AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsSelm: Self-Evolving Library Management for Multi-Skill Agent Optimization
arXiv:2610.24630v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly use skills as a lightweight way to acquire task-specific knowledge. Recent work enables skills to evolve from task trajectori…
- arXiv cs.AIagentsGood Judgment, Bad Advice: Decision-Model Controllers Raise Agent Cost Without Improving Success
arXiv:2610.24649v1 Announce Type: new Abstract: Agent scaffolds increasingly delegate small decisions (whether to stop, verify, or change approach) to cheaper models. We ask whether a typed decision model, Jev 1.13, cons…
- arXiv cs.AIagentsCan AI agents make law? Evaluating AI legal relevance and creativity
arXiv:2610.24661v1 Announce Type: new Abstract: Large language models, the artificial intelligence (AI) systems behind modern chatbots, are trained on vast amounts of legal text, but can they build on it to write law for…
- arXiv cs.AIagentsNeuro-Inspired Inhibitory Message Passing for Accurate and Token-Efficient Multi-Agent Reasoning
arXiv:2610.24718v1 Announce Type: new Abstract: Multi-agent systems promise to solve problems by bringing together information beyond the reach of any individual agent. Retrieval and tool use can distribute this informat…
- arXiv cs.AIagentsWhere Natural Language Ends in Agents: Executable Skill Composition with MetaSkill
arXiv:2610.24723v1 Announce Type: new Abstract: Agent Skills express open-ended task knowledge in natural language, but composing them requires persistent execution order and upstream-output handoffs. We propose selectiv…
- arXiv cs.AIagentsCan LLM Agents Resolve Service Failures? Simulating Multi-Turn Complaint Handling under Information Asymmetry
arXiv:2610.24733v1 Announce Type: new Abstract: LLM agents are widely adopted in service chatbots for rule-guided support and information access, yet emotionally demanding complaint handling still relies heavily on human…
- arXiv cs.AIagentsReTRACE: Feedback-Guided LLM Agent in a Chemistry Harness for Retrosynthesis Planning
arXiv:2610.24744v1 Announce Type: new Abstract: Computer-aided retrosynthesis has advanced through single-step prediction, tree search and route scoring, but complex targets still require judgement over the whole route,…
- arXiv cs.AIagentsDialogMem: Evaluating Dialogue-Grounded Embodied Memory in 3D Environments
arXiv:2610.24767v1 Announce Type: new Abstract: Embodied agents in human-centered environments require memory that integrates what they observe with what humans tell them over time. However, existing embodied memories ar…
- arXiv cs.AIagentsTeaching Code Agents to Ask Without Leaking Private Code
arXiv:2610.24824v1 Announce Type: new Abstract: Local coding agents can extend their reach by consulting a stronger cloud model. Such consultations, however, often expose the repository's private identifiers, file paths,…
- arXiv cs.AIagentsBellman Language Models: Solving the Recurring Decisions of LLM Agents
arXiv:2610.24871v1 Announce Type: new Abstract: A general observation in agentic workflows, especially bounded ones, is that the decision space of the LLM, when we separate a decision from generation, starts approaching…
- arXiv cs.AIagentsThe One-Pixel of Reasoning: One Truthful Relation Is Enough to Mislead LLM Agents
arXiv:2610.24873v1 Announce Type: new Abstract: Can one sentence manipulate an agent's belief by targeting a single implicit warrant linking evidence to a conclusion? In this paper, we introduce the one-relation attack (…
- arXiv cs.AIagentsHybridResearcher: Bridging the Structured-Unstructured Boundary for Deep Research Agents
arXiv:2610.24899v2 Announce Type: replace Abstract: While LLM agents are increasingly capable of gathering and synthesizing evidence from a single homogeneous source, such as structured tables or unstructured data, real-…
- arXiv cs.AIagentsMemPrice: Online Memory Allocation via Adaptive Pricing for Long-Horizon Agents
arXiv:2610.24955v1 Announce Type: new Abstract: Long-horizon LLM agents face a fundamental allocation problem as interaction histories grow: which information to retain, how much context to devote to it, and at what gran…
