AI news archive.
4,966 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsToward Interactive Understanding of Code APIs
arXiv:2609.32081v1 Announce Type: new Abstract: Empowered by advances in Language Model agents, systems have made substantial strides in code generation and understanding. However, these approaches often rely on read acc…
- arXiv cs.AIagentsMemory as Middleware for Self-Improving AI Agents
arXiv:2609.32091v1 Announce Type: new Abstract: AI agents are stateless across sessions by default and therefore operationally amnesic: each session begins with little durable knowledge of prior failures, repairs, prefer…
- arXiv cs.AIagentsGameBoyWorlds: A Testbed for Self-Improvement in Embodied Video Games
arXiv:2609.32093v1 Announce Type: new Abstract: Powered by expert guidance, agents can operate in interactive environments; however, it is unclear whether they can learn autonomously from their own experience. To evaluat…
- arXiv cs.AIagentsPastForward: Faster On-Device GUI Agents via Computational Experience Reuse
arXiv:2609.32166v1 Announce Type: new Abstract: Running GUI agents on edge devices can keep sensitive screens and interaction histories local, but the computational cost of inference at every action step makes deployment…
- arXiv cs.AIagentsAI Harness: Certification under Proposal-Conditioned Information for Foundation-Model Agents
arXiv:2609.32184v1 Announce Type: new Abstract: Foundation-model agents are often modeled as policies over an observed state. In deployed systems, however, a runtime may intervene only after the model has emitted a seman…
- arXiv cs.AIagentsCoMemBench: Benchmarking Collaborative Memory Boundaries across Multi-Agent Workflow Topologies
arXiv:2609.32192v2 Announce Type: replace Abstract: Multi-agent workflows require task-relevant information to be shared across agents, while irrelevant, stale, unverified, or incompatible information must remain isolate…
- arXiv cs.AIagentsWitness: Discovery, Deciphering, and Epiphany in Interactive Puzzle Environments
arXiv:2609.32208v1 Announce Type: new Abstract: Automated science needs agents that can work out the rules of an unfamiliar environment by interacting with it. Interactive rule-discovery puzzles offer a controlled settin…
- arXiv cs.AIagentsClarify the User or Verify the World? Uncertainty Routing for Proactive Agents
arXiv:2609.32255v1 Announce Type: new Abstract: Tool-using LLM agents must decide not only whether additional information is needed, but also which source can resolve the uncertainty. Existing proactive approaches often…
- arXiv cs.AIagentsLAM: Efficient Lossy Agent Memory Framework With A Retrieval-Score Error Bound
arXiv:2609.32256v1 Announce Type: new Abstract: Agent memory grows as agents read inputs, reason, and call tools. Longer histories increase inference cost and eventually exceed the context window. LLM-based summarization…
- arXiv cs.AIagentsPrefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMs
arXiv:2609.32259v2 Announce Type: replace Abstract: Recent multi-agent LLM systems increasingly combine heterogeneous models for specialized agent roles. However, text-based communication requires each receiver to prefil…
- arXiv cs.AIagentsWhen Does a Skill Add Value? Task-Conditional Gain Prediction for Selective Skill Use
arXiv:2609.32274v1 Announce Type: new Abstract: Agent skills are expected to improve task performance. Yet we find that they often provide no benefit, and can even hurt performance while incurring additional token costs.…
- arXiv cs.AIagentsGLIDE: Generalized Layer-wise Intrinsic Distributional Evaluation for Heterogeneous LLM Agents
arXiv:2609.32295v1 Announce Type: new Abstract: LLM agents require reliable step-level evaluation to compare candidate branches and allocate computation effectively. However, lightweight evaluation remains challenging. E…
- arXiv cs.AIagentsAgentsensus: Consensus-Compressed Shared Memory for Multi-Agent Story Worlds
