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All fields2263Multimodal454AI agents427Frontier LLMs283Inference & serving203Teacher policy distillation178Medical image segmentation130Physics informed neural109Supervised & diffusion models98Voice AI42Forecasting time40Machine learned interatomic38Electrocardiogram ecg37Remote sensing34Deepfake detection31Federated learning30All 25 fields

Open problems across fields

  • Interface lock-in in multimodal spatial reasoning1 paper raises it · 1 addresses it

    Specializing a model on one spatial task (e.g., side detection) degrades performance on others (e.g., distance estimation), limiting general spatial intelligence.

    • ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligenceraises · addresses
  • Lack of unified evaluation for work agents1 paper raises it · 1 addresses it

    Existing benchmarks fail to measure reliable work execution across tools, workflows, and open environments, hindering progress in agent systems.

    • Work Agents: Systems, Capabilities, and Optimizationraises · addresses
  • Memory tax in long-horizon agents1 paper raises it · 1 addresses it

    Dedicated memory systems often underperform naive retrieval, as reorganizing information can obscure evidence and reduce net value.

    • Memory Tax: Dissecting the Net Value of Agent Memoryraises · addresses
  • Credit assignment in diffusion language models1 paper raises it · 2 address it

    Iterative denoising in DLMs makes it hard to attribute rewards to individual tokens, limiting reinforcement learning effectiveness.

    • DTCA: Denoising Token Credit Assignment for Reinforcement Learning in Diffusion Language Modelsraises · addresses
    • Multiscale Policy Optimization for Long-Horizon LLM Agentsaddresses
  • Grounded significance testing for temporal hypotheses1 paper raises it · 1 addresses it

    Language models assert temporal patterns without rigorous statistical validation, risking incorrect conclusions in high-stakes domains like healthcare.

    • Testimony from Timelines: Evidence-Grounded Significance Testing of Temporal Clinical Hypothesesraises · addresses

Named by an AI from these papers' abstracts.

Papers

  • arXiv cs.CVOct 9

    QCLR: A Plug-in Robustification Module via Quantization for Contrastive Learning in Self-Supervised Skeleton-Based Human Action Recognition

    Nanzhu Chen, Feixiang He, Yunfeng Diao, Duygu Sarikaya, He Wang

    Skeleton-based human action recognition has gained increasing attention due to the stability of skeleton data across diverse environments and advances in motion capture technologies. Following the success of supervised learning approaches, self-supervised learning approaches, particularly contrastive learning ones, have achieved impressive performance. However, they remain vulnerable to adversarial attacks. In this work, we propose a plug-in robustification module via quantization for contrastive learning in self-supervised skeleton-based human action recognition (QCLR), which enhances the robustness of representations against adversarial attacks while preserving their representational capability. Specifically, we employ a dual-level quantization scheme across the encoding pipeline, which breaks the gradient propagation and absorbs small adversarial perturbations within the quantization bins. To mitigate the impact of inevitable quantization error, we further design an attention-guided multi-faceted quantization strategy to maintain the representation quality. Experiments on several benchmark datasets demonstrate that integrating QCLR into a progressive lineage of mainstream…

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  • arXiv cs.LGOct 9

    MHR-Router: Multimodal Heterogeneous Retrieval-Enhanced Adaptive Routing for VLM Inference

    Wenjuan Zhu, Zheming Yang, Yunqing Hu, Qi Guo

    Vision-language models (VLMs) exhibit complementary capabilities, yet selecting the most suitable expert for a given image-question query remains challenging due to heterogeneous multimodal inputs and varying expert strengths. Existing routing approaches rely on text-based cues or static model profiles, failing to capture fine-grained visual evidence and expert-specific failure patterns. To address this, we propose MHR-Router, a multimodal heterogeneous retrieval-enhanced adaptive routing framework for VLM inference. It accurately dispatches each multimodal query to the most suitable VLM expert via local retrieval evidence. MHR-Router consists of two stages: (1) Multimodal Routing Representation, which fuses frozen multimodal features into a comprehensive query representation; and (2) Heterogeneous Retrieval-Enhanced Adaptive Routing, which retrieves expert-specific success and failure memories as local evidence and employs a shared scorer to adaptively select the optimal expert. Experiments show that MHR-Router effectively achieves heterogeneous modality routing and consistently improves accuracy over traditional baselines.

