AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- Tom's Guide AIagentsI gave ChatGPT $100 and 72 hours to get me out of the house — and it found something I realized I've been missing for years
Consumer AI launches, tools, and practical coverage.
- The Register AI + MLagentsEmbedding the human factor into AI agent adoption
SPONSORED FEATURE: Making the link between business strategy and how the workforce adopts technology will be crucial to business success in the agentic AI era
- arXiv cs.AIagentsAdaptive Workflow Intelligence: A Cognitive Architecture for Context-Driven Enterprise Automation
arXiv:2610.08793v1 Announce Type: new Abstract: Enterprise systems increasingly rely on automated workflows, yet many AI-driven solutions remain brittle under non-stationary conditions, evolving policies, and delayed ope…
- arXiv cs.AIagentsAn Empirical Study of Agent Skills' Downstream Utility
arXiv:2610.08875v2 Announce Type: replace Abstract: Agent Skills package procedural guidance and resources for reuse, but a relevant Skill does not necessarily improve task performance. Existing studies characterize Skil…
- arXiv cs.AIagentsHumanize: Judgement Engineering for Agentic Coding
arXiv:2610.08900v2 Announce Type: replace Abstract: Agentic coding makes code generation cheap, but reliable completion remains difficult: the agent that writes the code is a weak judge of whether it is done. We present…
- arXiv cs.AIagentsSequential Probabilistic Uncertainty Estimation for Parallel Multi-Agent Reasoning Systems
arXiv:2610.08901v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) have attracted growing attention for improving reasoning through interaction among multiple agents. In this work, we focus on parallel m…
- arXiv cs.AIagentsAgent Plasticity: Measuring Self-Improvement Through Experience
arXiv:2610.08902v1 Announce Type: new Abstract: AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can d…
- arXiv cs.AIagentsCan AI Agents Make Open-Ended Scientific Discovery? Evidence from Station
arXiv:2610.08927v1 Announce Type: new Abstract: Recent AI systems have made rapid progress in scientific discovery when given well-defined metrics, but whether they can autonomously undertake open-ended scientific discov…
- arXiv cs.AIagentsVerify Less, Evolve More: Training Idea-Level Critics for Verification-Efficient ML Evolving Agents
arXiv:2610.08993v1 Announce Type: new Abstract: As large language models become more powerful, self-evolving agents are able to tackle challenging tasks including AI for machine learning (AI4ML). In AI4ML, while empirica…
- arXiv cs.AIagentsHow Fragile Is On-Device Language Model Safety? Localizing Safety-Critical Parameters for Sparse Fault Analysis
arXiv:2610.09000v1 Announce Type: new Abstract: As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as components of agentic systems, the integrity of loca…
- arXiv cs.AIagentsLearning to Report Unsafe Tasks in a Multi-Agent Game
arXiv:2610.09002v2 Announce Type: replace Abstract: When a team of agents shares a reward for completed tasks, reporting and stopping unsafe work reduces the team's reward. Random audits that penalize silence can make un…
- arXiv cs.AIagentsWhose Memory Is It? Scope-Aware Commit Rules for Long-Term LLM Memory
arXiv:2610.09008v1 Announce Type: new Abstract: Persistent memory allows an LLM agent to carry experience across conversations, but it also turns a local reasoning mistake into a durable one. During deliberation, an agen…
- arXiv cs.AIagentsNot Every Call Needs a Frontier Model: Per-Call-Site Evaluation of Small Language Models in a Deployed Agentic Home-Automation System
arXiv:2610.09021v1 Announce Type: new Abstract: An agentic system issues several structurally different kinds of LLM calls. It routes intent, classifies actions, grounds language in a device registry, plans multi-agent p…
- arXiv cs.AIagentsShared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network
arXiv:2610.09034v1 Announce Type: new Abstract: Multi-agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency. However, traditional roadmap generation method…
- arXiv cs.AIagentsWhen the Governor Becomes the Disturbance: Control-Generated Disturbance and Cost-Aware Backoff in Governed Tool-Using Agents
arXiv:2610.09037v1 Announce Type: new Abstract: Supervisory governors can interfere with the tool-using agents they regulate. We study this possibility in a controlled file-recovery environment where increases in regulat…
- arXiv cs.AIagentsRippleCP: Measuring Counterfactual Checkpoint Advantage in Coding Agents
arXiv:2610.09088v1 Announce Type: new Abstract: Agent checkpoint systems decide what state is recovery-relevant, how to snapshot it, and whether rollback is admissible. None decides which of the safe boundaries they expo…
- arXiv cs.AIagentsConstraint Tree Exploration for Learning from Language Feedback
arXiv:2610.09107v1 Announce Type: new Abstract: Natural-language feedback in interactive learning often explains why an action failed by pointing to violated requirements. Misinterpreting this feedback can lead an agent…
- arXiv cs.AIagentsGeoNatureAgent (GNA): A Framework and Benchmark for Pre-Production Evaluation of Tool-Using Agents on Geospatial and Environmental Tasks
