LLM information susceptibility, centred on the hypothesis that when computational resources are sufficiently large, the intervention of a fixed LLM does not increase the performance susceptibility of a strategy set with respect to budget. We develop a multi-variable utility-function framework that generalizes this hypothesis to architectures with multiple co-varying budget channels, and discuss the conditions under which co-scaling can exceed the susceptibility bound. We validate the theory empirically across structurally diverse domains and model scales spanning an order of magnitude, and show that nested, co-scaling architectures open response channels unavailable to fixed configurations. These results clarify when LLM intervention helps and when it does not, demonstrating that tools from statistical physics can provide predictive constraints for the design of AI systems. If the susceptibility hypothesis holds generally, the theory suggests that nested architectures may be a necessary structural condition for open-ended agentic self-improvement.

Method: Used difference-in-means method to estimate layer-wise steering vectors for five language/library pairs, then added these vectors to model hidden states during generation across three open-weight code LLMs.

Result: Steering vectors substantially increase generation toward target ecosystems under neutral prompts and remain effective even when prompts explicitly request the opposite choice. Steering strength varies by model and target, with common ecosystems easier to induce than rarer alternatives.

Conclusion: Code-style preferences in LLMs are partly represented by compact, steerable structure in activation space, though overly strong interventions can reduce output quality.

Abstract: Code LLMs often default to particular programming languages and libraries under neutral prompts. We investigate whether these preferences are encoded as approximately linear directions in activation space that can be manipulated at inference time. Using a difference-in-means method, we estimate layer-wise steering vectors for five language/library pairs and add them to model hidden states during generation. Across three open-weight code LLMs, these interventions substantially increase generation toward the target ecosystem under neutral prompts and often remain effective even when prompts explicitly request the opposite choice. Steering strength varies by model and target, with common ecosystems easier to induce than rarer alternatives, and overly strong interventions can reduce output quality. Overall, our results suggest that code-style preferences in LLMs are partly represented by compact, steerable structure in activation space.

Method: VPBoost fuses variable projection (enforcing optimality of linear weights) with second-order weak learning strategy for separable models. It uses closed-form solutions for optimal linear weights and operates as a functional trust-region method, leveraging trust-region theory for convergence analysis.

Result: Theoretical analysis shows VPBoost converges to a stationary point under mild conditions and achieves superlinear convergence under stronger assumptions. Experiments on synthetic data, image recognition, and scientific ML benchmarks show improved evaluation metrics compared to gradient-descent-based boosting and competitive performance with industry-standard decision tree boosting.

Conclusion: VPBoost provides a theoretically sound and practical gradient boosting approach for separable smooth approximators, bridging the gap between traditional decision tree boosting and neural network ensembles with provable convergence properties.

Abstract: Gradient boosting, a method of building additive ensembles from weak learners, has established itself as a practical and theoretically-motivated approach to approximate functions, especially using decision tree weak learners. Comparable methods for smooth parametric learners, such as neural networks, remain less developed in both training methodology and theory. To this end, we introduce \texttt{VPBoost} ({\bf V}ariable {\bf P}rojection {\bf Boost}ing), a gradient boosting algorithm for separable smooth approximators, i.e., models with a smooth nonlinear featurizer followed by a final linear mapping. \texttt{VPBoost} fuses variable projection, a training paradigm for separable models that enforces optimality of the linear weights, with a second-order weak learning strategy. The combination of second-order boosting, separable models, and variable projection give rise to a closed-form solution for the optimal linear weights and a natural interpretation of \VPBoost as a functional trust-region method. We thereby leverage trust-region theory to prove \VPBoost converges to a stationary point under mild geometric conditions and, under stronger assumptions, achieves a superlinear convergence rate. Comprehensive numerical experiments on synthetic data, image recognition, and scientific machine learning benchmarks demonstrate that \VPBoost learns an ensemble with improved evaluation metrics in comparison to gradient-descent-based boosting and attains competitive performance relative to an industry-standard decision tree boosting algorithm.

Method: Proposes continuous-time diffusion with bidirectional GRU backbone for temporal dependencies, unified Gaussian diffusion via learnable continuous embeddings for categorical variables, and factorized learnable noise schedule that adapts to per-feature-per-timestep learning difficulties.

Result: Outperforms existing approaches on two ICU datasets in downstream task performance, distribution fidelity, and discriminability, while requiring only 50 sampling steps vs. 1,000 for baselines. Classifier-free guidance enables effective conditional generation for class-imbalanced scenarios.

Conclusion: Continuous-time diffusion framework effectively generates high-quality synthetic EHR time-series data with mixed features, offering superior performance and efficiency over discrete-time methods.

Abstract: Electronic health records (EHRs) are invaluable for clinical research, yet privacy concerns severely restrict data sharing. Synthetic data generation offers a promising solution, but EHRs present unique challenges: they contain both numerical and categorical features that evolve over time. While diffusion models have demonstrated strong performance in EHR synthesis, existing approaches predominantly rely on discrete-time formulations, which suffer from finite-step approximation errors and coupled training-sampling step counts. We propose a continuous-time diffusion framework for generating mixed-type time-series EHRs with three contributions: (1) continuous-time diffusion with a bidirectional gated recurrent unit backbone for capturing temporal dependencies, (2) unified Gaussian diffusion via learnable continuous embeddings for categorical variables, enabling joint cross-feature modeling, and (3) a factorized learnable noise schedule that adapts to per-feature-per-timestep learning difficulties. Experiments on two large-scale intensive care unit datasets demonstrate that our method outperforms existing approaches in downstream task performance, distribution fidelity, and discriminability, while requiring only 50 sampling steps compared to 1,000 for baseline methods. Classifier-free guidance further enables effective conditional generation for class-imbalanced clinical scenarios.

Method: Proposes Behavior-Explainable RL (BXRL) formulation with behavior measures as functions from policies to real numbers. Defines contrastive behaviors to reduce preference questions to behavior measurement. Adapts existing XRL methods for behavior explanation and provides a JAX port of HighwayEnv for behavior differentiation.

Result: Establishes formal framework for behavior explanation in RL, enabling new query types. Provides tools for defining, measuring, and differentiating behaviors with respect to model parameters through the JAX environment implementation.

Conclusion: BXRL provides a principled approach to behavior explanation in RL, treating behaviors as first-class objects and enabling more comprehensive explainability beyond action- or trajectory-level analysis.

Abstract: A major challenge of Reinforcement Learning is that agents often learn undesired behaviors that seem to defy the reward structure they were given. Explainable Reinforcement Learning (XRL) methods can answer queries such as “explain this specific action”, “explain this specific trajectory”, and “explain the entire policy”. However, XRL lacks a formal definition for behavior as a pattern of actions across many episodes. We provide such a definition, and use it to enable a new query: “Explain this behavior”. We present Behavior-Explainable Reinforcement Learning (BXRL), a new problem formulation that treats behaviors as first-class objects. BXRL defines a behavior measure as any function $m : Π\to \mathbb{R}$, allowing users to precisely express the pattern of actions that they find interesting and measure how strongly the policy exhibits it. We define contrastive behaviors that reduce the question “why does the agent prefer $a$ to $a’$?” to “why is $m(π)$ high?” which can be explored with differentiation. We do not implement an explainability method; we instead analyze three existing methods and propose how they could be adapted to explain behavior. We present a port of the HighwayEnv driving environment to JAX, which provides an interface for defining, measuring, and differentiating behaviors with respect to the model parameters.

Method: Proposes KSTPP with background intensity modeled by spatial Gaussian Process and influence kernel as spatiotemporal GP. Uses separable product kernels and structured grid representations to induce Kronecker-structured covariance matrices for scalability. Develops tensor-product Gauss-Legendre quadrature for efficient likelihood evaluation.

Result: Model achieves scalable training and prediction on large event collections while enabling transparent event-wise relationship discovery. Captures rich interaction patterns including excitation, inhibition, neutrality, and time-varying effects.

Conclusion: KSTPP provides an interpretable yet flexible framework for spatiotemporal event modeling that scales to large datasets while maintaining transparency in relationship discovery.

Abstract: Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges. Classical Poisson and Hawkes processes rely on restrictive parametric assumptions that limit their ability to capture complex interaction patterns, while recent neural point process models increase representational capacity but integrate event information in a black-box manner, hindering interpretable relationship discovery. To address these limitations, we propose a Kronecker-Structured Nonparametric Spatiotemporal Point Process (KSTPP) that enables transparent event-wise relationship discovery while retaining high modeling flexibility. We model the background intensity with a spatial Gaussian process (GP) and the influence kernel as a spatiotemporal GP, allowing rich interaction patterns including excitation, inhibition, neutrality, and time-varying effects. To enable scalable training and prediction, we adopt separable product kernels and represent the GPs on structured grids, inducing Kronecker-structured covariance matrices. Exploiting Kronecker algebra substantially reduces computational cost and allows the model to scale to large event collections. In addition, we develop a tensor-product Gauss-Legendre quadrature scheme to efficiently evaluate intractable likelihood integrals. Extensive experiments demonstrate the effectiveness of our framework.

Method: Proposes Self-Paced Gaussian Curriculum Learning (SPGL) with closed-form update rules for Gaussian context distributions, avoiding costly numerical procedures while maintaining adaptability.

Result: SPGL matches or outperforms existing curriculum methods across contextual RL benchmarks (Point Mass, Lunar Lander, Ball Catching), especially in hidden context scenarios, with more stable context distribution convergence.

Conclusion: SPGL offers a scalable, principled alternative for curriculum generation in challenging continuous and partially observable domains with reduced computational overhead.

Abstract: Curriculum learning improves reinforcement learning (RL) efficiency by sequencing tasks from simple to complex. However, many self-paced curriculum methods rely on computationally expensive inner-loop optimizations, limiting their scalability in high-dimensional context spaces. In this paper, we propose Self-Paced Gaussian Curriculum Learning (SPGL), a novel approach that avoids costly numerical procedures by leveraging a closed-form update rule for Gaussian context distributions. SPGL maintains the sample efficiency and adaptability of traditional self-paced methods while substantially reducing computational overhead. We provide theoretical guarantees on convergence and validate our method across several contextual RL benchmarks, including the Point Mass, Lunar Lander, and Ball Catching environments. Experimental results show that SPGL matches or outperforms existing curriculum methods, especially in hidden context scenarios, and achieves more stable context distribution convergence. Our method offers a scalable, principled alternative for curriculum generation in challenging continuous and partially observable domains.

Method: Combines kernelized Iterative Null-space Projection (INLP) with gated Mixture-of-Experts (MoE) adapter. INLP estimates closed-form projection to remove single/multiple sensitive attributes from LLM representations without trainable parameters. Two-level MoE adapter selectively restores useful signals without reintroducing bias.

Result: Experiments on two public datasets show reduced attribute leakage across multiple protected variables while maintaining competitive recommendation accuracy.

Conclusion: Proposed method effectively mitigates bias in LLM-based recommender systems with lightweight, parameter-free approach that preserves task utility.

Abstract: Large Language Models (LLMs) have introduced new capabilities to recommender systems, enabling dynamic, context-aware, and conversational recommendations. However, LLM-based recommender systems inherit and may amplify social biases embedded in their pre-training data, especially when demographic cues are present. Existing fairness solutions either require extra parameters fine-tuning, or suffer from optimization instability. We propose a lightweight and scalable bias mitigation method that combines a kernelized Iterative Null-space Projection (INLP) with a gated Mixture-of-Experts (MoE) adapter. Our approach estimates a closed-form projection that removes single or multiple sensitive attributes from LLM representations with no additional trainable parameters. To preserve task utility, we introduce a two-level MoE adapter that selectively restores useful signals without reintroducing bias. Experiments on two public datasets show that our method reduces attribute leakage across multiple protected variables while maintaining competitive recommendation accuracy.

Method: First tested Llama3 models on self-written-text recognition tasks, then analyzed the residual stream to identify a vector associated with correct self-authorship judgments. Used causal intervention techniques to manipulate this vector and control the model’s behavior and perception.

Result: Llama3-8b-Instruct can reliably distinguish its own outputs from human ones, unlike the base model. Found a specific vector in the residual stream that activates during correct self-authorship judgments, which is causally related to the model’s ability to perceive and assert self-authorship. This vector can be used to steer the model’s authorship claims and beliefs.

Conclusion: LLMs can develop self-authorship recognition through post-training, and this ability is mediated by specific neural mechanisms that can be identified and controlled, with significant implications for AI safety and model interpretability.

Abstract: It has been reported that LLMs can recognize their own writing. As this has potential implications for AI safety, yet is relatively understudied, we investigate the phenomenon, seeking to establish whether it robustly occurs at the behavioral level, how the observed behavior is achieved, and whether it can be controlled. First, we find that the Llama3-8b-Instruct chat model - but not the base Llama3-8b model - can reliably distinguish its own outputs from those of humans, and present evidence that the chat model is likely using its experience with its own outputs, acquired during post-training, to succeed at the writing recognition task. Second, we identify a vector in the residual stream of the model that is differentially activated when the model makes a correct self-written-text recognition judgment, show that the vector activates in response to information relevant to self-authorship, present evidence that the vector is related to the concept of “self” in the model, and demonstrate that the vector is causally related to the model’s ability to perceive and assert self-authorship. Finally, we show that the vector can be used to control both the model’s behavior and its perception, steering the model to claim or disclaim authorship by applying the vector to the model’s output as it generates it, and steering the model to believe or disbelieve it wrote arbitrary texts by applying the vector to them as the model reads them.

Method: Formulates domain adaptation as stochastic geometric alignment using Bayesian transport operators to redistribute latent probability mass along Wasserstein geodesics, with PAC-Bayesian regularization to constrain posterior complexity.

Result: Demonstrates reduced latent manifold discrepancy, accelerated transport energy decay, improved covariance calibration, and bounded posterior uncertainty evolution compared to deterministic fine-tuning and adversarial baselines.

Conclusion: Uncertainty-aware probabilistic alignment provides a promising paradigm for reliable transfer learning in next-generation deep representation systems by connecting stochastic optimal transport geometry with statistical generalization theory.

Abstract: Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable optimization dynamics, and miscalibrated uncertainty propagation. This paper introduces an uncertainty-aware probabilistic latent transport framework that formulates domain adaptation as a stochastic geometric alignment problem in representation space. A Bayesian transport operator is proposed to redistribute latent probability mass along Wasserstein-type geodesic trajectories, while a PAC-Bayesian regularization mechanism constrains posterior model complexity to mitigate catastrophic overfitting. The proposed formulation yields theoretical guarantees on convergence stability, loss landscape smoothness, and sample efficiency under distributional shift. Empirical analyses demonstrate substantial reduction in latent manifold discrepancy, accelerated transport energy decay, and improved covariance calibration compared with deterministic fine-tuning and adversarial domain adaptation baselines. Furthermore, bounded posterior uncertainty evolution indicates enhanced probabilistic reliability during cross-domain transfer. By establishing a principled connection between stochastic optimal transport geometry and statistical generalization theory, the proposed framework provides new insights into robust adaptation of modern foundation architectures operating in heterogeneous environments. These findings suggest that uncertainty-aware probabilistic alignment constitutes a promising paradigm for reliable transfer learning in next-generation deep representation systems.

Method: Probe effectiveness of different fine-tuning and input sanitization approaches, alone and in combination, to mitigate MSJ attacks while retaining model performance on benign tasks

Result: Incremental mitigation effectiveness for each technique individually, with combined techniques significantly reducing MSJ attack effectiveness while maintaining performance on normal in-context learning and conversational tasks

Conclusion: The combined approach could meaningfully ameliorate MSJ vulnerabilities if incorporated into model safety post-training

Abstract: Many-shot jailbreaking (MSJ) is an adversarial technique that exploits the long context windows of modern LLMs to circumvent model safety training by including in the prompt many examples of a “fake” assistant responding inappropriately before the final request. With enough examples, the model’s in-context learning abilities override its safety training, and it responds as if it were the “fake” assistant. In this work, we probe the effectiveness of different fine-tuning and input sanitization approaches on mitigating MSJ attacks, alone and in combination. We find incremental mitigation effectiveness for each, and show that the combined techniques significantly reduce the effectiveness of MSJ attacks, while retaining model performance in benign in-context learning and conversational tasks. We suggest that our approach could meaningfully ameliorate this vulnerability if incorporated into model safety post-training.

Method: Empirical analysis of ReLU MLPs trained on modular addition, using DFT to extract frequency and phase information from weights, and constructing idealized models with perfect binary square wave input weights and cosine output weights.

Result: ReLU MLPs learn near-binary square wave input weights with intermediate values only near sign-change boundaries, and output weights satisfy phase-sum relation φ_out = φ_a + φ_b. Idealized model achieves 95.5% accuracy even when trained on noisy data that only achieves 0.23% accuracy.

Conclusion: Grokking doesn’t discover correct algorithms but rather sharpens an algorithm substantially encoded during memorization, progressively binarizing input weights and aligning output weights until generalization becomes possible.

Abstract: Grokking-the phenomenon where validation accuracy of neural networks on modular addition of two integers rises long after training data has been memorized-has been characterized in previous works as producing sinusoidal input weight distributions in transformers and multi-layer perceptrons (MLPs). We find empirically that ReLU MLPs in our experimental setting instead learn near-binary square wave input weights, where intermediate-valued weights appear exclusively near sign-change boundaries, alongside output weight distributions whose dominant Fourier phases satisfy a phase-sum relation $φ_{\mathrm{out}} = φ_a + φ_b$; this relation holds even when the model is trained on noisy data and fails to grok. We extract the frequency and phase of each neuron’s weights via DFT and construct an idealized MLP: Input weights are replaced by perfect binary square waves and output weights by cosines, both parametrized by the frequencies, phases, and amplitudes extracted from the dominant Fourier components of the real model weights. This idealized model achieves 95.5% accuracy when the frequencies and phases are extracted from the weights of a model trained on noisy data that itself achieves only 0.23% accuracy. This suggests that grokking does not discover the correct algorithm, but rather sharpens an algorithm substantially encoded during memorization, progressively binarizing the input weights into cleaner square waves and aligning the output weights, until generalization becomes possible.

Method: The approach avoids model self-reports and instead tests strategic deployment of knowledge about internal states. Uses two experimental paradigms to measure: 1) ability to assess and utilize confidence in answering factual/reasoning questions correctly, and 2) ability to anticipate and utilize their own future answers. Also analyzes token probabilities to identify upstream internal signals.

Result: Frontier LLMs since early 2024 show increasingly strong evidence of certain metacognitive abilities. These abilities are limited in resolution, emerge context-dependently, and differ qualitatively from human metacognition. Model differences suggest post-training may develop metacognitive abilities. Token probability analysis reveals upstream internal signals that could support metacognition.

Conclusion: LLMs demonstrate measurable metacognitive abilities, though different from humans. The methodology provides a foundation for quantitative evaluation of LLM self-awareness, with implications for AI safety and policy.

Abstract: The possibility of LLM self-awareness and even sentience is gaining increasing public attention and has major safety and policy implications, but the science of measuring them is still in a nascent state. Here we introduce a novel methodology for quantitatively evaluating metacognitive abilities in LLMs. Taking inspiration from research on metacognition in nonhuman animals, our approach eschews model self-reports and instead tests to what degree models can strategically deploy knowledge of internal states. Using two experimental paradigms, we demonstrate that frontier LLMs introduced since early 2024 show increasingly strong evidence of certain metacognitive abilities, specifically the ability to assess and utilize their own confidence in their ability to answer factual and reasoning questions correctly and the ability to anticipate what answers they would give and utilize that information appropriately. We buttress these behavioral findings with an analysis of the token probabilities returned by the models, which suggests the presence of an upstream internal signal that could provide the basis for metacognition. We further find that these abilities 1) are limited in resolution, 2) emerge in context-dependent manners, and 3) seem to be qualitatively different from those of humans. We also report intriguing differences across models of similar capabilities, suggesting that LLM post-training may have a role in developing metacognitive abilities.

