AI news archive.
4,966 articles filed under Agents, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsEncrypted Skills Can Become New Security Exploits If AI Agents Know How to Decipher
arXiv:2610.18262v1 Announce Type: cross Abstract: Third-party skills can expose LLM agents to malicious instructions hidden by encoding. We introduce Ciphered Skill Injection (CSI), which embeds such instructions in skil…
- arXiv cs.AIagentsJevGPT: Thinking Fast and Slow in Autonomous Research
arXiv:2610.18272v1 Announce Type: cross Abstract: Autonomous research agents must acquire and reason over evidence within a limited budget. Yet relevant sources can remain uninspected or unused, leaving a gap between ret…
- arXiv cs.AIagentsUrbex: Agentic Spatial Grounding for City-Scale 3D Scenes
arXiv:2610.18356v1 Announce Type: cross Abstract: City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outdoor scenes. We pro…
- arXiv cs.AIagentsResource-Efficient 3D Shape and Orientation Estimation of Road Users using Roadside RGB Camera and 3D Radar
arXiv:2610.18417v1 Announce Type: cross Abstract: End-to-end deep learning pipelines for camera-radar 3D object detection are well established in driver assistance systems and autonomous vehicles; however, adapting them…
- arXiv cs.AIagentsA Global View of How LLMs Value Human Time
arXiv:2610.18515v3 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly act as personal assistants; however, little is known about how they capture national-level preferences on a global scale…
- arXiv cs.AIagentsOn Symmetry for Characterizing Agent Heterogeneity in Multi-Agent Systems
arXiv:2610.18538v1 Announce Type: cross Abstract: Understanding agent heterogeneity can inform the design of shared and agent-specific policy representations, however existing criteria provide no unified structural test.…
- arXiv cs.AIagentsJev-ToM: Evaluating Theory of Mind in Typed Decision Models
arXiv:2610.18610v1 Announce Type: cross Abstract: Dialogue and collaborative assistants repeatedly judge what a partner knows, believes, or intends. Typed decision models offer a potential alternative to generative large…
- arXiv cs.AIagentsKnowledge Graphs as Files: An Agent Harness Where Tool Creation and Tool Use Co-Evolve
arXiv:2610.18616v2 Announce Type: replace-cross Abstract: Searching text-rich knowledge graphs requires following relations while examining textual evidence. Predefined graph interfaces constrain what evidence agents can…
- arXiv cs.AIagentsProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning
arXiv:2610.18683v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (e.g., GRPO…
- arXiv cs.AIagentsRadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction
arXiv:2610.18686v1 Announce Type: cross Abstract: 3D semantic occupancy prediction is crucial for autonomous driving, but camera-based methods degrade in low-light conditions and LiDAR-based methods are sensitive to adve…
- arXiv cs.AIagentsSEAR: Self-Evolving Agentic Robotics
arXiv:2610.18792v1 Announce Type: cross Abstract: Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for the next task. We int…
- arXiv cs.AIagentsOptimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning
arXiv:2610.18808v1 Announce Type: cross Abstract: In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator of effective coordin…
- arXiv cs.AIagentsRoboSimStudio: An Interactive Toolkit for Human and Agent Rapid Real2Sim of Robot Manipulation Tasks
arXiv:2610.18835v2 Announce Type: replace-cross Abstract: It has been difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled cont…
- arXiv cs.AIagentsHybrid Reasoning Agents
arXiv:2610.18861v1 Announce Type: cross Abstract: How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through te…
- arXiv cs.AIagentsTev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards
arXiv:2610.18868v1 Announce Type: cross Abstract: Long-horizon agents have drawn growing attention for their ability to work autonomously -- from hours to days -- on economically valuable tasks, a capability essential to…
- arXiv cs.AIagentsChunkAct: Action Chunking for Agentic Robotics
arXiv:2610.18874v1 Announce Type: cross Abstract: Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under t…
- arXiv cs.AIagentsAgent-Environment Alignment via Automated Interface Generation
arXiv:2505.21055v3 Announce Type: replace Abstract: Large language model (LLM) agents have shown impressive reasoning capabilities in interactive decision-making tasks. These agents interact with environment through inte…
- arXiv cs.AIagentsAre LLM Agents Behaviorally Coherent? A Latent Profiling Approach for Social Simulation
arXiv:2509.03736v3 Announce Type: replace Abstract: LLM agents are increasingly proposed as substitutes for human participants in social research, usually on the strength of survey responses that match those of the human…
