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All fields3079Multimodal501Agents476Frontier LLMs415Inference & serving250Image segmentation1683D scene149Graph learning142Learning verifiable rewards142Voice AI91Classifier & causal discovery82Forecasting time74Flow matching64Evals60Teacher policy distillation46Federated learning46All 32 fields

Open problems across fields

  • General in-context learning for embodied agents1 paper raises it · 0 address it

    Improving adaptability of models to new tasks and environments without task-specific fine-tuning is critical for scalable embodied intelligence.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Cost and latency in agentic systems2 papers raise it · 1 addresses it

    High computational and time costs limit practical deployment of agentic systems for real-world tasks.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
    • PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviewsraises · addresses
  • Physical-state verification and failure recovery1 paper raises it · 0 address it

    Robustness to irreversible failures and accurate state tracking are essential for reliable embodied control.

    • Has GPT-6-Astra Achieved Embodied Intelligence?raises
  • Evidence-grounded multimodal reasoning benchmarks1 paper raises it · 1 addresses it

    Current benchmarks often fail to ensure models use necessary audio-visual evidence, weakening evaluation reliability.

    • EvaOmni: An Evidence-Grounded Benchmark for Audio-Visual Reasoningraises · addresses
  • Length generalization in sequential state tracking1 paper raises it · 1 addresses it

    Models struggle to maintain performance on problems much longer than their training data, limiting scalability.

    • PAR: Progress-Aligned Recurrence for Length Generalization in State Trackingraises · addresses

Named by an AI from these papers' abstracts.

Papers

  • arXiv cs.AIOct 16

    GOLD: Geometric Optimized Latent Diffusion for Structure-Aware RNA Inverse Folding

    Qi Si, Xuyang Liu, Penglei Wang, Shuo Su, Limei Han +4

    Designing RNA sequences that fold into specific 3D structures is a fundamental challenge in therapeutics. Current RNA inverse folding methods typically condition on 3D backbone coordinates but the generation is limited to 1D discrete sequences. This ignores the continuous side-chain orientations, which are essential for overall structural stability. To address this issue, we explicitly introduce side-chain geometry into the RNA generative process. Unlike co-design frameworks that diffuse across separate raw spaces, we propose Geometric Optimized Latent Diffusion (\textbf{GOLD}), which project sequence and structure into a unified latent manifold. GOLD employs a novel GeoVAE to elegantly integrate discrete RNA sequences with continuous side-chain angles. By mapping these modalities into a single, smooth latent manifold, GOLD naturally facilitates a synergistic co-diffusion process. To enhance structural validity, we further introduce a training-free, gradient-guided sampling mechanism. By decoding the generated sequence probabilities and side-chain geometries into structures during the sampling process, we compute analytical gradients from physical constraints. This dynamic…

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  • arXiv cs.CVOct 16

    A100: A Hybrid Vision Foundation Model for Agricultural Plant Image Analysis

    Mayur Pratim Das, Ajai Dubey, Aksshay Mathew P, Sana Afreen, Rishi Kumar Soni +3

    The use of accurate vision models for crop monitoring, disease diagnosis, and plant phenotyping in agriculture is hindered by the lack of expertly annotated data, especially in developing countries. We propose A100, an agricultural vision foundation model that is pre-trained using self-supervised learning on 3 million unlabeled images from fields covering more than 100 crop species. A100 uses a hybrid CNN-Transformer backbone that fuses MBConv stages for local feature extraction with Transformer stages for global contextual representation. A100 uses pre-training with the integration of DINO self-distillation, iBOT masked patch reconstruction, and KoLeo uniformity regularization, resulting in features that are better than ImageNet-12K supervised representations for agricultural applications without any fine-tuning. By utilizing a frozen backbone, the accuracy for crop classification of A100 is 95.37%, which is +2.56% better than ImageNet-12K, and reduces the leaf count error by 10.4% on the GroMo25 phenotyping benchmark, proving that the same backbone can be used for classification and regression tasks. After full fine-tuning, the performance of A100 is 99.90% on 32-class crop…

