AI news archive.
4,965 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsAuDQ: Auditable Data Quality through Agentic Rule Discovery
arXiv:2610.26031v1 Announce Type: new Abstract: High-stakes tabular pipelines rely on scattered, static quality checks that are hard to evolve and audit against policy intent. Regulated domains particularly suffer as the…
- arXiv cs.AIagentsDecision4Rec: A Jev-Inspired Framework of Decision Models on Knowledge and Active Graphs for Multi-Agent Recommender Systems
arXiv:2610.26035v1 Announce Type: new Abstract: Decision models, such as the commercial Jev, are language models that return one probability for each option of a question instead of generating text. We present Decision4R…
- arXiv cs.AIagentsCUA-Lite: An Open Platform for Computer Use Agents
arXiv:2610.26071v1 Announce Type: new Abstract: Open resources for computer use agents (CUAs) -- environments, datasets, and models -- remain scattered: without a standard, these resources cannot be integrated and scaled…
- arXiv cs.AIagentsFrom Code to Playable Systems: CEGame-Bench for Long-Horizon Game Creation Agents
arXiv:2610.26098v1 Announce Type: new Abstract: A game ships when a player can launch it, finish it, and return to a saved world, none of which a successful build guarantees, yet game-creation agents are mostly tested on…
- arXiv cs.AIagentsReliable Before Profitable: QuantHarness for Governing Financial Strategy Development with LLM Agents
arXiv:2610.26101v1 Announce Type: new Abstract: LLM agents can turn natural-language financial research requests into executable and backtested strategies, but a successful run may still implement the wrong rule, use inf…
- arXiv cs.AIagentsThe Scheduler Is a Hidden Retriever: How Asynchrony Shapes Evidence Acquisition in Search Agents
arXiv:2610.26310v1 Announce Type: new Abstract: Language-model agents increasingly solve complex tasks through parallel reasoning and asynchronous information gathering. Controlled replay across two model families and th…
- arXiv cs.AIagentsThe Control Layer Behind Rule-Following Agents Trading Across Market Conditions
arXiv:2610.26324v2 Announce Type: replace Abstract: Trading discipline often fails people. Our study tested whether four AI trading agents followed their strategies and risk rules. Offering traders the evidence and an ag…
- arXiv cs.AIagentsVibeTraining-Bench: Data-Trainer-Harness Co-Design for Iterative System Improvement
arXiv:2610.26342v2 Announce Type: replace Abstract: Recursive self-improvement (RSI) requires agents to diagnose system bottlenecks and coordinate improvements across training and inference. We refer to the agents' capab…
- arXiv cs.AIagentsAutonomous intelligence for mining productivity: a multi-agent AI approach to operational value creation
arXiv:2610.26369v1 Announce Type: new Abstract: Integrated Remote Operation Centres (IROCs) are emerging as the architectural frontier of Industry 4.0 for capital-intensive sectors such as mining, oil and gas, and energy…
- arXiv cs.AIagentsLearning What to Remember: Resource-Efficient Self-Improvement for Lifelong Agents
arXiv:2610.26453v2 Announce Type: replace Abstract: Self-improving agents aim to continuously improve their behavior by learning from experience after deployment. However, accumulating and reusing more experience does no…
- arXiv cs.AIagentsVeriCUA: Learning Proactive Verification for Reliable GUI Agent Rewards
arXiv:2610.26469v1 Announce Type: new Abstract: Reinforcement learning for computer-use agents increasingly relies on proactive reward agents that interact with the post-execution GUI environment to verify what recorded…
- arXiv cs.AIagentsScopeMem: Latent Memory That Learns Where to Revise Frozen GUI Agents
arXiv:2610.26503v1 Announce Type: new Abstract: Latent memory is gaining ground as a lightweight history supplement for GUI agents. It supplies history to a frozen vision-language model via prefix vectors trained with st…
