AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsLoud Failures, Quiet Failures: Fault Detection and Recovery in Tool-Using Language Model Agents
arXiv:2610.10062v1 Announce Type: new Abstract: Tool-using agents are usually scored on whether they finish a task while the tools work. Deployments are less forgiving: services time out, endpoints disappear, parameter n…
- arXiv cs.AIagentsHGP:An on-device personalized agent memory via hybrid graph storage
arXiv:2610.10071v1 Announce Type: new Abstract: LLM-based agents face challenges in personalized interactive tasks due to heterogeneous, multi-typed, and implicitly constrained long-term traces. Existing memory mechanism…
- arXiv cs.AIagentsSkillSandbox: Skill Verification via Dynamic Scenario Synthesis
arXiv:2610.10088v1 Announce Type: new Abstract: Self-evolving agents distill task-solving experience into skills for future reuse, but these skills can encode incorrect procedures or non-transferable knowledge. It is the…
- arXiv cs.AIagentsRewardWeaver: Long-Horizon Interactive Learning for Language Agents via Self-Evolving Reward Adaptation
arXiv:2610.10120v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in domains where task outcomes can be reliably evaluated, but long-horizon interaction…
- arXiv cs.AIagentsUniSkill: Learning Actor-Aligned Skill Proposals for an Evolving Policy
arXiv:2610.10164v1 Announce Type: new Abstract: Large language model agents can improve across tasks by retaining reusable skills distilled from prior interactions. Recent work jointly optimizes task execution and skill…
- arXiv cs.AIagentsAgentic AI-Assisted Modeling for Production Scheduling: Assessment in Constraint Programming
arXiv:2610.10184v1 Announce Type: new Abstract: Developing optimization models for production scheduling requires substantial expert effort. Research on large language models (LLMs) has followed two directions: specializ…
- arXiv cs.AIagentsOOM-RL II: Reality Is an Oracle, Not a Debugger Provenance-Constrained Diagnosis in Continually Evolving Agent-Engineered Systems
arXiv:2610.10256v1 Announce Type: new Abstract: Reality may establish that an outcome occurred without identifying which evolving procedure produced it or why. This distinction matters in production ML systems whose code…
- arXiv cs.AIagentsStale, Misattributed, or Late: Where Personal Memory Fails Before Generation
arXiv:2610.10265v1 Announce Type: new Abstract: Personal memory for language agents is usually judged by whether the final an- swer is correct. That score hides errors that arise before generation: the memory block may c…
- arXiv cs.AIagentsAI Safety Considerations for Agents With Limited Time to Act
arXiv:2610.10285v1 Announce Type: new Abstract: In the wake of the increasingly public discussion about AI alignment, recent work has tried to propose specific AI architectures that behave safely. However, the proposed a…
- arXiv cs.AIagentsSOTA: Stock Options Trading Agents Guided by Option-Implied Return Distributions
arXiv:2610.10407v1 Announce Type: new Abstract: As option markets grow and AI advances, agentic systems for option trading are gaining increasing attention. Language-model-based agents can reason over contextual informat…
- arXiv cs.AIagentsRunningTab: Direct Workspace Interaction with Environment-Side Tabs
arXiv:2610.10444v1 Announce Type: new Abstract: Much knowledge work produces new deliverables from files a workspace already holds, and LLM agents are beginning to take such work over. Through direct corpus interaction,…
- arXiv cs.AIagentsBefore They Can Solve: Predicting Post-Training Coding-Agent Performance from Base Models
arXiv:2610.10478v1 Announce Type: new Abstract: How can we predict which base checkpoint is worth an expensive round of agentic post-training? End-to-end pass@$K$ tests whether successful behavior already appears in a ba…
- arXiv cs.AIagentsSciExam for ENSO: Can AI Agents Build Climate Models?
