AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsAlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks
arXiv:2610.20675v1 Announce Type: new Abstract: Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory. Understanding agent performance…
- arXiv cs.AIagentsIneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation
arXiv:2610.20681v1 Announce Type: new Abstract: Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations. We introduce IneqAgent…
- arXiv cs.AIagentsARIA: Runtime Inspection and Amendment for Multi-Agent Systems
arXiv:2610.20780v1 Announce Type: new Abstract: In LLM-based multi-agent systems (MAS), a local mistake can propagate as other agents build on it, turning a correctable error into a failed task. Diagnosing completed runs…
- arXiv cs.AIagentsEvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds
arXiv:2610.20786v1 Announce Type: new Abstract: Multi-modal agents have made significant progress in interacting with open worlds. However, their ability to self-evolve remains limited. Supervised fine-tuning enables imi…
- arXiv cs.AIagentsA Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering
arXiv:2610.20809v1 Announce Type: new Abstract: A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, ev…
- arXiv cs.AIagentsEvolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents
arXiv:2610.20845v1 Announce Type: new Abstract: Skill-policy co-evolution, in which an agent's policy model and its skill bank improve each other during training, is a promising paradigm for self-improving agents. Curren…
- arXiv cs.AIagentsSkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills
arXiv:2610.20926v2 Announce Type: replace Abstract: Agent skills turn procedural knowledge into reusable artifacts, allowing LLM agents to accumulate and refine expertise through experience. Building on this, skill evolu…
- arXiv cs.AIagentsRAC: A Relational Agent Contract for Constraint-Governed Engineering Tasks
arXiv:2610.20935v1 Announce Type: new Abstract: Engineering agents must produce artifacts that satisfy exact, coupled constraints. Existing solutions often require agents to make semantic decisions and also derive conseq…
- arXiv cs.AIagentsMAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environments
arXiv:2610.20939v1 Announce Type: new Abstract: Online reinforcement learning (RL) with verifiable rewards has driven rapid progress in mathematics, coding, and tool use, but scaling this success to GUI agents remains ch…
- arXiv cs.AIagentsStepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning
arXiv:2610.20960v1 Announce Type: new Abstract: Safety in agentic systems is inherently sequential: whether an action is safe depends on prior actions, tool outputs, information disclosures, and interactions among agents…
- arXiv cs.AIagentsLearning When to Withdraw: Curriculum Coverage and Rule-Specific Failures in Fine-Tuned Language Agents
arXiv:2610.21045v1 Announce Type: new Abstract: Tool-using language agents must revise prepared actions when evidence changes or authority is revoked. Selective withdrawal means suspending affected actions, preserving su…
- arXiv cs.AIagentsAthanor: An Agent-Native System for Autonomous Post-Training Research
arXiv:2610.21055v1 Announce Type: new Abstract: Recursive self-improvement, in which AI systems build stronger AI systems, is one promising route to superintelligence. We study a weaker form that can be measured today: f…
- arXiv cs.AIagentsOmicsBench: a long-horizon multi-turn benchmark of AI agents on research-study-scale bioinformatics
arXiv:2610.21074v1 Announce Type: new Abstract: Computational biologists increasingly delegate substantial analyses to AI agents in long-horizon, multi-turn sessions, yet most benchmarks of agents' bioinformatics capabil…
- arXiv cs.AIagentsSci$\Pi$vot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch Agents
arXiv:2610.21082v1 Announce Type: new Abstract: Large language model (LLM) agents can keep scientific experiments running for months with little human supervision, but reliable autonomous research also requires scientifi…
- arXiv cs.AIagentsProbe-and-Resume: When and How to Evaluate Agent Execution
arXiv:2610.21088v1 Announce Type: new Abstract: Test-time search can improve agent performance by exploring alternative actions and using a critic to select promising branches. We study this approach in terminal-based co…
- arXiv cs.AIagentsMemComposer: Learning to Assemble Memories for LLM Agents via Reinforcement Learning
arXiv:2610.21091v2 Announce Type: replace Abstract: Agent memory enables large language model (LLM) agents to reuse past experiences without updating their parameters. However, semantic similarity alone does not guarante…
- arXiv cs.AIagentsPlanning Commitment of LLM under Partial Observability
arXiv:2610.21108v2 Announce Type: replace Abstract: LLM-based planning agents commit to plans of different lengths before interacting with a partially observable environment. Some aggressively commit to a long plan based…
