自进化智能体研究综述：迈向人工超级智能之路

摘要

大型语言模型（LLMs）已展现出强大的能力，但其本质上仍属静态，无法根据新任务、演进的知识领域或动态交互情境调整内部参数。随着LLMs越来越多地部署于开放、互动的环境中，这种静态特性已成为关键瓶颈，亟需能够实时适应、推理和进化的智能体。这一范式转变——从扩展静态模型到开发自我进化智能体——激发了人们对支持从数据、交互和经验中持续学习与适应的架构和方法的日益关注。本综述首次系统全面地回顾了自我进化智能体，围绕三大基础维度展开：进化什么、何时进化以及如何进化。我们探讨了智能体各组件（如模型、记忆、工具、架构）的进化机制，按阶段（如测试期间、测试间）分类适应方法，并分析了指导进化适应的算法与架构设计（如标量奖励、文本反馈、单智能体与多智能体系统）。此外，我们分析了专为自我进化智能体设计的评估指标与基准测试，强调了在编程、教育、医疗等领域的应用，并指出了在安全性、可扩展性及协同进化动力学方面的关键挑战与研究方向。通过提供一个理解与设计自我进化智能体的结构化框架，本综述为推进适应性智能体系统的研究与实际部署绘制了路线图，最终为迈向人工超级智能（ASI）的实现铺平道路，在此愿景下，智能体自主进化，在广泛任务中达到或超越人类智能水平。

English

Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static, unable to adapt their internal parameters to novel tasks, evolving knowledge domains, or dynamic interaction contexts. As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck, necessitating agents that can adaptively reason, act, and evolve in real time. This paradigm shift -- from scaling static models to developing self-evolving agents -- has sparked growing interest in architectures and methods enabling continual learning and adaptation from data, interactions, and experiences. This survey provides the first systematic and comprehensive review of self-evolving agents, organized around three foundational dimensions -- what to evolve, when to evolve, and how to evolve. We examine evolutionary mechanisms across agent components (e.g., models, memory, tools, architecture), categorize adaptation methods by stages (e.g., intra-test-time, inter-test-time), and analyze the algorithmic and architectural designs that guide evolutionary adaptation (e.g., scalar rewards, textual feedback, single-agent and multi-agent systems). Additionally, we analyze evaluation metrics and benchmarks tailored for self-evolving agents, highlight applications in domains such as coding, education, and healthcare, and identify critical challenges and research directions in safety, scalability, and co-evolutionary dynamics. By providing a structured framework for understanding and designing self-evolving agents, this survey establishes a roadmap for advancing adaptive agentic systems in both research and real-world deployments, ultimately shedding lights to pave the way for the realization of Artificial Super Intelligence (ASI), where agents evolve autonomously, performing at or beyond human-level intelligence across a wide array of tasks.

自进化智能体研究综述：迈向人工超级智能之路

A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence

摘要

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