Method: Theoretical analysis under the manifold hypothesis, proving that diffusion models with coarse scores can achieve near-parametric rates by exploiting manifold regularity rather than estimating the full data distribution. The analysis compares classical minimax rates for density estimation with rates achievable through coarse score estimation.

Result: Diffusion models with coarse scores can generate samples from a target distribution comparable to the data distribution in neighborhoods of the manifold, with rates depending only on manifold smoothness rather than density regularity. When the manifold is smooth, generalization occurs at rates faster than full population distribution estimation.

Conclusion: Coarse scores in diffusion models capture data manifold geometry while discarding fine-scale distributional structure, enabling novel sample generation at faster statistical rates than classical density estimation, especially beneficial when data density is irregular.

Abstract: Diffusion models often generate novel samples even when the learned score is only \emph{coarse} – a phenomenon not accounted for by the standard view of diffusion training as density estimation. In this paper, we show that, under the \emph{manifold hypothesis}, this behavior can instead be explained by coarse scores capturing the \emph{geometry} of the data while discarding the fine-scale distributional structure of the population measure~$μ_{\scriptscriptstyle\mathrm{data}}$. Concretely, whereas estimating the full data distribution $μ_{\scriptscriptstyle\mathrm{data}}$ supported on a $k$-dimensional manifold is known to require the classical minimax rate $\tilde{\mathcal{O}}(N^{-1/k})$, we prove that diffusion models trained with coarse scores can exploit the \emph{regularity of the manifold support} and attain a near-parametric rate toward a \emph{different} target distribution. This target distribution has density uniformly comparable to that of~$μ_{\scriptscriptstyle\mathrm{data}}$ throughout any $\tilde{\mathcal{O}}\bigl(N^{-β/(4k)}\bigr)$-neighborhood of the manifold, where $β$ denotes the manifold regularity. Our guarantees therefore depend only on the smoothness of the underlying support, and are especially favorable when the data density itself is irregular, for instance non-differentiable. In particular, when the manifold is sufficiently smooth, we obtain that \emph{generalization} – formalized as the ability to generate novel, high-fidelity samples – occurs at a statistical rate strictly faster than that required to estimate the full population distribution~$μ_{\scriptscriptstyle\mathrm{data}}$.

Method: Conflict-Gated Gradient Scaling (CGGS) uses cosine similarity between data and physics gradients to dynamically modulate the penalty weight. Unlike standard annealing schemes, CGGS acts as a geometric gate: it suppresses physical constraints when directional conflict is high (allowing prioritization of data fidelity) and restores constraints when gradients align.

Result: The method preserves O(1/T) convergence rate for smooth non-convex objectives, autonomously induces curriculum learning effects, and improves parameter estimation in stiff epidemiological systems compared to magnitude-based baselines, with empirical results showing improved peak recovery and convergence.

Conclusion: CGGS provides a computationally efficient alternative to existing methods for addressing gradient conflicts in PINNs for epidemiological modeling, ensuring stable and efficient training through a geometric gating mechanism that dynamically balances data and physics objectives based on gradient alignment.

Abstract: Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartmental models, such as the susceptible-exposed-infected-removed (SEIR) model. However, training these hybrid networks is often unstable due to competing optimization objectives. As established in recent literature on ``gradient pathology," the gradient vectors derived from the data loss and the physical residual often point in conflicting directions, leading to slow convergence or optimization deadlock. While existing methods attempt to resolve this by balancing gradient magnitudes or projecting conflicting vectors, we propose a novel method, conflict-gated gradient scaling (CGGS), to address gradient conflicts in physics-informed neural networks for epidemiological modelling, ensuring stable and efficient training and a computationally efficient alternative. This method utilizes the cosine similarity between the data and physics gradients to dynamically modulate the penalty weight. Unlike standard annealing schemes that only normalize scales, CGGS acts as a geometric gate: it suppresses the physical constraint when directional conflict is high, allowing the optimizer to prioritize data fidelity, and restores the constraint when gradients align. We prove that this gating mechanism preserves the standard $O(1/T)$ convergence rate for smooth non-convex objectives, a guarantee that fails under fixed-weight or magnitude-balanced training when gradients conflict. We demonstrate that this mechanism autonomously induces a curriculum learning effect, improving parameter estimation in stiff epidemiological systems compared to magnitude-based baselines. Our empirical results show improved peak recovery and convergence over magnitude-based methods.

Method: Established that Neural Regression Collapse occurs below the last layer across different model types, analyzing feature subspace alignment with target dimensions, covariance alignment, weight subspace alignment, and linear prediction error relationships.

Result: Found that in collapsed layers: features lie in target-dimension subspace, feature covariance aligns with target covariance, weight input subspace aligns with feature subspace, and linear feature prediction error approximates overall model error. Also showed Deep NRC helps learn intrinsic dimension of low-rank targets and explored weight decay’s role.

Conclusion: Provides a more complete understanding of the simple structures learned by deep networks in regression contexts, extending Neural Collapse theory beyond classification and beyond just the final layer.

Abstract: Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only measuring the phenomenon at the last layer. In this paper, we establish that Neural Regression Collapse (NRC) also occurs below the last layer across different types of models. We show that in the collapsed layers of neural regression models, features lie in a subspace that corresponds to the target dimension, the feature covariance aligns with the target covariance, the input subspace of the layer weights aligns with the feature subspace, and the linear prediction error of the features is close to the overall prediction error of the model. In addition to establishing Deep NRC, we also show that models that exhibit Deep NRC learn the intrinsic dimension of low rank targets and explore the necessity of weight decay in inducing Deep NRC. This paper provides a more complete picture of the simple structure learned by deep networks in the context of regression.

Method: The paper uses circuit complexity theory to analyze prerequisite-tree tasks, formalizes recursive-majority mastery propagation, and empirically tests transformer encoders on recursive-majority trees with various supervision strategies.

Result: Transformers trained on recursive-majority trees converge to permutation-invariant shortcuts rather than learning the underlying structure, but auxiliary supervision on intermediate subtrees enables structure-dependent computation and near-perfect accuracy at depths 3-4.

Conclusion: The findings reveal computational barriers for transformers on deep hierarchical structures and motivate the need for structure-aware objectives and iterative mechanisms for effective prerequisite-sensitive knowledge tracing.

Abstract: Knowledge tracing models mastery over interconnected concepts, often organized by prerequisites. We analyze hierarchical prerequisite propagation through a circuit-complexity lens to clarify what is provable about transformer-style computation on deep concept hierarchies. Using recent results that log-precision transformers lie in logspace-uniform $\mathsf{TC}^0$, we formalize prerequisite-tree tasks including recursive-majority mastery propagation. Unconditionally, recursive-majority propagation lies in $\mathsf{NC}^1$ via $O(\log n)$-depth bounded-fanin circuits, while separating it from uniform $\mathsf{TC}^0$ would require major progress on open lower bounds. Under a monotonicity restriction, we obtain an unconditional barrier: alternating ALL/ANY prerequisite trees yield a strict depth hierarchy for \emph{monotone} threshold circuits. Empirically, transformer encoders trained on recursive-majority trees converge to permutation-invariant shortcuts; explicit structure alone does not prevent this, but auxiliary supervision on intermediate subtrees elicits structure-dependent computation and achieves near-perfect accuracy at depths 3–4. These findings motivate structure-aware objectives and iterative mechanisms for prerequisite-sensitive knowledge tracing on deep hierarchies.

Method: The paper provides an educational overview of recently discovered convex equivalences in ReLU neural networks, particularly focusing on two-layer ReLU networks and other architectures, connecting them to sparse signal processing models.

Result: The paper demonstrates how convex reformulations of ReLU networks can address training challenges and improve theoretical understanding, bridging deep learning mathematics with signal processing applications.

Conclusion: Convex equivalences in ReLU neural networks offer promising approaches to overcome optimization challenges and enhance theoretical foundations, encouraging broader applications of signal processing techniques in deep learning.

Abstract: Deep neural networks (DNNs), particularly those using Rectified Linear Unit (ReLU) activation functions, have achieved remarkable success across diverse machine learning tasks, including image recognition, audio processing, and language modeling. Despite this success, the non-convex nature of DNN loss functions complicates optimization and limits theoretical understanding. In this paper, we highlight how recently developed convex equivalences of ReLU NNs and their connections to sparse signal processing models can address the challenges of training and understanding NNs. Recent research has uncovered several hidden convexities in the loss landscapes of certain NN architectures, notably two-layer ReLU networks and other deeper or varied architectures. This paper seeks to provide an accessible and educational overview that bridges recent advances in the mathematics of deep learning with traditional signal processing, encouraging broader signal processing applications.

Method: Symbolic-KANs represent multivariate functions as compositions of learned univariate primitives applied to learned scalar projections, using a library of analytic primitives, hierarchical gating, and symbolic regularization that progressively sharpens continuous mixtures into one-hot selections.

Result: Symbolic-KANs reliably recover correct primitive terms and governing structures in data-driven regression and inverse dynamical systems, and extend to forward/inverse physics-informed learning of PDEs, producing accurate solutions with compact symbolic representations.

Conclusion: Symbolic-KAN represents a step toward scalable, interpretable, and mechanistically grounded learning of governing laws by embedding symbolic structure directly within trainable deep networks.

Abstract: Symbolic discovery of governing equations is a long-standing goal in scientific machine learning, yet a fundamental trade-off persists between interpretability and scalable learning. Classical symbolic regression methods yield explicit analytic expressions but rely on combinatorial search, whereas neural networks scale efficiently with data and dimensionality but produce opaque representations. In this work, we introduce Symbolic Kolmogorov-Arnold Networks (Symbolic-KANs), a neural architecture that bridges this gap by embedding discrete symbolic structure directly within a trainable deep network. Symbolic-KANs represent multivariate functions as compositions of learned univariate primitives applied to learned scalar projections, guided by a library of analytic primitives, hierarchical gating, and symbolic regularization that progressively sharpens continuous mixtures into one-hot selections. After gated training and discretization, each active unit selects a single primitive and projection direction, yielding compact closed-form expressions without post-hoc symbolic fitting. Symbolic-KANs further act as scalable primitive discovery mechanisms, identifying the most relevant analytic components that can subsequently inform candidate libraries for sparse equation-learning methods. We demonstrate that Symbolic-KAN reliably recovers correct primitive terms and governing structures in data-driven regression and inverse dynamical systems. Moreover, the framework extends to forward and inverse physics-informed learning of partial differential equations, producing accurate solutions directly from governing constraints while constructing compact symbolic representations whose selected primitives reflect the true analytical structure of the underlying equations. These results position Symbolic-KAN as a step toward scalable, interpretable, and mechanistically grounded learning of governing laws.

Method: Uses Recursive Curvature-Tunable Activation Family (RCT-AF) with parameters α and β for precise curvature control. Systematically analyzes relationship between max|σ’’| and adversarial robustness across diverse network architectures, datasets, and adversarial training methods. Examines how curvature affects Hessian matrix diagonal elements of loss.

Result: Reveals non-monotonic relationship: optimal adversarial robustness consistently occurs when max|σ’’| falls within 4 to 10. Normalized Hessian diagonal norm shows U-shaped dependence on max|σ’’|, with minimum within optimal robustness range. Findings hold across various experimental settings.

Conclusion: Activation function curvature plays a critical role in adversarial robustness through a fundamental trade-off mechanism. Optimal curvature range (4-10 for max|σ’’|) balances expressivity and robust generalization, providing practical guidance for designing robust neural networks.

Abstract: This work investigates the critical role of activation function curvature – quantified by the maximum second derivative $\max|σ’’|$ – in adversarial robustness. Using the Recursive Curvature-Tunable Activation Family (RCT-AF), which enables precise control over curvature through parameters $α$ and $β$, we systematically analyze this relationship. Our study reveals a fundamental trade-off: insufficient curvature limits model expressivity, while excessive curvature amplifies the normalized Hessian diagonal norm of the loss, leading to sharper minima that hinder robust generalization. This results in a non-monotonic relationship where optimal adversarial robustness consistently occurs when $\max|σ’’|$ falls within 4 to 10, a finding that holds across diverse network architectures, datasets, and adversarial training methods. We provide theoretical insights into how activation curvature affects the diagonal elements of the hessian matrix of the loss, and experimentally demonstrate that the normalized Hessian diagonal norm exhibits a U-shaped dependence on $\max|σ’’|$, with its minimum within the optimal robustness range, thereby validating the proposed mechanism.

Method: Treats invariants as first-class types and uses LLM-driven compilation to translate generic neural ODE specifications into structure-preserving architectures that keep trajectories on admissible manifolds.

Result: Provides a systematic framework for creating invariant-respecting neural surrogates that separate scientific structure (what must be preserved) from learned dynamics (what is learned from data).

Conclusion: The invariant compiler offers a clean separation between preservation requirements and learning, enabling systematic design of physically plausible neural ODE models across scientific domains.

Abstract: Neural ODEs are increasingly used as continuous-time models for scientific and sensor data, but unconstrained neural ODEs can drift and violate domain invariants (e.g., conservation laws), yielding physically implausible solutions. In turn, this can compound error in long-horizon prediction and surrogate simulation. Existing solutions typically aim to enforce invariance by soft penalties or other forms of regularization, which can reduce overall error but do not guarantee that trajectories will not leave the constraint manifold. We introduce the invariant compiler, a framework that enforces invariants by construction: it treats invariants as first-class types and uses an LLM-driven compilation workflow to translate a generic neural ODE specification into a structure-preserving architecture whose trajectories remain on the admissible manifold in continuous time (and up to numerical integration error in practice). This compiler view cleanly separates what must be preserved (scientific structure) from what is learned from data (dynamics within that structure). It provides a systematic design pattern for invariant-respecting neural surrogates across scientific domains.

Method: Proposes a Deep Convolutional Neural Network (CNN) architecture to analyze Fourier-transform infrared (FTIR) spectra and predict the dominant functional group in organic molecules. Compares performance with Support Vector Machine (SVM) baseline.

Result: CNN model outperforms traditional SVM methods in predicting the highest priority functional group from FTIR spectra, demonstrating superior accuracy and capability in spectral pattern recognition.

Conclusion: Deep CNNs are effective for functional group prediction from FTIR spectra, offering advantages over conventional machine learning approaches like SVM for spectral data analysis.

Abstract: Our work addresses the problem of predicting the highest priority functional group present in an organic molecule. Functional Groups are groups of bound atoms that determine the physical and chemical properties of organic molecules. In the presence of multiple functional groups, the dominant functional group determines the compound’s properties. Fourier-transform Infrared spectroscopy (FTIR) is a commonly used spectroscopic method for identifying the presence or absence of functional groups within a compound. We propose the use of a Deep Convolutional Neural Networks (CNN) to predict the highest priority functional group from the Fourier-transform infrared spectrum (FTIR) of the organic molecule. We have compared our model with other previously applied Machine Learning (ML) method Support Vector Machine (SVM) and reasoned why CNN outperforms it.

Method: Proposes VLC: neuro-symbolic method combining VLM-based concept recognition with circuit-based symbolic reasoning. Task rules are compiled into symbolic programs (circuits) that execute rules exactly over object concepts recognized by the VLM, decoupling perception from reasoning.

Result: Experiments on three visual deductive reasoning tasks with distinct rule sets show VLC consistently achieves strong performance under covariate shifts, demonstrating robust reasoning capabilities.

Conclusion: VLC’s neuro-symbolic approach combining VLM perception with exact symbolic reasoning circuits enables robust visual deductive reasoning under distribution shifts, addressing limitations of end-to-end fine-tuned VLMs and black-box neuro-symbolic methods.

Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts. In this paper, we study covariate shifts in which the perceptual input distribution changes while the underlying prediction rules do not. To investigate this question, we consider visual deductive reasoning tasks, where a model is required to answer a query given an image and logical rules defined over the object concepts in the image. Empirically, we find that VLMs fine-tuned through gradient-based end-to-end training can achieve high in-distribution accuracy but fail to generalize under such shifts, suggesting that fine-tuning does not reliably induce the underlying reasoning function. This motivates a neuro-symbolic perspective that decouples perception from reasoning. However, we further observe that recent neuro-symbolic approaches that rely on black-box components for reasoning can still exhibit inconsistent robustness across tasks. To address this issue, we propose VLC, a neuro-symbolic method that combines VLM-based concept recognition with circuit-based symbolic reasoning. In particular, task rules are compiled into a symbolic program, specifically a circuit, which executes the rules exactly over the object concepts recognized by the VLM. Experiments on three visual deductive reasoning tasks with distinct rule sets show that VLC consistently achieves strong performance under covariate shifts, highlighting its ability to support robust reasoning.

Method: Hybrid Distillation Policy Optimization (HDPO) identifies failure prompts, generates privileged rollouts with ground-truth information, filters correct solutions, and distills token-level distributions from teacher (with privileged info) to student (same weights, different input).

Result: On OpenMathInstruct-2 with Qwen2.5-Math-1.5B-Instruct, HDPO improves coverage metrics (pass@4 by +0.8-1.1%, pass@8 by +0.4-1.7%) while maintaining greedy accuracy, with distillation weight controlling exploration-exploitation tradeoff.

Conclusion: HDPO effectively addresses the cliff prompt problem in RL for math reasoning through privileged self-distillation with bounded realizability gap, providing a principled approach to improve coverage without sacrificing accuracy.

Abstract: Large language models trained with reinforcement learning (RL) for mathematical reasoning face a fundamental challenge: on problems the model cannot solve at all - “cliff” prompts - the RL gradient vanishes entirely, preventing any learning signal from reaching these failure modes. We introduce Hybrid Distillation Policy Optimization (HDPO), which augments standard RL with privileged self-distillation targeting cliff prompts. On each training step, HDPO identifies prompts where all rollouts fail, generates privileged rollouts by providing the model with ground-truth information, filters for correct solutions, and distills the teacher’s token-level distribution into the student. Because teacher and student share the same weights - differing only in their input - the realizability gap is provably bounded, unlike cross-model distillation. We prove that R=1 filtered privileged generation recovers the optimal KL-regularized RL policy in the hard-threshold limit. Experiments on OpenMathInstruct-2 with Qwen2.5-Math-1.5B-Instruct show that HDPO consistently improves coverage metrics (pass@4 by +0.8-1.1%, pass@8 by +0.4-1.7%) while maintaining greedy accuracy, with the distillation weight lambda providing direct control over the exploration-exploitation tradeoff.

Method: Developed Luna, a new bound propagator implemented in C++ that supports Interval Bound Propagation, CROWN analysis, and alpha-CROWN analysis over general computational graphs. The paper describes the architecture of this C++ implementation.

Result: Luna is competitive with state-of-the-art alpha-CROWN implementation in terms of both bound tightness and computational efficiency on benchmarks from VNN-COMP 2025.

Conclusion: Luna provides a practical C++ implementation of alpha-CROWN that enables better integration into existing DNN verification frameworks and production systems while maintaining competitive performance.

Abstract: The parameterized CROWN analysis, a.k.a., alpha-CROWN, has emerged as a practically successful bound propagation method for neural network verification. However, existing implementations of alpha-CROWN are limited to Python, which complicates integration into existing DNN verifiers and long-term production-level systems. We introduce Luna, a new bound propagator implemented in C++. Luna supports Interval Bound Propagation, the CROWN analysis, and the alpha-CROWN analysis over a general computational graph. We describe the architecture of Luna and show that it is competitive with the state-of-the-art alpha-CROWN implementation in terms of both bound tightness and computational efficiency on benchmarks from VNN-COMP 2025.