- arXiv cs.AIagentsKolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows
arXiv:2610.21109v1 Announce Type: new Abstract: When humans collaborate with AI agents on long-horizon tasks, what happens to the agents' contributions? For solving long-horizon tasks, humans place greater importance on…
- arXiv cs.AIagentsAll-in-One Memory: Process-Rewarded Reinforcement Learning for Memory Agents
arXiv:2610.21147v2 Announce Type: replace Abstract: Large language models increasingly rely on external memory to answer queries grounded in long-horizon histories such as multi-session dialogues and lengthy documents. H…
- arXiv cs.AIagentsGRADS: Graph-Regulated Agentic Data Synthesis with Controllable Difficulty
arXiv:2610.21167v1 Announce Type: new Abstract: Deep research agents are increasingly deployed to retrieve precise information from large corpora, yet multi-hop, long-horizon tasks remain challenging. Synthetic data gene…
- arXiv cs.AIagentsMemScope: Evolving Agent Memory Architectures through Localized Code Repair
arXiv:2610.21170v1 Announce Type: new Abstract: Agent memory architectures define how LLM agents store and reuse experience across tasks, but they are typically fixed after deployment, limiting their adaptability across…
- arXiv cs.AIagentsAgentEval: Evidence-Bounded Assessment of Stateful Agents
arXiv:2610.21177v1 Announce Type: new Abstract: Task completion alone does not determine whether a stateful agent satisfied its obligations, and a recorded judgment does not guarantee that its supporting evidence remains…
- arXiv cs.AIagentsWho May Release an Agent's Promise? Standing over Commitments in Multi-Party Chat
arXiv:2610.21203v1 Announce Type: new Abstract: When an assistant serving a team makes a promise to Alice, who may cancel it? We call this standing: only the creditor (the person owed) or someone the creditor or operator…
- arXiv cs.AIagentsAutoOnboarder: Autonomously Onboarding LLM Agents
arXiv:2610.21218v1 Announce Type: new Abstract: Agentic large language models have been documented to lose reasoning capability when placed inside a harness, the software that mediates their observations and actions. To…
- arXiv cs.AIagentsAuditing Escalation Reliability in Healthcare LLM Agents: A Perturbation-Controlled Benchmark of Workflow-State Failures
arXiv:2610.21270v1 Announce Type: new Abstract: Trustworthy biomedical AI requires reliable action selection when evidence is missing, stale, conflicting, or late, not only accurate answers on static inputs. We instantia…
- arXiv cs.AIagentsDiviner: Agentic RCA with Human-Governed Adaptation for Large-Scale Infrastructure
arXiv:2610.21281v1 Announce Type: new Abstract: Root-cause analysis (RCA) in large-scale distributed systems is challenging because a single symptom can cross service, data, configuration, compute, and organizational bou…
- arXiv cs.AIagentsMallea: Building, Steering, and Memorizing Worlds with Coding Agent
arXiv:2610.21283v1 Announce Type: new Abstract: Code world models represent world dynamics through explicit state and executable rules. We investigate how to coordinate object representations, behavioral revisions, and v…
- arXiv cs.AIagentsBias Control and Injection for Multi-Agent Aggregation
arXiv:2610.21290v1 Announce Type: new Abstract: Multi-agent reasoning with large language models (LLMs) raises a central question for collective decision-making: when agents disagree, whose decision should be trusted? Re…
- arXiv cs.AIagentsConstrained Multi-Agent Reinforcement Learning in Imagination
arXiv:2610.21319v1 Announce Type: new Abstract: Predicting cost in a multi-agent world model moves most constrained policy updates into imagination: the stronger model-free baseline needs $10$ to $20$ times CA-MAMBA's in…
- arXiv cs.AIagentsDocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding
arXiv:2610.21327v1 Announce Type: new Abstract: Multimodal long-document question answering requires models to seek sparse evidence across pages and modalities, understand heterogeneous document content, and reason over…
- arXiv cs.AIagentsARGUS: An Agenda-Driven Framework for Reliable User Simulation
arXiv:2610.21346v1 Announce Type: new Abstract: High-fidelity user simulators are essential for evaluating and training agents in interactive environments. However, current LLM-based simulators struggle with reliability…
- arXiv cs.AIagentsFedSkillGit: Federated Skill Evolution via Version Control
arXiv:2610.21348v1 Announce Type: new Abstract: Large language model agents can improve their capabilities by evolving reusable natural-language skills from execution experience. In real-world deployments, this experienc…
- arXiv cs.AIagentsMemCut: Every Secret Leaves a Trail in the Long-Term Memory of LLM Agents
arXiv:2610.21405v1 Announce Type: new Abstract: Long-term memory has become a crucial component of large language model (LLM) agents, with most research focusing on retrieving relevant information accurately and efficien…
- arXiv cs.AIagentsJev-LiteMAS: Separating Decisions from Generation in Multi-Agent Workflows
