The paper introduces Population-Centered Evolution (PCE), a new principle for improving self‑improving AI agents by leveraging patterns of success, failure, and prior modifications across the entire population rather than focusing solely on selected parent agents. PCE distills task records and modification histories into a population block that guides enhancements to a chosen parent’s harness. Experiments show that PCE raises success rates on SWE‑bench Verified and Polyglot benchmarks, narrowing the gap between population coverage and best‑agent performance.
By Shuo Yan, Ningning Hou, Haoyu He, Haochen Pei, Shang Gao
Self-improving AI systems can revise the designs that govern their own behavior. Yet existing approaches center improvement on selected parent agents and their evolutionary experience, leaving the bro...
The paper introduces the concept of Evolutionary Safety for recursive self-improving AI, focusing on how safety properties evolve as an AI system and its successors change. It identifies key risks such as intent drift, error accumulation, and safety-property erosion, and presents a taxonomy covering agent state, model state, evaluation, environment, and update mechanisms. The authors propose methods for discovering and evaluating evolutionary risks, and outline governance principles for modification, selection, authorization, provenance, and recovery, while highlighting open problems for maintaining safety in persistent, adaptive, and recursively self-improving systems.
By Chang Gong, Jingping Bi, Di Yao, Xinjian Liang, Chao Xiang, Ruijie Guo
arXiv:2609.00129v1 Announce Type: cross
Abstract: The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem they were trained on drifts. This is a nea...
By J. M. Diederik Kruijssen (Allora Foundation)
FREEEVOLVE automates the optimization loop used by agent evolvers, allowing the system to decide what to test, how much evidence to gather, which candidates to pursue, and when to stop, all within specified resource limits. By treating these decisions as an editable evolution skill and improving them through meta‑evolution, the method learns a capability that replaces hand‑crafted search loops. Experiments on several benchmarks show that FREEEVOLVE improves primary metrics by an average of 13.6 points and that meta‑evolved skills add further gains, indicating transferability across environments.
By Lecheng Kong, Like Hui, Nikos Kanakaris, Prithwish Jana, Sahika Genc, Narayanan Sadagopan
arXiv:2606. 26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains.
By Alex Iacob, Andrej Jovanovi\'c, William F. Shen, Daniel Burkhardt, Meghdad Kurmanji, Nurbek Tastan, Lorenzo Sani, Niccol\`o Alberto Elia Venanzi, Ambroise Odonnat, Zeyu Cao, Bill Marino, Xinchi Qiu, Nicholas D. Lane
The paper introduces Auto‑Robotist, a self‑evolving large language model (LLM) agent that transforms evolutionary robot design search traces into an explicit natural‑language skill library. Each skill records a structural archetype, evidence‑grounded rules, and supporting designs, enabling the agent to retrieve and condition LLM edits during search while still using a genetic algorithm for exploration. Experiments on seven EvoGym tasks show that Auto‑Robotist outperforms standard genetic algorithms, especially when transferring learned skills to larger design spaces.
By Yunfei Wang, Xiaohao Xu, Yang Li, Xiaonan Huang
EvoIn is a fine‑tuning framework that improves agent decision‑making by evolving new procedures from execution traces, validating them in the harness, and then rewriting the traces to internalize the logic into the model. The approach removes reliance on the evolved harness at inference time, enabling stronger decision‑making that generalizes to unseen tasks. Experiments show consistent gains, raising in‑domain pass rates by 10.9 points and out‑of‑domain by 9.2 points, and the method works across different model families.
By Shihan Dou, Shaofan Liu, Zhonghang Lu, Jiahang Lin, Shichun Liu, Binghai Wang, Jiajie Jin, Guanting Dong, Tao Gui, Qi Zhang, Xuanjing Huang
arXiv:2610.29784v1 Announce Type: new
Abstract: Most contemporary artificial intelligence (AI) systems are released with frozen parameters and improve through external updates, whereas an ideal syste...
By Zhipeng Chen, Yunsen Liang, Yanxi Chen, Hongkang Zhao, Lingyun Feng, Ruiyang Ren, Guoxin Chen, Jia Deng, Feng Chang, Chuan Hao, Ran Tao, Jian Yang, Ji-Rong Wen, Bryan Dai, Wayne Xin Zhao
The paper introduces RuHarness, a framework that co‑evolves rubrics and harnesses to enable continual self‑improvement of autonomous agents that rely on large language models. RuHarness automatically discovers and reassesses behavioral criteria from execution trajectories, prioritizes unresolved weaknesses, and uses these insights to guide targeted harness updates while keeping the task objective unchanged. Experiments across varied agent tasks and backbone models demonstrate that RuHarness outperforms existing harness‑evolution baselines, especially on unseen held‑out tasks, and that its adaptive rubric prioritization supports sustained improvement and stable generalization over successive evolution rounds.
By Hao Chen, Yukun Yan, Ziyu Han, Ye He, Chuanzhe Guo, Shangzhan Li, Qi Shi, Qingfu Zhu, Maosong Sun, Wanxiang Che
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
arXiv:2610.13991v1 Announce Type: cross
Abstract: Self-evolving agents improve through repeated cycles of modification and evaluation. However, existing methods receive feedback on modifications only...
By Zixuan Xie, Chang Xu, Yuhua Jiang, Li Zhao