- arXiv cs.AIagentsA Semantic Knowledge Graph Construction Pipeline for Vehicle Intention Prediction in nuScenes
arXiv:2610.26528v1 Announce Type: new Abstract: Knowledge graphs (KGs) are increasingly used in autonomous driving (AD) to make traffic scene entities and relations explicit for prediction and reasoning. Existing traffic…
- arXiv cs.AIagentsAgentic Orchestration of EDC Data Space Retrieval and Natural Language Driven Data Processing
arXiv:2610.26548v1 Announce Type: new Abstract: Participating in a data space still requires the Dataspace Protocol (DSP) negotiation flow to be hand-coded against each provider's access policy. We present a multi-agent…
- arXiv cs.AIagentsBetaMem: Learning to Remember a Changing World for Embodied Agents
arXiv:2610.26554v1 Announce Type: new Abstract: Long-horizon embodied agents face temporal confusion and incomplete or distorted records, recurring errors from fixed memory rules, and growing storage and retrieval costs.…
- arXiv cs.AIagentsReward Objectives for Robust (PO)MDPs: A Law-Preserving Equivalence with (Partially Observable) Games
arXiv:2610.26568v1 Announce Type: new Abstract: Robust partially observable Markov decision processes model agents that act under partial observability while adversarial environment resolves uncertainty in the transition…
- arXiv cs.AIagentsWhen the harness decides success: state that cannot be re-observed
arXiv:2610.26632v1 Announce Type: new Abstract: Agent harnesses decide what a language model sees across turns: they drop old turns, summarize them, or replay the model's own earlier reasoning. We ask when these choices…
- arXiv cs.AIagentsTandem: Serving Hybrid Linear-Attention GUI Agents on Mobile NPUs
arXiv:2610.26660v1 Announce Type: new Abstract: Smartphone GUI agents complete tasks in mobile apps by repeatedly reading the screen and asking a model for the next action. Mobile neural processing units (NPUs) enable lo…
- arXiv cs.AIagentsAllPath: Layered Verification for a Deployed, Self-Revising LLM Trading Agent under Human-Gated and Autonomous Regimes
arXiv:2610.26662v1 Announce Type: new Abstract: LLM trading agents are evaluated almost exclusively in backtests and autonomous benchmark harnesses. Deployment, where actions are irreversible, raises a different question…
- arXiv cs.AIagentsHoneypots for AI Agent Insider Threats
arXiv:2610.26664v1 Announce Type: new Abstract: AI agents deployed inside organizations can pose an insider threat if they make mistakes, are compromised by an attacker, or pursue misaligned objectives. We propose honeyp…
- arXiv cs.AIagentsStepBench: Diagnosing Real-World Procedural Understanding in AI Agents
arXiv:2610.26679v1 Announce Type: new Abstract: Evaluating an agent's planning ability remains challenging for open-domain tasks, where verifying procedures through execution is often impractical. Existing evaluations ty…
- arXiv cs.AIagentsAgent World Model in the Wild: Computer-Use Agent Environment Synthesis
arXiv:2610.26828v1 Announce Type: new Abstract: Agent learning is shifting from learning from human data toward learning through interaction with the world. However, scaling such learning directly in real-world computer…
- arXiv cs.AIagentsProteinGeoBench: Frontier Structural-Biology Agents Are Fluent at Lookup but Lost in Geometry
arXiv:2610.26829v1 Announce Type: new Abstract: Frontier agents retrieve structural metadata well and compute geometry from coordinates less reliably. We ask how much of a benchmark score reflects that gap and how much r…
- arXiv cs.AIagentsSame Simulation, Different Evidence: Where Retrieved Tool Output Costs LLM Agents Accuracy
arXiv:2610.26836v1 Announce Type: new Abstract: LLM agents that compute the right answer can still answer wrongly. Agent harnesses increasingly keep long tool outputs out of the context window, saving them to files that…
- arXiv cs.AIagentsAnyGoal: On World Model Generalization Beyond Expert Rollouts
arXiv:2610.26844v2 Announce Type: replace Abstract: The goal of a world model is to capture the dynamics of an environment. This has the great appeal that it allows agents to plan and simulate a course of actions with th…
