代理进化已成为改进程序,工作流程,和科学解决方案的强大范例，通过迭代生成候选,评估它们,并使用反馈来指导未来的搜索。然而，, 现有方法通常被实例化为模块化但严格的固定手工设计程序,，或者被实例化为灵活集成反馈但可能在长期演化中漂移的通用代理。这两种形式随着时间的推移积累了丰富的证据,，包括候选人,反馈,痕迹,和失败,，但缺乏一个稳定的界面来组织这些证据并修改推动未来演变的机制。我们通过将代理进化制定为交互式环境,来解决此限制，其中累积的进化上下文用作进程级状态。我们引入了 AEvo, 一个利用元编辑框架，其中元代理观察此状态，并且不是通过直接提出下一个候选, 而是通过编辑控制未来演化的过程或代理上下文来采取行动。这个统一的界面使 AEvo 能够引导基于程序和基于代理的演变,，使积累的证据可用于长期搜索。对代理和推理基准的实证评估表明，AEvo 优于五个进化基线,，比最强基线实现了 26 的相对改进。在三个开放式优化任务中，, AEvo 进一步优于四个进化基线，并在相同的迭代预算下实现了最先进的性能。
Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions by iteratively generating candidates, evaluating them, and using feedback to guide future search. However, existing methods are typically instantiated either as fixed hand-designed procedures that are modular but rigid, or as general-purpose agents that flexibly integrate feedback but can drift in long-horizon evolution. Both forms accumulate rich evidence over time, including candidates, feedback, traces, and failures, yet lack a stable interface for organizing this evidence and revising the mechanism that drives future evolution. We address this limitation by formulating agentic evolution as an interactive environment, where the accumulated evolution context serves as a process-level state. We introduce AEvo, a harnessed meta-editing framework in which a meta-agent observes this state and acts not by directly proposing the next candidate, but by editing the procedure or agent context that controls future evolution. This unified interface enables AEvo to steer both procedure-based and agent-based evolution, making accumulated evidence actionable for long-horizon search. Empirical evaluations on agentic and reasoning benchmarks show that AEvo outperforms five evolution baselines, achieving a 26 relative improvement over the strongest baseline. Across three open-ended optimization tasks, AEvo further outperforms four evolution baselines and achieves state-of-the-art performance under the same iteration budget.
科目: 人工智能 (cs.AI); 机器学习 (cs.LG)
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)