- arXiv cs.AIagentsCoworkEnv: Synthesizing Training Environments for Long-Horizon Cowork Agents
arXiv:2610.24960v1 Announce Type: new Abstract: AI coworkers must coordinate tools, communicate across turns, and act on changing workspace state while respecting workflow constraints. Constructing training experience fo…
- arXiv cs.AIagentsOne Skill Takes Over: Clashing Agent Skills and a One-File Fix
arXiv:2610.25064v1 Announce Type: new Abstract: Agent Skills are instruction files that tell a coding agent how to do one job. Users install skills from many authors, so two skills for the same job can be active at the s…
- arXiv cs.AIagentsDAGLoop: Aligning Method Design with Experimental Feedback in Automated AI Research
arXiv:2610.25089v1 Announce Type: new Abstract: Automated AI research agents aim to improve machine learning methods by iteratively proposing, implementing, and evaluating candidate methods. Existing work uses the outcom…
- arXiv cs.AIagentsToward Agentic Retrieval-Augmented Generation for Enterprise Retail Workflows over Heterogeneous Data Sources
arXiv:2610.25096v1 Announce Type: new Abstract: Enterprise retail workflows require agents to chain evidence across heterogeneous sources, from structured product and order records to unstructured inquiries and reviews,…
- arXiv cs.AIagentsExplore or Confirm? LLM Agents Misallocate Measurements Under Noise
arXiv:2610.25111v1 Announce Type: new Abstract: As model reasoning gets cheaper, the physical experiment becomes the slow and costly step in discovery. Each experiment returns a noisy reading: a high value can mean a goo…
- arXiv cs.AIagentsMemory, Know Your PLACE: Governing Lifecycle State and Query-Time Eligibility in Persistent-Memory Agents
arXiv:2610.25179v1 Announce Type: new Abstract: Persistent memory turns prior interactions into durable agent state, but existing systems provide limited control over whether that state remains valid and whether it shoul…
- arXiv cs.AIagentsBrandEval: A Two-Track Multi-Agent Benchmark for Risk-Sensitive LLM Crisis Communication
arXiv:2610.25208v1 Announce Type: new Abstract: Evaluating LLMs for crisis communication requires more than judging isolated responses, because public feedback and competitor actions can reshape trust over time. Existing…
- arXiv cs.AIagentsBridging Concepts: Multi-Agent Reasoning over Scientific Concept Graphs for High-Impact Research Prediction
arXiv:2610.25281v1 Announce Type: new Abstract: Within a research community, new topics often emerge when previously unconnected concepts are first studied together. We construct scientific concept graphs (SCGs)--domain…
- arXiv cs.AIagentsOutcome-Anchored Credit Reallocation for Agentic Knowledge Graph Question Answering
arXiv:2610.25384v1 Announce Type: new Abstract: Agentic knowledge graph question answering (KGQA) requires large language models (LLMs) to solve complex questions through multi-turn interactions with a knowledge graph (K…
- arXiv cs.AIagentsSystem X: An LLM Decision Model That Replies from Its Own KV Cache
arXiv:2610.25456v1 Announce Type: new Abstract: Decision models return probabilities over supplied options, but a deployed agent may also need to reply in words. The present study introduces System X, a large language mo…
- arXiv cs.AIagentsBud ContextCompact 2.0: Context Compaction for Agent Success and Accuracy with Small Language Models and Constrained Context Windows
arXiv:2610.25516v1 Announce Type: new Abstract: Agents that work for many turns outgrow the context window of the model that serves them, and on small language models (SLMs) and constrained inference systems that window…
- arXiv cs.AIagentsStopLoss: Teaching Agents to Predict Failure and Stop Early
arXiv:2610.25525v1 Announce Type: new Abstract: Coding agents keep working on runs that will fail verification, and asking a frontier model to watch for this is expensive in its own right. We introduce StopLoss, a small…
- arXiv cs.AIagentsAuditing Tool Traces in Agentic LLMs: Source Localization and Counterfactual Dependence Testing
arXiv:2610.25540v1 Announce Type: new Abstract: Agentic LLMs increasingly support scientific prediction by grounding decisions in external evidence. In biology, they promise accurate, interpretable predictions for import…
