AI news archive.
4,966 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsConcealing LLM-Based Multi-Agent Topology via Phantom Structure Injection
arXiv:2609.37567v1 Announce Type: cross Abstract: Driven by the rapid advancement of large language models (LLMs), LLM-based multi-agent systems (MAS) have emerged as a powerful paradigm for collaborative reasoning over…
- arXiv cs.AIagentsRLTLDR: Self-improvement by Internalizing Self-generated Feedback
arXiv:2609.37633v2 Announce Type: replace-cross Abstract: The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards the successfu…
- arXiv cs.AIagentsA Benchmark & Dataset for Detecting AI-Manipulated Visual Evidence in the Court System
arXiv:2609.37783v1 Announce Type: cross Abstract: Photographic evidence is becoming increasingly vulnerable to forms of alteration and fabrication that existing legal and technical workflows are not well equipped to eval…
- arXiv cs.AIagentsExplore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents
arXiv:2609.37810v1 Announce Type: cross Abstract: Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization to unseen tasks remains challenging. More recentl…
- arXiv cs.AIagentsMaking Duplicate Reimbursement Unrepresentable: A Verified Ethereum E-Invoice System for Humans and AI Agents
arXiv:2609.37819v1 Announce Type: cross Abstract: Electronic invoices are replacing paper invoices worldwide, but today's centralized architectures leave three problems unsolved on the consumption side: an invoice can be…
- arXiv cs.AIagentsAgentBug-Smith: Automatically Reproducing Real-World Harness Bugs in Agentic Systems
arXiv:2609.37864v1 Announce Type: cross Abstract: Agent harness bugs exhibit unique characteristics and remain challenging for state-of-the-art software agents to repair. Progress in this area is further hindered by exis…
- arXiv cs.AIagentsExceptionDrive: A Planning-Oriented Counterfactual Corner-Case Benchmark for Autonomous Driving
arXiv:2609.37871v1 Announce Type: cross Abstract: Average performance on routine driving benchmarks does not establish planner reliability under rare, safety-critical hazards. We proposed ExceptionDrive, a counterfactual…
- arXiv cs.AIagentsBITEM at the NTCIR-19 R2C2 Task: Predicting Confidence from Agentic RAG Pipeline Signals
arXiv:2609.37993v2 Announce Type: replace-cross Abstract: The BITEM team entered both subtasks of the NTCIR-19 R2C2 task with a single agentic pipeline, in which a model searches, reads and records evidence over a movie…
- arXiv cs.AIagentsdoPlan: A Variable-Horizon Dataset for Multi-Stage Language-Conditioned Planning in Autonomous Driving
arXiv:2609.38028v1 Announce Type: cross Abstract: Autonomous vehicles interacting with passengers through natural language must reason beyond immediate commands. Passenger intent may span multiple stages of behavior, dep…
- arXiv cs.AIagentsJaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
arXiv:2609.38065v1 Announce Type: cross Abstract: Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides a precise and struct…
- arXiv cs.AIagentsCorrect Answers, Invalid Traces: What Verifiable Grade-School Math Reveals About Chain-of-Thought Traces
arXiv:2609.38107v1 Announce Type: cross Abstract: Chain-of-thought traces are widely read as records of how models reach their answers, informing debugging, agent auditing, and claims about reasoning. Testing this interp…
- arXiv cs.AIagentsSkill-Space Shooting for Autonomous Robot Policy Improvement
arXiv:2609.38178v1 Announce Type: cross Abstract: Robots deployed in the physical world must be able to improve beyond their initial training as they encounter new situations and failures. For this improvement to scale a…
- arXiv cs.AIagentsAgentic AI for Clustering and Relationship Discovery in Prediction Markets
arXiv:2512.02436v4 Announce Type: replace Abstract: Prediction market platforms list thousands of contracts whose logical and economic connections are implicit in their resolution rules. We introduce a hierarchical agent…
- arXiv cs.AIagentsEvaluating Test-Time Scaling of General LLM Agents
arXiv:2602.18998v2 Announce Type: replace Abstract: LLM agents are increasingly expected to operate as general-purpose systems that resolve real-world user requests, yet their dynamic scaling behavior in realistic enviro…
- arXiv cs.AIagentsRead the Paper, Write the Code: Agentic Reproduction of Social-Science Results
arXiv:2604.21965v2 Announce Type: replace Abstract: Recent work has used LLM agents to reproduce empirical social science results with access to both the data and code. We broaden this scope by asking: Can they reproduce…
- arXiv cs.AIagentsProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries
arXiv:2605.06223v4 Announce Type: replace Abstract: Natural-language instance navigation becomes challenging when the initial user request does not uniquely specify the target instance. A practical agent should reduce th…
- arXiv cs.AIagentsProcess Matters more than Output for Distinguishing Humans from Machines
arXiv:2605.06524v3 Announce Type: replace Abstract: Reliable human-machine discrimination is becoming increasingly important as Large Language Models and autonomous agents are deployed in online settings. Existing approa…
