AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsJockeyBench: Evaluating LLM Agents on Horse-Racing Decision-Making
arXiv:2610.19589v1 Announce Type: new Abstract: Repeated event forecasting requires extracting predictive signals from noisy evidence and evaluating their value across successive decisions. We introduce JockeyBench, a be…
- arXiv cs.AIagentsL3M: A Verifiable Harness and Runtime for Self-Extending and Self-Improving Agents
arXiv:2610.19591v1 Announce Type: new Abstract: The lasting gains in AI have come from methods that absorb more computation, and language models already scale computation within a call. Useful work, though, happens acros…
- arXiv cs.AIresearchAre AI Decision Models Biased? A Case Study of Jev
arXiv:2610.19619v1 Announce Type: new Abstract: Do AI models designed for fast, structured decisions exhibit the cognitive and social biases observed in human judgment? We evaluate Jev, a model described as a System One…
- arXiv cs.AIagentsModel What Matters: Decision-Guided Selective World Modeling for Language Agents
arXiv:2610.19642v1 Announce Type: new Abstract: Reinforcement learning provides a direct mechanism for optimizing task objectives in large language model-driven agents, but task rewards are typically sparse trajectory-le…
- arXiv cs.AIresearchLearning When to Trust Medical Knowledge: Regression-Safe Routing for Clinical Reasoning
arXiv:2610.19663v1 Announce Type: new Abstract: Clinical reasoning LLMs need external medical knowledge, but retrieved evidence and reasoning chains can be outdated, poisoned, or hallucinated. Harmful knowledge can cause…
- arXiv cs.AIagentsMCAIR: Monitoring and Correcting Agent Unfaithfulness via Internal Representations
arXiv:2610.19679v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in completing complex tasks through multi-step reasoning and tool interactions. However, r…
- arXiv cs.AIresearchReFormBench: Benchmarking the Stability of Response-Conditioned Format Correction in Large Language Models
arXiv:2610.19693v1 Announce Type: new Abstract: Reliable use of large language models requires responses to satisfy formatting requirements across changing instructions. Standard compliance scores do not distinguish pres…
- arXiv cs.AIagentsC3: Rethinking Process Supervision via Confidence-Guided Credit Correction in Multi-Turn Reinforcement Learning
arXiv:2610.19695v1 Announce Type: new Abstract: Large language models are increasingly trained as multi-turn agents that interact with external tools. However, outcome-only reinforcement learning can produce a form of mi…
- arXiv cs.AIagentsRuleTrek: Benchmarking Agentic Rule Reasoning in Complex Scenarios
arXiv:2610.19698v1 Announce Type: new Abstract: Large language models (LLMs) increasingly tackle complex real-world tasks in specialized domains. However, identifying and correctly applying domain-specific rules remains…
- arXiv cs.AIresearchRuntime Monitoring of Supermartingale Certificates
arXiv:2610.19701v1 Announce Type: new Abstract: Supermartingale certificates formally guarantee temporal properties of discrete and continuous-time stochastic dynamical systems. However, these guarantees rely on a mathem…
- arXiv cs.AIresearchLearning Individual Scientific Taste with Privileged Hindsight Distillation
arXiv:2610.19719v1 Announce Type: new Abstract: Scientific taste shapes which research directions researchers pursue, and it varies across individuals. Existing work evaluates research directions along multiple evaluatio…
- arXiv cs.AIresearchArticulation Upper Bounds for Language Models
arXiv:2610.19721v1 Announce Type: new Abstract: Language captures only a subset of the perceptions, intuitions and intents that form meaning between speakers. What, then, are the limits of language? We take steps towards…
- arXiv cs.AIresearchLabOPBench: A Multimodal Benchmark for Evidence-Grounded Laboratory Decision Making
arXiv:2610.19780v1 Announce Type: new Abstract: Laboratory experiments involve more than following predefined protocols; they require adaptive decision-making as the experimental state changes. Existing scientific benchm…
- arXiv cs.AIagentsLearning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning
arXiv:2610.19796v1 Announce Type: new Abstract: In Theory-of-Mind (ToM) reasoning, large language models may correctly infer the agent's mental state but fail to use that inference when predicting or judging the agent's…
- arXiv cs.AIagentsReproBAIT: Can coding agents build their own biological AI tools?
