AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsEvoQuantLab: Training-Free Dual-Loop Evolution for Auditable Quantitative Research
arXiv:2610.17130v2 Announce Type: replace Abstract: Automated alpha factor discovery is critical for modern quantitative research. While large language models (LLMs) have increasingly been integrated into quantitative wo…
- arXiv cs.AIagentsAutoMARS: Automatic and Scalable Multi-Agent Large Language Model Framework for Supply Chain Risk Assessment
arXiv:2610.17206v1 Announce Type: new Abstract: We propose AutoMARS (Automatic Multi-Agent Risk Assessment), an agentic framework for automatic and scalable comparative risk assessment in supply chain networks. The frame…
- arXiv cs.AIagentsPhysCodeGym: Benchmarking Coding Agents for Physically Verifiable 4D Reconstruction
arXiv:2610.17288v1 Announce Type: new Abstract: Building physical simulations from video requires coding agents to translate observations into executable models and refine them through tool use. We introduce PhysCodeGym,…
- arXiv cs.AIagentsThe Agent Knew: Internal States Forecast Unauthorized Actions and Stop Them, and the Lies That Follow
arXiv:2610.17301v1 Announce Type: new Abstract: Language-model agents sometimes act beyond their authorization and then misreport what they did. On CAPE-Bench, matched agent tasks in which one fact decides whether an act…
- arXiv cs.AIagentsReliable Text-to-SQL in Resource-Constrained Settings with Small LLMs via Iterative Retry Workflows
arXiv:2610.17305v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently improved Text-to-SQL generation, but their computational cost can hinder deployment in resource-constrained settings. This paper…
- arXiv cs.AIagentsBeyond Successful Play: Diagnosing Opponent Modeling in Large Language Models
arXiv:2610.17310v1 Announce Type: new Abstract: Language-model agents are expected to adapt to others whose behavior changes through interaction. Yet adaptation may rely on learned behavioral patterns rather than theory…
- arXiv cs.AIagentsDo Theorem Provers Need Theorem-Proving Agents? mini-Lean-agent: A Coding-Agent Baseline for Lean
arXiv:2610.17331v1 Announce Type: new Abstract: General-purpose coding agents use standard shell and file interactions, yet formal theorem-proving agents often rely on specialized, ad-hoc tooling. This makes it difficult…
- arXiv cs.AIagentsBudgeted KV-Cache Repair for Evolving Contexts
arXiv:2610.17338v1 Announce Type: new Abstract: Language-model agents revise their working context as information becomes obsolete, but deleting text does not erase its influence on cached computation. Later key-value (K…
- arXiv cs.AIagentsWhen Goals Leak Across Relationships: How Cached Goal Representations Shape Cross-Client Decisions
arXiv:2610.17365v1 Announce Type: new Abstract: As LLM agents take on more autonomous roles, they may interact with multiple clients and counterparties whose goals and incentives conflict. If an agent maintains shared in…
- arXiv cs.AIagentsGet In, Decide, Prove: A Merchant-Side Readiness Benchmark for Agentic Commerce
arXiv:2610.17378v1 Announce Type: new Abstract: AI agents now browse and buy on people's behalf, and benchmarks measure how well the agents perform. Far less is known about the other side of the transaction: whether a me…
- arXiv cs.AIagentsAgency as a New Organizing Abstraction for Computer Science
arXiv:2610.17410v1 Announce Type: new Abstract: Advances in large-scale machine learning have enabled agentic AI systems: persistent, autonomous computational entities that pursue goals, initiate actions, and interact wi…
- arXiv cs.AIagentsSchemaWeaver: Accelerating On-Device Mobile Agentic AI via Schema-Guided Parallel LLM Decoding
arXiv:2610.17436v1 Announce Type: new Abstract: Large language models (LLMs) on mobile devices can act as agents that call external software tools to complete complex tasks, but the major challenge of supporting on-devic…
- arXiv cs.AIagentsLarge Language Models Extend Known Research But Do Not Create New Research Directions
arXiv:2610.17439v1 Announce Type: new Abstract: The success of autonomous research systems relies on the quality of the scientific hypotheses they generate. Recent studies report that human reviewers often rate LLM-gener…
- arXiv cs.AIagentsVerifier-Harness Co-Evolution for Training-Free Self-Improvement on Open-Ended Tasks
arXiv:2610.17609v1 Announce Type: new Abstract: Training-free self-improvement lets an agent use feedback to revise the response it is about to return and to update the harness state that shapes how it handles later task…
- arXiv cs.AIagentsMAS-Gate: Unlocking Multi-Agent System Lifecycle via Node-Centric Quantification
arXiv:2610.17613v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) can solve complex problems beyond the reach of a single agent by decomposing tasks and coordinating complementary…
- arXiv cs.AIagentsExecutable Prefix Values for Locating Agent Failures
arXiv:2610.17633v1 Announce Type: new Abstract: Work on debugging LLM agents treats the "first mistake" marked by a human or LLM judge as ground truth for where a trajectory failed. We ask whether such labels point at ca…
- arXiv cs.AIagentsWhen Should Enterprise Policy Decisions Be Learned, and When Should They Be Reasoned?