- arXiv cs.AIagentsTrain An Imaginative Agent
arXiv:2610.26845v1 Announce Type: new Abstract: A seemingly reasonable push in Sokoban can make the puzzle unsolvable. To avoid such mistakes, an agent has to anticipate the consequences of its actions before execution,…
- arXiv cs.AIagentsWeb Agent Agentic Reinforcement Learning Decision Model Under Multi-Cost and Failure Risk
arXiv:2610.23857v1 Announce Type: cross Abstract: Intelligent agent interactions in real-world web environments are commonly constrained by request budgets, time delays, anti-crawling restrictions, and operational failur…
- arXiv cs.AIagentsContext-Aware RGB-Thermal-Radar Obstacle Perception for Resource-Constrained ASVs
arXiv:2610.23873v1 Announce Type: cross Abstract: Reliable obstacle perception is challenging for small autonomous surface vessels (ASVs): reflections and glare confound visible-light cameras, vessel motion shifts the ap…
- arXiv cs.AIagentsLatent Multimodal Memory Reasoning
arXiv:2610.23890v1 Announce Type: cross Abstract: Long-term personalized agents are required to reason over distributed history across modalities and evolving dialogue states. While existing flat cross-modal methods comp…
- arXiv cs.AIagentsEMMA: Event-Driven Multimodal Memory System with Multi-Agent Retrieval
arXiv:2610.23892v1 Announce Type: cross Abstract: Long video understanding requires reasoning over hours of content that far exceed the context window of multimodal large language models (MLLMs). Existing approaches comp…
- arXiv cs.AIagentsReTTT: Reflection-Driven Test-Time Training via Experience-Based Parametric Memory
arXiv:2610.23942v1 Announce Type: cross Abstract: LLM agents accumulate valuable task-specific information through their actions, observations, and environmental feedback during multi-turn task execution. When effectivel…
- arXiv cs.AIagentsRL Teaches VLMs Where to Attend but Not How to Perceive
arXiv:2610.23983v1 Announce Type: cross Abstract: Although reinforcement learning (RL) effectively trains Vision-Language Model (VLM) agents, the mechanisms behind these capability gains are poorly understood. Across fou…
- arXiv cs.AIagentsAgentic Interpretation: Harness-Governed Evidence Construction for Software Agents
arXiv:2610.23996v1 Announce Type: cross Abstract: Software agents answer behavioral questions about code, such as whether a patch passes tests or which lines caused a failure. These tasks require repository-wide evidence…
- arXiv cs.AIagentsThe Window Is in the Reasoning: When LLM Agents Decide to Follow Prompt Injections
arXiv:2610.24048v1 Announce Type: cross Abstract: LLM agents that read untrusted content can be hijacked by prompt injections. Runtime monitors that try to catch a hijack before the agent acts, and benchmarks that score…
- arXiv cs.AIagentsRed-Teaming Multi-Agent Attacks for Loss-of-Control Risks
arXiv:2610.24052v1 Announce Type: cross Abstract: AI agents are growing more capable and persistent, and are being deployed in ever larger numbers. As more start collaborating, AI control measures must remain effective w…
- arXiv cs.AIagentsOR-License: Evaluating LLM Agents as Real-World Operations Research Practitioners
arXiv:2610.24087v1 Announce Type: cross Abstract: Can large language model (LLM) agents deliver expert-level operations research (OR) solutions directly from original client materials, without prior expert reformulation?…
- arXiv cs.AIagentsDoes AI-Assisted Development Homogenize Software? An Empirical Study of Code Similarity, Quality, and Evolution
arXiv:2610.24093v1 Announce Type: cross Abstract: AI-assisted software development is evolving from interactive LLM assistance toward agentic systems that autonomously perform software engineering tasks, yet the effects…
- arXiv cs.AIagentsCoDA: A Human-LM Collaborative Codebook Development Assistant for Domain-Specific Text Annotation
