arXiv AI August 18, 2026 By Pouya Ghiasnezhad Omran, Michael Zimmermann, Duncan Cambridge, Ashmita Kapoor, Tanya Dixit

Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback

arXiv:2608. 15591v1 Announce Type: new Abstract: Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 30

Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem

arXiv:2606. 29116v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly being adopted in low-code and no-code automation platforms, where non-expert users design workflows that combine natural language understanding with external services and APIs.

By Yutian Tang, Yuming Zhou, Huaming Chen

llms agents safety

arXiv AI

Jul 21

AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows

arXiv:2607. 16345v1 Announce Type: cross Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task.

By Tejas Singh Anand, Yuet Ying Christina Wang, Wanting Jiang, Steve Masson, Tian Zheng, Bingjie Zhou

llms agents safety

arXiv AI

Jul 7

AgentLTL: A Trace-Verification Framework for Measuring, Enforcing, and Training Procedural Compliance in Tool-Using LLM Agents

arXiv:2607. 02599v1 Announce Type: cross Abstract: Tool-using LLM agents are usually evaluated by final-answer correctness or LLM judges.

By La\"ila Elkoussy (LRE, EPITA), Julien Perez (LRE)

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Jun 16

GRACE-DS: a Guarded Reward-guided Agent Correction Environment in Data Science

arXiv:2606. 16000v1 Announce Type: cross Abstract: We introduce GRACE-DS, a Guarded Reward-guided Agent Correction Environment in Data Science for pre-deployment evaluation of LLM-powered AutoML agents.

By Aleksandr Tsymbalov, Danis Zaripov, Artem Epifanov, Anastasya Palienko

llms agents safety

arXiv AI

Aug 7

Agentic Software Issue Resolution with Large Language Models: A Survey

arXiv:2512. 22256v2 Announce Type: replace-cross Abstract: Software issue resolution aims to address real-world issues in software repositories based on natural language descriptions provided by users, and represents a key aspect of software maintenance.

By Zhonghao Jiang, David Lo, Zhongxin Liu

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 16

Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework

arXiv:2607. 13091v1 Announce Type: cross Abstract: LLM-based coding agents repeat the same classes of mistakes across sessions because they lack a mechanism to retain corrections from human review feedback.

By Aditya Aggarwal, Nahid Farhady Ghalaty

llms agents benchmarks

Related stories

Characterizing Large Language Model Agentic Workflows: A Study on N8n Ecosystem

AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows

AgentLTL: A Trace-Verification Framework for Measuring, Enforcing, and Training Procedural Compliance in Tool-Using LLM Agents

GRACE-DS: a Guarded Reward-guided Agent Correction Environment in Data Science

Agentic Software Issue Resolution with Large Language Models: A Survey

Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework