The paper introduces BiPA, a method to evaluate skill optimizers that rewrite natural‑language procedures for agents performing long‑horizon tasks. BiPA replays the optimizer’s acceptance rule on identical candidates to isolate selection effects and records each episode as a bipartite graph of turns and read text, revealing that learned skills only marginally outperform hand‑written seeds and that many gains vanish on test data. The study finds optimizers add extra input tokens and turns, and proposes four design principles for more reliable skill editing.
arXiv:2610.35434v1 Announce Type: new
Abstract: Recursive self-improvement (RSI), in which a system also improves the way it improves, is a long-standing aspiration on the path to AGI. Language agent...
By Wangcheng Tao, Zhanglu Yan, Han Wu, Weng-Fai Wong
SkillNEO introduces a non‑elitist evolutionary framework for refining agent skills, allowing lower‑reward versions to receive further edits and preserve useful partial repairs. By separating the selection of a version to use from the selection of versions to improve, the method identifies which steps to keep and which failures to fix, even revisiting earlier versions with new evidence. Experiments on 86 SkillsBench tasks across eight domains show significant reward gains for GPT‑5.4 Mini and substantial improvements when the evolved skills are reused by stronger models such as GPT‑5.5 and GPT‑5.6 Luna.
By Enze Ma, Chunyu Miao, Yankai Chen, Jie Yang, Liancheng Fang, Kening Zheng, Hanrong Zhang, Muyao Xu, Xinle Yu, Chengze Li, Wei-Chieh Huang, Ziwei Ren, Yunhao Du, Zhixiang Shen, Xue Liu, Philip S. Yu, Zhen Wang
SkillGate is a method that trains agents to select the correct skill from a large slate during an episode by separating credit signals for skill selection and execution. It addresses the problem of selector credit starvation, where traditional outcome-rewarded RL fails to give sufficient credit to the skill-naming tokens, especially in long-horizon tasks. Experiments on five benchmarks show that SkillGate improves a 9B policy’s success rate from 40.8% to 53.2%, reduces exposure to misleading candidates, and requires fewer skill reads.
By Qingyao Li, Wenxiang Jiao, Shuai Shao, Kangning Zhang, Yuan Lu, Yi Guo, Weiwen Liu, Weinan Zhang, Yong Yu
The paper introduces VERSE, a Verified Self‑Evolving optimizer that enhances LLM agent harnesses by allowing the optimizer to test edits, replay failures, and perturb steps while tracking fixes and regressions. VERSE builds its own tools for failure analysis, verification, training audits, and workflow control, and uses this feedback to revise the harness’s prompts, skills, tools, hooks, and notes without changing model weights. In experiments across five executors and multiple languages, VERSE improves all evaluated harness optimizers, achieving higher accuracy on held‑out and out‑of‑distribution tasks compared to the strongest baselines.
By Zekai Wang, Yingqiang Ge, Zekun Wang, Hai Wang, Yuhui Xu, Joshua Frandsen, Shancong Fu, Ashia C. Wilson, Chandan K. Reddy
arXiv:2608. 20318v1 Announce Type: new Abstract: Recursive self-improvement (RSI) asks whether an AI system can improve the process that produces AI systems, so that the next system inherits the improvement.
By Yizhe Chi, Wenyi Li, Deyao Hong, Xiaoqiu Wang, Mingju Gao, Kaisen Yang, Bingxiang He, Youjie Zheng, Calvin Xiao, Qinhuai Na