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  • arXiv cs.HCOct 16

    PersonaCUABench: Benchmarking Computer-Use Agents Serving Diverse Personas

    Haoming Li, Defu Cao, Xu Hu, Yingtao Luo, Yiyang Wang +21

    Computer-use agents (CUAs) are commonly evaluated against fixed task specifications, but users may omit requirements, add constraints, or require approval before consequential actions. Completing the benchmark instruction does not guarantee serving the person who asked. We introduce PersonaCUABench, which holds 74 tasks across 11 domains fixed and varies only who asks. A persona agent, playing one of 44 personas in four groups, rewrites each instruction in that user's own words and answers the CUA's questions, while the CUA sees only the dialogue, never the profile or the original instruction. Across nine backbones (3,330 runs), mean task completion falls by 36% relative to the original requests. The losses have two sources: in 59.9% of cells that lose points, the agent engages with what the user asked for and still gets it wrong, and 11.2% are stops the user requested that the checker scores as failures. Even when a persona request is fully completed, the user gives the top satisfaction rating in only 62.7% of cells, against 84.1% for the original requests. CUAs should therefore be evaluated not only on task completion but also on whether they meet the requirements and…

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  • arXiv cs.AIOct 16

    From Approval to Abuse: Privilege Escalation Attacks on Harness-Protected Coding Agents

    Xianglin Yang, Yechao Zhang, Jiahao Liu, Yulin Chen, Yuxin Cao +2

    Coding agents use execution harnesses to constrain the consequences of prompt injection by enforcing permission boundaries. These systems deterministically intercept any out-of-sandbox action and prompt the user for approval. To reduce approval fatigue, users can approve allow-rules proposed by the agent, after which any future action matching the rule is allowed without a prompt. In this work, we show that this mechanism introduces a new risk: untrusted context can influence the permissions presented for approval, with consequences extending beyond the immediate task. We investigate privilege escalation targeting this permission-update process. Specifically, adversarial context planted in a repository can reliably steer an agent performing a routine task into requesting a plausibly justified, yet dangerously broad allow-rule. If the user approves the request, the saved rule can effectively blind the harness, enabling adversaries to execute malicious actions that would otherwise require approval, without triggering human oversight. Across production platforms, including Claude Code and Codex CLI, we demonstrate that agents successfully elicit the target allow-rules in 58% of…

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  • arXiv cs.CLOct 16

    Prism Attention: Proposal-Refined Index Sharing Mechanism for Efficient LLMs Inference

    Zhenxu Tian, Kebin Liu, Zhengwu Yang, Yi Su, Qingqing Dang +6

    Long-context decoding in large language models (LLMs) is increasingly bottlenecked by the memory and computation required to attend over an ever-growing Key-Value (KV) cache at each autoregressive step. Although token-level sparse attention reduces this burden by restricting computation to a small subset of relevant past tokens, its practical gains are often offset by repeatedly recomputing layer-wise sparse indices as the context grows. A common remedy is to share sparse indices across layers by computing them only at designated anchor layers, but such static reuse ignores the layer-wise evolution of attention patterns, causing the reused indices to progressively deviate from the layer-specific optima and incur substantial attention loss. By revisiting cross-layer sparsity from the perspective of attention dynamics, we find that inter-layer salient token shift is highly localized: although the exact top-$κ$ indices differ across layers, the core high-attention tokens remain stable, with changes concentrated near the expanded boundary of previously selected regions. Motivated by this, we propose Prism Attention, a proposal-refined sparse attention framework. It replaces rigid…

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  • arXiv cs.CLOct 16

    Self-Evolving Agents Can Repair Failures but Struggle to Generalize from Them

    Weixiang Zhao, Yichen Zhang, Yanyan Zhao, Jinming Bai, Bin Liang +3

    Experience-driven self-evolution offers a promising path for large language model (LLM) agents to continually improve through interaction. Yet existing approaches often rely on successful trajectories or external evaluation signals, leaving unclear whether agents can evolve when such positive anchors are unavailable. We study this question through Cold-Start Failure-Only Evolution (CFOE), where agents start without accumulated experience and learn only from failed interactions without privileged guidance. Across three self-evolving paradigms, multiple LLM backbones, and four interactive environments, we uncover a consistent repair-generalization gap: failure-derived experience can help repair previously failed tasks, but rarely yields sustained improvements on unseen tasks. Further analysis shows that agents often misinterpret failures, while even verified-correct experience remains difficult to transfer. Relaxing CFOE reveals that verifier feedback and additional interaction provide limited gains, whereas successful trajectories substantially improve transfer. These findings suggest that current self-evolving agents are better at repairing observed failures than building…