arXiv:2610.09112v1 Announce Type: new Abstract: Before tool-using LLM agents are deployed in environmental and geospatial workflows, teams need evidence that an agent reliably selects the right operations against real AP…
- arXiv cs.AIagentsFrom Uncertainty to Action: Learning to Steer LLM Agents
arXiv:2610.09115v1 Announce Type: new Abstract: Steering an LLM agent means deciding whether to correct it, at which step, and with which mechanism. Uncertainty is often used to decide when to correct an agent, but wheth…
- arXiv cs.AIagentsWhich Buildings Are Artificial Intelligence-Ready? A Measurement-Based Assessment Framework for AI Question Answering and Actuation
arXiv:2610.09119v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) systems are becoming the interface to buildings, answering questions and controlling operations, but a building's readiness for them ha…
- arXiv cs.AIagentsCADFather: Autonomous CAD Reconstruction through Coordinated Tool Use
arXiv:2610.09127v1 Announce Type: new Abstract: Reconstructing an editable CAD model from a 3D shape remains a challenging engineering task. Existing methods can propose CAD operations, but no single source of proposals…
- arXiv cs.AIagentsSpecGuard: Proving a Task Is Broken Before the Agent Cheats
arXiv:2610.09159v1 Announce Type: new Abstract: As autonomous coding agents get increasingly deployed, the risk that accidental or adversarially injected misspecifications in tasks lead to dangerous agent behavior is cri…
- arXiv cs.AIagentsBookkeeping, Composition, or Unreachable Gold? Reading MemoryAgentBench's Conflict-Resolution Scores Against a Frozen Last-Write Resolver
arXiv:2610.09193v1 Announce Type: new Abstract: MemoryAgentBench's Conflict Resolution split is read as measuring "selective forgetting". We execute the benchmark's own rule - the newest statement about a fact wins - as…
- arXiv cs.AIagentsFreeEvolve: Learning to Evolve Beyond Fixed Loops
arXiv:2610.09197v2 Announce Type: replace Abstract: Agent evolvers automate the design of the prompts, skills and workflows around language model agents, yet the optimization process they follow is still designed by hand…
- arXiv cs.AIagentsRLDISCOVER: LLM-driven co-evolution of reinforcement learning algorithms
arXiv:2610.09218v1 Announce Type: new Abstract: LLM-guided program evolution has enabled discoveries in mathematics and computational optimization, raising the prospect of reinforcement learning (RL) algorithms that self…
- arXiv cs.AIagentsTrajectory Abstraction for the Science of Language Agent Behavior
arXiv:2610.09237v1 Announce Type: new Abstract: Scientific studies of language agents need behavioral variables that support hypotheses across tasks and models. We formulate this research problem as learning and testing…
- arXiv cs.AIagentsWe Query, Therefore We Compute: On Oracle Computation beyond the Machine, with an Application to Agents
arXiv:2610.09243v1 Announce Type: new Abstract: Agentic systems use large language models (LLMs) to carry out concrete tasks. Prior work often borrows abstractions such as scheduling, caching or isolation piecemeal from…
- arXiv cs.AIagentsRT-Safe: Benchmarking Agent Safety in Real-Time Embodied Environment
arXiv:2610.09294v1 Announce Type: new Abstract: Rapid progress in AI agents has brought growing attention to agent safety, with extensive evaluation focused on digital environments. As agents move into the physical world…
- arXiv cs.AIagentsSearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models
arXiv:2610.09335v1 Announce Type: new Abstract: Autonomous unmanned aerial vehicle (UAV) object search involves a closed loop of perception, decision-making, and action under partial observability. Urban environments pos…
- arXiv cs.AIagentsRelevance Is Not Sufficiency: What Actually Closes the Evidence Gap in Long-Term Memory QA
arXiv:2610.09348v1 Announce Type: new Abstract: LLM agents that interact with a user across many sessions accumulate histories that exceed their context window, so they store past interactions in an external memory and a…
- arXiv cs.AIagentsDUDA-Bench: Benchmarking LLM Agents on Multimodal Data-Driven Urban Diagnosis
arXiv:2610.09374v1 Announce Type: new Abstract: Urban diagnosis integrates heterogeneous observations to identify urban problems, localize affected areas, and investigate contributing factors, informing evidence-based ur…
- arXiv cs.AIagentsFrom Plausible Hierarchies to Useful Taxonomies: Evaluating Agentic Harnesses on Customer Feedback
arXiv:2610.09377v1 Announce Type: new Abstract: Taxonomies are the symbolic representations through which AI systems organize evidence, aggregate patterns, and answer questions over large document collections. Over custo…
- arXiv cs.AIagentsRSI-Forge: From Research Papers to Environments for Recursive Self-Improvement
arXiv:2610.09426v1 Announce Type: new Abstract: Environments are the foundation of recursive self-improvement: they provide the problems agents work on and the feedback used to evaluate progress. Yet constructing challen…
- arXiv cs.AIagentsMIMESIS: Learning User Simulators as Training Environments for Interactive Agents
