AI agents

Tool use, function calling, orchestration and the protocols that let models act rather than only answer.

16,183 stories · RSS feed

arXiv AI

Jun 11

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application

arXiv:2606. 12191v1 Announce Type: cross Abstract: Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model capabilities.

By Jiachun Li, Zhuoran Jin, Tianyi Men, Yupu Hao, Kejian Zhu, Lingshuai Wang, Dongqi Huang, Longxiang Wang, Shengjia Hua, Lu Wang, Jinshan Gao, Hongbang Yuan, Ruilin Xu, Kang Liu, Jun Zhao

llms agents

arXiv AI

Jun 11

Runtime Enforcement of Hybrid System Properties

arXiv:2606. 12022v1 Announce Type: cross Abstract: Runtime enforcement has emerged as a promising approach for ensuring the safety of autonomous and cyber-physical systems operating in uncertain and dynamic environments.

By Mir Md Sajid Sarwar, Srinivas Pinisetty, Rajarshi Ray, Thierry J\'eron

agents safety

arXiv Machine Learning

Jun 11

Multi-agent rendezvous in fluid flows via reinforcement learning

arXiv:2606. 11274v1 Announce Type: cross Abstract: Rendezvous is a critical task for multi-agent systems, requiring agents to coordinate to meet at an unspecified location.

By Bocheng Li, Jingran Qiu, Lihao Zhao

agents reinforcement-learning

arXiv Machine Learning

Jun 11

My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents

arXiv:2606. 11256v1 Announce Type: cross Abstract: Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes.

By C\'esar Ojeda, Darius A. Faroughy, Maryam Karimi, Payam Zarrintaj, Mir Mehdi Seyedebrahimi, Mart\'in Carballo-Pacheco

llms agents fine-tuning benchmarks

arXiv AI

Jun 11

Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness

arXiv:2606. 11686v1 Announce Type: cross Abstract: End-to-end task-success is the dominant way to evaluate LLM agents, but one aggregate number tells you that an agent regressed, not where.

By Sawyer Zhang, Alexander Wang, Sophie Lei

llms agents safety

arXiv Machine Learning

Jun 11

Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria

arXiv:2606. 11284v1 Announce Type: cross Abstract: Real-world multi-agent systems, from traffic coordination to resource allocation, are often modeled as general-sum games where individual incentives conflict with collective welfare.

By Wongyu Lee, Francesco Lelli, Omran Ayoub, Massimo Tornatore

agents reinforcement-learning safety

arXiv AI

Jun 11

AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable

arXiv:2606. 11456v1 Announce Type: cross Abstract: The deployment of LLM-based agents in scientific analysis raises opposing concerns: that agents may reduce methodological diversity, or that they may amplify the analytic flexibility through which researchers reach motivated conclusions.

By Meysam Alizadeh, Fabrizio Gilardi, Mohsen Mosleh, Enkelejda Kasneci

llms agents safety

arXiv AI

Jun 11

An Ethical eValuation Agent (EeVA): Results of a Proof-of-Concept Test on a Prototype Agentic-like Workflow to Assist Ethical Deliberations

arXiv:2606. 11218v1 Announce Type: cross Abstract: Ethical deliberation is often misunderstood as a search for single right or wrong answers, creating difficulties for non-ethically trained personnel who must address ethically laden challenges.

By Stephen Milford, B. Zara Malgir, Miguel Vazquez

llms agents safety

arXiv Machine Learning

Jun 11

Context-Driven Incremental Compression for Multi-Turn Dialogue Generation

arXiv:2606. 12411v1 Announce Type: cross Abstract: Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with conversation length.

By Yeongseo Jung, Jaehyeok Kim, Eunseo Jung, Jiachuan Wang, Yongqi Zhang, Ka Chun Cheung, Simon See, Lei Chen

agents nlp efficiency benchmarks

arXiv AI

Jun 11

Human-Enhanced Loop Modeling (HELM): Agent-Based Finite Element Modeling of Concrete Bridge Barriers

arXiv:2606. 12025v1 Announce Type: new Abstract: Finite element (FE) modeling of safety-critical infrastructure such as bridge barriers requires high-fidelity nonlinear dynamic analysis, yet the current FE modeling process remains labor-intensive and lacks automation.