arXiv:2610.21411v2 Announce Type: replace Abstract: Multi-agent LLM systems rely on generative models for expert execution, workflow planning, and artifact review. Yet many coordination operations require only bounded de…
- arXiv cs.AIagentsJevAgentHarness: Benchmarking, Deploying and Training Decision Models That Share Control with LLMs
arXiv:2610.21412v1 Announce Type: new Abstract: Large language model (LLM) agents now fix software, operate computers and serve customers, but their harnesses, the programs that run them, call a large LLM at every step,…
- arXiv cs.AIagentsDo Agents Need a New Kind of Paper? SPRA: Structured Paper Representation for Agents
arXiv:2610.21424v2 Announce Type: replace Abstract: Scientific agents must connect research problems, methods, and findings dispersed through narrative articles. We introduce SPRA (Structured Paper Representation for Age…
- arXiv cs.AIagentsMetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling
arXiv:2610.21492v1 Announce Type: new Abstract: Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging. Existing methods either optimize…
- arXiv cs.AIagentsISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing
arXiv:2610.21502v1 Announce Type: new Abstract: Internal solitary wave (ISW) remote sensing requires task parameters and spatial information to be transferred across image acquisition, detection, stripe extraction, and p…
- arXiv cs.AIagentsQREF: Improving DeepSearch Agents by Aligning Search with Reasoning via Query Refinement and Evidence Filtering
arXiv:2610.21512v1 Announce Type: new Abstract: DeepSearch agents achieve strong performance on complex information-seeking tasks through iterative search and reasoning. However, they often issue intent-misaligned, under…
- arXiv cs.AIagentsCriticaLLM: Using LLMs as Autonomous Game Master for Tabletop Role-Playing Games
arXiv:2610.21525v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown impressive capabilities across many tasks, including processing large amounts of text, following instructions, and generating ideas.…
- arXiv cs.AIagentsAgent Status Protocol (ASP): A Lightweight Specification for State Reporting in Multi-Agent Systems
arXiv:2610.21538v1 Announce Type: new Abstract: The Agent Status Protocol (ASP) defines a lightweight way for heterogeneous AI agents to report their operational state to a status server, and for observers such as dashbo…
- arXiv cs.AIagentsClaimScope: Label-Blind Witness Verification for Mathematical Claim Auditing
arXiv:2610.21581v1 Announce Type: new Abstract: Mathematical agents are commonly evaluated by answer accuracy or proof acceptance. We introduce ClaimScope, a 72-task benchmark for auditing proposed claims through a verdi…
- arXiv cs.AIagentsEvaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration
arXiv:2610.21604v1 Announce Type: new Abstract: Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex systems. Large langu…
- arXiv cs.AIagentsAgentPerturb: Evaluating LLM Agent Robustness under Controlled Interaction Perturbations
arXiv:2610.21624v2 Announce Type: replace Abstract: Real-world tasks are often complex and noisy, posing substantial challenges for LLM agents. Yet existing evaluations lack a systematic framework for jointly characteriz…
- arXiv cs.AIagentsThe Self-Improvement Problem
arXiv:2610.21646v1 Announce Type: new Abstract: With the rise of large language models, agentic systems are becoming increasingly autonomous, as they write code, call tools, revise prompts, and propose modifications to t…
- arXiv cs.AIagentsData World: Scaling Data Environments from Scratch
arXiv:2610.21656v1 Announce Type: new Abstract: Data agents use code to process heterogeneous files and derive analytical insights. Scaling their training faces costly environment curation, privacy and intellectual-prope…
- arXiv cs.AIagentsDeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents
arXiv:2610.21755v1 Announce Type: new Abstract: Large Language Model agents operate through closed-loop action policies whose decision space is defined by an agent harness. We investigate how this operating space shapes…
- arXiv cs.AIagentsFrom Research Goals to Scientific Closure: Evaluating Agents Through Evidence-Supported Physics Research
arXiv:2610.21767v1 Announce Type: new Abstract: Can AI agents organize scientific investigations that lead to well-supported conclusions? To evaluate this capability, we introduce PhysClosureBench, a benchmark of 50 expe…
- arXiv cs.AIagentsRSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent
arXiv:2610.21791v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capabilities in complex tasks, including code generation, tool use, and multi-hop question answering. Existing se…
- arXiv cs.AIagentsCoWorld Reasoner: Agentic Multimodal Reasoning with the Evolving Video World Model-Driven Harness
arXiv:2610.21910v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance, they are increasingly expected to perform spatiotemporal reasoning over motion, actions, and view changes beyond what the input im…