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  • arXiv cs.IROct 9

    Beyond the Final Embedding: Retrieving Memories from Token-Level Evidence

    Han Luo, Zhan Zhuang, Xu He, Jujia Zhao, Yu Li +3

    Memory-augmented assistants retrieve evidence from stored conversations and documents to answer queries. Most embedding-based retrievers compare single-vector representations of queries and memories, even when relevance depends on a small part of the content. Such local evidence can be matched to queries using the contextual token representations already computed by the same embedding models. Our method uses sparse token matching to improve retrieval without additional training. Each query token retrieves a small set of matching tokens across candidate memories. The matching scores are aggregated for each memory and combined with whole-memory similarity to rank memories. An optional low-rank query adapter can be trained with supervision while keeping the encoder and stored memory representations fixed. Our method improves retrieval across eight benchmarks and multiple model families without additional training. Query adaptation further increases the gains to 13.3 and 14.3 percentage points in average Recall@10 and NDCG@10 on Qwen3-Embedding-4B. These retrieval gains translate into higher question-answering accuracy on long-term memory benchmarks.

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  • arXiv cs.LGOct 9

    CopRoute: Dependence-Aware Conformal Routing for Language Models

    Siva Rajesh Kasa, Santhosh Kumar Kasa, Karan Gupta

    Set-based language model routing aims to increase the coverage of the event where at least one model answers a query correctly while controlling the number of inference calls made. Existing risk-controlled routing policies bound the all-miss risk (the probability that every model in the selected set fails) by calibrating a single conformal threshold and applying it to a nested family of model sets. Because each model is scored in isolation, these policies ignore the dependence structure of model failures. Models that share training data or architecture tend to fail on the same queries, so two individually strong models can be jointly redundant while a weaker but complementary model covers the queries that the others miss. To account for this dependence, which existing risk-controlled policies ignore, we propose CopRoute, which orders models by how many new queries each one answers correctly beyond those already selected, decides per query how far down that order to go, and then picks the smallest cutoff that still meets the target risk. We show that CopRoute's ordering attains, on the design data, the best guarantee any efficient fixed-ordering algorithm can give for the average…

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  • arXiv cs.SEOct 9

    Agent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis

    Yongjian Tang, Costel Moraru, Doruk Tuncel, Hanya Elhashemy, Steffen Klepke +1

    API specifications serve as critical documentation for modern software development, directly impacting integration efficiency and client code generation. While recent advancements have leveraged Large Language Models (LLMs) to generate initial specifications from source code, existing approaches treat this process as a one-time event - overlooking the needs for continuous validation, long-term maintenance, and market-driven evolution of these artifacts. To address this issue, we present Agent4Spec, an autonomous multi-agent framework featuring two distinct pipelines: (1) a validation pipeline that detects and self-corrects OpenAPI syntax errors, and (2) a market analysis pipeline that conducts competitive research, identifies functional gaps against industry leaders, and automatically generates missing API-spec functionalities. We evaluate Agent4Spec on two curated datasets of API specifications spanning both general-purpose and industrial scenarios. Among 9 SOTA LLMs, the top-performer achieves 100% syntax error detection rates and over 95% implementation success for proposed corrections, while securing over 50% adoption rates for suggested endpoint improvements, representing a…

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  • arXiv cs.CVOct 9

    OWLET: On-Policy Weighted Learning via Evidence Transfer for Video Large Language Model Post-Training

    Qitan Lv, Ke Fan, Hong Wang, Wen Wu, Yilin Wang +3

    Post-training has become a key stage for eliciting reasoning capabilities in Video Large Language Models (Video-LLMs). Existing post-training methods mainly follow two paradigms: reinforcement learning (RL) and on-policy distillation (OPD). However, they do not fully exploit visual evidence for effective training. RL supervises the policy with a single trajectory-level reward, which is too coarse-grained for video reasoning: the same response-level advantage is applied to every generated token, disregarding that the tokens of a video response are grounded in the visual stream to very different degrees. OPD provides token-level supervision through output-distribution matching, but lacks direct supervision of the spatiotemporal representations that produce those outputs. We observe across extensive experiments that the same model reasons more accurately when its video view combines global temporal context with question-relevant details. Comparing the same student-generated response under this view and uniform sampling reveals changes in token likelihoods and internal representations, providing a training signal for evidence use. Building on this observation, we propose OWLET, an…