- arXiv cs.AIagentsToward Agents That Reason About Their Computation
arXiv:2510.22833v3 Announce Type: replace Abstract: While reinforcement learning agents can achieve superhuman performance in many complex tasks, they typically do not become more computationally efficient as they improv…
- arXiv cs.AIagentsMulti-agent Undercover Gaming: Hallucination Removal via Counterfactual Test for Multimodal Reasoning
arXiv:2511.11182v2 Announce Type: replace Abstract: Hallucination continues to pose a major obstacle in the reasoning capabilities of large language models (LLMs). Although the Multi-Agent Debate (MAD) paradigm offers a…
- arXiv cs.AIagentsReCreate: Reasoning and Creating Domain Agents Driven by Experience
arXiv:2601.11100v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are reshaping the industrial landscape. However, most practical agents still rely on human-designed scaffolds, making their creation a…
- arXiv cs.AIagentsRisky-Bench: Evaluating Agentic Safety Risks in Real-World Life-Assist Deployments
arXiv:2602.03100v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments, introducing safety risks beyond linguistic harm through tool u…
- arXiv cs.AIagentsLogarithmic Scores, Power-Law Discoveries: Disentangling Measurement from Coverage in Agent-Based Evaluation
arXiv:2604.00477v2 Announce Type: replace Abstract: LLM-based agent judges are an emerging approach to evaluating conversational AI, yet a fundamental uncertainty remains: can we trust their assessments, and if so, how m…
- arXiv cs.AIagentsAgentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization
arXiv:2604.25083v2 Announce Type: replace Abstract: Computer Architecture research has traditionally required the exploration of vast, highly complex design spaces through manual human effort. Large Language Models (LLMs…
- arXiv cs.AIagentsVibeServe: Can AI Agents Build Bespoke LLM Serving Systems?
arXiv:2605.06068v2 Announce Type: replace Abstract: For years, we have built LLM serving systems like any other critical infrastructure, with a single general-purpose stack hand-tuned over many engineer-years to support…
- arXiv cs.AIagentsTools as Continuous Flow for Evolving Agentic Reasoning
arXiv:2605.07339v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in orchestrating tools for reasoning tasks. However, existing methods rely on a step-wise paradig…
- arXiv cs.AIagentsMemQ: Integrating Q-Learning into Self-Evolving Memory Agents over Provenance DAGs
arXiv:2605.08374v4 Announce Type: replace Abstract: Episodic memory allows LLM agents to accumulate and retrieve experience, but current methods treat each memory independently, i.e., evaluating retrieval quality in isol…
- arXiv cs.AIagentsRewardHarness: Self-Evolving Agentic Post-Training
arXiv:2605.08703v2 Announce Type: replace Abstract: Aligning generative models with human preferences remains a central challenge. While RLHF is widely adopted, it relies on large-scale preference data. This limitation i…
- arXiv cs.AIagentsVerifiable Process Rewards for Agentic Reasoning
arXiv:2605.10325v3 Announce Type: replace Abstract: Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet existing approaches predominantly rel…
- arXiv cs.AIagentsArgumentation-Based Person-Tailored Storytelling for Older Adults: A Reflective Hybrid AI Architecture
arXiv:2605.10531v2 Announce Type: replace Abstract: This work investigates knowledge-driven, large language model (LLM)-based storytelling for older adults. We present a reflective storytelling agent integrating knowledg…
- arXiv cs.AIagentsEnergy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems
arXiv:2605.22883v2 Announce Type: replace Abstract: Current AI energy benchmarks measure consumption per model invocation. For agentic systems, where a single user goal may trigger multistep orchestration, tool calls, re…
- arXiv cs.AIagentsTADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews
arXiv:2605.26911v2 Announce Type: replace Abstract: LLM-generated peer reviews are increasingly common at major venues, yet their deficiencies are hard to detect because they are uniformly fluent and well-structured. Exi…
- arXiv cs.AIagentsCoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Web Cloud-Edge Systems
arXiv:2606.00756v2 Announce Type: replace Abstract: LLM-based Web agents are increasingly deployed on edge servers to carry out long-horizon tasks on behalf of users with low latency. However, the lightweight models that…
- arXiv cs.AIagentsBusiness World Model
arXiv:2606.10044v3 Announce Type: replace Abstract: World model has emerged as a powerful paradigm in artificial intelligence, enabling agents to represent their environments, predict future states, and evaluate possible…
- arXiv cs.AIagentsPseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience
arXiv:2606.18060v2 Announce Type: replace Abstract: As Large Language Model based agents enter autonomous scientific research, their ability to resist pseudoscience becomes increasingly important. Otherwise, such systems…
- arXiv cs.AIagentsDecoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents
arXiv:2606.18947v2 Announce Type: replace Abstract: Production LLM agents increasingly depend on real-time search, yet native search grounding bundles retrieval policy, provider choice, evidence injection, cost, latency,…
- arXiv cs.AIagentsReward as An Agent for Embodied World Models
arXiv:2606.19990v3 Announce Type: replace Abstract: While RL has become a promising tool for refining world models, existing methods largely rely on conservative rollouts near the training distribution, limiting explorat…
- arXiv cs.AIagentsWhen Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents
arXiv:2606.22936v2 Announce Type: replace Abstract: Tool-using LLM agents make early choices about what to search for, which evidence to trust, and what to do next. A wrong early choice can steer every later tool call, s…
- arXiv cs.AIagentsPlans Fade Fast: Why Context Management Is Load-Bearing for LLM Agents
arXiv:2606.22953v2 Announce Type: replace Abstract: Context managers summarize or evict old messages so agents can operate beyond a finite window. This creates a reliability problem: removing history that still shapes th…
- arXiv cs.AIagentsLitmus: Zero-Label, Code-Driven Metric Specification for Evaluating AI Systems
arXiv:2606.23403v2 Announce Type: replace Abstract: As agentic LLM systems move from prototypes to deployment, evaluating them has become more important and more difficult. Evaluation goals are often left implicit. Witho…
- arXiv cs.AIagentsUnderstanding Rollout Error in Graph World Models
arXiv:2606.27780v3 Announce Type: replace Abstract: World models are increasingly used for planning by predicting future states under candidate actions. Many planning environments, however, are naturally graph-structured…
- arXiv cs.AIagentsMedEvoEval: Evaluating Continual Evolution of Doctor Agents through Simulated Clinical Episodes
arXiv:2606.28900v3 Announce Type: replace Abstract: Doctor agents are moving beyond single-turn answer generation toward evolving clinical decision systems that acquire evidence within episodes and reuse experience acros…
- arXiv cs.AIagentsWhen LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
arXiv:2606.29354v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) improves large language models (LLMs) on difficult reasoning tasks, but it often incurs long natural-language rationales that are poorly aligned…
- arXiv cs.AIagentsLong-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading
arXiv:2607.08964v3 Announce Type: replace Abstract: AI agents have become capable of autonomously completing short, well-specified tasks. However, existing terminal benchmarks largely focus on simple problems that finish…
- arXiv cs.AIagentsTracing Agentic Failure from the Flow of Success
arXiv:2607.12747v2 Announce Type: replace Abstract: Failure attribution for LLM-based agentic systems, i.e., identifying which steps in a failure trajectory caused the task to fail, is critical for debugging and improvin…
- arXiv cs.AIagentsPRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning
arXiv:2607.20064v3 Announce Type: replace Abstract: Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This gap is reflected…
- arXiv cs.AIagentsAgent-UCT: Upper Confidence Bounds Applied to Trees for Cost-Aware Agentic Workflow Optimization
arXiv:2607.24162v3 Announce Type: replace Abstract: Workflow search pays for complete quality observations, whose execution cost changes as reusable prefixes accumulate. AgentUCT uses an economic model of the remaining e…
- arXiv cs.AIagentsMIND: Lightweight and Effective Memory Injection Defense for LLM Agents via Intent-Aware Information Bottleneck
arXiv:2607.28103v2 Announce Type: replace Abstract: Memory-augmented LLM-based agents are vulnerable to memory injection attacks: Agents may retrieve poisoned memory from attackers, which diverts their behavior from init…
- arXiv cs.AIagentsCockpitHAT: Dependency-Graph-Driven Hierarchical Attribution for Embodied Multi-Agent Cockpits
arXiv:2608.01805v2 Announce Type: replace Abstract: LLM multi-agent systems suffer from Correctness Collapse, where high task-level accuracy conceals severe process-level failures. This is especially hazardous in safety-…
- arXiv cs.AIagentsSearchMaster: Grounded and Regulated Self-Play for Search Agents
arXiv:2608.01822v2 Announce Type: replace Abstract: Training LLM-based search agents requires high-quality search data: tasks that demand genuine multi-hop retrieval and trajectories that use search tools effectively. Ex…