能動系統中的共同演化：邁向超越人類設計的自我導向演化

摘要

代理系統日益被期望在部署後持續改進，然而單一實體的自我進化往往受限於靜態的學習情境，例如固定的任務與回饋。本綜述聚焦於代理系統中的共同進化，這是一種多組件形式的自我進化，其中多個代理及其環境彼此施加適應壓力。為組織現有文獻，我們提出一個漸進式的三階段分類架構，用以追蹤系統如何逐步擺脫人為設計的限制。代理—代理共同進化研究代理如何透過動態同儕互動而適應，包括對抗性、協作性與組織性適應。代理—環境共同進化將此循環延伸至隨代理一同變化的適應性任務、回饋與互動空間。後設共同進化則進一步探索使進化機制本身具備可進化性的可能性。我們亦討論了在評估此類系統、將其擴展至多個組件，以及確保日益自主的進化過程安全且可控等方面的開放性挑戰。本綜述為建構穩健且開放式的代理系統提供了統一的基礎，使系統能夠超越固定的人為設計路徑持續改進。

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.