智能体系统中的共同演化：迈向超越人类设计的自导演化

摘要

智能体系统日益被期望在部署后能够持续改进，然而单一实体的自我演化往往受限于静态学习环境，例如固定任务和固定反馈。本综述聚焦于智能体系统中的共演化——一种多组件的自我演化形式，其中多个智能体及其环境相互施加适应性压力。为系统组织现有文献，我们提出一个渐进式三阶段分类法，追踪系统如何逐步摆脱人类工程设计的约束。**智能体—智能体共演化**研究智能体如何通过动态同伴进行适应，包括对抗性、协作性和组织性适应。**智能体—环境共演化**将该演化循环拓展至随智能体变化的适应性任务、反馈和交互空间。**元共演化**进一步探索使演化机制本身成为可演化对象的可能性。我们还讨论了评估此类系统、在多组件间进行规模化扩展，以及保持日益自主的演化过程安全可控等方面的开放性挑战。本综述为构建超越固定人类设计路径、具有鲁棒性和开放性的智能体系统提供了统一基础。

English

Agentic systems are increasingly expected to improve after deployment, yet single-entity self-evolution is often bounded by a static learning context, such as fixed tasks and feedback. This survey focuses on co-evolution in agentic systems, a multi-component form of self-evolution in which multiple agents and their environment impose adaptive pressure on one another. To organize existing papers, we propose a progressive three-stage taxonomy that traces how the system gradually sheds human-engineered constraints. Agent--Agent Co-Evolution studies how agents adapt through dynamic peers, including adversarial, collaborative, and organizational adaptation. Agent--Environment Co-Evolution extends this loop to adaptive tasks, feedback, and interaction spaces that change with the agents. Meta Co-Evolution further explores the possibility of making the evolution mechanism itself evolvable. We also discuss open challenges in evaluating such systems, scaling them across multiple components, and keeping increasingly autonomous evolutionary processes safe and controllable. This survey provides a unified foundation for building robust and open-ended agentic systems that can improve beyond fixed human-designed paths.