- arXiv cs.AIagentsEquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium
arXiv:2605.09278v2 Announce Type: replace Abstract: Multi-agent debate (MAD) systems increasingly rely on shared memory to support long-horizon reasoning, but this convenience opens a critical vulnerability: a single cor…
- arXiv cs.AIagentsAre Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows
arXiv:2605.14322v4 Announce Type: replace Abstract: Language agents are increasingly deployed in professional workflows, yet tutoring remains a high-stakes capability that existing evaluations only partially capture. Eff…
- arXiv cs.AIagentsCOLLATOR: Compositional Multi-Agent Orchestration with Counterfactual Reinforcement Learning
arXiv:2605.14483v2 Announce Type: replace Abstract: Large language models (LLMs) provide a flexible foundation for multi-agent systems, but their effectiveness and computational cost depend critically on orchestration de…
- arXiv cs.AIagentsOttilie: A Socially Intelligent Virtual Host for Sales-Driven Live Commerce
arXiv:2605.14542v2 Announce Type: replace Abstract: A skilled live-commerce host is not merely a narrator, but a sales agent who converts viewer curiosity into purchase intent through expert product knowledge, emotionall…
- arXiv cs.AIagentsHarnessing LLM Agents with Skill Programs
arXiv:2605.17734v2 Announce Type: replace Abstract: Equipping LLM agents with reusable skills derived from past experience has become a popular and successful approach for tackling complex and long-horizon tasks. However…
- arXiv cs.AIagentsDemoEvolve: Demonstration-Guided Harness Evolution under Sparse Feedback
arXiv:2605.24539v2 Announce Type: replace Abstract: Harness evolution enables frozen language model agents to adapt to unfamiliar tasks by modifying the external programs that govern their behavior. For long-horizon task…
- arXiv cs.AIagentsKLineage: Recovering the Missing When of Kernel Optimization by Deoptimizing Experts
arXiv:2605.28213v2 Announce Type: replace Abstract: LLM-based agents are increasingly used to generate GPU kernels, but they often struggle to determine when an optimization is sound because its required code state and d…
- arXiv cs.AIagentsDeepSurvey: Agent-Oriented Automated Survey Generation with Analytical Depth and Citation Reliability
arXiv:2605.29522v2 Announce Type: replace Abstract: As scientific literature grows rapidly and research increasingly involves AI agents, automated survey generation has become a key capability for both agents and human r…
- arXiv cs.AIagentsSoftSkill: Behavioral Compression for Contextual Adaptation
arXiv:2606.20333v2 Announce Type: replace Abstract: Natural-language skills let agents reuse task knowledge, yet deploying a long Markdown document makes the model interpret that knowledge anew on every call. We ask whet…
- arXiv cs.AIagentsThe Hitchhiker's Guide to Agentic AI: From Foundations to Systems
arXiv:2606.24937v3 Announce Type: replace Abstract: The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems, covering the full stack from first principles to pr…
- arXiv cs.AIagentsWhen Do Agent Loops Mistake Stagnation for Progress? Self-Evaluation Bias and Externally Grounded Verification in Long-Running Autonomous LLM Agent Loops
arXiv:2607.25152v2 Announce Type: replace Abstract: Long-running autonomous agents plan, act, and judge their own completion without human intervention. When an agent grades its own work, self-evaluation bias takes hold:…
- arXiv cs.AIagentsXBridge: Entity-Grounded Latent Bridge for Heterogeneous LLM Communication
arXiv:2608.11676v2 Announce Type: replace Abstract: Heterogeneous multi-agent LLM systems, where agents are powered by different model families, can outperform homogeneous configurations by reducing redundant reasoning p…
- arXiv cs.AIagentsBenevolent Bias in Multi-Turn Human-Agent Dialogue
arXiv:2608.29206v2 Announce Type: replace Abstract: Bias in human-agent interaction can manifest not only through hostile language but also as benevolent bias, whereby unequal treatment hides behind a warm, positive tone…
- arXiv cs.AIagentsSRPO: Setwise Relative Policy Optimization for Multi-Agent Systems
arXiv:2609.08452v2 Announce Type: replace Abstract: Multi-agent systems enable complex reasoning and tool use by coordinating agents that divide roles and refine candidate solutions. Existing methods typically update ind…
- arXiv cs.AIagentsAgentGrad: Intervention-guided Prompt Optimization for Multi Agent Systems
arXiv:2609.08572v2 Announce Type: replace Abstract: Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the pro…
- arXiv cs.AIagentsRoot-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures
arXiv:2609.13463v2 Announce Type: replace Abstract: The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reliability, as it t…
- arXiv cs.AIagentsMeta-SecAlign: Training LLMs against Prompt Injection for Robust Agents
arXiv:2507.02735v4 Announce Type: replace-cross Abstract: Prompt injection attacks, where untrusted data contains an injected prompt to manipulate the system, have been listed as the top security threat to AI agents. By…
- arXiv cs.AIagentsEvaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?