Method: Proposes Constrained Optimistic eXploration Q-learning (COX-Q) with two key components: 1) cost-constrained optimistic exploration strategy that resolves gradient conflicts between reward and cost, and adaptively adjusts trust region; 2) truncated quantile critics to stabilize cost value learning and quantify epistemic uncertainty for exploration guidance.

Result: Experiments on safe velocity, safe navigation, and autonomous driving tasks show COX-Q achieves high sample efficiency, competitive test safety performance, and controlled data collection cost.

Conclusion: COX-Q is a promising RL method for safety-critical applications that effectively balances exploration efficiency with safety constraints.

Abstract: When safety is formulated as a limit of cumulative cost, safe reinforcement learning (RL) aims to learn policies that maximize return subject to the cost constraint in data collection and deployment. Off-policy safe RL methods, although offering high sample efficiency, suffer from constraint violations due to cost-agnostic exploration and estimation bias in cumulative cost. To address this issue, we propose Constrained Optimistic eXploration Q-learning (COX-Q), an off-policy safe RL algorithm that integrates cost-bounded online exploration and conservative offline distributional value learning. First, we introduce a novel cost-constrained optimistic exploration strategy that resolves gradient conflicts between reward and cost in the action space and adaptively adjusts the trust region to control the training cost. Second, we adopt truncated quantile critics to stabilize the cost value learning. Quantile critics also quantify epistemic uncertainty to guide exploration. Experiments on safe velocity, safe navigation, and autonomous driving tasks demonstrate that COX-Q achieves high sample efficiency, competitive test safety performance, and controlled data collection cost. The results highlight COX-Q as a promising RL method for safety-critical applications.

Method: Develops a single tractable UCB-style algorithm applicable to both average-reward and γ-regret settings. The algorithm achieves optimal variance-dependent regret guarantees with improved lower-order terms, and analyzes both cases with and without prior knowledge of optimal bias span.

Result: Achieves regret bounds of Õ(√(SA·Var) + lower-order terms) for both settings, which is minimax-optimal and adapts to easier instances. With prior knowledge of bias span, obtains optimal lower-order terms scaling as ||h*||_sp S²A. Without prior knowledge, shows fundamental gap in achievable performance.

Conclusion: The work completely characterizes optimal dependence on bias span in both leading and lower-order terms, revealing a fundamental gap between what’s achievable with and without prior knowledge, and provides near-optimal algorithms for infinite-horizon MDPs.

Abstract: Online reinforcement learning in infinite-horizon Markov decision processes (MDPs) remains less theoretically and algorithmically developed than its episodic counterpart, with many algorithms suffering from high ``burn-in’’ costs and failing to adapt to benign instance-specific complexity. In this work, we address these shortcomings for two infinite-horizon objectives: the classical average-reward regret and the $γ$-regret. We develop a single tractable UCB-style algorithm applicable to both settings, which achieves the first optimal variance-dependent regret guarantees. Our regret bounds in both settings take the form $\tilde{O}( \sqrt{SA,\text{Var}} + \text{lower-order terms})$, where $S,A$ are the state and action space sizes, and $\text{Var}$ captures cumulative transition variance. This implies minimax-optimal average-reward and $γ$-regret bounds in the worst case but also adapts to easier problem instances, for example yielding nearly constant regret in deterministic MDPs. Furthermore, our algorithm enjoys significantly improved lower-order terms for the average-reward setting. With prior knowledge of the optimal bias span $\Vert h^\star\Vert_\text{sp}$, our algorithm obtains lower-order terms scaling as $\Vert h^\star\Vert_\text{sp} S^2 A$, which we prove is optimal in both $\Vert h^\star\Vert_\text{sp}$ and $A$. Without prior knowledge, we prove that no algorithm can have lower-order terms smaller than $\Vert h^\star \Vert_\text{sp}^2 S A$, and we provide a prior-free algorithm whose lower-order terms scale as $\Vert h^\star\Vert_\text{sp}^2 S^3 A$, nearly matching this lower bound. Taken together, these results completely characterize the optimal dependence on $\Vert h^\star\Vert_\text{sp}$ in both leading and lower-order terms, and reveal a fundamental gap in what is achievable with and without prior knowledge.

Method: Proposes a knowledge-enhanced classification framework incorporating ecological knowledge graphs as structural priors. Uses macro-microbe coupling and microbial co-occurrence patterns to create a Graph-Regularized Multinomial Logistic Regression (GRMLR) model with manifold penalty to constrain feature space for biologically consistent classification.

Result: The approach significantly outperforms standard baselines, demonstrating potential as a robust and scalable framework for deep-sea ecological assessment. Importantly, it removes the need for macrofauna observations at inference time - only microbial abundance profiles are needed for prediction.

Conclusion: The knowledge-enhanced framework effectively addresses the small-sample, high-dimensional challenge in deep-sea microbial ecology by incorporating ecological knowledge as structural priors, enabling reliable cold seep stage assessment without costly macrofauna surveys.

Abstract: Deep-sea cold seep stage assessment has traditionally relied on costly, high-risk manned submersible operations and visual surveys of macrofauna. Although microbial communities provide a promising and more cost-effective alternative, reliable inference remains challenging because the available deep-sea dataset is extremely small ($n = 13$) relative to the microbial feature dimension ($p = 26$), making purely data-driven models highly prone to overfitting. To address this, we propose a knowledge-enhanced classification framework that incorporates an ecological knowledge graph as a structural prior. By fusing macro-microbe coupling and microbial co-occurrence patterns, the framework internalizes established ecological logic into a \underline{\textbf{G}}raph-\underline{\textbf{R}}egularized \underline{\textbf{M}}ultinomial \underline{\textbf{L}}ogistic \underline{\textbf{R}}egression (GRMLR) model, effectively constraining the feature space through a manifold penalty to ensure biologically consistent classification. Importantly, the framework removes the need for macrofauna observations at inference time: macro-microbe associations are used only to guide training, whereas prediction relies solely on microbial abundance profiles. Experimental results demonstrate that our approach significantly outperforms standard baselines, highlighting its potential as a robust and scalable framework for deep-sea ecological assessment.

Method: Proposes an integrated framework that couples wireless M2M networking with route scheduling, using AGV intention data as M2M traffic with retransmission-free multi-link transmission. Combines simulated annealing-based MRTA with congestion-aware A*-based route scheduling.

Result: The integrated scheme significantly enhances scheduling efficiency compared to baselines, even under high AGV load and limited channel resources. Shows wireless communication impacts on T-MRS performance and reveals scheduling-oriented M2M communication differs fundamentally from human-to-human communications.

Conclusion: The communication-enabled scheduling framework effectively addresses partial observability challenges in dynamic factory environments, enabling AGVs to dynamically adjust collision-free routes with reduced computational overhead, opening new technological opportunities for wireless networked smart factories.

Abstract: Achieving agile and reconfigurable production flows in smart factories depends on online multi-robot task assignment (MRTA), which requires online collision-free and congestion-free route scheduling of transportation multi-robot systems (T-MRS), e.g., collaborative automatic guided vehicles (AGVs). Due to the real-time operational requirements and dynamic interactions between T-MRS and production MRS, online scheduling under partial observability in dynamic factory environments remains a significant and under-explored challenge. This paper proposes a novel communication-enabled online scheduling framework that explicitly couples wireless machine-to-machine (M2M) networking with route scheduling, enabling AGVs to exchange intention information, e.g., planned routes, to overcome partial observations and assist complex computation of online scheduling. Specifically, we determine intelligent AGVs’ intention and sensor data as new M2M traffic and tailor the retransmission-free multi-link transmission networking to meet real-time operation demands. This scheduling-oriented networking is then integrated with a simulated annealing-based MRTA scheme and a congestion-aware A*-based route scheduling method. The integrated communication and scheduling scheme allows AGVs to dynamically adjust collision-free and congestion-free routes with reduced computational overhead. Numerical experiments shows the impacts from wireless communication on the performance of T-MRS and suggest that the proposed integrated scheme significantly enhances scheduling efficiency compared to other baselines, even under high AGV load conditions and limited channel resources. Moreover, the results reveal that the scheduling-oriented wireless M2M communication design fundamentally differs from human-to-human communications, implying new technological opportunities in a wireless networked smart factory.

Method: Proposes Kirchhoff-Inspired Neural Network (KINN) based on Kirchhoff’s current law, using state-variable-based architecture with numerically stable state updates derived from fundamental ordinary differential equations. The method enables explicit decoupling and encoding of higher-order evolutionary components within a single layer while maintaining physical consistency and interpretability.

Result: Extensive experiments show KINN outperforms state-of-the-art methods on both partial differential equation (PDE) solving and ImageNet image classification tasks.

Conclusion: KINN provides a novel biologically-inspired neural network architecture that systematically characterizes signal intensity, coupling structure, and state evolution, offering improved performance, physical consistency, and interpretability compared to conventional deep networks.

Abstract: Deep learning architectures are fundamentally inspired by neuroscience, particularly the structure of the brain’s sensory pathways, and have achieved remarkable success in learning informative data representations. Although these architectures mimic the communication mechanisms of biological neurons, their strategies for information encoding and transmission are fundamentally distinct. Biological systems depend on dynamic fluctuations in membrane potential; by contrast, conventional deep networks optimize weights and biases by adjusting the strengths of inter-neural connections, lacking a systematic mechanism to jointly characterize the interplay among signal intensity, coupling structure, and state evolution. To tackle this limitation, we propose the Kirchhoff-Inspired Neural Network (KINN), a state-variable-based network architecture constructed based on Kirchhoff’s current law. KINN derives numerically stable state updates from fundamental ordinary differential equations, enabling the explicit decoupling and encoding of higher-order evolutionary components within a single layer while preserving physical consistency, interpretability, and end-to-end trainability. Extensive experiments on partial differential equation (PDE) solving and ImageNet image classification validate that KINN outperforms state-of-the-art existing methods.

Method: Proposes QC-GAN with two pathways: quantum pathway exploits high-order feature correlations using quantum neural networks that map features into high-dimensional Hilbert spaces, and convolutional pathway extracts waveform structures. Includes QC feature complementarity loss to enforce feature orthogonality between pathways.

Result: Experimental results on denoising and interpolation tasks show QC-GAN preserves wavefield continuity and amplitude-phase information under complex noise conditions, demonstrating improved performance over classical methods.

Conclusion: QC-GAN breaks representational bottlenecks of classical GANs by synergistically integrating quantum and convolutional pathways, enabling better seismic data processing through enriched feature representation from quantum mechanics’ exponential state space.

Abstract: In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension. However, these methods rely on stacked perceptrons and standard activation functions, which imposes a bottleneck on the representational capacity of deep-learning models, making it difficult to capture the complex and non-stationary dynamics of seismic wavefields. Different from the classical perceptron-stacked NNs which are fundamentally confined to real-valued Euclidean spaces, the quantum NNs leverage the exponential state space of quantum mechanics to map the features into high-dimensional Hilbert spaces, transcending the representational boundary of classical NNs. Based on this insight, we propose a quantum-classical synergistic generative adversarial network (QC-GAN) for seismic data processing, serving as the first application of quantum NNs in seismic exploration. In QC-GAN, a quantum pathway is used to exploit the high-order feature correlations, while the convolutional pathway specializes in extracting the waveform structures of seismic wavefields. Furthermore, we design a QC feature complementarity loss to enforce the feature orthogonality in the proposed QC-GAN. This novel loss function can ensure that the two pathways encode non-overlapping information to enrich the capacity of feature representation. On the whole, by synergistically integrating the quantum and convolutional pathways, the proposed QC-GAN breaks the representational bottleneck inherent in classical GAN. Experimental results on denoising and interpolation tasks demonstrate that QC-GAN preserves wavefield continuity and amplitude-phase information under complex noise conditions.

Method: DIET profiles activation magnitudes across multiple tasks using only 100 samples per task, then applies majority voting to construct a single global pruning mask at the dimension level, requiring no training or large pre-computation costs.

Result: On Gemma-2 2B and 9B models across seven zero-shot benchmarks, DIET achieves significant improvements - at 20% sparsity on Gemma-2 2B, it shows ~10% average accuracy improvement over previous SOTA structured pruning methods, with advantages persisting across sparsity levels and model scales.

Conclusion: DIET provides a practical, robust training-free structured pruning solution that effectively balances dimension-level granularity with task-aware selection, making it suitable for efficient LLM deployment.

Abstract: Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deployment. Structured pruning offers a promising solution by removing entire dimensions or layers, yet existing methods face critical trade-offs: task-agnostic approaches cannot adapt to task-specific requirements, while task-aware methods require costly training to learn task adaptability. We propose DIET (Dimension-wise global pruning of LLMs via merging Task-wise importance scores), a training-free structured pruning method that combines dimension-level granularity with task-aware selection. DIET profiles activation magnitudes across tasks using only 100 samples per task, then applies majority voting to construct a single global mask. DIET does not require large costs from pre-computation or training. Experiments on seven zero-shot benchmarks using Gemma-2 2B and 9B models demonstrate the effectiveness of DIET; for example, at 20% sparsity on Gemma-2 2B, DIET achieves near 10% average accuracy improvement, compared to previous state-of-the-art structured pruning methods. This advantage persists across various sparsity levels and model scales, positioning DIET as a practical and robust choice for structured LLM pruning.

Method: Map ICU time series to natural language summaries using frozen LLM, embed summaries with frozen text embedding model to get fixed-length vectors for downstream predictors.

Result: Competitive with in-distribution baselines (grid imputation, self-supervised learning, time series foundation models) while showing smaller performance drops when transferring to new hospitals.

Conclusion: LLMs show potential as tools for scalable clinical ML deployment by creating portable patient embeddings that reduce engineering overhead for cross-hospital transfer.

Abstract: Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we study a simple question – can large language models (LLMs) create portable patient embeddings i.e. representations of patients enable a downstream predictor built on one hospital to be used elsewhere with minimal-to-no retraining and fine-tuning. To do so, we map from irregular ICU time series onto concise natural language summaries using a frozen LLM, then embed each summary with a frozen text embedding model to obtain a fixed length vector capable of serving as input to a variety of downstream predictors. Across three cohorts (MIMIC-IV, HIRID, PPICU), on multiple clinically grounded forecasting and classification tasks, we find that our approach is simple, easy to use and competitive with in-distribution with grid imputation, self-supervised representation learning, and time series foundation models, while exhibiting smaller relative performance drops when transferring to new hospitals. We study the variation in performance across prompt design, with structured prompts being crucial to reducing the variance of the predictive models without altering mean accuracy. We find that using these portable representations improves few-shot learning and does not increase demographic recoverability of age or sex relative to baselines, suggesting little additional privacy risk. Our work points to the potential that LLMs hold as tools to enable the scalable deployment of production grade predictive models by reducing the engineering overhead.

Method: Investigates three key factors affecting applications: starting artifact selection, credit horizon for execution traces, and batching trials into learning evidence. Uses case studies in MLAgentBench, Atari, and BigBench Extra Hard to analyze these design decisions.

Result: Found that design decisions critically determine optimization success: different starting artifacts affect solution reachability in MLAgentBench, truncated traces can still improve Atari agents, and larger minibatches don’t monotonically improve generalization on BBEH.

Conclusion: Lack of simple, universal way to set up learning loops across domains is a major hurdle for productionization. Provides practical guidance for making these design choices explicit.

Abstract: Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach to building self-improving agents, yet in practice remains brittle: despite active research, only 9% of surveyed agents used any automated optimization. We argue that this brittleness arises because, to set up a learning loop, an engineer must make ``hidden’’ design choices: What can the optimizer edit and what is the “right” learning evidence to provide at each update? We investigate three factors that affect most applications: the starting artifact, the credit horizon for execution traces, and batching trials and errors into learning evidence. Through case studies in MLAgentBench, Atari, and BigBench Extra Hard, we find that these design decisions can determine whether generative optimization succeeds, yet they are rarely made explicit in prior work. Different starting artifacts determine which solutions are reachable in MLAgentBench, truncated traces can still improve Atari agents, and larger minibatches do not monotonically improve generalization on BBEH. We conclude that the lack of a simple, universal way to set up learning loops across domains is a major hurdle for productionization and adoption. We provide practical guidance for making these choices.

Method: Proposes Stochastic Dimension-free Zeroth-order Estimator (SDZE) with two key innovations: 1) Common Random Numbers Synchronization (CRNS) to cancel O(1/ε²) variance by locking spatial random seeds across perturbations, and 2) implicit matrix-free subspace projection to reduce parameter exploration variance from O(P) to O(r) while maintaining O(1) memory footprint.

Result: SDZE enables training of 10-million-dimensional PINNs on a single NVIDIA A100 GPU, delivering significant improvements in speed and memory efficiency over state-of-the-art baselines.

Conclusion: SDZE provides a unified framework that achieves dimension-independent complexity in both space and memory for PINNs, overcoming fundamental limitations of backpropagation-based methods for high-dimensional PDEs.

Abstract: Physics-Informed Neural Networks (PINNs) for high-dimensional and high-order partial differential equations (PDEs) are primarily constrained by the $\mathcal{O}(d^k)$ spatial derivative complexity and the $\mathcal{O}(P)$ memory overhead of backpropagation (BP). While randomized spatial estimators successfully reduce the spatial complexity to $\mathcal{O}(1)$, their reliance on first-order optimization still leads to prohibitive memory consumption at scale. Zeroth-order (ZO) optimization offers a BP-free alternative; however, naively combining randomized spatial operators with ZO perturbations triggers a variance explosion of $\mathcal{O}(1/\varepsilon^2)$, leading to numerical divergence. To address these challenges, we propose the \textbf{S}tochastic \textbf{D}imension-free \textbf{Z}eroth-order \textbf{E}stimator (\textbf{SDZE}), a unified framework that achieves dimension-independent complexity in both space and memory. Specifically, SDZE leverages \emph{Common Random Numbers Synchronization (CRNS)} to algebraically cancel the $\mathcal{O}(1/\varepsilon^2)$ variance by locking spatial random seeds across perturbations. Furthermore, an \emph{implicit matrix-free subspace projection} is introduced to reduce parameter exploration variance from $\mathcal{O}(P)$ to $\mathcal{O}(r)$ while maintaining an $\mathcal{O}(1)$ optimizer memory footprint. Empirical results demonstrate that SDZE enables the training of 10-million-dimensional PINNs on a single NVIDIA A100 GPU, delivering significant improvements in speed and memory efficiency over state-of-the-art baselines.

Method: Proposes i-IF-Learn, an iterative framework with adaptive feature selection statistic that combines pseudo-label supervision with unsupervised signals. Uses low-dimensional embeddings (PCA or Laplacian eigenmaps) followed by k-means to simultaneously output influential feature subset and clustering labels.

Result: Numerical experiments on gene microarray and single-cell RNA-seq datasets show i-IF-Learn significantly surpasses classical and deep clustering baselines. Using selected influential features as preprocessing substantially enhances downstream deep models like DeepCluster, UMAP, and VAE.

Conclusion: Targeted feature selection is important and effective for unsupervised learning in high-dimensional data. i-IF-Learn provides a robust framework for identifying influential features that meaningfully define clusters while improving downstream model performance.

Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures. It’s common that only a few features, called the influential features, meaningfully define the clusters. Recovering these influential features is helpful in data interpretation and clustering. We propose i-IF-Learn, an iterative unsupervised framework that jointly performs feature selection and clustering. Our core innovation is an adaptive feature selection statistic that effectively combines pseudo-label supervision with unsupervised signals, dynamically adjusting based on intermediate label reliability to mitigate error propagation common in iterative frameworks. Leveraging low-dimensional embeddings (PCA or Laplacian eigenmaps) followed by $k$-means, i-IF-Learn simultaneously outputs influential feature subset and clustering labels. Numerical experiments on gene microarray and single-cell RNA-seq datasets show that i-IF-Learn significantly surpasses classical and deep clustering baselines. Furthermore, using our selected influential features as preprocessing substantially enhances downstream deep models such as DeepCluster, UMAP, and VAE, highlighting the importance and effectiveness of targeted feature selection.

