The paper introduces an online skill‑evolution framework that transforms interaction traces and evaluator feedback into a persistent, versioned library of reusable procedures for computer‑use agents. By executing each iteration against a frozen library snapshot, the system updates skills without altering the underlying model parameters. Experiments across four OSWorld domains show that the evolving library consistently outperforms an empty‑library baseline, with gains ranging from 5.7 to 18.6 percentage points, while also revealing domain‑specific temporal stability and challenges in skill retrieval and revision.
By Longtao Hu, Xiao Liang, Linchao Zhu
Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification. Yet current evaluations do not isolate this form of transfer.
AgentEvolver is a system that enables agents to develop reusable capabilities while executing tasks, without altering the underlying foundation model. It uses eight entity families to expose operations, methods, agents, control flow, interfaces, and state for revision, coordinated by a shared Runtime and persistent planning. In evaluations on SWE-bench Pro Public, it achieved an 82.08% resolution rate, demonstrating retained capabilities across subsequent website, game, and research tasks, though some objectives remained incomplete.
By Wentao Zhang, Fuchao Yang, Yilei Zhao, Xinrun Wang, Bo An
The paper introduces ReScopeBench, a benchmark designed to evaluate how agents manage the lifecycle of experience in evolving environments. It differentiates between structural experience—knowledge about how an environment is organized—and rule experience—principles that guide decisions across tasks. Using an experience-first construction approach, the study tracks how agents acquire, reuse, and revise experience across task streams, revealing that while retained experience boosts performance, agents often fail to keep it current when environments change.
By Shijue Huang, Hangyu Guo, Hengheng Zhang, Guo An, Zhitao He, Qi Tian, Yiren Feng
arXiv:2607. 05202v1 Announce Type: new Abstract: Agent self-evolution in long-horizon LLM systems is largely procedural: useful experience is not merely stored information, but reusable procedures for searching, debugging, and verification.
By Xingze Gao, Chuanrui Hu, Hongda Chen, Pengfei Yao, Zhao Wang, Yi Bai, Zhengwei Wu, Yunyun Han, Xiaofeng Cong, Jie Gui, Yafeng Deng, Teng Li
arXiv:2609.24663v1 Announce Type: new
Abstract: Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As t...
By Hongqiang Lin, Chao Liu, Xiaofan Bai, Xuan Jin, Yuhong Li, Nenggan Zheng, Xipeng Cao
Darwin‑Claw is a new framework for large language model agents that enables environment‑adaptive, self‑evolving behavior. It systematically distills episodic experiences into prototype vectors through a hierarchical experience consolidation system, providing context‑sensitive perception patches and explicit optimization signals for skill refinement. The method is deployable with mainstream agent systems and, according to experiments on WildClawBench, delivers significant performance gains while maintaining controllability and stability.
By Mingze Yin, Xiaohan Wang, Dian Li, Longfei Luo, Jiahong Yan, Yiheng Zhu, Chang-Yu Hsieh
The paper investigates how agent harness evolution supports recursive self‑improvement without changing model weights, assessing four methods across general and coding tasks. Repeated tests and component interventions show that all evolved agents acquire new tasks while losing some previously mastered ones, with reliability gains sometimes offset by higher deployment costs. The study highlights that the highest‑scoring agent does not always deliver the greatest mean improvement, and that stricter admission criteria can eliminate useful alternatives; it proposes preservation‑aware evaluation and a follow‑up tool, EvoAudit, to refine agent design.
By Xiaobo Wang, Zimu Wang, Siyu Ye, Jiaqi Li, Min Tang, Jun Bai, Yuling Shi, Ruobing Wang, Jingjing Zhang, Chaofan Wang, Kai Wu, Mengnan Qi, Kai Cai, Xiaodong Gu, Qi Liu, Zilong Zheng
arXiv:2605.24539v2 Announce Type: replace
Abstract: Harness evolution enables frozen language model agents to adapt to unfamiliar tasks by modifying the external programs that govern their behavior....
By Lirong Che, Yuzhe yang, Peiwen lin, Xu Cao, Chuang wang, Xueqian wang, Jian su
Self-evolving agents convert interaction feedback into persistent artifacts, such as memories or skills, which in turn guide subsequent decisions. As these artifacts are iteratively updated throughout...
Language agents are expected to solve increasingly complex tasks, creating a growing need for continual improvement. One promising approach is to evolve the agent harness, the software that governs to...
The paper introduces ScholarEvolve, a framework that evolves the software harness of language agents by automatically incorporating insights from recent research papers. It organizes harness improvements into functional modules, uses topic modeling to identify distinct strategies, and evaluates combinations to boost task performance. Experiments show significant gains on AppWorld and Tau2-Bench, raising Qwen3.5-27B completion rates from 49.6% to 63.6% and GPT-5.4-mini pass@1 from 72.7% to 81.9%.
By Jingbo Yang, Kwei-Herng Lai, Xiaowen Wang, Yaar Harari, Evgeniy Gabrilovich, Shiyu Chang