AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsRITE: Evaluating End-to-End Resilience to Silent Tool Errors in E-Commerce Agentic Systems
arXiv:2610.27777v1 Announce Type: new Abstract: Tool-using LLM agents increasingly answer e-commerce queries through multi-step tool interactions. The expected output of a tool call can shift between agent development an…
- arXiv cs.AIagentsGRADUS: Discovering and Recovering Agent Lessons from the Environment's Latent Syllabus
arXiv:2610.27781v1 Announce Type: new Abstract: Training LLM agents for policy-constrained tool environments, such as an airline backend with booking, refund, and cancellation policies, requires tasks that exercise the e…
- arXiv cs.AIagentsToward Agentic AI for the 6G Physical Layer: From Algorithm Discovery to Verified Baseband Silicon
arXiv:2610.27797v1 Announce Type: new Abstract: 6G will be designed faster than it can be built: AI-driven search already proposes physical-layer algorithms at machine speed, while carrying a single one onto embedded bas…
- arXiv cs.AIagentsLatBridge: Efficient and Stable Model Replacement in Latent-Communication Multi-Agent Systems
arXiv:2610.27813v1 Announce Type: new Abstract: Multi-agent systems rely on communication to complete tasks, and latent communication passes continuous representations to reduce text generation and re-encoding. However,…
- arXiv cs.AIagentsCOMPASS: Routing via Capability-Oriented Model Profiling with Adaptive Search, Selection, and Tool Use
arXiv:2610.27822v1 Announce Type: new Abstract: LLM routing aims to select a capable model at low cost, but specialized routers can require updates as models and tasks change. We propose COMPASS, an agentic router in whi…
- arXiv cs.AIagentsSAPD: Learning from Agent Experience without Environment Interaction
arXiv:2610.27847v1 Announce Type: new Abstract: Scaling the training of Large Language Model (LLM) agents typically requires a large collection of executable environments, whose construction, maintenance, and operation c…
- arXiv cs.AIagentsMMEnterprise-Bench: Evaluating Enterprise Agents over Evolving Multimodal Records
arXiv:2610.27911v1 Announce Type: new Abstract: Enterprise agents answer questions over workplace records that evolve through team collaboration. As information is shared, corrected, and revised across documents, chats,…
- arXiv cs.AIagentsLost in Recovery: Demystifying Recovery Behavior of Computer-Use Agents via Goal-Oriented Action Replay
arXiv:2610.27954v1 Announce Type: new Abstract: Recent advances in large (vision) language models have enabled computer-use agents that automate digital workflows via fine-grained keyboard and mouse operations. However,…
- arXiv cs.AIagentsWhen Do Humans and Machines Truly Complement Each Other? Formal Conditions and Empirical Evidence for Human-AI Synergy
arXiv:2610.27965v1 Announce Type: new Abstract: Can humans and artificial intelligence cooperate such that their combination yields greater accuracy than either agent alone? This result, termed \emph{synergy} or \emph{co…
- arXiv cs.AIagentsInterruption-Aware Recurrent Reinforcement Learning for UAS Navigation Under Temporary Overrides
arXiv:2610.28121v1 Announce Type: new Abstract: Safe autonomous navigation of UASs requires tactical deconfliction to address conflicts that strategic coordination cannot prevent. This task is challenging under partial o…
- arXiv cs.AIagentsMaD-RSI: Model-as-Data for Recursive Self-Improvement of Agent Harnesses
arXiv:2610.28201v1 Announce Type: new Abstract: Recursive self-improvement (RSI) of agent harnesses has emerged as an effective approach to enhancing the capabilities of large language model (LLM) agents. By searching th…
- arXiv cs.AIagentsChartEvo: Self-Evolving Agent Harnesses for Long-Tail Chart Reasoning
arXiv:2610.28282v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have substantially advanced chart understanding and reasoning, yet their performance remains highly uneven across chart types. Whil…