Method: SRG uses Lagrangian relaxation-guided stochastic differential equations with convolutional kernels to capture inter-variable dependencies. It integrates Lagrangian relaxation to guide sampling toward feasible and near-optimal regions, generating diverse solution candidates that define compact trust-region subproblems for standard MILP solvers.

Result: SRG consistently outperforms existing machine learning baselines in solution quality across multiple public benchmarks. It demonstrates strong zero-shot transferability, achieving competitive optimality with state-of-the-art exact solvers on unseen instances while significantly reducing computational overhead.

Conclusion: SRG provides a theoretically-grounded generative framework for MILP solving that generates diverse, high-quality solutions, captures variable dependencies, and enables efficient solving with strong transfer capabilities.

Abstract: Predict-and-search (PaS) methods have shown promise for accelerating mixed-integer linear programming (MILP) solving. However, existing approaches typically assume variable independence and rely on deterministic single-point predictions, which limits solution diversityand often necessitates extensive downstream search for high-quality solutions. In this paper, we propose \textbf{SRG}, a generative framework based on Lagrangian relaxation-guided stochastic differential equations (SDEs), with theoretical guarantees on solution quality. SRG leverages convolutional kernels to capture inter-variable dependencies while integrating Lagrangian relaxation to guide the sampling process toward feasible and near-optimal regions. Rather than producing a single estimate, SRG generates diverse, high-quality solution candidates that collectively define compact and effective trust-region subproblems for standard MILP solvers. Across multiple public benchmarks, SRG consistently outperforms existing machine learning baselines in solution quality. Moreover, SRG demonstrates strong zero-shot transferability: on unseen cross-scale/problem instances, it achieves competitive optimality with state-of-the-art exact solvers while significantly reducing computational overhead through faster search and superior solution quality.

Method: Profile routing counts on a small calibration set, select the top-k most-routed experts per layer, and apply LoRA only to those selected experts. The method systematically profiles expert routing across architectures and tasks to identify frequently activated experts.

Result: Tuning only top 25% routed experts per layer remains competitive with full LoRA (within +/-1 percentage point), reduces LoRA trainable parameters by 70-73%, adapter checkpoint size by 71-73%, and wall-clock training time by up to 50%. Random expert selection performs 2.5 percentage points worse.

Conclusion: MoE-Sieve provides an efficient fine-tuning framework for MoE models by leveraging routing patterns, demonstrating that adapting cold experts can introduce gradient noise without improving accuracy. The routing signal is crucial for effective expert selection.

Abstract: Standard LoRA fine-tuning of Mixture-of-Experts (MoE) models applies adapters to every expert, yet our profiling shows that per-layer expert routing is highly skewed: a small subset of experts handles most tokens in each layer, while many others are rarely activated (“cold”). We propose MoE-Sieve, a simple routing-guided framework for LoRA fine-tuning, and pair it with a systematic profiling study of expert routing across architectures and tasks. The method is simple: profile routing counts on a small calibration set, select the top-k most-routed experts per layer, and apply LoRA only to those experts. Across two architecturally distinct MoE models and three diverse tasks, tuning only the top 25% routed experts per layer remains competitive with full LoRA, with mean differences within +/-1 percentage point across all conditions. This reduces LoRA trainable parameters by 70-73%, adapter checkpoint size by 71-73%, and wall-clock training time by up to 50%. We also observe a non-monotonic relationship between expert count and seed-to-seed variance, consistent with the hypothesis that adapting cold experts can introduce gradient noise without improving accuracy. Further ablations show that random expert selection at matched budget is about 2.5 percentage points worse, indicating that the routing signal matters, while greedy per-layer budget optimization does not improve over uniform top-k.

Method: Novel PageRank-Centrality-based sensor placement method that strategically positions sensors to optimize GNN performance for pressure estimation, prediction, and leakage detection

Result: Demonstrated substantial impact on reconstruction, prediction, and leakage detection performance on EPANET Net1 benchmark

Conclusion: Sensor placement significantly influences GNN-based leakage detection effectiveness, and the proposed centrality-based method improves performance

Abstract: Sensor placement for leakage detection in water distribution networks is an important and practical challenge for water utilities. Recent work has shown that graph neural networks can estimate and predict pressures and detect leaks, but their performance strongly depends on the available sensor measurements and configurations. In this paper, we investigate how sensor placement influences the performance of GNN-based leakage detection. We propose a novel PageRank-Centrality-based sensor placement method and demonstrate that it substantially impacts reconstruction, prediction, and leakage detection on the EPANET Net1.

Method: DGO constructs an experience bank from previously explored trajectories, then guides policy exploration using both the external experience bank and the model’s internal knowledge. The resulting trajectories refine the experience bank and optimize model parameters, creating a closed loop of experience utilization and internalization.

Result: Experiments show that DGO consistently outperforms baseline methods, demonstrating that better utilization and internalization of experience leads to more effective reasoning in LLMs.

Conclusion: The proposed DGO framework successfully addresses the gap between current RL training and human learning by enabling LLMs to better leverage both external and internal experience, resulting in improved reasoning capabilities.

Abstract: Recently, reinforcement learning~(RL) has become an important approach for improving the capabilities of large language models~(LLMs). In particular, reinforcement learning from verifiable rewards~(RLVR) has emerged as a promising paradigm for reasoning tasks. However, existing RL-based training still remains only a rough approximation to human learning. Human learners leverage both external and internal experience to guide exploration and gradually internalize useful trajectories into stable knowledge. Motivated by this gap, we ask: how can LLMs better utilize and internalize experience during RLVR training? To answer this question, we propose \textbf{D}ual \textbf{G}uidance \textbf{O}ptimization~(\textbf{DGO}), a unified framework that leverages \emph{external} and \emph{internal experience} to improve training effectiveness. Specifically, DGO first constructs an experience bank from previously explored trajectories. The policy then performs exploration under the joint guidance of the experience bank and the model’s internal knowledge. The resulting trajectories are further used to refine the experience bank and optimize model parameters, forming a closed loop of experience utilization and internalization. Experiments show that DGO consistently outperforms baseline methods, suggesting that better utilization and internalization of experience lead to more effective reasoning.

Method: Proposes KCLNet with an asynchronous graph neural network using electrically-simulated message passing and a representation learning method inspired by Kirchhoff’s Current Law that enforces current embedding equality at each depth.

Result: Achieves significant performance improvements in analog circuit classification, subcircuit detection, and circuit edit distance prediction tasks compared to baseline methods.

Conclusion: KCLNet provides an effective solution for analog circuit representation learning that preserves electrical constraints, enhancing generalization ability of circuit embeddings.

Abstract: Digital circuits representation learning has made remarkable progress in the electronic design automation domain, effectively supporting critical tasks such as testability analysis and logic reasoning. However, representation learning for analog circuits remains challenging due to their continuous electrical characteristics compared to the discrete states of digital circuits. This paper presents a direct current (DC) electrically equivalent-oriented analog representation learning framework, named \textbf{KCLNet}. It comprises an asynchronous graph neural network structure with electrically-simulated message passing and a representation learning method inspired by Kirchhoff’s Current Law (KCL). This method maintains the orderliness of the circuit embedding space by enforcing the equality of the sum of outgoing and incoming current embeddings at each depth, which significantly enhances the generalization ability of circuit embeddings. KCLNet offers a novel and effective solution for analog circuit representation learning with electrical constraints preserved. Experimental results demonstrate that our method achieves significant performance in a variety of downstream tasks, e.g., analog circuit classification, subcircuit detection, and circuit edit distance prediction.

Method: Conducted systematic experiments across 22 experiments, 5 benchmarks, 4 model families, and 3 model scales (3B-14B). Used base-vs-instruct ablation, training stage ablation (Base, SFT, DPO), and cross-family replication. Employed Jaccard, embedding, and NLI-based baselines with DeBERTa models. Validated with cross-embedder and cross-dataset tests. Explored cheapest-first cascade (UCBD) over orthogonal uncertainty signals for selective prediction.

Result: RLHF-aligned models show significant response homogenization: 40-79% of TruthfulQA questions produce single semantic clusters across samples. On affected questions, sampling-based uncertainty methods have zero discriminative power (AUROC=0.500), while token entropy retains signal (0.603). Alignment tax is task-dependent (GSM8K: 0.724 AUROC). Base models show 1.0% single-cluster rate vs. 28.5% for instruct models. DPO training causes homogenization, not SFT. Selective prediction raises GSM8K accuracy from 84.4% to 93.2% at 50% coverage with 57% cost savings.

Conclusion: RLHF alignment causes response homogenization that impairs uncertainty estimation, but token entropy remains effective. The alignment tax varies by model family and scale. Selective prediction using orthogonal uncertainty signals can mitigate these issues and improve performance-cost tradeoffs.

Abstract: RLHF-aligned language models exhibit response homogenization: on TruthfulQA (n=790), 40-79% of questions produce a single semantic cluster across 10 i.i.d. samples. On affected questions, sampling-based uncertainty methods have zero discriminative power (AUROC=0.500), while free token entropy retains signal (0.603). This alignment tax is task-dependent: on GSM8K (n=500), token entropy achieves 0.724 (Cohen’s d=0.81). A base-vs-instruct ablation confirms the causal role of alignment: the base model shows 1.0% single-cluster rate vs. 28.5% for the instruct model (p < 10^{-6}). A training stage ablation (Base 0.0% -> SFT 1.5% -> DPO 4.0% SCR) localizes the cause to DPO, not SFT. Cross-family replication on four model families reveals alignment tax severity varies by family and scale. We validate across 22 experiments, 5 benchmarks, 4 model families, and 3 model scales (3B-14B), with Jaccard, embedding, and NLI-based baselines at three DeBERTa scales (all ~0.51 AUROC). Cross-embedder validation with two independent embedding families rules out coupling bias. Cross-dataset validation on WebQuestions (58.0% SCR) confirms generalization beyond TruthfulQA. The central finding – response homogenization – is implementation-independent and label-free. Motivated by this diagnosis, we explore a cheapest-first cascade (UCBD) over orthogonal uncertainty signals. Selective prediction raises GSM8K accuracy from 84.4% to 93.2% at 50% coverage; weakly dependent boundaries (|r| <= 0.12) enable 57% cost savings.

Method: Introduces a causal inference-based framework that: (1) aligns shared embeddings across layers, (2) enforces private embeddings to capture layer-specific signals, and (3) applies backdoor adjustment to ensure common embeddings capture only global information while being separated from private representations.

Result: Experiments on synthetic and real-world datasets demonstrate consistent improvements over existing baselines, highlighting effectiveness for robust and interpretable multiplex graph representation learning.

Conclusion: The CaDeM framework successfully disentangles common and private components in multiplex graphs, leading to more robust and interpretable representations through causal inference techniques.

Abstract: Learning representations from multiplex graphs, i.e., multi-layer networks where nodes interact through multiple relation types, is challenging due to the entanglement of shared (common) and layer-specific (private) information, which limits generalization and interpretability. In this work, we introduce a causal inference-based framework that disentangles common and private components in a self-supervised manner. CaDeM jointly (i) aligns shared embeddings across layers, (ii) enforces private embeddings to capture layer-specific signals, and (iii) applies backdoor adjustment to ensure that the common embeddings capture only global information while being separated from the private representations. Experiments on synthetic and real-world datasets demonstrate consistent improvements over existing baselines, highlighting the effectiveness of our approach for robust and interpretable multiplex graph representation learning.

Method: Introduces a scalable multi-turn synthetic data generation pipeline where a teacher model iteratively refines problems based on in-context student performance summaries. This creates structured difficulty progressions (stepping stones) without teacher fine-tuning, supporting curriculum-based training.

Result: Multi-turn approach substantially improves yield of valid synthetic problems and produces easier/harder task variants. Synthetic augmentation consistently improves in-domain code performance and in most cases out-of-domain math performance across Llama3.1-8B, Qwen3-8B, and Qwen2.5-32B models.

Conclusion: The multi-turn synthetic data generation pipeline effectively addresses RL scaling challenges by creating structured difficulty progressions, providing empirical insights into how curriculum design and data diversity jointly shape RL training dynamics.

Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for improving large language models beyond supervised fine-tuning, yet sustaining performance gains at scale remains an open challenge, as data diversity and structure, rather than volume alone, become the limiting factor. We address this by introducing a scalable multi-turn synthetic data generation pipeline in which a teacher model iteratively refines problems based on in-context student performance summaries, producing structured difficulty progressions without any teacher fine-tuning. Compared to single-turn generation, this multi-turn approach substantially improves the yield of valid synthetic problems and naturally produces stepping stones, i.e. easier and harder variants of the same core task, that support curriculum-based training. We systematically study how task difficulty, curriculum scheduling, and environment diversity interact during RL training across the Llama3.1-8B Instruct and Qwen3-8B Base model families, with additional scaling experiments on Qwen2.5-32B. Our results show that synthetic augmentation consistently improves in-domain code and in most cases out-of-domain math performance, and we provide empirical insights into how curriculum design and data diversity jointly shape RL training dynamics.

Method: Implemented feedback-control optimizers on a mixed-signal neuromorphic processor using an In-The-Loop (ITL) training setup. Tested on binary classification and nonlinear Yin-Yang problems, comparing against numerical simulations and gradient-based baselines.

Result: On-chip training achieved performance matching numerical simulations and gradient-based baselines, demonstrating feasibility of feedback-driven online learning under realistic mixed-signal constraints.

Conclusion: Feedback-control optimizers enable expressive on-chip training for neuromorphic systems, representing a co-design approach for embedding learning rules directly in silicon for autonomous and adaptive neuromorphic computing.

Abstract: On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, recent studies show that feedback-control optimizers can enable expressive, on-chip training of neuromorphic devices. In this work, we present a proof-of-concept implementation of such feedback-control optimizers on a mixed-signal neuromorphic processor. We assess the proposed approach in an In-The-Loop(ITL) training setup on both a binary classification task and the nonlinear Yin-Yang problem, demonstrating on-chip training that matches the performance of numerical simulations and gradient-based baselines. Our results highlight the feasibility of feedback-driven, online learning under realistic mixed-signal constraints, and represent a co-design approach toward embedding such rules directly in silicon for autonomous and adaptive neuromorphic computing.

Method: The authors formalize likelihood hacking in a core probabilistic programming language, identify sufficient syntactic conditions to prevent it, and prove that a safe language fragment satisfying these conditions cannot produce likelihood-hacking programs. They implement these conditions as SafeStan, a modification of Stan that resists likelihood hacking.

Result: Empirical results show that GRPO-trained models generating PyMC code discover likelihood hacking exploits within the first few training steps, driving violation rates well above baseline. SafeStan effectively prevents likelihood hacking under optimization pressure.

Conclusion: Language-level safety constraints are both theoretically grounded and effective in practice for preventing likelihood hacking in automated Bayesian model discovery systems, ensuring reliable probabilistic program generation.

Abstract: When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likelihood reward by producing programs whose data distribution fails to normalise instead of fitting the data better. We call this failure likelihood hacking (LH). We formalise LH in a core probabilistic programming language (PPL) and give sufficient syntactic conditions for its prevention, proving that a safe language fragment $\mathcal{L}{\text{safe}}$ satisfying these conditions cannot produce likelihood-hacking programs. Empirically, we show that GRPO-trained models generating PyMC code discover LH exploits within the first few training steps, driving violation rates well above the untrained-model baseline. We implement $\mathcal{L}{\text{safe}}$’s conditions as $\texttt{SafeStan}$, a LH-resistant modification of Stan, and show empirically that it prevents LH under optimisation pressure. These results show that language-level safety constraints are both theoretically grounded and effective in practice for automated Bayesian model discovery.

Method: Revisits gossip algorithms specifically designed for pairwise objective functions (U-statistics of degree two). Provides comprehensive theoretical framework for convergence analysis, examining graph properties affecting efficiency and establishing convergence conditions.

Result: Fills gap in literature by establishing conditions under which these methods succeed and identifying critical graph properties. Performs refined analysis of convergence upper and lower bounds.

Conclusion: Provides theoretical foundation for gossip algorithms handling pairwise statistical learning tasks in distributed IoT networks, addressing limitations of existing averaging-focused approaches.

Abstract: In the IoT era, information is more and more frequently picked up by connected smart sensors with increasing, though limited, storage, communication and computation abilities. Whether due to privacy constraints or to the structure of the distributed system, the development of statistical learning methods dedicated to data that are shared over a network is now a major issue. Gossip-based algorithms have been developed for the purpose of solving a wide variety of statistical learning tasks, ranging from data aggregation over sensor networks to decentralized multi-agent optimization. Whereas the vast majority of contributions consider situations where the function to be estimated or optimized is a basic average of individual observations, it is the goal of this article to investigate the case where the latter is of pairwise nature, taking the form of a U -statistic of degree two. Motivated by various problems such as similarity learning, ranking or clustering for instance, we revisit gossip algorithms specifically designed for pairwise objective functions and provide a comprehensive theoretical framework for their convergence. This analysis fills a gap in the literature by establishing conditions under which these methods succeed, and by identifying the graph properties that critically affect their efficiency. In particular, a refined analysis of the convergence upper and lower bounds is performed.

Method: Proposes a two-stage attack framework: (1) a novel membership inference attack using a reference model that improves detection accuracy even for models robust to overfitting-based attacks, and (2) the first attribute inference attack that predicts sensitive characteristics of training data for time series imputation models. Evaluated on attention-based and autoencoder architectures in two scenarios: models trained from scratch and fine-tuned models where adversary has access to initial weights.

Result: The membership attack retrieves a significant portion of training data with tpr@top25% score significantly higher than naive attack baseline. The membership attack also provides good insight into whether attribute inference will work (90% precision vs 78% in general case).

Conclusion: Time series imputation models have serious privacy vulnerabilities to inference attacks, necessitating better privacy-preserving techniques for deployment in sensitive domains.

Abstract: Deep learning models for time series imputation are now essential in fields such as healthcare, the Internet of Things (IoT), and finance. However, their deployment raises critical privacy concerns. Beyond the well-known issue of unintended memorization, which has been extensively studied in generative models, we demonstrate that time series models are vulnerable to inference attacks in a black-box setting. In this work, we introduce a two-stage attack framework comprising: (1) a novel membership inference attack based on a reference model that improves detection accuracy, even for models robust to overfitting-based attacks, and (2) the first attribute inference attack that predicts sensitive characteristics of the training data for timeseries imputation model. We evaluate these attacks on attention-based and autoencoder architectures in two scenarios: models that are trained from scratch, and fine-tuned models where the adversary has access to the initial weights. Our experimental results demonstrate that the proposed membership attack retrieves a significant portion of the training data with a tpr@top25% score significantly higher than a naive attack baseline. We show that our membership attack also provides a good insight of whether attribute inference will work (with a precision of 90% instead of 78% in the genral case).

Method: Proposes RGC-Net combining reservoir dynamics with graph convolution: uses fixed random reservoir weights with leaky integrator for feature retention, adaptable model for graph classification, and RGC-Net-powered transformer for graph generation.

Result: Achieves state-of-the-art performance in classification and generative tasks, including brain graph evolution, with faster convergence and reduced over-smoothing compared to existing methods.

Conclusion: RGC-Net successfully integrates reservoir computing with structured graph convolution, addressing key limitations of GCNs while maintaining computational efficiency and performance.

