arXiv:2610.22984v1 Announce Type: new
Abstract: Experience-driven self-evolution offers a promising path for large language model (LLM) agents to continually improve through interaction. Yet existing...
By Weixiang Zhao, Yichen Zhang, Yanyan Zhao, Jinming Bai, Bin Liang, Ruifeng Xu, Bing Qin, Ting Liu
arXiv:2607. 26598v1 Announce Type: cross Abstract: Large language model (LLM) agents may recover from a failure within an episode or after a retry, yet the same execution failure can recur in later tasks because post-episode feedback rarely revises the persistent harness that guides future interactions.
By Yuetian Du, Yucheng Wang, He Xu, Jiexu Xu, Shanwen Tan, Bing Zhao, Boyu Yang, Zhijie Xu, Ming Kong, Hu Wei, Jie Liu, Qiang Zhu
LegoEnv introduces a new paradigm for creating agentic environments by recombining reusable atoms—intent, rule, data, and server—rather than building each environment from scratch. The system automatically evolves these atoms when an environment fails to provide reliable feedback, leading to a significant increase in solvable environments from 71.6% to 88.3% after one evolution round. Experiments on AutomationBench show that training on LegoEnv environments doubles the performance of large language models, with SFT boosting Qwen3.6-27B from 16.0% to 32.0% and RL improving Qwen3.8-27B from 37.6% to 40.3%.
whyItMatters":"LegoEnv’s modular, self‑evolving approach enables scalable, generalizable environment construction that directly enhances the training and performance of LLM agents on diverse tasks."
By Zhuosong Jiang, Chengxu Wu, Junqi Liu, Yujie Yi, Siqin Wang, Zhixuan Huang, Jinqian Jiang, Lijun Xiao, Jianxun Li, Yongyang Pan, Jieran Wang, Shiqi Shen, Fei Liu, Xinyu Lin
VeriEvolve is an evaluator-first evolution framework that first builds an executable evaluator from task instructions, then uses this evaluator as a frozen fitness signal while evolving agents. The system generates challenge states and counterfactual test cases to refine the evaluator across rounds, producing 30 evaluators for 30 MCPMark Filesystem tasks with perfect agreement on generated suites and high agreement on held-out states. Evaluator-guided evolution significantly improves externally verified success rates for GLM-5.2 and Claude, and cross-environment studies reveal complementary limits from observability, state representation, and search/execution capacity.
By Yueshen Li, Kamer Ali Yuksel, Gokhan Tur, Dilek Hakkani-T\"ur, Jo\~ao Pedro Maia
arXiv:2610.17609v1 Announce Type: new
Abstract: Training-free self-improvement lets an agent use feedback to revise the response it is about to return and to update the harness state that shapes how...
By Tianyi Xu, Yaolun Zhang, Yiran Wu, Joe Nguyen, Shengyu Dai, Saba Moeinizade, Zhenwen Shao, Qingyun Wu, Huazheng Wang
arXiv:2608. 07545v1 Announce Type: cross Abstract: An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and control flow.
By Yifan Zhang, Yutong Dai, Juntao Tan, Luyu Yang, Rishi Mullur, Thai Hoang, Zhiyuan Hu, James Zhu, Phil Mui, Silvio Savarese, Ran Xu, Zeyuan Chen