- arXiv cs.AIagentsS-Agent 2.0: Spatial Reasoning with a General Coding Agent
arXiv:2610.28354v1 Announce Type: new Abstract: Spatial reasoning asks a model to work out where objects are in 3D space, how they relate to one another, and how they move. Agentic VLMs have recently brought large improv…
- arXiv cs.AIagentsFALCA: Falsification-Aware Reinforcement Learning with Hierarchical Credit Assignment for Long-Horizon Deep Search Agents
arXiv:2610.28361v1 Announce Type: new Abstract: Long-horizon deep search requires LLM agents to iteratively decompose complex information needs, gather evidence from external sources, and maintain a reliable research sta…
- arXiv cs.AIagentsToolflation: Seller Agents Distort Tool Markets Through Spontaneous Generative Engine Optimization
arXiv:2610.28454v1 Announce Type: new Abstract: Agents are starting to sell to other agents. Seller agents write the listings of tools, services and products, and buyer agents read these listings when they choose what to…
- arXiv cs.AIagentsClaims of emergence in AI should be grounded in information decomposition
arXiv:2610.28479v1 Announce Type: new Abstract: When a language model develops capabilities not traceable to its individual components, when a multi-agent AI system outperforms each agent on its own, or when a human-AI p…
- arXiv cs.AIagentsProof-Carrying Contracts: Commitments AI Agents Cannot Break
arXiv:2610.28484v1 Announce Type: new Abstract: AI agents increasingly act on behalf of people or other agents, purchasing services, exchanging information and delegating work. What an agent must respect is written in in…
- arXiv cs.AIagentsAgentic Ensemble with Vision-Language Models for Visual Anomaly Detection
arXiv:2610.28560v1 Announce Type: new Abstract: Visual anomaly detection aims to detect abnormal objects, while anomaly samples are often scarce during model training. Assembling multiple pretrained detectors can help by…
- arXiv cs.AIagentsSage-A1.5: From Long-Horizon Execution to Scientific Exploration with Capability Composition
arXiv:2610.28578v1 Announce Type: new Abstract: In this report, we introduce Sage-A1.5, a 35B Mixture-of-Experts agentic model that builds on A1 to advance long-horizon problem solving through adaptive capability composi…
- arXiv cs.AIagentsEvolving Agent Interfaces
arXiv:2610.28601v1 Announce Type: new Abstract: Computer-use agents are limited by the interfaces humans give them. An agent may type a spreadsheet cell by cell when a single line of code would do, or print all two thous…
- arXiv cs.AIagentsSemantic Observability for Autonomous Systems Diagnostics
arXiv:2610.28611v1 Announce Type: new Abstract: Modern distributed microservice deployments generate massive volumes of heterogeneous, multi-format observability logs across physically and logically partitioned infrastru…
- arXiv cs.AIagentsLarge Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle
arXiv:2507.05723v1 Announce Type: cross Abstract: The emergence of Large Language Models (LLMs) with increasingly sophisticated natural language understanding and generative capabilities has sparked interest in the Agent…
- arXiv cs.AIagentsINCARBench: A Benchmark for Scientific Configuration in VASP INCAR by Large Language Models
arXiv:2606.23571v2 Announce Type: cross Abstract: Large language models (LLMs) are increasingly being integrated into first-principles computational workflows, yet their ability to configure scientific calculations remai…
- arXiv cs.AIagentsAptamers as a sandbox for lab-in-the-loop agentic molecular discovery
arXiv:2610.24806v1 Announce Type: cross Abstract: Despite breakthroughs in AI-driven molecular generation, existing discovery pipelines operate largely in silico, with wet-lab feedback reserved for validation rather than…
- arXiv cs.AIagentsMeasuring Consensus in AI Agent Networks
arXiv:2610.24833v1 Announce Type: cross Abstract: As AI agents become increasingly connected through networks and take on tasks that require negotiation and joint decision-making, a systematic approach is needed to asses…