arXiv:2610.10513v2 Announce Type: replace Abstract: Language-model agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric, or a la…
- arXiv cs.AIagentsTaming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots
arXiv:2610.08812v1 Announce Type: cross Abstract: We present Go2-DrivoR, a goal-conditioned adaptation of the end-to-end autonomous driving trajectory planning framework DrivoR for urban navigation with quadrupedal robot…
- arXiv cs.AIagentsAutonomous Driving Research Requires a Community-Driven Data Paradigm
arXiv:2610.08825v1 Announce Type: cross Abstract: Autonomous driving has made remarkable progress, with recent AI advances enabling commercial deployments that are reshaping urban mobility. Yet the field remains far from…
- arXiv cs.AIagentsCredLeakBench: Evaluating Credential Leakage and Recovery in LLM Agents
arXiv:2610.08871v1 Announce Type: cross Abstract: Language model agents are increasingly deployed to automate everyday digital chores from managing emails and social media to handling banking and bills allowing users to…
- arXiv cs.AIagentsMulti-Aspect Runtime Verification for Simulation-Based V&V of LLM-Enabled Autonomous Agents
arXiv:2610.08928v1 Announce Type: cross Abstract: LLM-based agents are entering decision-support roles in defence staff work, where the obligations they must respect are already written down and binding, and where retrai…
- arXiv cs.AIagentsToward Evidence-Driven Human-Agent-Robot Teaming for Earth-Independent Anomaly Triage
arXiv:2610.08933v1 Announce Type: cross Abstract: Deep-space crews cannot rely on real-time ground support for urgent off-nominal events. Initial alerts may underdetermine cause, while discriminating evidence may reside…
- arXiv cs.AIagentsRACER: Reflective Agent Coupling Query Interpretation and Tool-Based Retrieval for Frame Selection in Long Video Understanding
arXiv:2610.08954v1 Announce Type: cross Abstract: Video large language models (Vid-LLMs) excel at diverse video-language tasks by reasoning over selected frames. However, frame selection for long videos remains challengi…
- arXiv cs.AIagentsGraphOPD: Graph-Augmented On-Policy Distillation for LLM Agents
arXiv:2610.08959v2 Announce Type: replace-cross Abstract: On-policy distillation post-trains large language model agents by supplying dense, step-level guidance from a teacher policy when the reinforcement-learning rewar…
- arXiv cs.AIagentsOn KL-Regularized Policy Optimization
arXiv:2610.08963v1 Announce Type: cross Abstract: Asynchronous reinforcement learning (RL) for large language model (LLM) agents trains one policy on trajectories generated by another: rollouts come from stale checkpoint…
- arXiv cs.AIagentsVisual Memory Attacks Can Persist Through The KV Cache
arXiv:2610.09027v1 Announce Type: cross Abstract: Modern language model systems operate autonomously over increasingly long contexts containing untrusted text and images. Can an adversarial input continue to steer a mode…
- arXiv cs.AIagentsSwarmReconGuard: Black-Box Detection of Distributed Collective Reconnaissance by Individually Benign-Looking Agent Populations
arXiv:2610.09138v1 Announce Type: cross Abstract: Autonomous and agentic clients can distribute reconnaissance across many identities so that each request remains valid, low-rate, and benign-looking while the population…
- arXiv cs.AIagentsToolRACER: A Robust Agentic Conversation Emulation Resource for Agent Training and Evaluation
arXiv:2610.09163v2 Announce Type: replace-cross Abstract: Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when users exhib…
- arXiv cs.AIagentsAdversarial Images Hijack Web Agents from Visual Grounding to Browser Execution
arXiv:2610.09240v1 Announce Type: cross Abstract: Modern web agents built on large vision-language models process webpages, select relevant UI elements, and translate model outputs into browser actions. Existing visual r…
- arXiv cs.AIagentsPackage Hallucination Attacks on Coding Agents through Prompt Injection in Rule Files
arXiv:2610.09264v1 Announce Type: cross Abstract: Modern agentic coding frameworks increasingly rely on community-shared rule files (e.g., AGENTS.md or .cursorrules) to guide autonomous code generation, yet the security…
- arXiv cs.AIagentsLeCuration: A Tiny World Model as a Data Curation Multi-Tool
arXiv:2610.09285v1 Announce Type: cross Abstract: Many applications of physical AI run within finite or closed physical worlds with a limited set of physical laws governing object behavior. Examples include robots workin…
- arXiv cs.AIagentsThe Confidence Game: Strategic Miscalibration in Human-AI Delegation
arXiv:2610.09371v1 Announce Type: cross Abstract: Calibrated uncertainty quantification is essential to ensuring AI agents are trustworthy and reliable. However, when agents seek to maximize user engagement or revenue, c…
- arXiv cs.AIagentsSecure-CUA: Controlling Untrusted Influence in Computer-Use Agents
arXiv:2610.09469v1 Announce Type: cross Abstract: Computer-use agents (CUAs) perform tasks across applications (such as desktops, mobile apps, and web browsers) by observing graphical interfaces and issuing commands such…
- arXiv cs.AIagentsNot All Uncertainty Matters: Simulation-in-the-Loop Fast-Slow Reasoning for Decision-Critical Autonomous Driving System
arXiv:2610.09520v1 Announce Type: cross Abstract: Large vision-language models (VLMs) provide powerful open-world perception and reasoning for autonomous driving, but their high computational cost and inference latency m…
- arXiv cs.AIagentsCorrect Answers, Unsupported Findings: Evidence Binding in Forensic Reconstruction of LLM Agent Logs
arXiv:2610.09581v1 Announce Type: cross Abstract: Forensic reconstruction of LLM-agent actions requires not only recovering the correct value, but establishing which preserved record supports that finding. Tool logs, gen…