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  • arXiv cs.CVOct 9

    ConClick: Element-Wise Contrastive Learning Improves GUI Grounding

    Ruotian Zhang, Yuhui Zhang, Junzhe Huang, Yu Xin, Hehe Fan +1

    GUI grounding, the task of locating the interface element specified by a user instruction, is a fundamental capability of GUI agents. However, existing grounding models remain error-prone, often causing downstream task failures. We systematically evaluate 10 grounding models across five datasets and identify a major error pattern: most incorrect clicks hit non-target interface elements rather than the background, with different models repeatedly selecting the same distractors. Motivated by these findings, we propose ConClick, a contrastive learning framework that teaches models to distinguish the target element from plausible distractors. We construct ConClick-Data, a dataset containing 4,292 challenging target-distractor pairs, and train the model using complementary listwise ranking and pairwise margin objectives. ConClick improves the grounding accuracy of Qwen3-VL-8B from 54.71% to 66.03% on ScreenSpot-Pro, from 83.72% to 86.28% on MMBench-GUI L2, and from 63.33% to 70.20% on OSWorld-G, outperforming prior methods built on the same or similarly sized backbones. We further introduce ReClick, an inference-time strategy that revisits promising regions and compares predictions…

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  • arXiv q-fin.RMOct 9

    Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps

    Ravil Akhtyamov

    A defined-benefit pension is only as safe as its sponsor, yet sponsor distress is under-instrumented relative to plan-side modelling. We present a transparent, reproducible early-warning pipeline built entirely on public data. A Sponsor Vulnerability Index (SVI), a gradient-boosted classifier over point-in-time SEC EDGAR balance-sheet fundamentals and two macro series, predicts a filings-based distress state one to two quarters ahead. On 36 U.S. sponsors in five sector groups (1,418 firm-quarters, 2015Q1-2025Q3) it reaches an out-of-fold AUC-ROC of 0.887 and PR-AUC of 0.711 (base rate 11.9%) under leave-one-firm-out cross-validation. A Monte Carlo VaR with a stylized transition-stress overlay shows that climate-type stress is negligible for a diversified plan but adds 20-43% to one-year 95% VaR when 22.5% of assets are held in sponsor securities. A dynamic self-investment cap maps the SVI to a limit that falls from 8% to below 1% in acute distress and recovers with the sponsor, which we illustrate on PG&E's 2019 bankruptcy and Boeing's persistent fragility. We report the method's failure modes alongside its results (book-equity labels that also fire on non-credit events such as…

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  • arXiv cs.AIOct 9

    Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents

    Lei Liu, Yunji Liang, Xiaowen Zhang, Jingqi Liu, Qi Li +2

    Large language model (LLM) agents are highly vulnerable to memory-poisoning attacks. Existing defenses primarily rely on external modules for either static memory isolation or continuous online auditing, resulting in low memory efficiency and high computational overhead. To address these limitations, inspired by neuroscientific mechanisms that weak reactivation can destabilize memories and induce decay in the absence of reinforcement, we propose an agent-intrinsic training-free memory repair framework, DREAM (Dynamic Reactivation for Engram Attenuation in Memory), that enables selective functional forgetting of poisoned memories. Specifically, DREAM implements a three-stage pipeline: perturbation-induced implicit reactivation to reveal the structural fragility of malicious memories, adaptive anomaly diagnosis based on multi-dimensional activation patterns to detect topological anomalies, and a dynamic memory repair module that selectively suppresses harmful memories while reinforcing benign ones. Extensive experiments across diverse attackers and real-world tasks demonstrate that DREAM reduces attack success rates by over 95\% against backdoor poisoning while preserving strong…