- arXiv cs.AIagentsExpert-Guided Rubrics as Dense Reward Signals for Reinforcement Learning in Non-Verifiable Domains
arXiv:2610.26932v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must interpret user intent, r…
- arXiv cs.AIagentsHow Intervention History Changes AI Agent Sabotage on Subsequent Tasks
arXiv:2610.26935v1 Announce Type: cross Abstract: AI control evaluations often reset agents' memory between tasks, although deployed agents may retain records of prior interventions. We ask whether learning that an earli…
- arXiv cs.AIagentsVisual Task Modeling and Automated Planning for Humanoid Robots in Education of Individuals with Autism Spectrum Disorder
arXiv:2610.27162v1 Announce Type: cross Abstract: In robot-assisted education, a robot must understand how to complete a task in order to effectively guide a student through it. We present a novel method that enables a s…
- arXiv cs.AIagentsLoWAgent: A Low-Resource Multilingual Benchmark for Web Agents
arXiv:2610.27205v2 Announce Type: replace-cross Abstract: Web agents are typically evaluated on English-language interfaces, leaving their ability to operate across the world's multilingual websites largely unexplored. T…
- arXiv cs.AIagentsMMedAgent-Plus: Scaling Multimodal Medical Agents across Modalities, Tasks and Workflows
arXiv:2610.27233v1 Announce Type: cross Abstract: Medical workflows often require multiple interdependent analyses, but existing specialist and generalist AI models are not designed to coordinate them. We introduce MMedA…
- arXiv cs.AIagentsAutoRedClaw: Automated Red-Teaming for Claw-Style Agents
arXiv:2610.27244v1 Announce Type: cross Abstract: Agent systems are shifting from task-scoped tool-use agents to persistent, personalized assistants such as OpenClaw. These claw-style agents maintain configuration files…
- arXiv cs.AIagentsCertifying Repeatable Entry-Replay Witnesses for COBOL-to-Java Migration
arXiv:2610.27272v1 Announce Type: cross Abstract: Producing synthetic data for comparison testing a migrated COBOL application is a major challenge that agentic migrations face today. Our prior paper, the Agentic Method…
- arXiv cs.AIagentsEvaluating Context Compaction on the Questions That Come Later
arXiv:2610.27301v1 Announce Type: cross Abstract: Language-model agents compact their context for the question at hand, but the compacted context must also serve questions that come later. We evaluate compaction on these…
- arXiv cs.AIagentsRefuses When Asked, Complies When Acting: A Runtime Safety Check for Robot Control Loops
arXiv:2610.27350v1 Announce Type: cross Abstract: Frontier models can control robots through agent harnesses that loop on one question: what motion comes next toward the user's goal? We show that this default loop carrie…
- arXiv cs.AIagentsSelf-Evolving Agents through Privileged Retrospective Distillation
arXiv:2610.27495v1 Announce Type: cross Abstract: Self-evolving agents aim to improve from their own experience rather than remain fixed after deployment, but naturally collected trajectories often lack ground-truth solu…
- arXiv cs.AIagentsWORTH Benchmark: Evidence Discovery under Not-Fully-Cooperative Information -- A Two-Episode Pilot
arXiv:2610.27597v1 Announce Type: cross Abstract: Existing reasoning evaluations typically provide task-relevant information to models as available premises, rarely examining concurrently how models discover evidence aut…
- arXiv cs.AIagentsAuditing the Product, Not the Recursion: The Evaluation Evidence Behind Recursive Self-Improvement Claims
arXiv:2610.27610v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) promises AI systems, such as self-evolving LLM agents, that become better at producing further improvements. Evaluating this promise requ…
- arXiv cs.AIagentsLocal Allow, Global Deny: Why Per-Action Authorization Does Not Compose in LLM Agents