- arXiv cs.AIagentsEditing as Language: Teaching LLM Agents a Grammar for Native Context Editing
arXiv:2610.25550v1 Announce Type: new Abstract: A synopsis in novels folds a story into a few lines; a function in code gathers many steps with a single execution. Context editing is native to language itself. In this pa…
- arXiv cs.AIagentsSymbolic Constraint Agents: Mining Formal Tool-Call Constraints to Prevent Agent Errors
arXiv:2610.25552v1 Announce Type: new Abstract: Symbolic constraints (programmatic, deterministic checks for tool calls) enable deterministic verification of LLM agents' tool calls, but existing approaches primarily enfo…
- arXiv cs.AIagentsLearning Which Device to Call: Online Tool Grounding for LLM Agents in IoT
arXiv:2610.25563v1 Announce Type: new Abstract: The Internet of Things (IoT) gives large language model (LLM) agents access to physical observations, but one requested capability is usually offered by several devices, an…
- arXiv cs.AIagentsDesign and Cross-Domain Evaluation of an Agent Execution Harness
arXiv:2610.25568v1 Announce Type: new Abstract: Completing complex tasks with language-model agents requires coordinating planning, execution, and review so the final result addresses the user's original objective. We pr…
- arXiv cs.AIagentsToolOPD: Credit-Aware On-Policy Distillation for Tool-Use Agents
arXiv:2610.25693v1 Announce Type: new Abstract: On-policy distillation (OPD) enhances the capabilities of small language agents by providing dense, token-level supervision from a teacher model on student-generated trajec…
- arXiv cs.AIagentsWhose Credential Is It? How Owner Attribution in Agent MemoryShapes Cross-User Account Misuse
arXiv:2610.25707v1 Announce Type: new Abstract: Shared long-term memory lets an LLM agent serving one user inadvertently retrieve another user's credentials, but it is unclear when such exposure leads to actual harm. We…
- arXiv cs.AIagentsTimelineAgent: An LLM-based Framework for Query-Centric Event Timeline Extraction Across Multiple Documents
arXiv:2610.25733v1 Announce Type: new Abstract: Query-centric cross-document event timeline extraction aims to construct event timelines from multiple documents under diverse query focuses. The task is very challenging d…
- arXiv cs.AIagentsEvalRx: Agentic Diagnosis and Verified Repair of Foundation Models
arXiv:2610.25767v1 Announce Type: new Abstract: Model evaluation reveals where models fail, but rarely why or whether a proposed fix genuinely works. We introduce EvalRx, an open-source agentic framework for statisticall…
- arXiv cs.AIagentsFrom Trace to Fix: Residual Task State in Long-Horizon Agents
arXiv:2610.25776v1 Announce Type: new Abstract: Recovering from failures in long-horizon, stateful language agents is challenging because the action introducing a defect, the symptom exposing it, and the persistent state…
- arXiv cs.AIagentsStanding on the Shoulders of Giants: Enhancing LLM Agents for Automated Fact-Checking with Human Expert Skills
arXiv:2610.25777v1 Announce Type: new Abstract: Automated fact-checking (AFC) systems powered by large language model (LLM) agents still face challenges in evidence retrieval and claim verification. Existing approaches i…
- arXiv cs.AIagentsOpenSaC: A Python Library for Search as Code in Deep Research Agents
arXiv:2610.25779v1 Announce Type: new Abstract: Deep research agents investigate complex questions through iterative search, source examination, and evidence synthesis. A conventional workflow alternates reasoning with i…
- arXiv cs.AIagentsFrontierRefactor: Benchmarking Multi-Scale Performance-Oriented Codebase Refactoring
arXiv:2610.25834v1 Announce Type: new Abstract: Autonomous codebase refactoring requires coding agents to restructure working software while strictly preserving externally observable behavior and prioritizing execution p…
- arXiv cs.AIagentsMASIF: Implicit Failures for Process Observability in Multi-Agent Systems
arXiv:2610.25852v1 Announce Type: new Abstract: Multi-agent systems (MAS) can complete a task despite local execution errors that are corrected, bypassed, or carried forward without individually determining its outcome.…
- arXiv cs.AIagentsAre Self-Evolving Agents Continual Learners?