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  • arXiv cs.CVOct 16

    UniField++: A Unified Agentic Framework with Loop Reasoning for MRI Field-Strength Enhancement

    Yiyang Lin, Chenhui Wang, Tianliang Yao, Shuaibo Li, Zijun Sun +2

    MRI field-strength enhancement seeks to combine the accessibility of low-field scanners with the image quality of high-field scanners. Yet, existing methods remain prone to anatomical hallucinations, as uniform processing leaves difficult brain regions insufficiently examined and scarce paired data limit their learning of how tissue contrast and anatomical visibility change across field strengths. To address these challenges, we propose UniField++, a two-agent framework that unifies enhancement tasks across multiple field-strength transitions, including 64mT-to-3T and 3T-to-7T. Unified learning allows both the VLM-based planner and the flow-matching-based enhancer to draw on field-dependent image characteristics observed across tasks, rather than relying on a single transition. Trained with radiologists' annotations of hallucination-prone regions and corresponding correction plans, the planner predicts regional risk and formulates targeted restoration strategies. Guided by these plans, the enhancer performs loop reasoning along the flow-matching trajectory, repeatedly processing high-risk regions for additional refinement. Together, cross-transition learning and risk-guided…

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  • arXiv cs.CVOct 16

    How Murky Before a VLM Knows It Cannot See? Accuracy, Confidence and Abstention on a Physical Visibility Axis

    Mochamad Rizal Fauzan, Irgi Surya

    Robustness studies of vision-language models (VLMs) grade degradation on nominal severity scales ("level 1-5") that carry no physical units, and they rarely ask whether a model's confidence follows the loss of visibility. We measure accuracy, internal (token-probability) confidence, verbalized confidence and an explicit abstain option on a visibility axis in metres: fog follows the Koschmieder model, an underwater arm scales the absorption spectrum of pure water, both are applied with a monocular metric depth map, and the visibility range V is swept over eight log-spaced levels from 250 m to 1.95 m. On 1000 COCO scenes and five small open VLMs (2-5 B parameters), accuracy on a boxed-object identification task falls from near its clean value to chance within a narrow band (half-way visibility V_acc of 12.1-15.9 m in fog and 20.5-38.6 m under water) while, for the four models that are confident on clean images, the confidence stays high: at V=1.95 m in fog their accuracy is at chance but their mean internal confidence is 0.68-0.84 and their verbalized confidence 0.61-1.00, a blind-spot width W=log10(V_acc/V_aware) of at least 0.79 decades, i.e. a factor of 6.2 in visibility. The…

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  • arXiv cs.CLOct 16

    KiranaBench: Can a Language Model Run a Neighbourhood Shop?

    Bhuvan Arora, Ankita Karmakar, Chaitanya Jain, Dilesh Churiwal, Harshit Jain +8

    Large language model (LLM) agents are increasingly evaluated on long-horizon tasks, including autonomously running a business. Existing benchmarks for this setting are almost exclusively English-language, Western in retail context, and single-agent, with the agent observing the business directly. We study how these results transfer to a multi-agent Indic setting. We introduce KiranaBench, a simulated Indian general store in which four roles (manager, cashier, helper, and bookkeeper) are each played by a separate LLM agent. Interactions with customers and suppliers occur in English, Hindi, or Hinglish, and product demand is calibrated to India's national Household Consumption Expenditure Survey. Evaluating four open-weight LLMs, we find that all fall far short of a fixed-rule shopkeeper baseline: the best model earns only one-eighth of the baseline's profit, and two models finish below an unmanaged shop. Performance is also consistently lower for Hindi and Hinglish than for English supplier communication. KiranaBench offers evidence that current LLM agents are not yet reliable for autonomously operating small businesses in India, and provides a testbed for developing agents suited…