arXiv:2610.09484v2 Announce Type: replace Abstract: Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and difficult to scale. Simula…
- arXiv cs.AIagentsReam: Unfolding Mutual Awareness in Human-Agent Workspaces
arXiv:2610.09497v1 Announce Type: new Abstract: As AI agents work alongside humans in shared workspaces, a mutual awareness challenge arises: agents act at speeds that outpace human monitoring, and users' evolving intere…
- arXiv cs.AIagentsDrugTargetWorld: A Synthetic Biobank for Training and Benchmarking AI Scientists
arXiv:2610.09558v1 Announce Type: new Abstract: Drug target discovery requires distinguishing molecules that causally drive disease from those that are merely associated with it. Training and evaluating AI agents to perf…
- arXiv cs.AIagentsWorld Potential Model: Pretrained World Knowledge as Progress Potentials
arXiv:2610.09560v1 Announce Type: new Abstract: Long-horizon language agents often receive supervision only from terminal task outcomes, leaving little signal for distinguishing productive intermediate behavior from stag…
- arXiv cs.AIagentsLearning Situation-Conditioned Thinking Policies for Long-Term LLM Agents
arXiv:2610.09590v1 Announce Type: new Abstract: Long-running autonomous agents must reuse accumulated reasoning experience without allowing explicit historical memory and LLM context to grow indefinitely. However, existi…
- arXiv cs.AIagentsHow Do Agentic LLMs Decide to Call Tools? A Tool-Call Vector Shaped by Suppression
arXiv:2610.09624v2 Announce Type: replace Abstract: Tool calling, invoking external tools on demand, is central to agentic LLMs, yet the mechanism that decides whether a model calls a tool or responds directly remains po…
- arXiv cs.AIagentsMeshSIPP: Efficient Lattice Planning in Dynamic Environment
arXiv:2610.09652v1 Announce Type: new Abstract: Autonomous navigation in dynamic environments requires computing spatiotemporal trajectories that satisfy non-holonomic motion constraints. When the trajectories of the mov…
- arXiv cs.AIagentsShared and structured inputs undermine collective random choice by reasoning AI agents
arXiv:2610.09667v1 Announce Type: new Abstract: Random selection is widely used in resource allocation and auditing, making reliable implementation essential for AI-agent systems. Behavioural tests across six reasoning m…
- arXiv cs.AIagentsCost-Efficient Theorem Proving via Agent Orchestration in Program Verification
arXiv:2610.09681v1 Announce Type: new Abstract: Program verification establishes software correctness through machine-checkable proofs constructed in theorem provers. It's a guarantee especially valuable for code generat…
- arXiv cs.AIagentsSystem Switch: When Should a Fast Decision Model Stop and Think?
arXiv:2610.09683v1 Announce Type: new Abstract: Dual-process agents pair a fast policy with a slow deliberative model. In real-time settings the slow model usually runs continuously; in turn-based agents and robot planne…
- arXiv cs.AIagentsSkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles
arXiv:2610.09832v2 Announce Type: replace Abstract: Memory-augmented reinforcement learning strengthens LLM agents' ability to solve complex long-horizon tasks. Skills are one such form of memory, pairing instructions wi…
- arXiv cs.AIagentsSelf-Evolve With a Reference:Anchored Training of Tool-Integrated Agents
arXiv:2610.09856v1 Announce Type: new Abstract: Self-evolving tool-integrated agents learn from tasks and feedback generated within their own training loop. A Curriculum Agent generates tasks, while an Executor Agent lea…
- arXiv cs.AIagentsLiveMACE: Process-Aware Evaluation of LLM Agent Capabilities in Evolving Markets
arXiv:2610.09872v1 Announce Type: new Abstract: Evaluating agents by outcomes alone can obscure the capabilities that produce them. This problem is especially pronounced in evolving environments, where outcomes reflect a…
- arXiv cs.AIagentsOutperformance Inverse Optimization: Learning Objective Functions that Outperform Agent Decisions
arXiv:2610.09890v1 Announce Type: new Abstract: Inverse optimization estimates the weights of an objective function that explain observed decisions as optimal solutions, and is used in a variety of fields. For mixed-inte…
- arXiv cs.AIagentsAgentTime: Can Agents Estimate and Control Their Own Runtime?
arXiv:2610.09944v2 Announce Type: replace Abstract: An essential control of AI agents is their ability to manage runtime. This ability requires a sense of time-awareness, to predict and estimate wall-clock time and to co…
- arXiv cs.AIagentsFrom Expected Harmfulness to Likelihood: A Probabilistic Reformulation of Jailbreaking LLM Agents
arXiv:2610.09973v1 Announce Type: new Abstract: When the harmfulness of an LLM agent's output can be quantified, a natural jailbreaking objective is to maximize expected harmfulness over admissible input modifications. A…
- arXiv cs.AIagentsLearning to Accumulate Knowledge with Mutual Information
arXiv:2610.10042v1 Announce Type: new Abstract: Large language model (LLM) agents can improve their performance by reusing knowledge distilled from past interactions. However, curating new experiences into a knowledge ba…