AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchBeyond Pixel Reconstruction: Retrieval-Guided Glyph-Aware Restoration for Low-Resource Manchu Historical Documents
arXiv:2610.00315v1 Announce Type: cross Abstract: Historical Manchu documents preserve invaluable linguistic and cultural heritage, yet their digitization is hindered by severe degradations and the scarcity of paired tra…
- arXiv cs.AIagentsDuplexSpeechBench-Document Grounding: Benchmarking Document Grounding and Hallucinations in Voice Agents
arXiv:2610.00316v1 Announce Type: cross Abstract: Voice agents enable low-latency, natural interaction, yet their ability to faithfully ground responses in external documents remains underexplored. We introduce DuplexSpe…
- arXiv cs.AIresearchDriftOPD: Sequence-Level Reverse-KL Distillation for One-Step VLA Policies
arXiv:2610.00317v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models increasingly rely on action experts that generate short action chunks under receding-horizon control. While chunk-level training is co…
- arXiv cs.AIagentsActions with Receipts: Jointly Binding Claims, Evidence, and Execution for Replayable Tool-Agent Auditing
arXiv:2610.00327v2 Announce Type: replace-cross Abstract: Tool-using agents can expose citations and execution logs while leaving a critical association unaudited: whether the claim shown to a user is the claim emitted b…
- arXiv cs.AIresearchThe Weakest Link: Distilling LLM Reasoning with Worst-Case Constrained Reinforcement Learning
arXiv:2610.00332v1 Announce Type: cross Abstract: Distilling the reasoning capabilities of large language models (LLMs) into smaller students is a central challenge for efficient deployment. Current approaches face a fun…
- arXiv cs.AIresearchLEGO-OPD: Factorized Teacher Composition for Multimodal On-Policy Distillation
arXiv:2610.00333v1 Announce Type: cross Abstract: Multimodal on-policy distillation (OPD) aims to improve visual grounding while preserving the strong reasoning capabilities of language models. Recent multi-teacher appro…
- arXiv cs.AIagentsAuthorization for Self-Modifying AI Agent Populations: Conserving Authority across Replacement, Forking, and Rollback
arXiv:2610.00347v1 Announce Type: cross Abstract: Self-modifying AI agents can replace, fork, and roll back identity-bearing software while descendants remain executable. Per-successor authorization does not constrain th…
- arXiv cs.AIagentsProof-Gated Signing: Solver-Checked Transaction Guards that Hold Under State Drift for Onchain AI Agents
arXiv:2610.00354v1 Announce Type: cross Abstract: AI agents that control wallets read attacker-reachable content, so they can be steered into proposing harmful transactions. The usual last line of defense is a pre-signin…
- arXiv cs.AIresearchDiffusion Editing with Soft Mask: Pixel Level Redo of Image and Video with Adjustable Strength
arXiv:2610.00359v1 Announce Type: cross Abstract: Diffusion models with prompt and reference image-guided editing have seen rapid progress, yet they remain too coarse for pixel-level control. One promising direction is t…
- arXiv cs.AIresearchDeep Learning for Anomaly Detection in Railway Systems: A Structured Survey
arXiv:2610.00363v1 Announce Type: cross Abstract: Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep learning has emerged as…
- arXiv cs.AIresearchManifold-Constrained Initial Noise Optimization for Efficient Generative Model Alignment
arXiv:2610.00365v1 Announce Type: cross Abstract: Recent advances in distillation and flow-map models have enabled deterministic one- or few-step generation for high-quality data, facilitating a new branch of reward alig…
- arXiv cs.AIresearchDeepJEPA: Scaling World Models from Within
arXiv:2610.00368v1 Announce Type: cross Abstract: World-model planners typically scale outward by rolling farther, sampling more trajectories, or optimizing longer, while assigning the same computation to every imagined…
- arXiv cs.AIresearchA Shared Taste for Model-Written Text: The Generator-by-Selector Matrices of "AI-AI Bias" Show No Detectable Own-Model Premium
arXiv:2610.00369v1 Announce Type: cross Abstract: Laurito et al. (PNAS 2025) showed that large language models choosing between two descriptions of the same product, paper or film prefer the description written by a lang…
- arXiv cs.AIagentsDeny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems
arXiv:2610.00371v1 Announce Type: cross Abstract: Multi-agent systems derive their capabilities from sharing evidence, delegating tasks, and combining information across agents. The same process creates a safety problem:…
- arXiv cs.AIresearchFaithful Chart Generation for Multimodal Deep Research: Frame-Evidence Co-Adaptation
arXiv:2610.00374v1 Announce Type: cross Abstract: Analytical charts in multimodal deep research encode quantitative claims, requiring every visualized value to be faithfully grounded in supporting evidence. Unlike retrie…
- arXiv cs.AIresearchFAER: Auditable Utility-Aligned Trajectory Replay for Language Model Post-Training
arXiv:2610.00385v2 Announce Type: replace-cross Abstract: Replay selectors often rank cached trajectories by format feedback, confidence, freshness, or response length, although cache-level correctness and downstream lea…
- arXiv cs.AIagentsT2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning
arXiv:2610.00388v1 Announce Type: cross Abstract: Reinforcement learning enables large language model (LLM) agents to learn multi-step behaviors through interaction with their environments. However, rewards in many inter…
- arXiv cs.AIresearchMatrixReward: Reward from Rubric Matrix for Open-Ended Generation
arXiv:2610.00389v1 Announce Type: cross Abstract: Open-ended query generation lacks standard answers, thus necessitating an effective reward mechanism. Pointwise scoring rubrics provide limited information about the rela…
- arXiv cs.AIresearchInterpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps
arXiv:2610.00391v3 Announce Type: replace-cross Abstract: Synthetic medical tabular data generation has become essential for developing and validating computer-based medical systems (CBMSs) when real clinical data is res…
- arXiv cs.AIagentsRepresentation Transitions Reveal Emerging Safety Risks in Multi-Turn LLM Agents
arXiv:2610.00400v1 Announce Type: cross Abstract: Multi-turn attacks on agentic systems can compose individually permissible actions into harmful outcomes, challenging defenses that assess actions or states in isolation.…
- arXiv cs.AIresearchScores That Hold, Benchmarks That Leak: Measuring Dataset Contamination in Public Brain-Tumor MRI Classification
arXiv:2610.00421v1 Announce Type: cross Abstract: Automated classification of brain tumors from MRI is a heavily published application of deep learning in medical imaging, with reported accuracies on public benchmarks ro…
- arXiv cs.AIresearchLearning to Cover Locally: Graph Neural Combinatorial Optimization under a Hard Information Horizon
arXiv:2610.00422v1 Announce Type: cross Abstract: Neural combinatorial optimization typically assumes a centralized solver that reads the whole instance. We study the opposite: combinatorial optimization under a hard inf…
- arXiv cs.AIresearchThe Life Cycle of a Massive Activation: Stochastic Birth, Weight-Decay-Driven Growth, and Competitive Consolidation
arXiv:2610.00423v2 Announce Type: replace-cross Abstract: Massive activations, residual-stream coordinates with magnitudes far larger than typical activations, are associated with attention sinks in transformers, but how…
- arXiv cs.AIresearchTarget-Dependent Limits of Causal Repair: A Leading-Log Frontier in a Gaussian Model
arXiv:2610.00424v1 Announce Type: cross Abstract: Knowing how much a causal predictor could improve need not reveal the gain of the repair actually learned. We quantify this gap in a scalar Gaussian causal experiment wit…
- arXiv cs.AIagentsCode That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Software
arXiv:2610.00425v1 Announce Type: cross Abstract: Code generation has emerged as a central capability of large language models, with coding agents now able to produce functionally correct software projects from natural l…
- arXiv cs.AIagentsMemetic Trojans: Social Contagions as Carriers of Adversarial Payloads in Agent Networks
arXiv:2610.00430v2 Announce Type: replace-cross Abstract: Autonomous large language model (LLM) agents increasingly interact in network environments where adversarial content can propagate between agents. Known attacks i…
- arXiv cs.AIresearchXOR-Trellis: Ultra-Low-Complexity Dequantization and Curvature-Aware Hadamard-Free LLM Quantization
arXiv:2610.00432v1 Announce Type: cross Abstract: Trellis-coded quantization enables high-dimensional compression of large language model (LLM) weights at ultra-low bit widths without the exponentially large codebooks re…
- arXiv cs.AIagentsHow AI Agents Discover Scientific Equations: From Hydrotope Rediscovery to New Water-Wave Amplitudes
arXiv:2610.00435v1 Announce Type: cross Abstract: We study how AI agents discover and validate scientific formulas using a controlled case study of the hydrotope, a recently discovered geometric formula that combines the…
- arXiv cs.AIresearchPACT: End-to-End Learning of Human Pose, Contacts, and Forces from Video
arXiv:2610.00451v3 Announce Type: replace-cross Abstract: Human motion, environmental contacts, and interaction forces are governed by common physical laws, yet existing approaches typically separate visual pose reconstr…
- arXiv cs.AIagentsEurekaBench: Measuring Agentic Ability to Discover New Scientific Insights
arXiv:2610.00492v1 Announce Type: cross Abstract: When Isaac Newton discovered the law of gravitation, he did so through an iterative process of analyzing observed data such as planetary patterns, finding the underlying…
- arXiv cs.AIresearchGumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps
arXiv:2610.00497v1 Announce Type: cross Abstract: We present Gumbel Straight Flow (GSF), a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive language (AR) model. We…
- arXiv cs.AIresearchRules Amortize, Pairings Don't: Linguistic Structure Determines What Latent Task Representations Can Replace In-Context Learning
arXiv:2610.00526v1 Announce Type: cross Abstract: In-context learning (ICL) can be amortized into latent objects (task vectors, function vectors, context vectors) that recover few-shot behavior at zero-shot inference cos…
- arXiv cs.AIagentsMulti-agent Auditory Scene Analysis: Improved Localization Speed and Robustness by Multi-beamformed Speech Quality Feedback