arXiv:2609.32297v1 Announce Type: new Abstract: A agentic story world is a dynamic system simulating who learned what, when, and from whom -- yet the standard design gives each character a private memory stream. A shared…
- arXiv cs.AIagentsEnabling Timely Guidance before Skill Retrieval: Retaining Helpful Warm Tips in Agent Context
arXiv:2609.32339v1 Announce Type: new Abstract: Reusable skills help LLM-based agents solve complex tasks, but the agent must receive guidance before it commits to an ineffective approach. Existing skill mechanisms often…
- arXiv cs.AIagentsFrom Trajectories to Grounded Preferences: Process Preference Synthesis via Interaction Element Graphs for Web PRMs
arXiv:2609.32351v1 Announce Type: new Abstract: Comparative Process Reward Models (PRMs) provide critical step-level guidance for autonomous web agents by evaluating state-conditioned preferences between candidate action…
- arXiv cs.AIagentsAuthorityLens: Rethinking LLM-Based Agent Systems Through the Lens of Authority
arXiv:2609.32378v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed with authority over consequential resources and decisions in real systems. These agents often operate alongside human and LLM-bas…
- arXiv cs.AIagentsReward Hacking and Agent Containment Failure: A Monte Carlo Study Based on the 2026 Hugging Face Incident
arXiv:2609.32390v1 Announce Type: new Abstract: The July 2026 intrusion into Hugging Face production infrastructure showed how reward hacking can become an external cybersecurity incident when a capable agent encounters…
- arXiv cs.AIagentsSCLATE: a Substrate for Continual-Learning Agent Training and Evaluation
arXiv:2609.32391v2 Announce Type: replace Abstract: Continual-learning agents are systems of models, harnesses, and memory operating over long multi-session horizons. Evaluating and training them requires interleaving ta…
- arXiv cs.AIagentsBeyond Scripted Search: Sample-Efficient Reward Discovery via Agentic Black-box Optimization
arXiv:2609.32394v1 Announce Type: new Abstract: Designing dense reward functions for low-level reinforcement learning (RL) control remains difficult. Recent work uses large language models (LLMs) to iteratively generate…
- arXiv cs.AIagentsPluginRSI: Recursive Improvement of Agent Harnesses with Reusable Plugins
arXiv:2609.32423v2 Announce Type: replace Abstract: The harness surrounding a language model is a central determinant of agent performance. Recent methods optimize harnesses by searching over complete programs, where ind…
- arXiv cs.AIagentsAuthorization Closure Graph: Minimal Repair for LLM Agents with Evolving User Instructions
arXiv:2609.32428v1 Announce Type: new Abstract: Tool-using large language model (LLM) agents increasingly perform state-changing actions that require user authorization. Yet existing approaches do not provide a principle…
- arXiv cs.AIagentsMulti-Agent System Search via Active Substructure-aware Policy Optimization
arXiv:2609.32430v1 Announce Type: new Abstract: LLMs enable multi-agent systems (MAS) to tackle complex tasks, but manually designing agent roles, prompts, and communication structures requires substantial expertise and…
- arXiv cs.AIagentsRepoMAS: Solving Progressively Specified Tasks with Issue-Driven Multi-Agent Systems
arXiv:2609.32490v2 Announce Type: replace Abstract: LLM-based multi-agent systems (MASs) have shown strong potential for solving complex tasks, but most assume that task requirements are sufficiently specified before exe…
- arXiv cs.AIagentsDAAF: From Failure Localization to Editable System Assets in LLM Agents
arXiv:2609.32498v1 Announce Type: new Abstract: Deployed LLM agents increasingly rely on persistent, versioned system assets such as routing rules, knowledge segments, prompt instructions, and reusable skills. Failure-lo…
- arXiv cs.AIagentsLearning from Others, Acting for You: Cross-User Memory Sharing for LLM Agents
arXiv:2609.32511v1 Announce Type: new Abstract: Large language model (LLM) agents serving different users often solve related tasks, yet separate user histories can leave reusable experience inaccessible to other agents.…