- arXiv cs.AIagentsCoding-Agent Benchmarks Should Match Their Users' Task Flows
arXiv:2610.09633v2 Announce Type: replace-cross Abstract: The evaluation of coding agents generally strives to be as realistic as possible. In our study, we collect 4,782 agent sessions of real software engineers in JetB…
- arXiv cs.AIagentsCircuitATLAS: Agentic reasoning over a systems neuroscience knowledge graph for target discovery in circuitopathies
arXiv:2610.09643v1 Announce Type: cross Abstract: Drug discovery for neurological disease has traditionally centered on the molecules altered by disease. But the molecules that cause pathology are not necessarily the bes…
- arXiv cs.AIagentsFrom Pareto to Preference: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery
arXiv:2610.09684v1 Announce Type: cross Abstract: Test-time scaling (TTS) improves the reasoning capabilities of large language models by allocating additional inference computation. Existing approaches to improving TTS…
- arXiv cs.AIagentsBeyond Policy Support: Interaction Constrained Offline Reinforcement Learning for Autonomous Driving
arXiv:2610.09763v2 Announce Type: replace-cross Abstract: Offline reinforcement learning enables reward-driven policy improvement from fixed datasets without requiring online exploration, making it particularly attractiv…
- arXiv cs.AIagentsDecoupling Logic from Persona: Structural Immunity of Edge LLM Agents to Context Pollution
arXiv:2610.09772v2 Announce Type: replace-cross Abstract: Edge language-model agents must hold a persona and reason correctly at once, in one context window that fills with history and persona instructions. We study what…
- arXiv cs.AIagentsTraining Advisors for LLM Agents from Task Outcomes
arXiv:2610.09858v1 Announce Type: cross Abstract: Large language model agents tackle multi-step tasks by interleaving reasoning and tool calls with observations from the environment. Prior work has shown that natural-lan…
- arXiv cs.AIagentsDeadline-Aware Multi-Agent Reinforcement Learning for TSN-Based Vehicular Edge Networks
arXiv:2610.09870v1 Announce Type: cross Abstract: Vehicular edge computing (VEC) enables latency-sensitive applications by bringing computing and networking resources closer to vehicles. However, existing approaches ofte…
- arXiv cs.AIagentsAn AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
arXiv:2610.09871v1 Announce Type: cross Abstract: Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results. Fo…
- arXiv cs.AIagentsKnow the Shape, Find the Fault: Topology-Conditioned Diagnosis of Multi-Agent LLM Failures
arXiv:2610.10126v1 Announce Type: cross Abstract: Multi-agent LLM systems coordinate task execution through exchanges of information among agents. When coordination breaks down, similar symptoms in execution traces can r…
- arXiv cs.AIagentsWhen Algorithmic Exploration Becomes Cheap: A Case Study of Agentic Research in EDA
arXiv:2610.10129v1 Announce Type: cross Abstract: As EDA researchers, we conducted eight deliberate trials of agentic algorithm exploration, selecting several topics outside our areas of depth. One faculty member and sev…
- arXiv cs.AIagentsBeyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents
arXiv:2610.10179v1 Announce Type: cross Abstract: Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions. Reinforcement Learning with Verifiable Rewa…
- arXiv cs.AIagentsWhy Software Engineering Is Indispensable in the Age of Coding Agents
arXiv:2610.10226v2 Announce Type: replace-cross Abstract: Can AI make Software Engineering (SE) -- the discipline -- obsolete? And can it make software engineers -- the professionals -- redundant? This paper argues that…
- arXiv cs.AIagentsTestGRAD: Evolving Test Suites via Failure Pattern Momentum for SWE-Agent Ensemble
arXiv:2610.10242v1 Announce Type: cross Abstract: SWE-agent ensembles improve issue resolution by combining candidate patches from different agents with complementary strengths. The central problem is therefore test-base…
- arXiv cs.AIagentsQuSema: Detecting Silent Bugs in Quantum Libraries via Quantum-knowledge-enhanced Agents
arXiv:2610.10258v1 Announce Type: cross Abstract: Quantum libraries are now critical infrastructure for quantum algorithm development, yet their correctness remains difficult to test. Existing testing techniques mainly r…
- arXiv cs.AIagentsMIRA: A Musical Intent Refinement Agent for Aligning Text-to-Music Generation with User Intent
arXiv:2610.10355v1 Announce Type: cross Abstract: Text-to-music systems produce increasingly convincing audio, yet evaluation reveals little about whether the result matches user intent. A global text-audio relevance sco…
- arXiv cs.AIagentsRoboQuest: Generalist Physical Agents that Search, Inspect and Test
arXiv:2610.10388v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks. However, successful operation in an u…
- arXiv cs.AIagentsA Society of Researchers: Designing Institutions for Populations of Autonomous Research Agents
arXiv:2610.10468v1 Announce Type: cross Abstract: Deployments of research agents are moving to populations of thousands that share one pool of compute, while most current systems organize one project at a time or leave t…
- arXiv cs.AIagentsFREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledge
arXiv:2308.00505v4 Announce Type: replace Abstract: Agent Based Models (ABMs) often deal with systems where there is a lack of quantitative data or where quantitative data alone may be insufficient to fully capture the c…
- arXiv cs.AIagentsPersuasion Propagation: Does Persuasion Change AI Agent Behavior?
arXiv:2602.00851v4 Announce Type: replace Abstract: AI agents combine normal conversation with autonomous task execution, allowing earlier context to shape how later tasks are executed. Yet studying this possibility is c…