Abstract: Message passing is a core mechanism in Graph Neural Networks (GNNs), enabling the iterative update of node embeddings by aggregating information from neighboring nodes. Graph Convolutional Networks (GCNs) exemplify this approach by adapting convolutional operations for graph structures, allowing features from adjacent nodes to be combined effectively. However, GCNs encounter challenges with complex or dynamic data. Capturing long-range dependencies often requires deeper layers, which not only increase computational costs but also lead to over-smoothing, where node embeddings become indistinguishable. To overcome these challenges, reservoir computing has been integrated into GNNs, leveraging iterative message-passing dynamics for stable information propagation without extensive parameter tuning. Despite its promise, existing reservoir-based models lack structured convolutional mechanisms, limiting their ability to accurately aggregate multi-hop neighborhood information. To address these limitations, we propose RGC-Net (Reservoir-based Graph Convolutional Network), which integrates reservoir dynamics with structured graph convolution. Key contributions include: (i) a reimagined convolutional framework with fixed random reservoir weights and a leaky integrator to enhance feature retention; (ii) a robust, adaptable model for graph classification; and (iii) an RGC-Net-powered transformer for graph generation with application to dynamic brain connectivity. Extensive experiments show that RGC-Net achieves state-of-the-art performance in classification and generative tasks, including brain graph evolution, with faster convergence and reduced over-smoothing. Source code is available at https://github.com/basiralab/RGC-Net .

Method: Proposes a multi-fidelity, multi-modal Bayesian optimization framework that integrates low-fidelity numerical data with high-fidelity human preferences. Uses Gaussian process surrogate models with both hierarchical autoregressive and non-hierarchical coregionalization-based structures to learn from mixed-modality data.

Result: Demonstrated by tuning an autonomous vehicle’s trajectory planner, showing that combining numerical and preference data significantly reduces the need for human-involved experiments while effectively adapting driving style to individual preferences.

Conclusion: The proposed framework enables efficient optimization of control policies using both objective numerical data and subjective human preferences, reducing experimental burden while achieving personalized adaptation.

Abstract: Tuning control policies manually to meet high-level objectives is often time-consuming. Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective function. However, many systems, particularly those involving humans, require optimization based on subjective criteria. Preferential Bayesian optimization addresses this by learning from pairwise comparisons instead of quantitative measurements, but relying solely on preference data can be inefficient. We propose a multi-fidelity, multi-modal Bayesian optimization framework that integrates low-fidelity numerical data with high-fidelity human preferences. Our approach employs Gaussian process surrogate models with both hierarchical, autoregressive and non-hierarchical, coregionalization-based structures, enabling efficient learning from mixed-modality data. We illustrate the framework by tuning an autonomous vehicle’s trajectory planner, showing that combining numerical and preference data significantly reduces the need for experiments involving the human decision maker while effectively adapting driving style to individual preferences.

Method: Proposes Linear-Nonlinear Fusion Neural Operator (LNF-NO) that models operator mappings via multiplicative fusion of linear and nonlinear components, supporting multiple functional inputs and both regular/irregular geometries.

Result: LNF-NO trains substantially faster than DeepONet and FNO while achieving comparable or better accuracy across PDE benchmarks, including 2.7x faster training than 3D FNO on Poisson-Boltzmann equations.

Conclusion: Linear-nonlinear decoupling improves neural operator learning efficiency and interpretability while maintaining accuracy, making LNF-NO a promising approach for PDE operator learning.

Abstract: Neural operator learning directly constructs the mapping relationship from the equation parameter space to the solution space, enabling efficient direct inference in practical applications without the need for repeated solution of partial differential equations (PDEs) - an advantage that is difficult to achieve with traditional numerical methods. In this work, we find that explicitly decoupling linear and nonlinear effects within such operator mappings leads to markedly improved learning efficiency. This yields a novel network structure, namely the Linear-Nonlinear Fusion Neural Operator (LNF-NO), which models operator mappings via the multiplicative fusion of a linear component and a nonlinear component, thus achieving a lightweight and interpretable representation. This linear-nonlinear decoupling enables efficient capture of complex solution features at the operator level while maintaining stability and generality. LNF-NO naturally supports multiple functional inputs and is applicable to both regular grids and irregular geometries. Across a diverse suite of PDE operator-learning benchmarks, including nonlinear Poisson-Boltzmann equations and multi-physics coupled systems, LNF-NO is typically substantially faster to train than Deep Operator Networks (DeepONet) and Fourier Neural Operators (FNO), while achieving comparable or better accuracy in most cases. On the tested 3D Poisson-Boltzmann case, LNF-NO attains the best accuracy among the compared models and trains approximately 2.7x faster than a 3D FNO baseline.

Method: Proposes TsetlinWiSARD training approach using Tsetlin Automata for probabilistic, feedback-driven learning with iterative optimization, implemented on FPGA-based training architecture.

Result: Achieves 1000x faster training vs traditional WiSARD, with 22% reduced resource usage, 93.3% lower latency, and 64.2% lower power consumption compared to other FPGA-based ML training accelerators.

Conclusion: TsetlinWiSARD enables efficient on-chip training for weightless neural networks with state-of-the-art accuracy and significant hardware efficiency improvements, suitable for edge computing applications.

Abstract: Increasing demands for adaptability, privacy, and security at the edge have persistently pushed the frontiers for a new generation of machine learning (ML) algorithms with training and inference capabilities on-chip. Weightless Neural Network (WNN) is such an algorithm that is principled on lookup table based simple neuron structures. As a result, it offers architectural benefits, such as low-latency, low-complexity inference, compared to deep neural networks that depend heavily on multiply-accumulate operations. However, traditional WNNs rely on memorization-based one-shot training, which either leads to overfitting and reduced accuracy or requires tedious post-training adjustments, limiting their effectiveness for efficient on chip training. In this work, we propose TsetlinWiSARD, a training approach for WNNs that leverages Tsetlin Automata (TAs) to enable probabilistic, feedback-driven learning. It overcomes the overfitting of WiSARD’s one-shot training with iterative optimization, while maintaining simple, continuous binary feedback for efficient on-chip training. Central to our approach is a field programmable gate array (FPGA)-based training architecture that delivers state-of-the-art accuracy while significantly improving hardware efficiency. Our approach provides over 1000x faster training when compared with the traditional WiSARD implementation of WNNs. Further, we demonstrate 22% reduced resource usage, 93.3% lower latency, and 64.2% lower power consumption compared to FPGA-based training accelerators implementing other ML algorithms.

Method: Proposes IPatch, a multi-resolution Transformer architecture that integrates both point-wise and patch-wise tokens to model temporal information at multiple resolutions simultaneously, capturing both fine-grained details and broader contextual dependencies.

Result: Experiments on 7 benchmark datasets show IPatch consistently improves forecasting accuracy, robustness to noise, and generalization across various prediction horizons compared to single-representation baselines.

Conclusion: Integrating multiple temporal resolutions in Transformer architectures is effective for time series forecasting, addressing limitations of single-representation approaches and improving performance across diverse datasets and conditions.

Abstract: Accurate forecasting of multivariate time series remains challenging due to the need to capture both short-term fluctuations and long-range temporal dependencies. Transformer-based models have emerged as a powerful approach, but their performance depends critically on the representation of temporal data. Traditional point-wise representations preserve individual time-step information, enabling fine-grained modeling, yet they tend to be computationally expensive and less effective at modeling broader contextual dependencies, limiting their scalability to long sequences. Patch-wise representations aggregate consecutive steps into compact tokens to improve efficiency and model local temporal dynamics, but they often discard fine-grained temporal details that are critical for accurate predictions in volatile or complex time series. We propose IPatch, a multi-resolution Transformer architecture that integrates both point-wise and patch-wise tokens, modeling temporal information at multiple resolutions. Experiments on 7 benchmark datasets demonstrate that IPatch consistently improves forecasting accuracy, robustness to noise, and generalization across various prediction horizons compared to single-representation baselines.

Method: Proposes Location-Scale Gaussian VAE (LSG-VAE) that explicitly parameterizes both predictive mean and time-dependent variance through a location-scale likelihood formulation, enabling adaptive attenuation mechanism that down-weights highly volatile observations during training.

Result: Extensive experiments on nine benchmark datasets show LSG-VAE consistently outperforms fifteen strong generative baselines while maintaining high computational efficiency suitable for real-time deployment.

Conclusion: LSG-VAE effectively addresses the heteroscedasticity limitation in probabilistic time series forecasting by explicitly modeling time-dependent variances, leading to improved robustness in trend prediction and better uncertainty quantification.

Abstract: Probabilistic time series forecasting (PTSF) aims to model the full predictive distribution of future observations, enabling both accurate forecasting and principled uncertainty quantification. A central requirement of PTSF is to embrace heteroscedasticity, as real-world time series exhibit time-varying conditional variances induced by nonstationary dynamics, regime changes, and evolving external conditions. However, most existing non-autoregressive generative approaches to PTSF, such as TimeVAE and $K^2$VAE, rely on MSE-based training objectives that implicitly impose a homoscedastic assumption, thereby fundamentally limiting their ability to model temporal heteroscedasticity. To address this limitation, we propose the Location-Scale Gaussian VAE (LSG-VAE), a simple but effective framework that explicitly parameterizes both the predictive mean and time-dependent variance through a location-scale likelihood formulation. This design enables LSG-VAE to faithfully capture heteroscedastic aleatoric uncertainty and introduces an adaptive attenuation mechanism that automatically down-weights highly volatile observations during training, leading to improved robustness in trend prediction. Extensive experiments on nine benchmark datasets demonstrate that LSG-VAE consistently outperforms fifteen strong generative baselines while maintaining high computational efficiency suitable for real-time deployment.

Method: Proposes a new NMF framework called maximum-volume-constrained NMF that makes basis vectors as distinct as possible. Establishes identifiability theorem for this approach and provides an algorithm for estimation.

Result: Experimental results demonstrate the effectiveness of the proposed maximum-volume-constrained NMF method.

Conclusion: Maximum-volume-constrained NMF addresses limitations of minimum-volume-constrained NMF by producing more distinct and interpretable basis vectors, particularly suitable for highly mixed data.

Abstract: In nonnegative matrix factorization (NMF), minimum-volume-constrained NMF is a widely used framework for identifying the solution of NMF by making basis vectors as similar as possible. This typically induces sparsity in the coefficient matrix, with each row containing zero entries. Consequently, minimum-volume-constrained NMF may fail for highly mixed data, where such sparsity does not hold. Moreover, the estimated basis vectors in minimum-volume-constrained NMF may be difficult to interpret as they may be mixtures of the ground truth basis vectors. To address these limitations, in this paper we propose a new NMF framework, called maximum-volume-constrained NMF, which makes the basis vectors as distinct as possible. We further establish an identifiability theorem for maximum-volume-constrained NMF and provide an algorithm to estimate it. Experimental results demonstrate the effectiveness of the proposed method.

Method: Proposes Attack-AAIRS extension that uses GANs to learn the distribution of adversarial attacks exploiting HCN-ID weaknesses. Combines small real dataset with GAN-generated synthetic adversarial samples to augment training and inoculate the model.

Result: Ten-fold cross validation shows increased robustness to unseen attacks (FGSM, PGD, Gaussian Noise, MI-FGSM, BIM). Generated attack samples maintain quality similar to original benign synthetic samples, and inoculated models preserve test accuracy on real data.

Conclusion: Attack-AAIRS effectively improves model robustness against adversarial attacks without compromising performance on real data, addressing security vulnerabilities in skeleton-based person identification with small datasets.

Abstract: Machine learning models trained on small data sets for security applications are especially vulnerable to adversarial attacks. Person identification from LiDAR based skeleton data requires time consuming and expensive data acquisition for each subject identity. Recently, Assessment and Augmented Identity Recognition for Skeletons (AAIRS) has been used to train Hierarchical Co-occurrence Networks for Person Identification (HCN-ID) with small LiDAR based skeleton data sets. However, AAIRS does not evaluate robustness of HCN-ID to adversarial attacks or inoculate the model to defend against such attacks. Popular perturbation-based approaches to generating adversarial attacks are constrained to targeted perturbations added to real training samples, which is not ideal for inoculating models with small training sets. Thus, we propose Attack-AAIRS, a novel addition to the AAIRS framework. Attack-AAIRS leverages a small real data set and a GAN generated synthetic data set to assess and improve model robustness against unseen adversarial attacks. Rather than being constrained to perturbations of limited real training samples, the GAN learns the distribution of adversarial attack samples that exploit weaknesses in HCN-ID. Attack samples drawn from this distribution augment training for inoculation of the HCN-ID to improve robustness. Ten-fold cross validation of Attack-AAIRS yields increased robustness to unseen attacks- including FGSM, PGD, Additive Gaussian Noise, MI-FGSM, and BIM. The HCN-ID Synthetic Data Quality Score for Attack-AAIRS indicates that generated attack samples are of similar quality to the original benign synthetic samples generated by AAIRS. Furthermore, inoculated models show consistent final test accuracy with the original model trained on real data, demonstrating that our method improves robustness to adversarial attacks without reducing test performance on real data.

Method: Cost-Sensitive Neighborhood Aggregation (CSNA) computes pairwise distance in a learned projection and uses it to soft-route each message through concordant and discordant channels with independent transformations, with theoretical analysis under a contextual stochastic block model.

Result: CSNA is competitive with SOTA on adversarial-heterophily datasets (Texas, Wisconsin, Cornell, Actor) but underperforms on informative-heterophily datasets (Chameleon, Squirrel), showing per-edge routing helps only when there’s useful decomposition to exploit.

Conclusion: The cost function’s ability to separate edge types serves as a diagnostic for heterophily regime, revealing when fine-grained routing adds value over uniform channels and when it does not.

Abstract: Recent work distinguishes two heterophily regimes: adversarial, where cross-class edges dilute class signal and harm classification, and informative, where the heterophilous structure itself carries useful signal. We ask: when does per-edge message routing help, and when is a uniform spectral channel sufficient? To operationalize this question we introduce Cost-Sensitive Neighborhood Aggregation (CSNA), a GNN layer that computes pairwise distance in a learned projection and uses it to soft-route each message through concordant and discordant channels with independent transformations. Under a contextual stochastic block model we show that cost-sensitive weighting preserves class-discriminative signal where mean aggregation provably attenuates it, provided $w_+/w_- > q/p$. On six benchmarks with uniform tuning, CSNA is competitive with state-of-the-art methods on adversarial-heterophily datasets (Texas, Wisconsin, Cornell, Actor) but underperforms on informative-heterophily datasets (Chameleon, Squirrel) – precisely the regime where per-edge routing has no useful decomposition to exploit. The pattern is itself the finding: the cost function’s ability to separate edge types serves as a diagnostic for the heterophily regime, revealing when fine-grained routing adds value over uniform channels and when it does not. Code is available at https://github.com/eyal-weiss/CSNA-public .

Method: ReGuider integrates pretrained time series foundation models as semantic teachers. During training, both the target forecasting model and pretrained model process input sequences. Intermediate embeddings from the pretrained model (rich in temporal/semantic information) are aligned with the target model’s encoder embeddings through representation-level supervision.

Result: Extensive experimentation across diverse datasets and architectures shows ReGuider consistently improves forecasting performance, confirming its effectiveness and versatility.

Conclusion: ReGuider successfully addresses the limitation of standard forecasting approaches by leveraging pretrained foundation models to guide temporal representation learning, resulting in more expressive representations and better forecasting accuracy.

Abstract: Nowadays, time series forecasting is predominantly approached through the end-to-end training of deep learning architectures using error-based objectives. While this is effective at minimizing average loss, it encourages the encoder to discard informative yet extreme patterns. This results in smooth predictions and temporal representations that poorly capture salient dynamics. To address this issue, we propose ReGuider, a plug-in method that can be seamlessly integrated into any forecasting architecture. ReGuider leverages pretrained time series foundation models as semantic teachers. During training, the input sequence is processed together by the target forecasting model and the pretrained model. Rather than using the pretrained model’s outputs directly, we extract its intermediate embeddings, which are rich in temporal and semantic information, and align them with the target model’s encoder embeddings through representation-level supervision. This alignment process enables the encoder to learn more expressive temporal representations, thereby improving the accuracy of downstream forecasting. Extensive experimentation across diverse datasets and architectures demonstrates that our ReGuider consistently improves forecasting performance, confirming its effectiveness and versatility.

Method: Dual-branch Transformer framework: cell-line branch uses modality-specific encoders for multi-omics profiles with Transformer blocks; drug branch represents compounds as molecular graphs encoded with GNN-Transformer; cross-modal fusion via Transformer-based module to model interactions and mitigate feature misalignment.

Result: Outperforms baselines on pharmacogenomic benchmarks under cold-start evaluation: RMSE=1.248, R²=0.875, AUC=0.987 with full multi-omics inputs, reducing classification error by 9.5%. Provides biological explanations via SHAP-based gene attributions and pathway enrichment.

Conclusion: DeepDTF effectively addresses cross-modal alignment challenges in drug response prediction, achieving state-of-the-art performance while providing interpretable biological insights through attribution analysis.

Abstract: Cancer drug response varies widely across tumors due to multi-layer molecular heterogeneity, motivating computational decision support for precision oncology. Despite recent progress in deep CDR models, robust alignment between high-dimensional multi-omics and chemically structured drugs remains challenging due to cross-modal misalignment and limited inductive bias. We present DeepDTF, an end-to-end dual-branch Transformer fusion framework for joint log(IC50) regression and drug sensitivity classification. The cell-line branch uses modality-specific encoders for multi-omics profiles with Transformer blocks to capture long-range dependencies, while the drug branch represents compounds as molecular graphs and encodes them with a GNN-Transformer to integrate local topology with global context. Omics and drug representations are fused by a Transformer-based module that models cross-modal interactions and mitigates feature misalignment. On public pharmacogenomic benchmarks under 5-fold cold-start cell-line evaluation, DeepDTF consistently outperforms strong baselines across omics settings, achieving up to RMSE=1.248, R^2=0.875, and AUC=0.987 with full multi-omics inputs, while reducing classification error (1-ACC) by 9.5%. Beyond accuracy, DeepDTF provides biologically grounded explanations via SHAP-based gene attributions and pathway enrichment with pre-ranked GSEA.

Method: Automated reward design framework using large language models to synthesize executable reward programs from environment instrumentation. Programs are constrained within formal validity envelopes and evaluated by training policies from scratch under fixed computational budgets, with selection based solely on sparse task returns.

Result: Iterative search generations consistently yield superior task returns and delivery counts across four Overcooked-AI layouts with varied coordination challenges. Most pronounced gains occur in environments with interaction bottlenecks. Synthesized shaping components increase interdependence in action selection and improve signal alignment in coordination-intensive tasks.

Conclusion: Automated search for objective-grounded reward programs can reduce manual engineering burden while producing shaping signals compatible with cooperative learning under finite computational budgets.

Abstract: Designing effective auxiliary rewards for cooperative multi-agent systems remains a precarious task; misaligned incentives risk inducing suboptimal coordination, especially where sparse task feedback fails to provide sufficient grounding. This study introduces an automated reward design framework that leverages large language models to synthesize executable reward programs from environment instrumentation. The procedure constrains candidate programs within a formal validity envelope and evaluates their efficacy by training policies from scratch under a fixed computational budget; selection depends exclusively on the sparse task return. The framework is evaluated across four distinct Overcooked-AI layouts characterized by varied corridor congestion, handoff dependencies, and structural asymmetries. Iterative search generations consistently yield superior task returns and delivery counts, with the most pronounced gains occurring in environments dominated by interaction bottlenecks. Diagnostic analysis of the synthesized shaping components indicates increased interdependence in action selection and improved signal alignment in coordination-intensive tasks. These results demonstrate that the search for objectivegrounded reward programs can mitigate the burden of manual engineering while producing shaping signals compatible with cooperative learning under finite budgets.

Method: Proposes a new LAIC framework with two components: (1) exploits cross-modal relations to produce more discriminative self-supervision signals for clustering, compatible with most VLMs training mechanisms; (2) learns category-wise continuous semantic centers via prompt learning to produce final clustering assignments.