- arXiv cs.AIagentsFrom Anomalies to Failures: Constructing Causal Error Graphs for Agentic Trace Diagnosis
arXiv:2609.32514v1 Announce Type: new Abstract: LLM-driven agents are increasingly deployed in complex applications, where long agentic traces make failures difficult to diagnose. Existing trace diagnosis methods often c…
- arXiv cs.AIagentsMemAgent: Learning to Manage Heterogeneous Memory Providers for LLM Agents
arXiv:2609.32521v1 Announce Type: new Abstract: Current agents remain largely stateless across tasks, limiting their ability to continually improve from prior interactions and making memory essential for long-horizon age…
- arXiv cs.AIagentsBeyond Dyadic Memory: Interaction-Aware Multimodal Memory with Adaptive Agentic Retrieval for Multi-Party Spoken Conversations
arXiv:2609.32522v1 Announce Type: new Abstract: Long-term memory enables agents to accumulate information and reason across sessions, yet existing research primarily focuses on dyadic text or image-text conversations, le…
- arXiv cs.AIagentsFail Loudly: An Auditable Runtime for Agentic Data Analysis
arXiv:2609.32528v1 Announce Type: new Abstract: Large language models (LLMs) have enabled data-science agents to automate multi-step analyses over heterogeneous files. However, incorrect choices regarding data sources, s…
- arXiv cs.AIagentsPorimon: An LLM-Based Pok\'emon Battle Agent Enhanced by Long/Short-Term Knowledge Augmented Generation
arXiv:2609.32544v2 Announce Type: replace Abstract: In this paper, we use Pok\'emon Battles as a case study to investigate how to improve the performance of LLM-based agents in tasks that require opponent-aware planning…
- arXiv cs.AIagentsCUE-Mem: Benchmarking Long-Term User Memory via Implicit Cues in Multimodal Conversations
arXiv:2609.32574v1 Announce Type: new Abstract: Long-term memory is essential for multimodal agents that interact with users across sustained conversations. However, user memories are not always explicitly stated: they m…
- arXiv cs.AIagentsEMIR$^2$: Evolution-Aware Memory with Intent-Guided Multi-Round Retrieval
arXiv:2609.32584v1 Announce Type: new Abstract: Long-term memory enables large language model (LLM) agents to leverage historical interactions for future tasks. However, existing memory systems struggle to utilize contin…
- arXiv cs.AIagentsMA-FPPO: Multi-Agent Flow-Pretrained Policy Optimization
arXiv:2609.32594v1 Announce Type: new Abstract: Multi-agent flow policies learn cooperative behavior from fixed offline datasets, but often struggle to complete tasks in situations not covered by the offline data. In the…
- arXiv cs.AIagentsCUA-SWE: When Computer-Use Agents Meet Visual Software Engineering
arXiv:2609.32600v1 Announce Type: new Abstract: Software development requires more than editing code: developers repeatedly run software, interact with its interfaces, visually inspect its behavior, and use these observa…
- arXiv cs.AIagents"You're Right, Let Me Fix It": How LLM Agents Damage Correct Work When Falsely Accused
arXiv:2609.32616v1 Announce Type: new Abstract: LLM agents increasingly keep working after a task succeeds as they resume after compaction or take over handoffs. Their finished work keeps receiving follow-up input that s…
- arXiv cs.AIagentsSWE-MILE: Asynchronous Potential-Induced Milestone Credit Assignment for Long-Horizon Software Engineering Agents
arXiv:2609.32631v1 Announce Type: new Abstract: Long-horizon software engineering (SWE) agents trained with reinforcement learning with verifiable rewards (RLVR) typically receive only terminal outcome supervision, makin…
- arXiv cs.AIagentsBusiness Compromise Detection with Agentic AI and LLM-driven Knowledge Discovery
arXiv:2609.32643v1 Announce Type: new Abstract: Detecting compromised business ad accounts is a challenge in digital advertising, as attackers exploit hijacked accounts to launch fraudulent campaigns. Large Language Mode…
