自我进化智能体概览：迈向人工超级智能之路

摘要

大型語言模型（LLMs）已展現出強大的能力，但其本質上仍屬靜態，無法根據新任務、演進的知識領域或動態的互動情境調整其內部參數。隨著LLMs越來越多地被部署於開放式、互動性的環境中，這種靜態特性已成為關鍵瓶頸，亟需能夠即時適應性推理、行動與演化的代理者。這一範式轉變——從擴展靜態模型轉向開發自我演化代理者——激發了對能夠從數據、互動與經驗中持續學習與適應的架構與方法日益增長的興趣。本綜述首次系統且全面地回顧了自我演化代理者，圍繞三個基本維度——演化什麼、何時演化及如何演化——進行組織。我們探討了代理者組件（如模型、記憶、工具、架構）間的演化機制，按階段（如測試期間內、測試期間外）分類適應方法，並分析了指導演化適應的算法與架構設計（如標量獎勵、文本反饋、單代理與多代理系統）。此外，我們分析了專為自我演化代理者定制的評估指標與基準，強調了在編程、教育、醫療等領域的應用，並指出了在安全性、可擴展性及共同演化動力學方面的關鍵挑戰與研究方向。通過提供一個結構化框架以理解與設計自我演化代理者，本綜述為在研究與實際部署中推進適應性代理系統建立了路線圖，最終為實現人工超級智能（ASI）——代理者自主演化，在廣泛任務中達到或超越人類智能水平——照亮了道路。

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.

自我进化智能体概览：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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