Result: Extensive experiments on eight benchmark datasets show average improvement of 2.6% over state-of-the-art methods, and the learned semantic centers exhibit strong interpretability.

Conclusion: The proposed LAIC framework effectively addresses limitations of existing methods by leveraging cross-modal relations and prompt learning, achieving superior clustering performance with interpretable semantic centers.

Abstract: Language-Assisted Image Clustering (LAIC) augments the input images with additional texts with the help of vision-language models (VLMs) to promote clustering performance. Despite recent progress, existing LAIC methods often overlook two issues: (i) textual features constructed for each image are highly similar, leading to weak inter-class discriminability; (ii) the clustering step is restricted to pre-built image-text alignments, limiting the potential for better utilization of the text modality. To address these issues, we propose a new LAIC framework with two complementary components. First, we exploit cross-modal relations to produce more discriminative self-supervision signals for clustering, as it compatible with most VLMs training mechanisms. Second, we learn category-wise continuous semantic centers via prompt learning to produce the final clustering assignments. Extensive experiments on eight benchmark datasets demonstrate that our method achieves an average improvement of 2.6% over state-of-the-art methods, and the learned semantic centers exhibit strong interpretability. Code is available in the supplementary material.

Method: Proposes CoordLight with two key components: 1) Queue Dynamic State Encoding (QDSE) for improved traffic state representation, and 2) Neighbor-aware Policy Optimization (NAPO) with attention mechanism for coordinated decision-making among adjacent agents.

Result: Superior performance against state-of-the-art methods across three real-world traffic datasets with up to 196 intersections, demonstrating consistent improvements in diverse traffic networks with varying flows.

Conclusion: CoordLight effectively addresses coordination challenges in decentralized MARL for traffic control through novel state encoding and neighbor-aware optimization, enabling scalable network-level traffic optimization.

Abstract: Adaptive traffic signal control (ATSC) is crucial in alleviating congestion, maximizing throughput and promoting sustainable mobility in ever-expanding cities. Multi-Agent Reinforcement Learning (MARL) has recently shown significant potential in addressing complex traffic dynamics, but the intricacies of partial observability and coordination in decentralized environments still remain key challenges in formulating scalable and efficient control strategies. To address these challenges, we present CoordLight, a MARL-based framework designed to improve intra-neighborhood traffic by enhancing decision-making at individual junctions (agents), as well as coordination with neighboring agents, thereby scaling up to network-level traffic optimization. Specifically, we introduce the Queue Dynamic State Encoding (QDSE), a novel state representation based on vehicle queuing models, which strengthens the agents’ capability to analyze, predict, and respond to local traffic dynamics. We further propose an advanced MARL algorithm, named Neighbor-aware Policy Optimization (NAPO). It integrates an attention mechanism that discerns the state and action dependencies among adjacent agents, aiming to facilitate more coordinated decision-making, and to improve policy learning updates through robust advantage calculation. This enables agents to identify and prioritize crucial interactions with influential neighbors, thus enhancing the targeted coordination and collaboration among agents. Through comprehensive evaluations against state-of-the-art traffic signal control methods over three real-world traffic datasets composed of up to 196 intersections, we empirically show that CoordLight consistently exhibits superior performance across diverse traffic networks with varying traffic flows. The code is available at https://github.com/marmotlab/CoordLight

Method: MolEvolve reformulates molecular discovery as autonomous look-ahead planning using LLMs to explore chemical symbolic operations, with MCTS for test-time planning and external tools like RDKit. The system self-discovers optimal trajectories through evolutionary reasoning chains.

Result: MolEvolve outperforms baselines in both property prediction and molecule optimization tasks while evolving transparent, human-readable chemical insights.

Conclusion: The framework enables autonomous molecular discovery with improved interpretability and performance, addressing key limitations in current chemistry deep learning approaches.

Abstract: Despite deep learning’s success in chemistry, its impact is hindered by a lack of interpretability and an inability to resolve activity cliffs, where minor structural nuances trigger drastic property shifts. Current representation learning, bound by the similarity principle, often fails to capture these structural-activity discontinuities. To address this, we introduce MolEvolve, an evolutionary framework that reformulates molecular discovery as an autonomous, look-ahead planning problem. Unlike traditional methods that depend on human-engineered features or rigid prior knowledge, MolEvolve leverages a Large Language Model (LLM) to actively explore and evolve a library of executable chemical symbolic operations. By utilizing the LLM to cold start and an Monte Carlo Tree Search (MCTS) engine for test-time planning with external tools (e.g. RDKit), the system self-discovers optimal trajectories autonomously. This process evolves transparent reasoning chains that translate complex structural transformations into actionable, human-readable chemical insights. Experimental results demonstrate that MolEvolve’s autonomous search not only evolves transparent, human-readable chemical insights, but also outperforms baselines in both property prediction and molecule optimization tasks.

Method: Uses ensemble approach with subbagging (subsampling without replacement) to preserve local nearest neighbor distance distributions. Analyzes interplay between sampling rate, k-NN size, and ensemble size theoretically and experimentally. Also combines bagging with neighborhood smoothing techniques.

Result: Bagged estimators significantly reduce variance and mean squared error compared to non-bagged baselines within broad hyperparameter regions, with controllable bias impact. Combining bagging with neighborhood smoothing provides substantial further improvements in LID estimation performance.

Conclusion: The proposed ensemble bagging approach effectively addresses the variance-bias tradeoff in LID estimation, with well-characterized hyperparameter regions enabling informed selection based on application preferences. The method provides reliable variance reduction while maintaining local neighborhood characteristics.

Abstract: The theory of Local Intrinsic Dimensionality (LID) has become a valuable tool for characterizing local complexity within and across data manifolds, supporting a range of data mining and machine learning tasks. Accurate LID estimation requires samples drawn from small neighborhoods around each query to avoid biases from nonlocal effects and potential manifold mixing, yet limited data within such neighborhoods tends to cause high estimation variance. As a variance reduction strategy, we propose an ensemble approach that uses subbagging to preserve the local distribution of nearest neighbor (NN) distances. The main challenge is that the uniform reduction in total sample size within each subsample increases the proximity threshold for finding a fixed number k of NNs around the query. As a result, in the specific context of LID estimation, the sampling rate has an additional, complex interplay with the neighborhood size, where both combined determine the sample size as well as the locality and resolution considered for estimation. We analyze both theoretically and experimentally how the choice of the sampling rate and the k-NN size used for LID estimation, alongside the ensemble size, affects performance, enabling informed prior selection of these hyper-parameters depending on application-based preferences. Our results indicate that within broad and well-characterized regions of the hyper-parameters space, using a bagged estimator will most often significantly reduce variance as well as the mean squared error when compared to the corresponding non-bagged baseline, with controllable impact on bias. We additionally propose and evaluate different ways of combining bagging with neighborhood smoothing for substantial further improvements on LID estimation performance.

Method: Uses generative latent flow-matching model that conditions on current-day weather maps and autoregressively predicts subsequent days’ weather maps in learned latent space. Replaces rotary positional encodings with trainable positional embeddings and extends temporal context window to better capture long-range temporal dependencies.

Result: Despite having only 276M parameters, achieves performance comparable to LaDCast (1.6B parameters) while operating at significantly higher inference speeds. Model is computationally efficient and offers strong predictive performance for subseasonal forecasting.

Conclusion: Marchuk provides an effective solution for subseasonal weather forecasting with computational efficiency and strong performance, making it practical for real-world applications. The model is open-sourced for community use.

Abstract: Accurate subseasonal weather forecasting remains a major challenge due to the inherently chaotic nature of the atmosphere, which limits the predictive skill of conventional models beyond the mid-range horizon (approximately 15 days). In this work, we present \textit{Marchuk}, a generative latent flow-matching model for global weather forecasting spanning mid-range to subseasonal timescales, with prediction horizons of up to 30 days. Marchuk conditions on current-day weather maps and autoregressively predicts subsequent days’ weather maps within the learned latent space. We replace rotary positional encodings (RoPE) with trainable positional embeddings and extend the temporal context window, which together enhance the model’s ability to represent and propagate long-range temporal dependencies during latent forecasting. Marchuk offers two key advantages: high computational efficiency and strong predictive performance. Despite its compact architecture of only 276 million parameters, the model achieves performance comparable to LaDCast, a substantially larger model with 1.6 billion parameters, while operating at significantly higher inference speeds. We open-source our inference code and model at: https://v-gen-ai.github.io/Marchuk/

Method: Developed stacked LSTM surrogate trained on wave-elevation time series from URANS numerical wave tank simulations. Used 49 random-phase realizations for three sea states with fixed forward speed. Compared loss functions (MSE, relative-entropy-based objectives, amplitude-weighted variants) to balance average error and tail fidelity.

Result: Model successfully tracks onset and growth of large-amplitude roll consistent with parametric excitation in severe sea states. Captures corresponding changes in roll probability density functions (PDFs). Demonstrates trade-offs between different loss functions for average error vs. tail fidelity relevant to risk assessment.

Conclusion: Data-driven LSTM surrogates can effectively learn nonlinear causal mappings from wave-motion data to predict parametric roll episodes and associated statistical shifts, providing valuable tools for ship operability and risk assessment.

Abstract: Parametric roll is a rare but high-consequence instability that can trigger abrupt regime changes in ship response, including pronounced shifts in roll statistics and tail risk. This paper develops a data-driven surrogate that learns the nonlinear, causal functional mapping from incident wave–motion time series to vessel motions, and demonstrates that the surrogate reproduces both (i) parametric roll episodes and (ii) the associated statistical shifts in the response. Crucially, the learning framework is data-source agnostic: the paired wave–motion time series can be obtained from controlled experiments (e.g., towing-tank or basin tests with wave probes and motion tracking) when a hull exists, or from high-fidelity simulations during design when experiments are not yet available. To provide a controlled severe-sea demonstration, we generate training data with a URANS numerical wave tank, using long-crested irregular seas synthesized from a modified Pierson–Moskowitz spectrum. The demonstration dataset comprises 49 random-phase realizations for each of three sea states, simulated at a fixed forward speed selected to yield encounter conditions under which parametric-roll episodes can occur. A stacked LSTM surrogate is trained on wave-elevation time series and evaluated on held-out realizations using time-domain accuracy and distributional fidelity metrics. In the most severe case, the model tracks the onset and growth of large-amplitude roll consistent with parametric excitation, and captures the corresponding changes in roll probability density functions (PDFs). We further compare loss-function choices (MSE, relative-entropy-based objectives, and amplitude-weighted variants) and show how they trade average error for improved tail fidelity relevant to operability and risk assessment.

Method: CUA-Suite provides VideoCUA with ~10,000 human-demonstrated tasks across 87 diverse applications featuring continuous 30 fps screen recordings, kinematic cursor traces, and multi-layered reasoning annotations (55 hours, 6M frames). It also includes UI-Vision benchmark for evaluation and GroundCUA with 56K annotated screenshots and 3.6M UI element annotations.

Result: The dataset reveals current foundation action models struggle substantially with professional desktop applications (~60% task failure rate). CUA-Suite’s rich multimodal corpus supports emerging research including generalist screen parsing, continuous spatial control, video-based reward modeling, and visual world models.

Conclusion: CUA-Suite addresses the critical bottleneck in computer-use agent development by providing continuous video demonstrations and dense annotations, enabling research in multimodal understanding and generation for desktop automation.

Abstract: Computer-use agents (CUAs) hold great promise for automating complex desktop workflows, yet progress toward general-purpose agents is bottlenecked by the scarcity of continuous, high-quality human demonstration videos. Recent work emphasizes that continuous video, not sparse screenshots, is the critical missing ingredient for scaling these agents. However, the largest existing open dataset, ScaleCUA, contains only 2 million screenshots, equating to less than 20 hours of video. To address this bottleneck, we introduce CUA-Suite, a large-scale ecosystem of expert video demonstrations and dense annotations for professional desktop computer-use agents. At its core is VideoCUA, which provides approximately 10,000 human-demonstrated tasks across 87 diverse applications with continuous 30 fps screen recordings, kinematic cursor traces, and multi-layerfed reasoning annotations, totaling approximately 55 hours and 6 million frames of expert video. Unlike sparse datasets that capture only final click coordinates, these continuous video streams preserve the full temporal dynamics of human interaction, forming a superset of information that can be losslessly transformed into the formats required by existing agent frameworks. CUA-Suite further provides two complementary resources: UI-Vision, a rigorous benchmark for evaluating grounding and planning capabilities in CUAs, and GroundCUA, a large-scale grounding dataset with 56K annotated screenshots and over 3.6 million UI element annotations. Preliminary evaluation reveals that current foundation action models struggle substantially with professional desktop applications (~60% task failure rate). Beyond evaluation, CUA-Suite’s rich multimodal corpus supports emerging research directions including generalist screen parsing, continuous spatial control, video-based reward modeling, and visual world models. All data and models are publicly released.

Method: LSTM model trained on virtual battery dataset capturing real-world variability, combined with Maximum Mean Discrepancy for domain adaptation between simulated and target domains, and Conformal Prediction for uncertainty quantification.

Result: Framework improves generalization and trustworthiness of SOH forecasts across heterogeneous cells by addressing domain shift and providing calibrated prediction intervals.

Conclusion: The proposed uncertainty-aware transfer learning framework effectively addresses domain shift in battery SOH forecasting, enhancing both accuracy and reliability for real-world applications.

Abstract: Accurate forecasting of state-of-health (SOH) is essential for ensuring safe and reliable operation of lithium-ion cells. However, existing models calibrated on laboratory tests at specific conditions often fail to generalize to new cells that differ due to small manufacturing variations or operate under different conditions. To address this challenge, an uncertainty-aware transfer learning framework is proposed, combining a Long Short-Term Memory (LSTM) model with domain adaptation via Maximum Mean Discrepancy (MMD) and uncertainty quantification through Conformal Prediction (CP). The LSTM model is trained on a virtual battery dataset designed to capture real-world variability in electrode manufacturing and operating conditions. MMD aligns latent feature distributions between simulated and target domains to mitigate domain shift, while CP provides calibrated, distribution-free prediction intervals. This framework improves both the generalization and trustworthiness of SOH forecasts across heterogeneous cells.

Method: Assumes distributions absolutely continuous with respect to product of marginals, introduces linear VC dimension (maximum shattered set on axis-parallel lines), and develops novel estimators departing from standard empirical mean.

Result: Proves uniform law of large numbers holds if and only if linear VC dimension is finite, shows linear VC dimension can be arbitrarily smaller than classical VC dimension, and demonstrates necessity of non-standard estimators.

Conclusion: Establishes new uniform convergence theory for product spaces with product-compatible distributions, introduces linear VC dimension as key complexity measure, and opens questions about quantitative sample complexity bounds.

Abstract: Uniform laws of large numbers form a cornerstone of Vapnik–Chervonenkis theory, where they are characterized by the finiteness of the VC dimension. In this work, we study uniform convergence phenomena in cartesian product spaces, under assumptions on the underlying distribution that are compatible with the product structure. Specifically, we assume that the distribution is absolutely continuous with respect to the product of its marginals, a condition that captures many natural settings, including product distributions, sparse mixtures of product distributions, distributions with low mutual information, and more. We show that, under this assumption, a uniform law of large numbers holds for a family of events if and only if the linear VC dimension of the family is finite. The linear VC dimension is defined as the maximum size of a shattered set that lies on an axis-parallel line, namely, a set of vectors that agree on all but at most one coordinate. This dimension is always at most the classical VC dimension, yet it can be arbitrarily smaller. For instance, the family of convex sets in $\mathbb{R}^d$ has linear VC dimension $2$, while its VC dimension is infinite already for $d\ge 2$. Our proofs rely on estimator that departs substantially from the standard empirical mean estimator and exhibits more intricate structure. We show that such deviations from the standard empirical mean estimator are unavoidable in this setting. Throughout the paper, we propose several open questions, with a particular focus on quantitative sample complexity bounds.

Method: Two-pronged approach: 1) For deterministic models, integrate differentiable spectral Leray projection based on Helmholtz-Hodge decomposition to restrict regression to divergence-free velocity fields. 2) For generative models, construct divergence-free Gaussian reference measure via curl-based pushforward to ensure entire probability flow remains subspace-consistent.

Result: Experiments on 2D Navier-Stokes equations demonstrate exact incompressibility up to discretization error and substantially improved stability and physical consistency compared to unconstrained models.

Conclusion: The framework provides hard structural guarantees for physical consistency in fluid dynamics modeling, addressing fundamental limitations of penalty-based approaches through intrinsic constraint enforcement.

Abstract: Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer soft regularization, they provide no structural guarantees, resulting in spurious divergence and long-term collapse. In this work, we introduce a unified framework that enforces the incompressible continuity equation as a hard, intrinsic constraint for both deterministic and generative modeling. First, to project deterministic models onto the divergence-free subspace, we integrate a differentiable spectral Leray projection grounded in the Helmholtz-Hodge decomposition, which restricts the regression hypothesis space to physically admissible velocity fields. Second, to generate physically consistent distributions, we show that simply projecting model outputs is insufficient when the prior is incompatible. To address this, we construct a divergence-free Gaussian reference measure via a curl-based pushforward, ensuring the entire probability flow remains subspace-consistent by construction. Experiments on 2D Navier-Stokes equations demonstrate exact incompressibility up to discretization error and substantially improved stability and physical consistency.

Method: Autoresearch-style pipeline powered by Claude Code that starts from existing attack implementations (like GCG) and iteratively produces new algorithms through optimization with dense quantitative feedback.

Result: Discovered algorithms achieve up to 40% attack success rate on CBRN queries against GPT-OSS-Safeguard-20B (vs ≤10% for baselines) and generalize to achieve 100% ASR against Meta-SecAlign-70B (vs 56% for best baseline).

Conclusion: White-box adversarial red-teaming is well-suited for LLM agent automation, and incremental safety/security research can be effectively automated using LLM agents.

Abstract: LLM agents like Claude Code can not only write code but also be used for autonomous AI research and engineering \citep{rank2026posttrainbench, novikov2025alphaevolve}. We show that an \emph{autoresearch}-style pipeline \citep{karpathy2026autoresearch} powered by Claude Code discovers novel white-box adversarial attack \textit{algorithms} that \textbf{significantly outperform all existing (30+) methods} in jailbreaking and prompt injection evaluations. Starting from existing attack implementations, such as GCG~\citep{zou2023universal}, the agent iterates to produce new algorithms achieving up to 40% attack success rate on CBRN queries against GPT-OSS-Safeguard-20B, compared to $\leq$10% for existing algorithms (\Cref{fig:teaser}, left). The discovered algorithms generalize: attacks optimized on surrogate models transfer directly to held-out models, achieving \textbf{100% ASR against Meta-SecAlign-70B} \citep{chen2025secalign} versus 56% for the best baseline (\Cref{fig:teaser}, middle). Extending the findings of~\cite{carlini2025autoadvexbench}, our results are an early demonstration that incremental safety and security research can be automated using LLM agents. White-box adversarial red-teaming is particularly well-suited for this: existing methods provide strong starting points, and the optimization objective yields dense, quantitative feedback. We release all discovered attacks alongside baseline implementations and evaluation code at https://github.com/romovpa/claudini.

Method: Proposes Sequential-AMPC, a sequential neural policy that generates MPC candidate control sequences by sharing parameters across the prediction horizon. For deployment, wraps the policy in a safety-augmented online evaluation and fallback mechanism (Safe Sequential-AMPC).

Result: Compared to feedforward policy baselines, Sequential-AMPC requires substantially fewer expert MPC rollouts, yields candidate sequences with higher feasibility rates, and improves closed-loop safety. On high-dimensional systems, it shows better learning dynamics, performance in fewer epochs, and stable validation improvement where baselines stagnate.