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  • arXiv cs.LGOct 16

    Towards Hyperparameter Transfer for Differentially Private Optimization

    Tianze Wang, Zhiqi Bu, Linjun Zhang

    Scaling laws and hyperparameter tuning remain central challenges in training large-scale models, particularly under differential privacy (DP), where optimization is further complicated by noise injection and per-sample gradient clipping. While Maximal Update Parametrization ($μ$P) allows for zero-shot hyperparameter transfer in standard training, we show that it fails under DP constraints due to the distinct spectral properties of high-dimensional noise versus low-rank gradients. In this work, we introduce DP-$μ$P, a theoretically grounded framework that extends $μ$P to the privacy-preserving setting. By analyzing the interaction between gradient clipping, noise injection, and feature learning, we derive a Signal-to-Noise Ratio (SNR) condition that governs layer-wise learning rate scaling. Combining this with our proposed spectral scaling formulation, we provide specific scaling rules for DP-SGD, DP-Adam, and DP-Muon optimizers. Empirically, we demonstrate that DP-$μ$P enables effective zero-shot transfer of learning rates across model widths on diverse architectures (MLP, ViT, GPT-2), matching or exceeding the performance of individually tuned baselines while significantly…

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  • arXiv cs.CVOct 16

    Sink-Sparse Hybrid Attention for Efficient Inference in T2I/T2V Diffusion Transformers

    Bingxin Xing, Yu Zhang, Jiatong Ding, Lei Lu, Ping Wang

    Diffusion Transformers (DiTs) have become a common backbone for high-quality text-to-image and text-to-video generation, but repeated dense self-attention remains a major inference bottleneck as token sequences grow and the same operator is invoked across many denoising steps. Our study suggests that dense attention in pretrained DiTs contains reusable layer/head-specific high-mass structure that can be identified from a small offline calibration set and reused across future generations. This leads to Sink-Sparse Hybrid Attention, a train-free attention replacement that reuses static sink masks and supplements them with a lightweight pooled-KV branch, while leaving model weights and sampling schedules unchanged. On Wan2.1-T2V-1.3B, the method attains 1.54$\times$ end-to-end speedup while maintaining generation quality. On Z-Image-Turbo, it attains 1.57$\times$ end-to-end speedup with competitive GenEval2 performance. These results indicate that reusable importance-aware sparse support can provide an effective train-free acceleration path for both T2V and T2I DiTs.

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  • arXiv cs.AIOct 16

    DiffusionRVGE: Reinforcement Learning via Regression-Based Value Gradient Estimation

    Jiayuan Wang, Tongyao Pang

    Reinforcement learning improves diffusion models using rewards on generated images, but repeated denoising and gradient estimation make training expensive. We introduce \method, an efficient method that constructs useful update directions by regressing centered rewards on centered final denoised images. It requires neither reward derivatives nor adjoint backpropagation through the sampling trajectory. A unified first-variation formulation identifies the value-gradient target with or without a trajectory-KL penalty. A population analysis under Gaussian noising explains the regression geometry, while a subspace analysis motivates clean-image coordinates. The implementation adapts rollout bandwidth and query weights to reward continuity: local rollouts suit smooth rewards, whereas broader groups provide informative contrasts for discrete rewards. Trained jointly on five rewards, one SD3.5-Medium model matches or exceeds DiffusionNFT's joint model on seven of eight metrics, reaching GenEval $0.94$, OCR $0.92$, and PickScore $23.82$ at about one fifth of its estimated training compute. In single-reward training, \method reaches matched reward thresholds $2.1$--$6.7\times$ faster than…

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  • arXiv cs.DBOct 16

    NL2SQL with Iterative Self-Refinement

    Anjali Vemula, Akshat Dasula

    Bridging the gap between human intent and machine logic remains a critical challenge in data access for non-technical users, yet Large Language Models (LLMs) remain unreliable: they hallucinate schema elements, generate syntactically invalid queries, or fail to capture user intent. These limitations are especially critical in domains such as insurance, where KPIs, regulatory constraints, and legacy relational systems demand high precision. We introduce a multi-agent, persona-driven NL2SQL framework that combines (1) domain relevance detection, (2) schema-grounded knowledge construction, (3) KPI-aware SQL synthesis, (4) syntax-level self-repair using sqlite execution feedback, and (5) semantic consistency checking via SQL-to-NL reverse engineering. Our system significantly reduces query hallucination and improves semantic fidelity compared to direct LLM prompting. Evaluated on 60 queries (50 from a HuggingFace text-to-SQL dataset and 10 custom domain queries), the system achieves 90% correctness using Mistral across seven coordinated LLM calls showing that this approach can substantially increase the reliability of NL2SQL pipelines in high-stakes environments.