arXiv:2610.19810v1 Announce Type: new Abstract: Large language models are increasingly competent at leveraging scientific knowledge and existing computational tools to execute complex workflows. At present, little inform…
- arXiv cs.AIagentsOne Monitor Does Not Fit All: Formalism-Agnostic Runtime Verification for Agentic AI
arXiv:2610.19818v1 Announce Type: new Abstract: Agentic AI systems combine model decisions, mutable state, tool calls, and delegation to external agents. Monitoring them requires observations from different execution bou…
- arXiv cs.AIagentsActionOPD: Distilling Decisions, Not Tokens
arXiv:2610.19820v1 Announce Type: new Abstract: Distilling capable language-model agents into smaller models requires aligning the student's behavior with the teacher's. This raises a fundamental question: at which state…
- arXiv cs.AIagentsTeaching Agents in their Own Style: Blackbox Teacher Distillation for Web Agents
arXiv:2610.19834v1 Announce Type: new Abstract: Frontier web agents using closed models have high inference and computational costs. Smaller open models can be an alternative, but to be performant, they require training…
- arXiv cs.AIagentsLAGO: Language Agents with Reusable Growing Options
arXiv:2610.19843v1 Announce Type: new Abstract: Long-horizon tasks expose a weakness of monolithic tool-use loops: one agent must decompose, execute, and verify a task within a single trajectory. We introduce LAGO, which…
- arXiv cs.AIagentsEuler: Towards Human-Centered Multi-Agent Mathematical Discovery
arXiv:2610.19864v1 Announce Type: new Abstract: Mathematical research drives scientific discovery, and recent advances in AI for mathematics have shown that language-model agents can solve challenging problems and develo…
- arXiv cs.AIagentsExplorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards
arXiv:2610.19868v1 Announce Type: new Abstract: Training LLM agents on long-horizon interactive tasks with reinforcement learning typically relies on human-annotated tasks and reliable verification, both expensive to con…
- arXiv cs.AIagentsHow GUI Agents Fail: GraceBench for Execution Stability Diagnosis
arXiv:2610.19900v1 Announce Type: new Abstract: As GUI agents are increasingly deployed for mobile task automation, their reliability depends not only on final task outcomes, but also on whether they reach these outcomes…
- arXiv cs.AIresearchSparse Transformer: a Controlled Testbed for Mechanistic Interpretability
arXiv:2610.19907v1 Announce Type: new Abstract: Mechanistic interpretability aims to decompose neural network computations into human-understandable features, yet progress is limited by the absence of ground-truth units…
- arXiv cs.AIresearchWhen Protection Overrides Consequences: Understanding Protection Mode in Large Language Model Decision-Making
arXiv:2610.19948v1 Announce Type: new Abstract: Frontier language models excel at complex tasks yet may prioritize protection over larger physical consequences in simple, urgent decisions. GPT-5.6 Sol, for example, moves…
- arXiv cs.AIagentsNeurosymbolic Learning of Interpretable World Models
arXiv:2610.19955v1 Announce Type: new Abstract: World models aim to learn compact representations of an environment from observations, enabling agents to predict future states and plan their behaviour. However, most exis…
- arXiv cs.AIagentsWhat Coding Agents Leave Unsaid: Measuring Task-State Gaps Between Execution and Reports
arXiv:2610.20003v2 Announce Type: replace Abstract: Human supervisors rely on coding agents' reports to assess progress, verify repairs, and decide when to intervene. But a supervisor cannot investigate a failure they we…
- arXiv cs.AIagentsMemorizing Who, Knowing How: Benchmarking Personal and Procedural Memory for Persistent Assistants
arXiv:2610.20022v2 Announce Type: replace Abstract: Memory systems for persistent personal agents must move beyond isolated memories, maintaining both personal memory about whom they serve and procedural memory about how…
- arXiv cs.AIresearchBeyond Correctness: Compute-Responsive Data Selection for Reasoning Model Post-Training
arXiv:2610.20057v1 Announce Type: new Abstract: Reasoning language models increasingly rely on test-time compute scaling, yet most existing work studies how to allocate inference computation while treating the model itse…
- arXiv cs.AIresearchDesigning Textual Interfaces To Enhance Llm Reasoning On Audio-Visual Question Answering
arXiv:2610.20085v1 Announce Type: new Abstract: Automatic prompt optimization (APO) has shown strong effectiveness for text-based LLMs and LLM systems, but its application to audio-visual understanding systems requires s…
- arXiv cs.AIagentsEvalBuilder: Can LLMs Build Efficient Benchmarks?