- arXiv cs.AIagentsFrom Scene Graphs to Answers: Selective Neuro-Symbolic Reasoning for Autonomous Driving
arXiv:2609.32645v1 Announce Type: new Abstract: Autonomous-driving question answering requires reasoning over structured scene information, yet existing vision-language approaches largely delegate heterogeneous reasoning…
- arXiv cs.AIagentsContract Memory Compiler: Resolve, Then Traverse
arXiv:2609.32658v1 Announce Type: new Abstract: External memory lets language-model agents answer questions about histories too long for the answer model's context window. Updates create a harder problem than retrieving…
- arXiv cs.AIagentsWhen Better Gets Worse: Improvement Fidelity for Self-Improving Agents in Adaptive Worlds
arXiv:2609.32677v1 Announce Type: new Abstract: Self-improving agents increasingly rely on proxy verifiers to choose policy updates, yet deployment can change the world in which those updates are evaluated. An update tha…
- arXiv cs.AIagentsDude, Where's My State? Execution Information Requirements for Stateful Agents
arXiv:2609.32687v2 Announce Type: replace Abstract: Long-running agents must preserve information that later steps depend on. We introduce the Execution Information Requirement (EIR), a lower bound on the information tha…
- arXiv cs.AIagentsWorld Agent: Can Language Models Keep a World Running?
arXiv:2609.32692v1 Announce Type: new Abstract: World models are moving from generating realistic frames to generating playable worlds, yet whether a delivered world can keep running is not tested anywhere. Existing eval…
- arXiv cs.AIagentsIGSD: Environment-Verified Hindsight Self-Distillation for Search Agents
arXiv:2609.32694v1 Announce Type: new Abstract: On-policy self-distillation densifies agent training without external teachers: a policy conditioned on privileged hindsight provides step-level guidance for its own unpriv…
- arXiv cs.AIagentsCAIRN: Dynamic Fact-Intent DAGs for Multi-Agent Exploration
arXiv:2609.32700v1 Announce Type: new Abstract: LLM-powered autonomous systems have demonstrated promising capabilities in mathematical reasoning, engineering, and cybersecurity. Yet how to organize these systems for eff…
- arXiv cs.AIagentsDespite Instructions: Frontier Agents Improvise Covert Channels at Test Time
arXiv:2609.32701v2 Announce Type: replace Abstract: In security-sensitive applications, language-model agents are often required to coordinate without disclosing confidential information. Yet repeated interactions may al…
- arXiv cs.AIagentsSkillVine: Agent Skill Evolution via Branching Exploration
arXiv:2609.32731v1 Announce Type: new Abstract: Agent skills encapsulate reusable procedural knowledge that enables LLM agents to perform tasks, and they can be improved automatically using trajectories from interactions…
- arXiv cs.AIagentsCUA-Sandbox: Efficient Environments for Computer-Use Agent Reinforcement Learning
arXiv:2609.32750v1 Announce Type: new Abstract: Reinforcement learning enables computer-use agents to improve through interaction with real software environments, including websites and desktop applications. However, con…
- arXiv cs.AIagentsAdaptive Consistency Graph for Long-Horizon Agents
arXiv:2609.32754v2 Announce Type: replace Abstract: Large language model agents can often make reasonable local decisions on short tasks, yet their performance degrades when success requires long sequences of dependent a…
- arXiv cs.AIagentsAgentic Network Traffic Monitoring
arXiv:2609.32778v1 Announce Type: new Abstract: As the use of agentic artificial intelligence increases in nearly every industry, there exists a widening attack surface. It is necessary to monitor agents to ensure that a…
- arXiv cs.AIagentsCLAIRE: A Schema-Grounded Hybrid Workflow for Healthcare Administrative Form Completion
arXiv:2609.32787v1 Announce Type: new Abstract: Healthcare administrative staff transfer structured information from electronic health records, referrals, claims systems, provider rosters, and work queues into dynamic fo…