現代智能體系統中的自我改進：綜述

摘要

自我改進自主代理正從研究原型邁向部署系統。其核心目標是透過經驗實現可控演化或適應，且過程中僅需極少甚至無需人類介入。本綜述將現代自我改進代理視為一種能將經驗轉化為累積能力增益的自適應系統。我們提出一套系統級框架，將現代代理表述為基礎模型與操作支架（包含提示詞、記憶、工具及控制邏輯）耦合而成的配置。在此框架下，自我改進被形式化為一個自我誘導的更新運算元，該運算元獲取並提交對模型參數或支架元件的更新。我們依更新目標與驅動變化的信號對既有研究進行分類，隨後回顧應用案例並探討評估方法，最後以開放問題與未來方向作結。為便於查閱，相關技術更新可參考 https://github.com/selfimproving-agent/awesome-Self-Improving-Agents。

English

Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that convert experience into accumulated capability gains. We offer a system-level framework that represents a modern agent as a configuration coupling a foundation model with an operational scaffold of prompts, memory, tools, and control logic. Within this framework, self-improvement is formalized as a self-induced update operator that obtains and commits updates to model parameters or scaffold components. We organize prior work by update target and by the signals that drive change, then review applications and discuss evaluation, before closing with open problems and future directions. For convenience, we track technical updates on https://github.com/selfimproving-agent/awesome-Self-Improving-Agents.