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  • arXiv cs.CVOct 16

    ANCHOR: Audio-Visually Grounded Chain-of-Thought Reasoning Benchmark

    Joel Julin, Souraja Kundu, Liza Dahiya, George Z. Wei, Liuyue Xie +4

    Multimodal large language models (MLLMs) perform well on audio-visual question answering, but correct answers do not necessarily imply that their stated reasoning is grounded in relevant perceptual evidence. Existing benchmarks mainly evaluate final-answer accuracy, offering limited insight into whether model-generated rationales correctly identify visual regions, temporal intervals, and audio events supporting predictions. We introduce ANCHOR, a benchmark and evaluation framework for measuring grounded-rationale alignment in multimodal reasoning, comprising 2,319 videos and 8,272 question-answer pairs with structured reasoning traces, temporal evidence, spatial bounding boxes, and salient audio cues, including a human-refined test set of 224 videos and 975 question-answer pairs. We separately measure answer correctness, hallucination, logical divergence, and conciseness, with direct spatial and temporal grounding measurements on a focused subset. Across the full benchmark, twelve contemporary MLLMs show a substantial alignment gap: models with comparable answer performance can differ markedly in whether their explanations are supported by annotated evidence. On a matched subset…

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  • arXiv cs.CLOct 16

    What Shapes Brain Alignment of Large Language Models during Dialogue? Insights from Language Production and Comprehension

    Yuko Nakagi, Yuya Chiba, Yasue Kishino

    Brain alignment of large language models (LLMs) offers a way to investigate representational similarities between LLMs and the human brain during language processing. While previous studies have mainly focused on passive language comprehension, little is known about such alignment during dialogue, in which speakers engage in both language production and comprehension. In this study, using fMRI data collected during dialogue, we examine brain alignment during language production and comprehension across LLM layers and brain regions. We assess its association with the intrinsic dimension (ID) of LLM representations and the contributions of several interpretable linguistic and communicative features. Our results reveal distinct patterns of brain alignment between production and comprehension, as well as strong associations with ID in brain regions associated with each processing mode. Furthermore, we show that the contributions of linguistic and communicative features also vary across processing modes, LLM layers, and brain regions.

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  • arXiv cs.CROct 16

    SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval

    Haoming Li, Dingyi Kang, Xu Hu, Jinxin Yang, Zhu Xu +6

    Large language model (LLM) agents increasingly rely on persistent memory to support long-term reasoning and personalized interactions. However, information collected from external sources can introduce security risks when untrusted claims (i.e., atomic statements) are later treated as authorized preferences, policies, or permissions. Existing defenses typically rely on content-based filtering or coarse-grained provenance tracking, which either fail to prevent unauthorized memory use or block legitimate information, sacrificing memory utility. To address these limitations, we propose SAFE-Mem, a fine-grained memory governance framework that improves security while preserving legitimate memory use. First, SAFE-Mem annotates each claim with three governance labels (i.e., kind, authority, and scope) to capture its provenance and permitted use. Second, governance-aware retrieval matches query requirements with claim permissions to block unauthorized influence while retaining legitimate information. Third, a governance-enabled memory graph connects source sentences, claims, and entities to support provenance tracking and efficient retrieval. Across four LLM backbones on MPBench and…

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  • arXiv eess.IVOct 16

    Stabilizing Illumination Conditioning in SSM-Based Low-Light Image Enhancement with Bottleneck Prompt Sidecars

    Wei Chen Ng, Qiyi Xie, Jian Ding Tan, Yan Kang Lee, Abdulrab Hakim Qaid Abdullah +1