By Quankai Wang, Yulin Xie, Tongfei Yang, Minghui Cheng, Ran Cao

agents safety

arXiv AI

Jun 11

Knowing When to Ask: Self-Gated Clarification for Hierarchical Language Agents

arXiv:2606. 11349v1 Announce Type: new Abstract: In hierarchical reasoning, failures often originate at intermediate decision points where the agent commits to a wrong branch without recognizing that it lacks critical information.

By Aijing Gao, Yiming Kang, Mengdie Flora Wang, Jae Oh Woo

llms agents benchmarks

arXiv AI

Jun 11

Can Open-Source LLM Agents Replace Static Application Security Testing Tools? An Empirical Assessment

arXiv:2606. 11672v1 Announce Type: cross Abstract: This paper explores the value of agentic AI tools for cybersecurity purposes.

By Derek Yohn, Luke Flancher, Mirajul Islam, Khaled Slhoub

llms agents reinforcement-learning

arXiv AI

Jun 11

Grounding Computer Use Agents on Human Demonstrations

arXiv:2511. 07332v2 Announce Type: replace-cross Abstract: Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements.

By Aarash Feizi, Shravan Nayak, Xiangru Jian, Kevin Qinghong Lin, Kaixin Li, Rabiul Awal, Xing Han L\`u, Johan Obando-Ceron, Juan A. Rodriguez, Nicolas Chapados, David Vazquez, Adriana Romero-Soriano, Reihaneh Rabbany, Perouz Taslakian, Christopher Pal, Spandana Gella, Sai Rajeswar

agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 11

Learning to Inject: Automated Prompt Injection via Reinforcement Learning

arXiv:2602. 05746v2 Announce Type: replace-cross Abstract: Prompt injection is a critical vulnerability in LLM agents, yet the strongest methods still rely on human red-teamers and hand-crafted prompts.

By Xin Chen, Jie Zhang, Florian Tram\`er

llms agents reinforcement-learning fine-tuning safety

arXiv AI

Jun 11

Goal-Autopilot: A Verifiable Anti-Fabrication Firewall for Unattended Long-Horizon Agents

arXiv:2606. 11688v1 Announce Type: cross Abstract: Long-horizon LLM agents are not trusted to run unattended: with no human watching, they confidently report success they never verified.

By Youwang Deng

llms agents

arXiv AI

Jun 11

Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents

arXiv:2604. 13733v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) enables high-frequency, closed-loop control for robotic manipulation, but scaling to long-horizon tasks with sparse or imperfect rewards remains difficult due to inefficient exploration and poor credit assignment.

By Angelo Moroncelli, Roberto Zanetti, Marco Maccarini, Loris Roveda

agents reinforcement-learning robotics efficiency multimodal safety

arXiv AI

Jun 11

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction

arXiv:2606. 11247v1 Announce Type: cross Abstract: Generative models are increasingly used to propose designs, data, and control actions for physical systems, yet many such systems are governed by hard physical constraints rather than by perceptual plausibility.

By Yaser Mike Banad, Sarah Sharif

agents diffusion multimodal benchmarks

arXiv Machine Learning

Jun 11

Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning

arXiv:2511. 14427v4 Announce Type: replace-cross Abstract: Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception.

By Rickmer Krohn, Vignesh Prasad, Gabriele Tiboni, Georgia Chalvatzaki

llms rag agents reinforcement-learning robotics multimodal

arXiv AI

Jun 11

ISE: An Execution-Grounded Recipe for Multi-Turn OS-Agent Trajectories

arXiv:2606. 11520v1 Announce Type: cross Abstract: Training capable OS agents requires data that simultaneously captures structured user intents, multi-turn task delegation, and grounded tool execution--properties absent from existing datasets.

By Siyuan Luo, Nairong Zheng, Lin Zhou, Tiankuo Yao, Shengyou Yuan, Haojia Yu, Cong Pang, Jiapeng Luo, Lewei Lu

llms rag agents fine-tuning benchmarks

arXiv AI

Jun 11

When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control

arXiv:2605. 26418v2 Announce Type: replace-cross Abstract: A properly calibrated rule-based autoscaler can beat every one of six mainstream deep reinforcement learning (DRL) algorithms on cost across every workload we test - so when, if ever, does DRL actually help?

By Guilin Zhang, Chuanyi Sun, Kai Zhao, Shahryar Sarkani, John Fossaceca

agents reinforcement-learning benchmarks

Get the daily briefing