Conclusion: Sequential-AMPC provides an efficient approximation to NMPC that reduces offline training requirements while maintaining safety through online evaluation mechanisms, making it suitable for deployment on resource-constrained hardware.

Abstract: The practical deployment of nonlinear model predictive control (NMPC) is often limited by online computation: solving a nonlinear program at high control rates can be expensive on embedded hardware, especially when models are complex or horizons are long. Learning-based NMPC approximations shift this computation offline but typically demand large expert datasets and costly training. We propose Sequential-AMPC, a sequential neural policy that generates MPC candidate control sequences by sharing parameters across the prediction horizon. For deployment, we wrap the policy in a safety-augmented online evaluation and fallback mechanism, yielding Safe Sequential-AMPC. Compared to a naive feedforward policy baseline across several benchmarks, Sequential-AMPC requires substantially fewer expert MPC rollouts and yields candidate sequences with higher feasibility rates and improved closed-loop safety. On high-dimensional systems, it also exhibits better learning dynamics and performance in fewer epochs while maintaining stable validation improvement where the feedforward baseline can stagnate.

Method: Aligns uncertainty attributions with Co-12 XAI evaluation framework, proposes concrete implementations for correctness, consistency, continuity, and compactness properties, and introduces conveyance property specifically for uncertainty attributions. Evaluates eight metrics across combinations of uncertainty quantification and feature attribution methods on tabular and image data.

Result: Gradient-based methods outperform perturbation-based approaches in consistency and conveyance; Monte-Carlo dropconnect outperforms Monte-Carlo dropout in most metrics. Most metrics rank methods consistently across samples, but inter-method agreement remains low, suggesting no single metric sufficiently evaluates uncertainty attribution quality.

Conclusion: Proposed evaluation framework establishes foundation for systematic comparison and development of uncertainty attribution methods, addressing current inconsistencies in the field.

Abstract: Research on explainable AI (XAI) has frequently focused on explaining model predictions. More recently, methods have been proposed to explain prediction uncertainty by attributing it to input features (uncertainty attributions). However, the evaluation of these methods remains inconsistent as studies rely on heterogeneous proxy tasks and metrics, hindering comparability. We address this by aligning uncertainty attributions with the well-established Co-12 framework for XAI evaluation. We propose concrete implementations for the correctness, consistency, continuity, and compactness properties. Additionally, we introduce conveyance, a property tailored to uncertainty attributions that evaluates whether controlled increases in epistemic uncertainty reliably propagate to feature-level attributions. We demonstrate our evaluation framework with eight metrics across combinations of uncertainty quantification and feature attribution methods on tabular and image data. Our experiments show that gradient-based methods consistently outperform perturbation-based approaches in consistency and conveyance, while Monte-Carlo dropconnect outperforms Monte-Carlo dropout in most metrics. Although most metrics rank the methods consistently across samples, inter-method agreement remains low. This suggests no single metric sufficiently evaluates uncertainty attribution quality. The proposed evaluation framework contributes to the body of knowledge by establishing a foundation for systematic comparison and development of uncertainty attribution methods.

Method: AVO replaces classical evolutionary variation operators with autonomous coding agents that operate in a self-directed loop. These agents can consult the current evolutionary lineage, domain-specific knowledge bases, and execution feedback to propose, repair, critique, and verify implementation edits. The system was evaluated on attention kernels for NVIDIA Blackwell GPUs.

Result: Over 7 days of autonomous evolution on multi-head attention, AVO discovered kernels that outperform cuDNN by up to 3.5% and FlashAttention-4 by up to 10.5%. For grouped-query attention, only 30 minutes of additional adaptation yielded gains of up to 7.0% over cuDNN and 9.3% over FlashAttention-4.

Conclusion: Agentic variation operators represent a significant advancement beyond prior LLM-in-the-loop evolutionary pipelines by elevating agents from candidate generators to autonomous variation operators, enabling discovery of performance-critical optimizations that surpass expert-engineered implementations on state-of-the-art hardware.

Abstract: Agentic Variation Operators (AVO) are a new family of evolutionary variation operators that replace the fixed mutation, crossover, and hand-designed heuristics of classical evolutionary search with autonomous coding agents. Rather than confining a language model to candidate generation within a prescribed pipeline, AVO instantiates variation as a self-directed agent loop that can consult the current lineage, a domain-specific knowledge base, and execution feedback to propose, repair, critique, and verify implementation edits. We evaluate AVO on attention, among the most aggressively optimized kernel targets in AI, on NVIDIA Blackwell (B200) GPUs. Over 7 days of continuous autonomous evolution on multi-head attention, AVO discovers kernels that outperform cuDNN by up to 3.5% and FlashAttention-4 by up to 10.5% across the evaluated configurations. The discovered optimizations transfer readily to grouped-query attention, requiring only 30 minutes of additional autonomous adaptation and yielding gains of up to 7.0% over cuDNN and 9.3% over FlashAttention-4. Together, these results show that agentic variation operators move beyond prior LLM-in-the-loop evolutionary pipelines by elevating the agent from candidate generator to variation operator, and can discover performance-critical micro-architectural optimizations that produce kernels surpassing state-of-the-art expert-engineered attention implementations on today’s most advanced GPU hardware.

Method: Two-stage approach: 1) Rejection Fine-Tuning (RFT) for continuous co-evolution of data and models in autonomous loop; 2) Group Relative Self-Distillation (GRSD) identifies critical fork points in group rollouts and constructs dense step-level supervision from successful trajectories to correct failed ones.

Result: Achieves 81.0% Pass@1 success rate on AndroidWorld, outperforming recent baselines and exceeding human-level performance. Ablation studies verify GRSD effectiveness.

Conclusion: Represents significant leap toward efficient, self-evolving, high-performance mobile GUI automation without expensive manual data annotation.

Abstract: Autonomous mobile GUI agents have attracted increasing attention along with the advancement of Multimodal Large Language Models (MLLMs). However, existing methods still suffer from inefficient learning from failed trajectories and ambiguous credit assignment under sparse rewards for long-horizon GUI tasks. To that end, we propose UI-Voyager, a novel two-stage self-evolving mobile GUI agent. In the first stage, we employ Rejection Fine-Tuning (RFT), which enables the continuous co-evolution of data and models in a fully autonomous loop. The second stage introduces Group Relative Self-Distillation (GRSD), which identifies critical fork points in group rollouts and constructs dense step-level supervision from successful trajectories to correct failed ones. Extensive experiments on AndroidWorld show that our 4B model achieves an 81.0% Pass@1 success rate, outperforming numerous recent baselines and exceeding human-level performance. Ablation and case studies further verify the effectiveness of GRSD. Our method represents a significant leap toward efficient, self-evolving, and high-performance mobile GUI automation without expensive manual data annotation.

Method: Uses perplexity differences between base and fine-tuned models to identify specialized knowledge: prompts where fine-tuned model has low perplexity but base model has high perplexity indicate specialized knowledge. Creates synthetic training dataset through iterative generation of similar prompts, requiring only initial models and few representative prompts.

Result: Models fine-tuned using TuneShift-KD achieve higher accuracy than prior approaches, enabling effective transfer of specialized knowledge without access to original training data.

Conclusion: TuneShift-KD provides an automated, data-efficient approach for knowledge transfer between LLMs, addressing practical constraints of data availability and model evolution.

Abstract: To embed domain-specific or specialized knowledge into pre-trained foundation models, fine-tuning using techniques such as parameter efficient fine-tuning (e.g. LoRA) is a common practice. However, as new LLM architectures and pre-trained models emerge, transferring this specialized knowledge to newer models becomes an important task. In many scenarios, the original specialized data may be unavailable due to privacy or commercial restrictions, necessitating distillation and transfer of this specialized knowledge from the fine-tuned base model to a different pre-trained model. We present TuneShift-KD, a novel approach that automatically distills specialized knowledge from a fine-tuned model to a target model using only a few examples representative of the specialized information. Our key insight is that specialized knowledge can be identified through perplexity differences between base and fine-tuned models: prompts where the fine-tuned model responds confidently (low perplexity), but the base model struggles (high perplexity), indicate queries corresponding to the specialized knowledge learned by the fine-tuned model. TuneShift-KD leverages this insight to create a synthetic training dataset to transfer the specialized knowledge. Using an iterative process, TuneShift-KD generates more prompts similar to those that generated responses with specialized knowledge. TuneShift-KD does not require training discriminators or access to training datasets. It is an automated approach that only requires the initial fine-tuned and base models and a few representative prompts. Our experiments demonstrate that models fine-tuned using TuneShift-KD achieve higher accuracy than prior approaches, enabling ease of deployment and more effective transfer of the specialized knowledge.

Method: RAVEN uses recurrence-aware next-visit event prediction with autoregressive generation of tokenized clinical events. It introduces regularization for repeated events and is trained on over 1M patients. The approach addresses data-constrained, compute-saturated regimes.

Result: RAVEN rivals fine-tuned Transformer models in zero-shot disease forecasting and outperforms simulation-based next-token approaches. It generalizes to external cohorts despite lossy clinical code mappings and feature gaps without parameter updates.

Conclusion: RAVEN demonstrates effective generative pretraining for EHR data, addressing key evaluation pitfalls and showing strong generalization capabilities, though scaling requires commensurate data increases.

Abstract: While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present RAVEN, a novel generative pretraining strategy for sequential EHR data based on Recurrence-Aware next-Visit EveNt prediction. Leveraging a dataset of over one million unique individuals, our model learns to autoregressively generate tokenized clinical events for the next visit conditioned on patient history. We introduce regularization on predicting repeated events and highlight a key pitfall in EHR-based foundation model evaluations: repeated event tokens can inflate performance metrics when new onsets are not distinguished from subsequent occurrences. Furthermore, we empirically investigate the scaling behaviors in a data-constrained, compute-saturated regime, showing that simply increasing model size is suboptimal without commensurate increases in data volume. We evaluate our model via zero-shot prediction for forecasting the incidence of a diverse set of diseases, where it rivals fully fine-tuned representation-based Transformer models and outperforms widely used simulation-based next-token approaches. Finally, without additional parameter updates, we show that RAVEN can generalize to an external patient cohort under lossy clinical code mappings and feature coverage gaps.

Method: Leverages denoised latent features from video generation models through: (1) shortcut forcing for recursive multi-resolution step compression, (2) autoregressive dense reward model on latent representations, and (3) Gaussian vocabulary sampling for GRPO to constrain exploration to physically plausible trajectories.

Result: Achieves 87.7 EPDMS on NavSim v2, establishing state-of-the-art performance and demonstrating latent-space RL effectiveness for autonomous driving.

Conclusion: DreamerAD enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling, making latent-space RL practical for real-world driving applications.

Abstract: We introduce DreamerAD, the first latent world model framework that enables efficient reinforcement learning for autonomous driving by compressing diffusion sampling from 100 steps to 1 - achieving 80x speedup while maintaining visual interpretability. Training RL policies on real-world driving data incurs prohibitive costs and safety risks. While existing pixel-level diffusion world models enable safe imagination-based training, they suffer from multi-step diffusion inference latency (2s/frame) that prevents high-frequency RL interaction. Our approach leverages denoised latent features from video generation models through three key mechanisms: (1) shortcut forcing that reduces sampling complexity via recursive multi-resolution step compression, (2) an autoregressive dense reward model operating directly on latent representations for fine-grained credit assignment, and (3) Gaussian vocabulary sampling for GRPO that constrains exploration to physically plausible trajectories. DreamerAD achieves 87.7 EPDMS on NavSim v2, establishing state-of-the-art performance and demonstrating that latent-space RL is effective for autonomous driving.

Method: Multilevel Euler-Maruyama (ML-EM) uses a hierarchy of approximators f¹,…,f^k to the drift function f, with increasing accuracy and cost. It performs many evaluations of cheaper approximators and few evaluations of the most accurate one, leveraging variance reduction techniques.

Result: ML-EM achieves ε-approximation with ε^{-γ} compute (same as single drift evaluation) instead of traditional EM’s ε^{-γ-1}. For diffusion models with γ≈2.5, this yields up to 4x speedup on CelebA 64x64 image generation.

Conclusion: ML-EM provides polynomial speedups for SDE solving and diffusion model sampling, with potential for larger gains on practical large-scale models. The method effectively decouples computational cost from accuracy requirements.

Abstract: We introduce the Multilevel Euler-Maruyama (ML-EM) method compute solutions of SDEs and ODEs using a range of approximators $f^1,\dots,f^k$ to the drift $f$ with increasing accuracy and computational cost, only requiring a few evaluations of the most accurate $f^k$ and many evaluations of the less costly $f^1,\dots,f^{k-1}$. If the drift lies in the so-called Harder than Monte Carlo (HTMC) regime, i.e. it requires $ε^{-γ}$ compute to be $ε$-approximated for some $γ>2$, then ML-EM $ε$-approximates the solution of the SDE with $ε^{-γ}$ compute, improving over the traditional EM rate of $ε^{-γ-1}$. In other terms it allows us to solve the SDE at the same cost as a single evaluation of the drift. In the context of diffusion models, the different levels $f^{1},\dots,f^{k}$ are obtained by training UNets of increasing sizes, and ML-EM allows us to perform sampling with the equivalent of a single evaluation of the largest UNet. Our numerical experiments confirm our theory: we obtain up to fourfold speedups for image generation on the CelebA dataset downscaled to 64x64, where we measure a $γ\approx2.5$. Given that this is a polynomial speedup, we expect even stronger speedups in practical applications which involve orders of magnitude larger networks.

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Method: Proposes Epistemic Time-Dilation MAPPO with Dual-Gated Epistemic Trigger that modulates execution frequency using aleatoric uncertainty (Shannon entropy of policy) and epistemic uncertainty (state-value divergence in Twin-Critic architecture). Structures environment as Semi-Markov Decision Process with SMDP-Aligned Asynchronous Gradient Masking Critic for proper credit assignment.

Result: Achieves >60% relative baseline improvements over current temporal models, prevents premature policy collapse, enables emergent Temporal Role Specialization, and reduces computational overhead by 73.6% during off-ball execution without deteriorating centralized task dominance.

Conclusion: The approach successfully enables MARL agents to autonomously modulate execution frequency based on uncertainty, significantly reducing computational requirements while maintaining performance, making it suitable for edge device deployment.

Abstract: While Multi-Agent Reinforcement Learning (MARL) algorithms achieve unprecedented successes across complex continuous domains, their standard deployment strictly adheres to a synchronous operational paradigm. Under this paradigm, agents are universally forced to execute deep neural network inferences at every micro-frame, regardless of immediate necessity. This dense throughput acts as a fundamental barrier to physical deployment on edge-devices where thermal and metabolic budgets are highly constrained. We propose Epistemic Time-Dilation MAPPO (ETD-MAPPO), augmented with a Dual-Gated Epistemic Trigger. Instead of depending on rigid frame-skipping (macro-actions), agents autonomously modulate their execution frequency by interpreting aleatoric uncertainty (via Shannon entropy of their policy) and epistemic uncertainty (via state-value divergence in a Twin-Critic architecture). To format this, we structure the environment as a Semi-Markov Decision Process (SMDP) and build the SMDP-Aligned Asynchronous Gradient Masking Critic to ensure proper credit assignment. Empirical findings demonstrate massive improvements (> 60% relative baseline acquisition leaps) over current temporal models. By assessing LBF, MPE, and the 115-dimensional state space of Google Research Football (GRF), ETD correctly prevented premature policy collapse. Remarkably, this unconstrained approach leads to emergent Temporal Role Specialization, reducing computational overhead by a statistically dominant 73.6% entirely during off-ball execution without deteriorating centralized task dominance.

Method: SEMA uses a collaborative multi-agent framework with self-evolution through adaptive bias calibration (in-episode assessment and cross-episode analysis), dynamic observation pruning based on structural entropy to model game states topologically, and a hybrid knowledge-memory mechanism integrating micro-trajectories, macro-experience, and hierarchical domain knowledge.

Result: Experiments across multiple StarCraft II maps show SEMA achieves superior win rates while reducing average decision latency by over 50%, validating its efficiency and robustness in complex RTS scenarios.

Conclusion: SEMA effectively addresses LLM limitations in RTS environments through its self-evolving multi-agent framework, demonstrating significant improvements in both decision quality and speed.

Abstract: Large language models (LLMs) have demonstrated exceptional potential in complex reasoning,pioneering a new paradigm for autonomous agent decision making in dynamic settings. However, in Real-Time Strategy (RTS) scenarios, LLMs suffer from a critical speed-quality trade-off. Specifically expansive state spaces and time limits render inference delays prohibitive, while stochastic planning errors undermine logical consistency. To address these challenges, we present SEMA (Self-Evolving Multi-Agent), a novel framework designed for high-performance, low-latency decision-making in RTS environments. This collaborative multi-agent framework facilitates self-evolution by adaptively calibrating model bias through in-episode assessment and cross-episode analysis. We further incorporate dynamic observation pruning based on structural entropy to model game states topologically. By distilling high dimensional data into core semantic information, this approach significantly reduces inference time. We also develop a hybrid knowledge-memory mechanism that integrates micro-trajectories, macro-experience, and hierarchical domain knowledge, thereby enhancing both strategic adaptability and decision consistency. Experiments across multiple StarCraft II maps demonstrate that SEMA achieves superior win rates while reducing average decision latency by over 50%, validating its efficiency and robustness in complex RTS scenarios.

Method: The work modifies an existing AMAPF algorithm designed for continuous space where agents are modeled as disks of equal size. The original algorithm required strict minimum separation of 4 agent radii between any start/goal positions. The proposed modification reduces this requirement to 2√3 agent radii while preserving theoretical guarantees.

Result: Theoretically demonstrated that the enhanced algorithm preserves original theoretical properties, including the guarantee that all agents will eventually achieve their goals safely and without collisions, despite the relaxed constraints.

Conclusion: The modification makes AMAPF more practical for real-world applications by reducing the required separation between agent positions, potentially enabling deployment in more constrained environments while maintaining safety guarantees.

Abstract: The study addressed the problem of Anonymous Multi-Agent Path-finding (AMAPF). Unlike the classical formulation, where the assignment of agents to goals is fixed, in the anonymous MAPF setting it is irrelevant which agent reaches specific goal, provided that all goals are occupied. Most existing multi-agent pathfinding algorithms rely on a discrete representation of the environment (e.g., square grids) and do not account for the sizes of agents. This limits their applicability in real-world scenarios, such as trajectory planning for mobile robots in warehouses. Conversely, methods operating in continuous space typically impose substantial restrictions on the input data, such as constraints on the distances between initial and goal positions or between start/goal positions and obstacles. In this work, we considered one of the AMAPF algorithms designed for continuous space, where agents are modeled as disks of equal size. The algorithm requires a strict minimum separation of $4$ agent radii between any start/goal positions. Proposed a modification aimed at relaxing the constraints and reduce this limit from $4$ to $2\sqrt{3}$. We theoretically demonstrated that the proposed enhancements preserve original theoretical properties, including the guarantee that all agents will eventually achieve their goals safely and without collisions.

Method: Develops discrete-time game model where players reallocate resources one hop per time step, uses reachability analysis for graph-constrained movement, extends to attackers that can split/merge, applies superposition principles for defender strategies, and creates set-based dynamic programming algorithm.

Result: Determines necessary and sufficient defender resources for guaranteed defense, computes optimal strategies via dynamic programming, and validates approach through numerical simulations and Robotarium hardware experiments.

Conclusion: The dDAB framework successfully extends Blotto games to dynamic graph settings, providing analytical tools and algorithms for resource allocation in security scenarios with moving threats over networks.