    Low-light image enhancement (LLIE) requires more than brightness amplification because real low-light images often exhibit spatially varying illumination, amplified noise, color distortion, and local detail loss. Recent SSM- and Mamba-based restorers provide efficient long-range modeling, yet the route for introducing illumination priors into such backbones remains insufficiently studied. This study addresses safe illumination conditioning for SSM-based LLIE through a bottleneck-side PromptAdapter. A lightweight illumination estimator first predicts a single-channel illumination map, which is then converted into prompt tokens and injected through a residual sidecar adapter at the bottleneck, thereby avoiding direct modification of the state-space core. Under a locked three-seed evaluation protocol on LOL-v1, LOLv2-Real, and LOLv2-Synthetic, the PromptAdapter improves over the Base model by 0.94 dB, 1.24 dB, and 0.93 dB in PSNR, respectively, while also improving SSIM, LPIPS, and masked dark-region metrics. The clearest real-data evidence appears on LOLv2-Real. A Learned-Fixed control on LOL-v1 indicates that the present results support the utility of the sidecar conditioning…

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  • arXiv cs.CVOct 16

    Query-adaptive Recurrent Tokenization

    Bavesh Balaji, Pranav Gopinath, Pouya Sadeghi, Sirisha Rambhatla

    Visual information worth extracting from an image and the token budget it warrants depend on the text query. Yet modern vision and vision-language models largely encode images independently of the query and allocate a fixed visual token budget to every input. We introduce the Query-Adaptive Recurrent Tokenizer (QART), a backbone-agnostic module that makes visual representations both query-steerable and budget-adaptive. QART steers the visual representation toward query-relevant evidence while adapting its token budget to the amount of information required. Its key principle is simple. Additional tokens are allocated until the visual features relevant to the query are represented with sufficient fidelity. Starting from frozen encoder features, QART recurrently constructs a compact representation and uses reconstruction quality over the query-relevant visual support to control token allocation. On Visual7W and VQAv2, QART achieves 81.96% and 76.37% accuracy using only about 29 visual tokens, reducing token counts by 20-25x relative to dense VLM inputs. It further reaches 92.0% P@1 on CORE and 36.3% PR-AUC on PODS, outperforming SteerViT on PODS by 8.4%. Together, these results show…

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  • arXiv cs.LGOct 16

    On the tradeoff between robustness and geometric accuracy in adversarial training

    Rachel Morris, Ryan Murray, Kerrek Stinson

    For non-parametric binary classification in the presence of an adversary, we prove an optimal quantitative estimate for adversarial classifiers controlling geometric accuracy, as measured by the Hausdorff distance to the Bayes classifier, in terms or their robustness, as encoded by the adversarial budget $\varepsilon$. This rigorously shows that robust classifiers sacrifice at most $O(\sqrt{\varepsilon})$ or $O(\varepsilon)$ in accuracy, depending on the regularity of the underlying Bayes classifier. In contrast to prior work on the incompatibility of robustness and accuracy over low-complexity hypothesis classes, our work shows that high-complexity hypothesis classes allow for robust classifiers with precisely quantified accuracy. Our results apply for adversaries determined by any norm on Euclidean space and in both the infinite and finite data settings.

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  • arXiv cs.CLOct 16

    Recognizing Atari and Assessing Sente: Controlled Evaluation Tasks for Language-Based Go Agents

    Taiyi Chen, Iskar Deng, Amandalynne Paullada

    Domain terminology provides concise descriptions of game states and strategic relationships, but whether game-playing language models can accurately recognize these concepts and relate them to move evaluation remains unclear. We construct a controlled evaluation set centered on atari and its relationship to sente in Go, examining terminology recognition under local board changes and move evaluation under different strategic conditions. We conduct our tests on LoGos, a Go language model, after fine-tuning it with terminology and commentary supervision. The model struggles to recognize changes in the atari relationship and assess its strategic implications. Its terminology predictions are associated with move preferences even when those preferences contradict the reference evaluation. Our controlled tests provide a framework for examining whether language-based Go agents connect domain terminology with precise board relationships and strategic move values.

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Rising research topics

  • Training & fine-tuning42+90.9%
  • AI safety29+61.1%
  • Computer vision29+31.8%
  • Evals60+25.0%
  • Federated learning46+21.1%
  • Code models13+18.2%
  • Quantum machine learning24+14.3%
  • Image & video generation20+5.3%