arXiv:2610.00538v1 Announce Type: cross Abstract: A real-time auditory scene analyzer (ASA) aims to carry out the tasks of locating, separating and classifying the sound sources present in a given acoustic environment. R…
- arXiv cs.AIresearchRandom Recursive Models
arXiv:2610.00541v1 Announce Type: cross Abstract: Recursive models create computational depth through parameter reuse, offering a parameter-efficient alternative to increasing model size. However, most recursive models r…
- arXiv cs.AIagentsNo One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agents
arXiv:2610.00557v1 Announce Type: cross Abstract: Autonomous red team agents increasingly stress-test AI-enabled cyber defenses by planning strategy and executing multistage attacks. Reinforcement learning (RL) and large…
- arXiv cs.AIresearchCan LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning
arXiv:2610.00562v1 Announce Type: cross Abstract: Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient histories. Although…
- arXiv cs.AIresearchBeyond Affine Transformations: A Soft Dominance Layer for Coordinate-Wise Neural Computation
arXiv:2610.00563v1 Announce Type: cross Abstract: This paper presents a preliminary study of an alternative to the affine transformation underlying conventional neural-network layers. In the proposed Soft Dominance Layer…
- arXiv cs.AIresearchEmergent Unfaithfulness: How Alignment Training Causes Language Models to Silently Override Task Faithfulness
arXiv:2610.00568v1 Announce Type: cross Abstract: Large language models are characterized by three key properties: capability, alignment, and faithfulness. Prior work studies the tradeoffs between capability and alignmen…
- arXiv cs.AIresearchInterpreting Reasoning of Large Language Models via Partial Information Decomposition
arXiv:2610.00571v1 Announce Type: cross Abstract: Large reasoning models (LRMs) have achieved substantial improvements in solving complex mathematical problems, but often produce lengthy, repetitive, or erroneous reasoni…
- arXiv cs.AIresearchQuery-efficient winner prediction in district-based elections
arXiv:2610.00577v1 Announce Type: cross Abstract: In a district-based election, N voters are partitioned into k districts, and each voter votes for one of m candidates. Each district elects a winner using the plurality r…
- arXiv cs.AIagentsTowards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution
arXiv:2610.00590v1 Announce Type: cross Abstract: An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability to generalize as netw…
- arXiv cs.AIresearchALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning
arXiv:2610.00592v1 Announce Type: cross Abstract: In partially observable reinforcement learning (RL), a later observation can make stored information obsolete or change what it implies for the next decision. Memory arch…
- arXiv cs.AIresearchWhen Reasoning Helps Action: Monitoring and Steering Chain-of-Thought in Vision-Language-Action Policies
arXiv:2610.00601v1 Announce Type: cross Abstract: Reasoning-enabled VLA policies expose chain-of-thought (CoT) traces that appear to explain and guide their actions, creating a potential interface for runtime safety thro…
- arXiv cs.AIresearchMIKASA-Robo-VLA: Benchmarking Memory in VLA Models for Long-Horizon Manipulation
arXiv:2610.00604v1 Announce Type: cross Abstract: Vision-language-action policies often see only one or a few recent frames, which makes it difficult to evaluate how they use information that disappears during a task. We…
- arXiv cs.AIresearchWhere's Waldo? Query-language Preference under Cross-lingual Knowledge Disparities
arXiv:2610.00606v1 Announce Type: cross Abstract: Large Language Models increasingly serve as interfaces for knowledge-intensive information seeking tasks across languages by synthesizing multilingual evidence. Prior wor…
- arXiv cs.AIresearchBeyond Supra-Competitive Outcomes: Collusive Behaviour in Deep Reinforcement Learning for Optimal Execution Games
arXiv:2610.00619v1 Announce Type: cross Abstract: In this paper, we extend earlier findings of supra-competitive outcomes in optimal-execution games by identifying a learned punitive mechanism that deters deviations and…
- arXiv cs.AIresearchMisalignment of Low-Loss Regions Causes Grokking
arXiv:2610.00620v1 Announce Type: cross Abstract: Grokking refers to the delayed emergence of validation-set generalization after a model has already overfit the training set. Although first observed in small algorithmic…
- arXiv cs.AIagentsSelf-Evolving Coding Rules for AI Coding Agents
arXiv:2610.00650v1 Announce Type: cross Abstract: The performance of AI coding agents is highly dependent on their underlying coding rules. However, existing coding rules are typically hand-crafted and fixed, making the…
- arXiv cs.AIresearchExploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models
arXiv:2610.00661v3 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) generate text by denoising a sequence or successive blocks, allowing several tokens to be revealed in parallel. Reinforcem…
- arXiv cs.AIresearchVisionQ: VLM-as-a-Judge Taxonomy, Dataset and Benchmark for Qualitative Analysis in Computer Vision
arXiv:2610.00666v1 Announce Type: cross Abstract: Qualitative comparison figures are central evidence in computer vision papers, and vision-language models (VLMs) are increasingly used to judge them. Yet existing benchma…