arXiv:2602.11988v3 Announce Type: replace-cross Abstract: A widespread practice in software development is to tailor coding agents to repositories using context files, such as AGENTS.md. Although this practice is strongl…
- arXiv cs.AIagentsAgentic AI for Scalable and Robust Optical Systems Control
arXiv:2602.20144v2 Announce Type: replace-cross Abstract: We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interp…
- arXiv cs.AIagentsCollective Opinion Dynamics in Structured LLM Populations
arXiv:2604.11312v3 Announce Type: replace-cross Abstract: Large Language Models are increasingly deployed as interacting agents in settings such as online platforms, recommendation systems, and multi-agent applications.…
- arXiv cs.AIagentsTrojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses in LLM Agents
arXiv:2605.01970v4 Announce Type: replace-cross Abstract: Memory systems enable otherwise stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. The Trojan Hippo attack…
- arXiv cs.AIagentsMissing Old Logits in Asynchronous Agentic RL: Semantic Mismatch and Repair Methods for Off-Policy Correction
arXiv:2605.12070v3 Announce Type: replace-cross Abstract: Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy optimization, but it a…
- arXiv cs.AIagentsAgentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis
arXiv:2605.18770v3 Announce Type: replace-cross Abstract: Public commercial registries are formally open, yet their practical analysis remains difficult because relevant facts are scattered across millions of records tha…
- arXiv cs.AIagentsTwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing
arXiv:2605.18859v5 Announce Type: replace-cross Abstract: LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single user request triggers…
- arXiv cs.AIagentsLearning to Assign Prediction Tasks to Agents with Capacity Constraints
arXiv:2605.27999v2 Announce Type: replace-cross Abstract: We address the problem of learning to assign prediction tasks to one agent from a set of available agents, including human decision-makers and AI models. We focus…
- arXiv cs.AIagentsDo Proactive Agents Need an LLM to Decide When to Act?
arXiv:2605.30152v2 Announce Type: replace-cross Abstract: Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipelines repeatedly inter…
- arXiv cs.AIagentsHANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
arXiv:2606.06493v4 Announce Type: replace-cross Abstract: Humanoid loco-manipulation benefits from one controller to coordinate locomotion, arm movements, and fall recovery. These behaviors are difficult to learn togethe…
- arXiv cs.AIagentsSkill-MAS: Evolving Meta-Skill for Automatic Multi-Agent Systems
arXiv:2606.18837v3 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based automatic Multi-Agent Systems (MAS) generation has become a crucial frontier for tackling complex tasks. However, existing method…
- arXiv cs.AIagentsIs Agent Code Less Maintainable Than Human Code?
arXiv:2606.21804v2 Announce Type: replace-cross Abstract: Maintainability is a core dimension of software engineering, shaping how code is written, reviewed, and developed over time. While coding agents have demonstrated…
- arXiv cs.AIagentsORCA-bench: How Ready Are Language Model Agents for Oncall?
arXiv:2607.28545v3 Announce Type: replace-cross Abstract: Large language models can write, patch, and search code, but oncall root cause analysis (RCA) demands something different: reasoning over noisy metrics, logs, tra…
- arXiv cs.AIagentsActive-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports
arXiv:2608.04682v2 Announce Type: replace-cross Abstract: Coding agents powered by large language models (LLMs) are increasingly adopted in software engineering (SWE) scenarios, capable of fixing a specific bug in large-…
- arXiv cs.AIagentsDon't Drop the BATON: Long-Horizon Robot Manipulation via Agentic Subtask Exploration and Transition-aware Memory
arXiv:2608.16889v3 Announce Type: replace-cross Abstract: Long-horizon robot manipulation chains many contact-rich skills into one multi-stage task. Vision-language-action (VLA) and world-action models (WAMs) increasingl…
- arXiv cs.AIagentsTraceML: What Auto-Research Agents Miss in Long-Horizon ML Development
arXiv:2608.26086v3 Announce Type: replace-cross Abstract: Auto-research agents now run machine-learning development unattended for hours, revising data pipelines, models, and validation from their own feedback, yet on mo…