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  • arXiv cs.CVOct 9

    CV-OPD: Contrastive Vision On-policy Distillation

    Haowei Wang, Hao Lin, Qi Zhu, Jiayi Ji, Deyi Ji +2

    On-policy distillation (OPD) reveals versatility and convenience during VLM post-training, providing dense supervision for student from teacher. However, we observed an anomaly that the distilled VLM could still answer the questions accurately even when the related visual regions were masked or replaced with noise. Building on this observation, our quantitative and qualitative experiments demonstrate that OPD, which uses the answer as a hint, has failed to teach students to genuinely seek out and rely on visual evidence. Our theoretical analysis suggests that this failure arises from a shortcut enabled by the lack of visual constraints during distillation. To address the issue, we introduce Contrastive Vision On-policy Distillation (CV-OPD), which compares teacher distributions induced by positive and negative crops while conditioning on the same student prefix and augments optimization with a margin-based contrastive constraint, which is modulated with discrepancy gating across token and sample level. Specifically, CV-OPD first introduces a negative teacher unrelated to the answer and designs a Contrastive Divergence Loss (CDL) that constrains the feature distribution of student…

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  • arXiv cs.CVOct 9

    SheafPrune: Sheaf-Guided Graph Propagation for Visual Token Pruning

    Xiaoyang Ma, Chi Wang, Hong Sun

    Multimodal large language models process the complete visual token sequence, increasing prefill cost even when many tokens are irrelevant to the instruction. Token-wise saliency captures limited inter-token dependence; graph-based selection instead depends on how relations govern importance propagation and ranking. We study support-constrained propagation with node-degree correction. Inspired by sheaf geometry, SheafPrune uses modality-specific maps and a sparse text--visual graph to transfer relevance, then propagates scores on a separate visual graph and corrects them for degree. It retains a hard top-$k$ subset once before the frozen language model. The selector has about $4.5$M parameters and is trained end-to-end through the frozen LLaVA-NeXT-8B backbone using only the language-modeling objective. Explicit retention-ratio conditioning lets one checkpoint serve every budget. Across four backbones, ten benchmarks and seven pruning ratios, with all baselines re-implemented under one protocol, SheafPrune achieves the best retention at $22$ of $28$ backbone--budget settings, with monotonically growing margins in the moderate-to-aggressive regime. On LLaVA-NeXT, retaining $10\%$…

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  • arXiv cs.ROOct 9

    Dual-Stream Memory for Vision-Language-Action Policies

    Yang Wang, Ping Liu, Ruiyu Wang, Xuhua Chen, Lingfeng Zhang +1

    Memory-dependent manipulation requires a policy to track execution state and recover visual evidence that is no longer observable. We propose dual-stream memory for vision-language-action policies, assigning these requirements to complementary representations. A task-agnostic Gated DeltaNet compresses history into one temporal token, supervised by progress prediction and action-conditioned feature reconstruction. A task-conditioned retriever retains frames above a fixed relevance threshold; linear multi-scale fusion encodes each frame into sixteen spatial tokens. We then freeze the temporal compressor and jointly optimize retrieval and policy learning. Across sixteen RoboMME tasks, the complete model achieves 49.22% mean success versus 44.51% for the published FrameSamp+Modul reference, improving fourteen tasks and all four suite means. On four real-robot tasks, mean success reaches 62.5% versus 37.5%, with the largest gains on repeated actions requiring execution-stage discrimination. These results support combining execution context with selective visual access, while failures under cup swaps expose persistent identity tracking as an unresolved requirement.

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  • arXiv cs.LGOct 9

    Data-Adaptive Mahalanobis Metric Learning for Cross-Head Attention in Transformers

    Zhenke Duan, Xin Li, Jiqun Pan, Dong Xiaofei, Hanwen Ning +1

    In this paper, we propose Data-Adaptive Mahalanobis Cross-Head Attention (DAMCHA), a simple and effective attention mechanism that generalizes Multi-Head Attention (MHA) through the lens of metric learning. Standard MHA implicitly learns a static, head-wise Euclidean similarity corresponding to the diagonal blocks of a unified cross-head metric matrix, discarding all off-diagonal interactions across heads. DAMCHA addresses this limitation via two key innovations. First, it learns the full cross-head metric matrix, inducing a Mahalanobis-like similarity that explicitly captures inter-head interactions beyond the restricted block-diagonal structure of MHA. Second, it parameterizes the metric matrix as an input-dependent function, enabling the attention geometry to dynamically adapt to the intrinsic manifold structure of the data rather than remaining fixed after training. We further introduce principled regularization strategies, most notably stack-wise parameter sharing, to ensure computational efficiency and stable optimization. DAMCHA serves as a drop-in replacement for MHA and its variants in various Transformer architectures, delivering superior expressive power and learning…

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  • arXiv cs.CVOct 9

    Beyond a Single Crop: Contextualized Embeddings for Player Re-Identification

    Ge Luo, Changhuo Yang, Weisi Fan, Rui Yang, Shaokai Wang

    Person re-identification is typically addressed by learning identity-discriminative embeddings from individual person crops. However, player re-identification in broadcast sports videos presents a unique challenge: players from the same team often share similar appearances, while video sequences provide repeated observations of the same players under different viewpoints and surrounding contexts. Existing crop-based approaches largely ignore this contextual information, which can be critical for resolving visually similar identities. Here, we propose ContextReID, a contextualized embedding framework that represents a player by jointly considering other players in the same frame and observations from different frames of the broadcast sequence. By incorporating both co-occurring players and cross-frame observations, the model learns identity features relative to the surrounding visual context rather than relying only on isolated appearance cues. We implement this framework with a vision-language model that takes sets of player crops as input and generates context-aware embeddings for individual players. A combined contextual learning and retrieval objective encourages the model to…

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  • arXiv cs.LGOct 9

    Neural Surrogate Modeling of Ultrasonic Wave Propagation in Heterogeneous Media

    Burla Nur Korkmaz, Petro Moilanen, Ari Salmi, Harsha Vardhan Tetali, Arto Klami

    Neural surrogates provide computationally efficient auto-regressive modeling of wave propagation, enabling e.g. fast inverse solutions. Despite recent progress, the current surrogates are not yet sufficient for the needs of ultrasonic sensing, characterized by high frequency and heterogeneous propagation media. We show how methods building on Fourier Neural Operators (FNO) fail to capture essential signal features, such as reflections, whereas U-Net -based methods learn the wave fields well but lack the ability to transfer information across different spatial scales. We then propose a new surrogate that captures the best of the two worlds, by integrating FNOs and local-integral resampling into a U-Net architecture. We show it can be trained on limited data, and that the predictions are robust to input noise and work across spatial scales.

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  • arXiv cs.LGOct 9

    CAST: Credit Alignment for Cross-Tokenizer On-Policy Distillation

    Jingru Yang, Jinhang Wu, Minjing Shi, Shengfeng He, Xuyan Li +2

    Cross-tokenizer on-policy distillation (OPD) requires aligning teacher and student sequences before their scores can be compared. Decoded-text alignment provides such correspondence at the chunk level, but leaves a distinct problem unresolved: credit alignment. A chunk-level signal specifies how much a span should change, but not how that signal should be distributed across student tokens, while tokens without teacher correspondence remain unconstrained. We propose CAST (Chunk-aware credit Assignment with Selective Token Anchoring), a shift-or-stay framework that addresses both issues. For aligned chunks, CAST redistributes each chunk's advantage across student tokens using predictive uncertainty and local teacher-signal smoothness, emphasizing learning opportunities supported by locally consistent teacher feedback while preserving the chunk's total advantage. The resulting weights exactly solve a KL-regularized credit-allocation problem. For unaligned positions, CAST selectively anchors the student to its initial policy through a KL penalty, limiting drift where teacher supervision is unavailable. Because credit assignment is separated from chunk construction, CAST also applies…

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  • arXiv cs.LGOct 9

    Tabular Foundation Models Are Effectively Shallow

    Irene Cannistraci, Julia E. Vogt

    In-context-learning Tabular Foundation Models (TFMs) are uniquely suited to scenarios with limited resources or privacy constraints, as they solve new tasks without additional training. However, their architecture consists of multiple transformer blocks, resulting in high computational and memory footprints. Meanwhile, traditional alternatives like Gradient-Boosted Decision Trees (GBDTs) remain highly competitive and run efficiently on CPUs, suggesting that tabular tasks may not inherently require deep architectures. This contrast raises a fundamental question: can TFMs be simplified by removing architectural redundancies without sacrificing performance? To answer this, we introduce a closed-form, label-free affine translator that replaces a contiguous window of up to 96% of the blocks, leaving the rest unchanged. Evaluating eight frontier TFMs on 171 classification datasets, we reveal that tabular transformers are vastly overparameterized: replacing half of a model's blocks costs at most $0.002$ median AUROC across all architectures, with the half-depth TabFM still outperforming every other full-depth TFMs and tuned GBDTs. Furthermore, different model families are…