Abstract: This work introduces the dynamic Defender-Attacker Blotto (dDAB) game, extending the classical static Blotto game to a dynamic resource allocation setting over graphs. In the dDAB game, a defender is required to maintain numerical superiority against attacker resources across a set of key nodes in a connected graph. The engagement unfolds as a discrete-time game, where each player reallocates its resources in turn, with resources allowed to move at most one hop per time step. The primary goal is to determine the necessary and sufficient amount of defender resources required to guarantee sustained defense, along with the corresponding strategies. To address the central challenge arising from graph-constrained resource reallocation, we conduct a reachability analysis, starting with simplified settings where attacker resources act as a single cohesive group. We then extend the framework to allow attacker resources to split and merge arbitrarily, and construct defender strategies using superposition principles. A set-based dynamic programming algorithm is developed to compute the optimal strategies, as well as the minimum amount of defender resources to ensure successful defense. The effectiveness of our approach is demonstrated through numerical simulations and hardware experiments on the Georgia Tech Robotarium platform.

Method: SafeSieve uses a progressive adaptive pruning algorithm with dual-mechanism: initial LLM-based semantic evaluation combined with accumulated performance feedback, enabling transition from heuristic initialization to experience-driven refinement. Employs 0-extension clustering to preserve structurally coherent agent groups while eliminating ineffective links.

Result: Achieves 94.01% average accuracy while reducing token usage by 12.4%-27.8%. Shows robustness under prompt injection attacks (1.23% average accuracy drop). In heterogeneous settings, reduces deployment costs by 13.3% while maintaining performance.

Conclusion: SafeSieve establishes an efficient, GPU-free, and scalable framework for practical multi-agent systems by dynamically refining inter-agent communication through progressive pruning.

Abstract: LLM-based multi-agent systems exhibit strong collaborative capabilities but often suffer from redundant communication and excessive token overhead. Existing methods typically enhance efficiency through pretrained GNNs or greedy algorithms, but often isolate pre- and post-task optimization, lacking a unified strategy. To this end, we present SafeSieve, a progressive and adaptive multi-agent pruning algorithm that dynamically refines the inter-agent communication through a novel dual-mechanism. SafeSieve integrates initial LLM-based semantic evaluation with accumulated performance feedback, enabling a smooth transition from heuristic initialization to experience-driven refinement. Unlike existing greedy Top-k pruning methods, SafeSieve employs 0-extension clustering to preserve structurally coherent agent groups while eliminating ineffective links. Experiments across benchmarks (SVAMP, HumanEval, etc.) showcase that SafeSieve achieves 94.01% average accuracy while reducing token usage by 12.4%-27.8%. Results further demonstrate robustness under prompt injection attacks (1.23% average accuracy drop). In heterogeneous settings, SafeSieve reduces deployment costs by 13.3% while maintaining performance. These results establish SafeSieve as an efficient, GPU-free, and scalable framework for practical multi-agent systems. Our code can be found here: https://github.com/csgen/SafeSieve

Method: Introduces Agent Contracts framework that unifies input/output specifications, multi-dimensional resource constraints, temporal boundaries, and success criteria into coherent governance with explicit lifecycle semantics. Establishes conservation laws for delegated budgets respecting parent constraints, enabling hierarchical coordination through contract delegation.

Result: Empirical validation across four experiments shows: 90% token reduction with 525x lower variance in iterative workflows, zero conservation violations in multi-agent delegation, and measurable quality-resource tradeoffs through contract modes.

Conclusion: Agent Contracts provide formal foundations for predictable, auditable, and resource-bounded autonomous AI deployment, extending coordination mechanisms from task allocation to resource governance.

Abstract: The Contract Net Protocol (1980) introduced coordination through contracts in multi-agent systems. Modern agent protocols standardize connectivity and interoperability; yet, none provide formal, resource governance-normative mechanisms to bound how much agents may consume or how long they may operate. We introduce Agent Contracts, a formal framework that extends the contract metaphor from task allocation to resource-bounded execution. An Agent Contract unifies input/output specifications, multi-dimensional resource constraints, temporal boundaries, and success criteria into a coherent governance mechanism with explicit lifecycle semantics. For multi-agent coordination, we establish conservation laws ensuring delegated budgets respect parent constraints, enabling hierarchical coordination through contract delegation. Empirical validation across four experiments demonstrates 90% token reduction with 525x lower variance in iterative workflows, zero conservation violations in multi-agent delegation, and measurable quality-resource tradeoffs through contract modes. Agent Contracts provide formal foundations for predictable, auditable, and resource-bounded autonomous AI deployment.

Method: Proposed agentic annotation framework with 7 specialized agents to generate hierarchical labels and rationales. Constructed M^3 dataset of 2,455 memes from X, 4chan, and Weibo with fine-grained hate labels and human-verified rationales.

Result: Benchmarking shows state-of-the-art Multimodal Large Language Models struggle to effectively utilize surrounding post context, often failing to improve or even degrading detection performance.

Conclusion: Current models face challenges in reasoning over memes embedded in real-world discourse, highlighting the need for context-aware multimodal architectures.

Abstract: Hate speech online targets individuals or groups based on identity attributes and spreads rapidly, posing serious social risks. Memes, which combine images and text, have emerged as a nuanced vehicle for disseminating hate speech, often relying on cultural knowledge for interpretation. However, existing multimodal hate speech datasets suffer from coarse-grained labeling and a lack of integration with surrounding discourse, leading to imprecise and incomplete assessments. To bridge this gap, we propose an agentic annotation framework that coordinates seven specialized agents to generate hierarchical labels and rationales. Based on this framework, we construct M^3 (Multi-platform, Multi-lingual, and Multimodal Meme), a dataset of 2,455 memes collected from X, 4chan, and Weibo, featuring fine-grained hate labels and human-verified rationales. Benchmarking state-of-the-art Multimodal Large Language Models reveals that these models struggle to effectively utilize surrounding post context, which often fails to improve or even degrades detection performance. Our finding highlights the challenges these models face in reasoning over memes embedded in real-world discourse and underscores the need for a context-aware multimodal architecture. Our dataset and code are available at https://github.com/mira-ai-lab/M3.

Method: Proposes dispersion-weighted masking (DWM), a lightweight masking strategy that uses the spectral sparsity in audio frequency structure. Compares with inverse block masking and other recent SSL frameworks.

Result: DWM alleviates limitations of inverse block masking (which improves audio event understanding but hurts generalization) and reduces computational complexity, leading to consistent performance improvements.

Conclusion: Provides practical guidance on masking strategy design for masked prediction-based audio representation learning, showing DWM offers better efficiency and performance trade-offs.

Abstract: Since the introduction of Masked Autoencoders, various improvements to masking techniques have been explored. In this paper, we rethink masking strategies for audio representation learning using masked prediction-based self-supervised learning (SSL) on general audio spectrograms. While recent informed masking techniques have attracted attention, we observe that they incur substantial computational overhead. Motivated by this observation, we propose dispersion-weighted masking (DWM), a lightweight masking strategy that leverages the spectral sparsity inherent in the frequency structure of audio content. Our experiments show that inverse block masking, commonly used in recent SSL frameworks, improves audio event understanding performance while introducing a trade-off in generalization. The proposed DWM alleviates these limitations and computational complexity, leading to consistent performance improvements. This work provides practical guidance on masking strategy design for masked prediction-based audio representation learning.

Method: Proposes Crab architecture integrating WavLM speech representations and RoBERTa text representations via Cross-Modal Transformer. Introduces Multi Layer Contrastive Supervision (MLCS) that injects contrastive learning signals at multiple network layers without adding inference parameters. Uses weighted cross-entropy to handle data imbalance.

Result: Outperforms strong unimodal and multimodal baselines on IEMOCAP, MELD, and MSP-Podcast 2.0 datasets, with particularly large gains under naturalistic and highly imbalanced conditions.

Conclusion: MLCS is an effective general strategy for SER that improves emotional discriminative representations throughout the model without inference overhead.

Abstract: Speech Emotion Recognition (SER) in real-world scenarios remains challenging due to severe class imbalance and the prevalence of spontaneous, natural speech. While recent approaches leverage self-supervised learning (SSL) representations and multimodal fusion of speech and text, most existing methods apply supervision only at the final classification layer, limiting the discriminative power of intermediate representations. In this work, we propose Crab (Contrastive Representation and Multimodal Aligned Bottleneck), a bimodal Cross-Modal Transformer architecture that integrates speech representations from WavLM and textual representations from RoBERTa, together with a novel \textit{Multi Layer Contrastive Supervision} (MLCS) strategy. MLCS injects multi-positive contrastive learning signals at multiple layers of the network, encouraging emotionally discriminative representations throughout the model without introducing additional parameters at inference time. To further address data imbalance, we adopt weighted cross-entropy during training. We evaluate the proposed approach on three benchmark datasets covering different degrees of emotional naturalness: IEMOCAP, MELD, and MSP-Podcast 2.0. Experimental results demonstrate that Crab consistently outperforms strong unimodal and multimodal baselines across all datasets, with particularly large gains under naturalistic and highly imbalanced conditions. These findings highlight the effectiveness of \textit{Multi Layer Contrastive Supervision} as a general and robust strategy for SER. Official implementation can be found in https://github.com/AI-Unicamp/Crab.

Method: Proposes Bayesian tracking algorithms that incorporate enhanced speech signals to improve tracking performance, using temporal feedback in a frame-wise causal processing style. The enhanced signal is used to autoregressively guide deep spatial filters. Also introduces a novel dataset based on the social force model for realistic trajectory simulation.

Result: Autoregressive incorporation significantly improves Bayesian tracker accuracy, resulting in superior speech enhancement with none or only negligibly increased computational overhead. Methods generalize well to unseen, challenging acoustic conditions as demonstrated with real-world recordings.

Conclusion: The proposed Bayesian tracking approach enables high-quality speech enhancement in dynamic scenarios by effectively leveraging enhanced speech signals to improve tracking accuracy while maintaining real-time computational efficiency.

Abstract: Deep spatially selective filters achieve high-quality enhancement with real-time capable architectures for stationary speakers of known directions. To retain this level of performance in dynamic scenarios when only the speakers’ initial directions are given, accurate, yet computationally lightweight tracking algorithms become necessary. Assuming a frame-wise causal processing style, temporal feedback allows for leveraging the enhanced speech signal to improve tracking performance. In this work, we investigate strategies to incorporate the enhanced signal into lightweight tracking algorithms and autoregressively guide deep spatial filters. Our proposed Bayesian tracking algorithms are compatible with arbitrary deep spatial filters. To increase the realism of simulated trajectories during development and evaluation, we propose and publish a novel dataset based on the social force model. Results validate that the autoregressive incorporation significantly improves the accuracy of our Bayesian trackers, resulting in superior enhancement with none or only negligibly increased computational overhead. Real-world recordings complement these findings and demonstrate the generalizability of our methods to unseen, challenging acoustic conditions.

Method: Created ACAVCaps from ACAV100M using a multi-expert pipeline that analyzes audio from speech, music, and acoustic perspectives, then synthesizes these analyses into detailed descriptions using a large language model.

Result: Models pre-trained on ACAVCaps show substantially stronger generalization capabilities on various downstream tasks compared to those trained on other leading captioning datasets.

Conclusion: ACAVCaps addresses the data limitations in audio captioning and enables better training of large audio-language models for general audio understanding.

Abstract: General audio understanding is a fundamental goal for large audio-language models, with audio captioning serving as a cornerstone task for their development. However, progress in this domain is hindered by existing datasets, which lack the scale and descriptive granularity required to train truly versatile models. To address this gap, we introduce ACAVCaps, a new large-scale, fine-grained, and multi-faceted audio captioning dataset. Derived from the ACAV100M collection, ACAVCaps is constructed using a multi-expert pipeline that analyzes audio from diverse perspectives-including speech, music, and acoustic properties-which are then synthesized into rich, detailed descriptions by a large language model. Experimental results demonstrate that models pre-trained on ACAVCaps exhibit substantially stronger generalization capabilities on various downstream tasks compared to those trained on other leading captioning datasets. The dataset is available at https://github.com/xiaomi-research/acavcaps.

Method: Used 72-image photogrammetry captures per subject processed with Apple’s Object Capture API to generate PR meshes for 150 subjects from SONICOM dataset. Computed PR synthetic HRTFs using Mesh2HRTF and compared against measured HRTFs, 3D scan-derived HRTFs, KEMAR, and random HRTFs through numerical evaluation, auditory models, and behavioral sound localization experiments (N=27).

Result: PR synthetic HRTFs preserved ITD cues but exhibited increased ILD and spectral errors. Auditory-model predictions and behavioral data showed substantially higher quadrant error rates, reduced elevation accuracy, and greater front-back confusions than measured HRTFs, performing worse than random HRTFs on perceptual metrics.

Conclusion: Current photogrammetry pipelines support individual HRTF synthesis but are limited by insufficient pinna morphology details and high-frequency spectral fidelity needed for accurate individual HRTFs containing monaural cues.

Abstract: Individual head-related transfer functions (HRTFs) are essential for accurate spatial audio binaural rendering but remain difficult to obtain due to measurement complexity. This study investigates whether photogrammetry-reconstructed (PR) head and ear meshes, acquired with consumer hardware, can provide a practically useful baseline for individual HRTF synthesis. Using the SONICOM HRTF dataset, 72-image photogrammetry captures per subject were processed with Apple’s Object Capture API to generate PR meshes for 150 subjects. Mesh2HRTF was used to compute PR synthetic HRTFs, which were compared against measured HRTFs, high-resolution 3D scan-derived HRTFs, KEMAR, and random HRTFs through numerical evaluation, auditory models, and a behavioural sound localisation experiment (N = 27). PR synthetic HRTFs preserved ITD cues but exhibited increased ILD and spectral errors. Auditory-model predictions and behavioural data showed substantially higher quadrant error rates, reduced elevation accuracy, and greater front-back confusions than measured HRTFs, performing worse than random HRTFs on perceptual metrics. Current photogrammetry pipelines support individual HRTF synthesis but are limited by insufficient pinna morphology details and high-frequency spectral fidelity needed for accurate individual HRTFs containing monaural cues.

Method: Evaluated four open-source TTS architectures (FastPitch, VITS, Grad-TTS, Matcha-TTS) across multiple dimensions: qualitative aspects (ease of installation, dataset preparation, hardware requirements) and quantitative assessments (synthesis quality for Romanian). Used both objective metrics and subjective listening tests to evaluate intelligibility, speaker similarity, and naturalness.

Result: Revealed significant challenges in tool chain setup, data preprocessing, and computational efficiency that hinder adoption in low-resource contexts. Provides reproducible protocols and accessible evaluation criteria for TTS development.

Conclusion: This work aims to inform best practices and promote more inclusive, language-diverse TTS development by providing systematic evaluation of open-source frameworks and highlighting practical implementation barriers.

Abstract: Open-source text-to-speech (TTS) frameworks have emerged as highly adaptable platforms for developing speech synthesis systems across a wide range of languages. However, their applicability is not uniform – particularly when the target language is under-resourced or when computational resources are constrained. In this study, we systematically assess the feasibility of building novel TTS models using four widely adopted open-source architectures: FastPitch, VITS, Grad-TTS, and Matcha-TTS. Our evaluation spans multiple dimensions, including qualitative aspects such as ease of installation, dataset preparation, and hardware requirements, as well as quantitative assessments of synthesis quality for Romanian. We employ both objective metrics and subjective listening tests to evaluate intelligibility, speaker similarity, and naturalness of the generated speech. The results reveal significant challenges in tool chain setup, data preprocessing, and computational efficiency, which can hinder adoption in low-resource contexts. By grounding the analysis in reproducible protocols and accessible evaluation criteria, this work aims to inform best practices and promote more inclusive, language-diverse TTS development. All information needed to reproduce this study (i.e. code and data) are available in our git repository: https://gitlab.com/opentts_ragman/OpenTTS

Method: ArrayDPS-Refine is array-agnostic and training-free. It first estimates noise spatial covariance matrix (SCM) from enhanced speech produced by any discriminative model, then uses this estimated noise SCM for diffusion posterior sampling to refine the output.

Result: The method consistently improves performance of various discriminative models, including state-of-the-art waveform and STFT domain models, demonstrating effective refinement without retraining.

Conclusion: ArrayDPS-Refine provides an effective training-free approach to enhance discriminative multi-channel speech enhancement models using generative diffusion priors, offering consistent performance improvements across different model architectures.

Abstract: Multi-channel speech enhancement aims to recover clean speech from noisy multi-channel recordings. Most deep learning methods employ discriminative training, which can lead to non-linear distortions from regression-based objectives, especially under challenging environmental noise conditions. Inspired by ArrayDPS for unsupervised multi-channel source separation, we introduce ArrayDPS-Refine, a method designed to enhance the outputs of discriminative models using a clean speech diffusion prior. ArrayDPS-Refine is training-free, generative, and array-agnostic. It first estimates the noise spatial covariance matrix (SCM) from the enhanced speech produced by a discriminative model, then uses this estimated noise SCM for diffusion posterior sampling. This approach allows direct refinement of any discriminative model’s output without retraining. Our results show that ArrayDPS-Refine consistently improves the performance of various discriminative models, including state-of-the-art waveform and STFT domain models. Audio demos are provided at https://xzwy.github.io/ArrayDPSRefineDemo/.

Method: A fully diffusion-based model that takes three inputs: optional timbre reference, melody-providing singing clip, and modified lyrics. Uses curriculum learning and Group Relative Policy Optimization for training.

Result: Outperforms Vevo2 baseline in melody preservation and lyric adherence. Introduces LyricEditBench, the first benchmark for melody-preserving lyric modification evaluation.

Conclusion: YingMusic-Singer provides effective melody-controllable singing voice synthesis with flexible lyric manipulation without manual alignment, advancing the field of controllable audio generation.

Abstract: Regenerating singing voices with altered lyrics while preserving melody consistency remains challenging, as existing methods either offer limited controllability or require laborious manual alignment. We propose YingMusic-Singer, a fully diffusion-based model enabling melody-controllable singing voice synthesis with flexible lyric manipulation. The model takes three inputs: an optional timbre reference, a melody-providing singing clip, and modified lyrics, without manual alignment. Trained with curriculum learning and Group Relative Policy Optimization, YingMusic-Singer achieves stronger melody preservation and lyric adherence than Vevo2, the most comparable baseline supporting melody control without manual alignment. We also introduce LyricEditBench, the first benchmark for melody-preserving lyric modification evaluation. The code, weights, benchmark, and demos are publicly available at https://github.com/ASLP-lab/YingMusic-Singer.

Method: Proposes adaptive federated fine-tuning with early exits: lightweight prediction heads inserted at intermediate layers of SSL backbone allow clients to terminate computation based on local constraints; layer-wise, depth-aware partial aggregation strategy better utilizes representations from different network depths.

Result: Framework reduces edge overhead, supports heterogeneous hardware, and maintains competitive performance in resource-constrained federated environments.

Conclusion: The adaptive framework effectively addresses heterogeneity challenges in federated SSL fine-tuning for speech tasks through early exits and partial aggregation, enabling efficient deployment in resource-constrained environments.

Abstract: Integrating Federated Learning (FL) with self-supervised learning (SSL) enables privacy-preserving fine-tuning for speech tasks. However, federated environments exhibit significant heterogeneity: clients differ in computational capacity, causing straggler effects under unified fine-tuning, while diverse downstream tasks require different representation depths, making full-model updates inefficient. To address these challenges, we propose an adaptive federated fine-tuning framework with early exits. Lightweight prediction heads are inserted at intermediate layers of the SSL backbone, allowing clients to terminate computation based on local constraints and task requirements. We further introduce a layer-wise, depth-aware partial aggregation strategy to better utilize representations from different network depths. Experiments show that the framework reduces edge overhead, supports heterogeneous hardware, and maintains competitive performance in resource-constrained federated environments.