Open-ended self-improving agents can autonomously modify their own structural designs to advance their capabilities and overcome the limits of pre-defined architectures, thus reducing reliance on human intervention. We introduce Group-Evolving Agents (GEA), a new paradigm for open-ended self-improvements, which treats a group of agents as the fundamental evolutionary unit, enabling explicit experience sharing and reuse within the group throughout evolution. Unlike existing open-ended self-evolving paradigms that adopt tree-structured evolution, GEA overcomes the limitation of inefficient utilization of exploratory diversity caused by isolated evolutionary branches. We evaluate GEA on challenging coding benchmarks, where it significantly outperforms state-of-the-art self-evolving methods (71.0% vs. 56.7% on SWE-bench Verified, 88.3% vs. 68.3% on Polyglot) and matches or exceeds top human-designed agent frameworks (71.8% and 52.0% on two benchmarks, respectively). Analysis reveals that GEA more effectively converts early-stage exploratory diversity into sustained, long-term progress, achieving stronger performance under the same number of evolved agents. Furthermore, GEA exhibits consistent transferability across different coding models and greater robustness, fixing framework-level bugs in 1.4 iterations on average, versus 5 for self-evolving methods.

翻译：开放式自我改进智能体能够自主修改其自身结构设计以提升能力并突破预定义架构的限制，从而减少对人类干预的依赖。本文提出群体演化智能体这一开放式自我改进新范式，其以智能体群体作为基本演化单元，支持在演化过程中实现群体内显式的经验共享与复用。与现有采用树状结构演化的开放式自演化范式不同，GEA克服了因演化分支孤立导致的探索多样性利用效率低下的局限。我们在具有挑战性的代码生成基准测试中评估GEA，其显著优于当前最先进的自演化方法（在SWE-bench Verified上达到71.0%对比56.7%，在Polyglot上达到88.3%对比68.3%），并与顶尖人工设计的智能体框架性能相当或更优（在两个基准测试中分别达到71.8%和52.0%）。分析表明，GEA能更有效地将早期探索多样性转化为持续的长期进步，在相同演化智能体数量下实现更强性能。此外，GEA在不同编码模型间展现出稳定的可迁移性和更强的鲁棒性，平均仅需1.4次迭代即可修复框架级错误，而自演化方法平均需要5次迭代。