arXiv:2610.24157v1 Announce Type: cross Abstract: We present CoDA, an open-source human-LM collaborative codebook development assistant that helps domain experts develop annotation guidelines for annotating new variables…
- arXiv cs.AIagentsModel Capacity Frontier: A Development Framework for LLM-Supported Social Simulation
arXiv:2610.24168v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly incorporated into agent-based models (ABMs) on the premise that richer, context-sensitive human behavior can im…
- arXiv cs.AIagentsSWE-Bench Pro V2: A Cleaner, Harder-to-Game Coding Benchmark
arXiv:2610.24206v1 Announce Type: cross Abstract: SWE-Bench Pro is a widely used benchmark of long-horizon software engineering tasks for coding agents. As agents improve, they reach task issues earlier agents never touc…
- arXiv cs.AIagentsLoopHIL: Looping Hardware into LLM-assisted Agents for Wireless Embedded Systems
arXiv:2610.24231v1 Announce Type: cross Abstract: LLM agents are widely used for software development and debugging, but their adoption in embedded systems, especially for wireless tasks, remains limited. This is because…
- arXiv cs.AIagentsWho Plans, Who Edits? Verified, Cost-Aware Division of Labor for LLM-Driven Analog Layout Repair
arXiv:2610.24234v1 Announce Type: cross Abstract: Language-model agents can repair analog layouts when sign-off tools check every edit, but which model should plan a repair, which should edit it, and at what cost remain…
- arXiv cs.AIagentsSPIDES: Information-Flow Control for Browser Agents
arXiv:2610.24277v1 Announce Type: cross Abstract: Browser agents automate complex workflows by reading and acting on arbitrary page content, but this flexibility exposes them to prompt injection attacks and unintended da…
- arXiv cs.AIagentsUnbounded Losses, Unchecked Inputs: Measuring the Safeguards of LLM Options-Trading Agents
arXiv:2610.24289v1 Announce Type: cross Abstract: Large language model (LLM) agents now place options orders at retail brokers, and developers publish open-source options agents faster than anyone has examined them. Prio…
- arXiv cs.AIagentsRATION: Towards Quantitative Risk-Based Access Control for AI Agents
arXiv:2610.24311v1 Announce Type: cross Abstract: AI agents increasingly interact with external environments to complete complex user tasks. These interactions can lead to unexpected behavior and expose agents to securit…
- arXiv cs.AIagentsVERAS: Verification-Guided Adaptive Search for Reliable Operations Research
arXiv:2610.24329v1 Announce Type: cross Abstract: Advances in reasoning, coding, and tool use enable LLM agents to solve operations research (OR) problems through iterative problem modeling, program construction, and ver…
- arXiv cs.AIagentsWrite Once, Check Many: Reusable Ledgers for Post-Hoc Policy Compliance Auditing of Agent Trajectories
arXiv:2610.24483v1 Announce Type: cross Abstract: Auditing LLM agents against policies is critical to ensure safety but requires rereading long trajectories once per policy. Can we instead record each trajectory once and…
- arXiv cs.AIagentsWhen Code Becomes an LLM Call: An Empirical Study of Call-Site Contracts in Agentic Systems
arXiv:2610.24545v1 Announce Type: cross Abstract: Software quality has long rested on specifications that state what code must do, and on testing and verification that check that the code does it. Agentic systems change…
- arXiv cs.AIagentsCARVS: A Congestion-Aware Algorithm for Online Multi-Agent Pickup-and-Delivery in Human-Collaborative Warehouses
arXiv:2610.24549v1 Announce Type: cross Abstract: Intelligent warehouse systems have transformed logistics by integrating robots with minimal infrastructure changes, boosting efficiency and optimizing workflows compared…
- arXiv cs.AIagentsScene-Adaptive VLA: Efficient Autonomous Driving via Dynamic Layer Routing
arXiv:2610.24590v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising paradigm for autonomous driving. However, the massive parameter scale of VLAs strains onboard computational resource…