Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing
AI 摘要
GEA提出了一种新的自进化Agent范式,通过群体进化和经验共享实现高效的持续改进。
主要贡献
- 提出Group-Evolving Agents (GEA) 范式
- 在经验共享的基础上实现自进化
- 显著优于现有自进化方法,并媲美人工设计的Agent框架
方法论
GEA将Agent群体作为进化单元,通过群体内的经验共享和复用,克服了树状进化中探索多样性利用不足的局限。
原文摘要
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.