arXiv:2610.27620v1 Announce Type: cross Abstract: Autonomous LLM agents act through tools under per-action authorization: a local guard screens each tool call in isolation. We show that local authorization does not compo…
- arXiv cs.AIagentsCovNav: Conformal Prediction-Guided Adaptive Inference for Object Goal Navigation
arXiv:2610.27677v1 Announce Type: cross Abstract: In object goal navigation (ObjectNav), an embodied agent must find a target object (e.g., "find a bed") in a previously unseen environment, relying on spatial commonsense…
- arXiv cs.AIagentsHearsay or Fact? How Framing and Elicitation Shape LLM Probability Updates
arXiv:2610.27745v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judgment assistants and as stand-in participants in psychology experiments, yet little is known about how much their…
- arXiv cs.AIagentsS$^2$-MAS: A Society-Scale Multi-Agent Simulation Framework with a 500-Million-Persona Resource
arXiv:2610.27746v5 Announce Type: replace-cross Abstract: Simulating economic responses at society scale requires decision rules that respond to household circumstances and accounting rules that track their consequences.…
- arXiv cs.AIagentsRELAY: Scaling Historical Build Restoration through Rule Reuse and Agentic Repair
arXiv:2610.27784v1 Announce Type: cross Abstract: Vulnerability benchmarks and software engineering research depend on rebuilding historical software. Yet builds rely on external resources, such as base images and packag…
- arXiv cs.AIagentsClawMMU: Protection-Coherent Virtual Memory for Stateful LLM Agents
arXiv:2610.28093v2 Announce Type: replace-cross Abstract: AI agents keep long-term memory, much of which comes from web pages and tool results. Agent harnesses, the software that runs the model, do not record who may act…
- arXiv cs.AIagentsFrom Code Generator to Engineering Companion: A Plant-aware Agent for PLC Programming
arXiv:2610.28160v2 Announce Type: replace-cross Abstract: Programmable Logic Controller (PLC) coding remains a manual, IDE-centric task that demands constant context switching between documentation, project files, and co…
- arXiv cs.AIagentsReframing Object-Centric Reinforcement Learning: From Absolute to Relative Representations
arXiv:2610.28197v1 Announce Type: cross Abstract: Object-centric reinforcement learning agents typically receive each object's position in a fixed global frame, even though their decisions usually depend on where objects…
- arXiv cs.AIagentsLocal Work, Global Progress: A Convergence Theory of AI Agent Collaboration
arXiv:2610.28286v1 Announce Type: cross Abstract: Teams of large language model (LLM) agents are increasingly applied to complex tasks, but their collaboration protocols are mostly designed empirically, without convergen…
- arXiv cs.AIagentsMAS-Guard: Unified Guardrails from Individual Agents to Multi-Agent LLM Systems
arXiv:2610.28397v1 Announce Type: cross Abstract: Agentic AI extends large language models (LLMs) from generating responses to planning and executing complex workflows, substantially expanding their utility while exposin…
- arXiv cs.AIagentsRewards for Budget-Aware Multi-Turn Agents That Adapt to Arbitrary Test-Time Constraints
arXiv:2610.28409v1 Announce Type: cross Abstract: Large language model agents are often given an inference budget and asked to maximise success within it. A multi-turn agent has to allocate this budget across many enviro…
- arXiv cs.AIagentsAfter the Attacker Leaves: Benchmarking Carryover Attacks on LLM Agents
arXiv:2610.28425v1 Announce Type: cross Abstract: Modern LLM agents persist state across sessions via two channels: long-lived memory files silently prepended to the system prompt, and scheduled tasks waking the agent wi…
- arXiv cs.AIagentsSceneRig: An Agentic System for Simulation-Ready 3D Scene Reconstruction from Single Images
arXiv:2610.28585v1 Announce Type: cross Abstract: Simulation is a powerful tool for robotics, but building diverse, simulation-ready scenes at scale remains difficult. Reconstructing scenes from single images is a promis…