arXiv:2610.20099v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used for long-horizon research and engineering, yet evaluating these capabilities remains expensive, time-consuming, and…
- arXiv cs.AIagentsMaximising Transparency in Context Compaction Improves Retention of Facts and Commitments for Lightweight Coding Agents
arXiv:2610.20116v1 Announce Type: new Abstract: Long coding agent sessions outgrow the model's context window, so agents compact: older history is replaced by something shorter, usually a model-written summary. A summary…
- arXiv cs.AIresearchLLM-Based Policy Compliance Verification of Loan Applications
arXiv:2610.20155v1 Announce Type: new Abstract: Banks rely on extensive internal policy frameworks to ensure that corporate lending decisions comply with regulatory requirements and internal risk standards. Reviewing loa…
- arXiv cs.AIresearchValue-Guided Repulsion for Offline Reinforcement Learning
arXiv:2610.20190v1 Announce Type: new Abstract: Drifting models have emerged as a highly efficient and stable paradigm for policy design in offline reinforcement learning (RL). However, standard drift policies determine…
- arXiv cs.AIagentsHEM-Med: A Hierarchical Agentic Memory Framework for Safe Multi-Center Medication Recommendation
arXiv:2610.20191v2 Announce Type: replace Abstract: Medication recommendation (MR) is critical for clinical decision support. While existing MR methods are mostly designed for single-center learning, where only one indep…
- arXiv cs.AIagentsDrivers of LLM Consensus Formation Under Competing Incentives: A Simulation Study
arXiv:2610.20200v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in multi-agent environments that produce unanimous agreement, both in intended interactions that need coordination an…
- arXiv cs.AIresearchRobust Counterfactuals for Markov Decision Processes under Statistical and Causal Uncertainties
arXiv:2610.20210v1 Announce Type: new Abstract: Counterfactual reasoning (asking what would have happened, had a decision been different) is a powerful tool for identifying causes of failure and explaining the behaviour…
- arXiv cs.AIresearchDesign Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement
arXiv:2610.20224v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) and Reinforcement Learning (RL) have recently been explored for VLSI placement optimization, but their effectiveness for post-legalization plac…
- arXiv cs.AIagentsCASI: Credit Assignment via Set Intervention for Agentic Retrieval
arXiv:2610.20293v1 Announce Type: new Abstract: Large language models increasingly generate multiple retrieval actions jointly, while downstream systems often provide only one reward for the resulting set. Broadcasting t…
- arXiv cs.AIagentsReflect After Failure: Brain-Inspired Self-Evolving Agents for Embodied Spatial Intelligence
arXiv:2610.20312v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have made substantial progress in spatial reasoning, and self-evolving agents further extend these capabilities to embodied environments by le…
- arXiv cs.AIresearchArgument Graph Uncertainty: Quantifying Uncertainty from the Logical Structure of Reasoning Chains
arXiv:2610.20328v1 Announce Type: new Abstract: Reliable uncertainty estimation is essential for deploying large language models in high-stakes reasoning tasks, yet most existing approaches rely on expensive sampling str…
- arXiv cs.AIagentsTreeFlex: Agents Navigating Their Own Search Trees
arXiv:2610.20334v1 Announce Type: new Abstract: A ReAct-style agent accumulates observations, intermediate results, and failed attempts in a single context. As this context grows, stale and off-path information consumes…
- arXiv cs.AIresearchLifted State Policy Optimization: Internalizing Self-Correction through Transition Geometry
arXiv:2610.20350v1 Announce Type: new Abstract: Preference optimization ranks final answers and trajectory RL maximizes aggregate returns, yet neither credits the local change that turns a flawed reasoning state into a b…
- arXiv cs.AIagentsFrom Relevance to Validity: Zero-Shot Tool Selection via Functional and Contextual Validation
arXiv:2610.20388v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly face tasks from unseen domains and execution contexts, making zero-shot tool selection essential for identifying tools that c…
- arXiv cs.AIagentsSwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm
arXiv:2610.20431v2 Announce Type: replace Abstract: Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their own experimental experience. Scientific agent swarms…
- arXiv cs.AIresearchSafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift
arXiv:2610.20473v2 Announce Type: replace Abstract: Opponent histories can mislead after behavioral shifts. SafeAdapt-Poker formulates budgeted adaptation under opponent shift as a dual-counterfactual sequential-control…
- arXiv cs.AIagentsThe Wealth of Agents: Scaling Agentic AI Across Developers
arXiv:2610.20518v1 Announce Type: new Abstract: Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating agents. While this simplifies development,…
- arXiv cs.AIagentsActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps
arXiv:2610.20532v2 Announce Type: replace Abstract: A multimodal agent must decide whether visual evidence is worth acquiring, which candidate or resolution to query, and when further interaction can no longer change its…
- arXiv cs.AIagentsPhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition
arXiv:2610.20560v1 Announce Type: new Abstract: 3D object composition is pivotal to digital content creation and robot simulation, while also serving as a critical testbed for physical intelligence in foundation models.…
- arXiv cs.AIagentsScaling Discovery Requires Forgetting
arXiv:2610.20563v1 Announce Type: new Abstract: Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are capable of discovering…
- arXiv cs.AIagentsCo-Evolving Acting and Forecasting for Language Agents
arXiv:2610.20597v2 Announce Type: replace Abstract: Training language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar…