AI 开始自己进化自己，不再等人设计
现在的 AI 系统部署后还能继续变强，但通常被固定任务和反馈框死。这篇综述提出一个更野的方向：让多个 AI 互相施加压力、共同进化——不只是 AI 之间对抗或合作，连任务、反馈、甚至进化机制本身都可以跟着变。研究者把这种「多组件共同进化」分成三个阶段：AI 与 AI 互相适应、AI 与环境互相改变、最后连进化规则都能自我演化。它不是你明天能用上的东西，但它指向一个关键转变：AI 的改进路径正在从「人类设计好」走向「系统自己长出来」。
📄 原文摘要(英文)
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.