arXiv:2610.17643v1 Announce Type: new Abstract: An enterprise agent that answers under a written policy must choose an action before it says anything: answer, ask for a missing fact, ask for confirmation, call one or sev…
- arXiv cs.AIagentsCan Agents Truly Forget? Explore Machine Unlearning for LLM Agents
arXiv:2610.17659v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong capabilities in interactive problem solving, tool use, and long-horizon task execution. However, they may also acquire s…
- arXiv cs.AIagentsCowork-Gym: Synthesizing Interactive Work Environments at Scale
arXiv:2610.17670v1 Announce Type: new Abstract: Cowork agents navigate dependencies across tools and files while adapting to changing user requirements and edits to shared workspaces. Scaling such environments requires m…
- arXiv cs.AIagentsAdaptive-Group Process Rewards for GUI Agents
arXiv:2610.17710v2 Announce Type: replace Abstract: Training graphical user interface (GUI) agents with reinforcement learning suffers from sparse outcome rewards, leading to inefficient learning. Process rewards offer a…
- arXiv cs.AIagentsKeep Your Receipts: Trustworthy Cleanup of Agent-Built Knowledge Graphs
arXiv:2610.17719v1 Announce Type: new Abstract: Agents build knowledge graphs as memory, and building them has become easy. Maintaining them has not. When a source is retracted, overruled, or erased, everything built on…
- arXiv cs.AIagentsUpstream Influence, Downstream Re-Anchoring in LLM Agent Chains
arXiv:2610.17723v1 Announce Type: new Abstract: People act on evaluative judgments produced by LLM agents, and in multi-agent systems those judgments pass through several agents before reaching a person. We show that a w…
- arXiv cs.AIagentsEvidence-First Agentic Verification
arXiv:2610.17724v1 Announce Type: new Abstract: Long-horizon, semi-verifiable agentic tasks often rely on rubric-based evaluation, but the evidence needed to assess these tasks can be spread across the environment state…
- arXiv cs.AIagentsSafety for Whom? Benchmarking Understanding Beyond Refusal in Egocentric Multimodal Assistants
arXiv:2610.17728v1 Announce Type: new Abstract: Egocentric multimodal assistants see through the user's eyes and guide what the user does next, so their outputs directly shape physical actions, including those that affec…
- arXiv cs.AIagentsInstructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents
arXiv:2610.17757v1 Announce Type: new Abstract: Large language model agents can follow complex human instructions to solve real-world tasks. In practice, however, initial instructions are often incomplete. In our analysi…
- arXiv cs.AIagentsAutonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms
arXiv:2610.17762v1 Announce Type: new Abstract: Utility-scale photovoltaic (PV) inspection is dominated by aerial surveys, which are effective for identifying module-level hot cells but provide limited visibility of crit…
- arXiv cs.AIagentsMemory Echo in Single-Agent Long-Term Memory: Lifecycle Evaluation and Provenance-Guided Mitigation
arXiv:2610.17763v1 Announce Type: new Abstract: Long-term memory supports agents across sessions. Repeated retrieval and write-back can let one historical claim occupy multiple retrieval positions and influence current a…
- arXiv cs.AIagentsAgentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis
arXiv:2610.17786v1 Announce Type: new Abstract: We study industrial fault diagnosis as a classification task that assigns sensor time-series windows to predefined health states or fault types. Scarce labels and changing…
- arXiv cs.AIagentsEmpowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution
arXiv:2610.17816v2 Announce Type: replace Abstract: Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-hosted models. H…
- arXiv cs.AIagentsBenchmarking and Training Reward Models for Cross-Source Evidence Grounding in Search Agents
arXiv:2610.17827v1 Announce Type: new Abstract: Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back to retrieved evidence.…
- arXiv cs.AIagentsLearning Adaptive Deliberation Control for Reliable Recognition with Multi-Agent Language Models
arXiv:2610.17840v1 Announce Type: new Abstract: Multi-agent deliberation can improve recognition by criticizing and revising candidate answers, but fixed controls cannot respond to changes in disagreement or reasoning qu…
- arXiv cs.AIagentsSocratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills
arXiv:2610.17860v1 Announce Type: new Abstract: Agent Skills improve LLM agents by making task knowledge reusable across sessions. Learning transferable Skills requires evidence about a procedure and its conditions of us…
- arXiv cs.AIagentsECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning
arXiv:2610.17869v1 Announce Type: new Abstract: Multi-agent Large Language Model (LLM) systems typically commit to a deliberation protocol before inference. A fixed protocol cannot allocate computation differently to res…
- arXiv cs.AIagentsTraining LLMs for Multi-Agent Collaboration with Verifiable Information Rewards