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  • arXiv cs.CVOct 9

    Vision Transformers Learn Word Meaning from Images Alone

    Anders Gjølbye, Sebastian Ray Mason, Stella Frank, Lars Kai Hansen

    Can a vision model learn what words mean from pixels alone? We show that the two largest DINOv3 vision transformers organize words rendered in images by word meaning, despite being pretrained using self-supervision on images only, without paired captions, class labels, or other language supervision. We derive word representations from embedding differences between word images and matched controls, then test whether semantically related words are neighbors within each model's embedding space, while controlling for spelling similarity. We find that brand names in the same industry group together, as do nouns from the same semantic domain. Country-capital pairs exhibit consistent vector offsets, similarly to word embeddings trained on text. These word representations also align with the representations of the visual object categories they name. DINOv3-H and DINOv3-7B are, to our knowledge, the first self-supervised vision models to show substantial semantic structure for rendered words. Comparisons with text and speech encoders reveal shared structure among representations of the same words. Contrastive alignment with either encoder further strengthened DINOv3-H's word-level…

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  • arXiv cs.SDOct 9

    What Do Audio-Language Judges of Generated Music Listen For? Four Probes with Controls from Expert Ratings

    Mateusz Modrzejewski, Tomasz Radzikowski

    Open audio-language models are used as judges of generated music: given a clip and its text prompt, the model rates the clip, and the judge is validated by how well its ratings correlate with expert ratings. The correlation does not show what the judge responds to: the music, the text prompt, or the way the question is asked. We propose four probes that test each of these, three of them read against a control computed from the human ratings: removing the audio, replacing the clip with another model's clip for the same prompt, swapping the order of two clips in pairwise judging, and rewording the instruction. We apply them to five open judges on all 2,748 clips of MusicEval, and the last two to the SongEval songs. Without audio, no judge exceeds what the prompt alone predicts from the human ratings, and for prompt alignment every judge falls below it. With audio, every judge follows the clip it hears when rating prompt alignment, two do when rating musical quality, and the strongest judge's quality score tracks the experts' alignment rating more than their quality rating. The pairwise verdicts of the four smaller judges follow the position of a clip in the pair (its slot) rather…

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  • arXiv cs.CVOct 9

    Deep Learning Approach for RealTime Contraction Detection in Laboring Dairy Cattle

    Sebastian Puchala, Dominik Gronkiewicz, Marcin Gołębiewski, Grzegorz Grodkowski, Grzegorz Gwardys

    Currently, the animal production sector is undergoing rapid development of techniques that allow automatic monitoring of animal behaviour and health. This paper presents a computer vision-based approach for the automatic recognition of labor contractions in cows. During the course of the study, videos depicting 100 cow births were collected. From the obtained videos, 8500 cow contraction actions and about 25 000 non-contraction sequences were extracted which constituted the dataset. Leveraging image processing techniques and deep learning algorithms like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), our methodology extracts relevant features from video frames to detect and classify contractions. Our study demonstrates a high accuracy rate of over 95% in detecting labor contractions. This automated system has the potential to enhance productivity and animal welfare in the dairy industry by providing real-time monitoring and facilitating timely intervention during difficult calvings. The video demonstration of the system can be found at the following URL: https://www.youtube.com/watch?v=8fTSG5D7dZ0

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Rising research topics

  • AI tutors17+41.7%
  • Character recognition OCR12+20.0%
  • Channel state information14+7.7%
  • Electrocardiogram ecg37−5.1%
  • Supervised & diffusion models98−13.3%
  • Machine learned interatomic38−15.6%
  • Stochastic gradient descent26−21.2%
  • Quantum machine learning15−25.0%