arXiv:2610.25858v1 Announce Type: new Abstract: Large language model agents are often deployed in environments where new knowledge, tools, and workflows keep arriving. Such an agent is therefore expected to adapt continu…
- arXiv cs.AIagentsWhen Should AI Chime In? Benchmarking Timely Contribution of Proactive Agents in Multi-Party Chat
arXiv:2610.25896v2 Announce Type: replace Abstract: Proactive agents can support group collaboration by surfacing useful information without waiting to be asked, but even relevant contributions can be disruptive if poorl…
- arXiv cs.AIagentsTESS: LLM-guided Translation, Exploration, Simulation, and Search for Embodied Task Planning
arXiv:2610.25930v1 Announce Type: new Abstract: In this work we focus on the problem of embodied task planning, which requires an agent to accomplish a complex task by separating it into atomic actions. Recent studies ha…
- arXiv cs.AIagentsEvoSkill v2: Fully Agentic Skill Evolution from Benchmark Adaptation to a Stateful Evolver
arXiv:2610.25931v2 Announce Type: replace Abstract: EvoSkill v2 is a three-agent system that edits the instructions (skills) an agent is given, leaving its model and harness intact. The user starts a run by describing a…
- arXiv cs.AIagentsClaim Laundering: When Agent Decision Models Trust Records Over Reality
arXiv:2610.25947v1 Announce Type: new Abstract: Agent decision models select actions in a single forward pass from inputs that combine environmental observations with the agent's own record of previous actions. When an a…
- arXiv cs.AIagentsCXBench: A Benchmark for Process-Compliant LLM Agents in Regulated Customer-Experience Workflows
arXiv:2610.25954v1 Announce Type: new Abstract: Tool-using language agents increasingly perform regulated customer-service workflows, where a correct final state is insufficient: agents must also provide disclosures, obt…
- arXiv cs.AIagentsProductive State-Space Exploration for Multi-turn RL in LLM Agents
arXiv:2610.25957v1 Announce Type: new Abstract: Reinforcement Learning (RL) for Large Language Model (LLM) agents in multi-turn, long-horizon environments remains challenging due to inefficient action selection and repet…
- arXiv cs.AIagentsEmbodiedWorld-200K: Toward Embodied World Agents via Embodied-Planning Dataset and Interactive World Models
arXiv:2610.25959v2 Announce Type: replace Abstract: Building embodied agents that can perceive, reason, and act across diverse 3D worlds is a central research direction toward artificial general intelligence. Yet most ex…
- arXiv cs.AIagentsClueFlow: Learning Long-Horizon Search through Attributed Research-State Transitions
arXiv:2610.25980v1 Announce Type: new Abstract: Long-horizon search requires agents to acquire relevant evidence and integrate it into an evolving research state. Existing methods maintain research states to guide explor…
- arXiv cs.AIagentsMap-Distribution Mixture-of-Experts for Multi-Agent Pathfinding
arXiv:2610.25991v1 Announce Type: new Abstract: Designing capable neural policies for multi-agent pathfinding (MAPF), which map local observations to action distributions, represents an important challenge in multi-agent…
- arXiv cs.AIagentsProcedural Memory for Language Agents: A Neurosymbolic Perspective on Explicit Goal Structure
arXiv:2610.25996v1 Announce Type: new Abstract: Large language models remain unreliable on long-horizon tasks requiring procedural reasoning. We argue that a key cause is the absence of explicit procedural memory: access…