arXiv:2610.17893v1 Announce Type: new Abstract: Effective collaboration among large language model (LLM) agents in multi-agent systems requires sharing relevant evidence, requesting missing information, and incorporating…
- arXiv cs.AIagentsLearning How to Remember: Memory Modality Selection for GUI Agents
arXiv:2610.17928v1 Announce Type: new Abstract: Recent advances in vision-language models (VLMs) have substantially improved the capabilities of GUI agents, while memory augmentation has emerged as a promising approach f…
- arXiv cs.AIagentsCan Agents Forget When Asked? A Benchmark for Memory Unlearning in LLM Agents
arXiv:2610.18052v1 Announce Type: new Abstract: Long-term memory has become a core component of large language model (LLM) agents. As agents accumulate personal information over long-term use, users increasingly ask them…
- arXiv cs.AIagentsWorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution
arXiv:2610.18067v1 Announce Type: new Abstract: Scaling interactive environments is important for training long-horizon agents through reinforcement learning. These environments often require further testing and refineme…
- arXiv cs.AIagentsAReaL-RSI: Adaptive Scaling and Coordination of Recursive Self-Improvement
arXiv:2610.18083v1 Announce Type: new Abstract: Scaling recursive self-improvement requires additional computation to produce useful research progress in reaching persistent objectives. Intuitively, more researcher agent…
- arXiv cs.AIagentsResearchFlow-Eval: Tracing the Execution Gap in End-to-End Multi-Claim Autoresearch
arXiv:2610.18088v2 Announce Type: replace Abstract: Autoresearch systems increasingly undertake entire research projects. In practice, scientific research requires systems to pursue multiple interdependent claims through…
- arXiv cs.AIagentsAM-Bench: Understanding Agents under Task Underspecification across Models and Harnesses
arXiv:2610.18090v1 Announce Type: new Abstract: Ambiguous or incomplete task specifications complicate both execution and clarification for language-model agents. Yet agents' responses to these information gaps remain po…
- arXiv cs.AIagentsLifeSearch: Benchmarking Deep Research Agents for Real-World Life and Productivity
arXiv:2610.18092v1 Announce Type: new Abstract: Deep research (DR) agents address open-ended information needs through multi-step reasoning, long-horizon web search, and evidence synthesis. However, existing benchmarks p…
- arXiv cs.AIagentsWhen and How Order-Flow Interventions Work After Dealers Adapt
arXiv:2610.18158v1 Announce Type: new Abstract: Learning dealer agents can settle on persistently wide quotes, raising what customers pay to trade. A venue can route more orders to tighter quotes, but a wide quote may re…
- arXiv cs.AIagentsNeuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences
arXiv:2610.18176v1 Announce Type: new Abstract: Proactive human-robot collaboration requires autonomous systems to infer not only immediate actions but also the underlying hierarchical intentions governing human behavior…
- arXiv cs.AIagentsBeyond Answer Sharing: TrueMAD for Targeted Peer Engagement in Multi-Agent Debate
arXiv:2610.18240v1 Announce Type: new Abstract: Multi-agent debate (MAD) aims to improve large language model reasoning by exposing agents to alternative solutions and enabling iterative revision. However, standard broad…
- arXiv cs.AIagentsOutput Discipline Sets the Floor: A Falsification-Guided, Model-Agnostic Agent Protocol for Auditable Hypothesis-Driven Decision Making
arXiv:2610.18298v2 Announce Type: replace Abstract: What binds large language models on auditable, hypothesis-driven decision tasks, where every candidate can be checked against the evidence? On the public TCMEval-SDT be…
- arXiv cs.AIagentsAssetForge: Asset-Augmented Task Synthesis for Business Automation Agents
arXiv:2610.18330v2 Announce Type: replace Abstract: Training business automation agents requires executable tasks that connect a natural-language request to an application state and a verifiable end state. Traditional Ru…
- arXiv cs.AIagentsChoosing LLMs for Strategic Agent Interactions
arXiv:2610.18354v1 Announce Type: new Abstract: Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capability dimensions. Howev…
- arXiv cs.AIagentsABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution
arXiv:2610.18355v2 Announce Type: replace Abstract: We introduce ABot Embodied Reasoning (ABot-ER), a unified embodied reasoning foundation model that breaks the tension between breadth and depth in embodied reasoning. E…
- arXiv cs.AIagentsDelegating Earth science model calibration to knowledge-guided LLM agents
arXiv:2610.18378v1 Announce Type: new Abstract: Applying process-based Earth science models to new sites and questions often requires expert calibration decisions. Reliable delegation to language-model agents must preser…
- arXiv cs.AIagentsAutonomous Science Bench: Evaluating AI Agents for Autonomous Scientific Discovery
arXiv:2610.18449v1 Announce Type: new Abstract: AI agents for autonomous scientific discovery must decide which experiments to run under a limited budget. Most benchmarks for science agents provide fixed datasets, while…