现代智能体系统中的自我改进：综述

摘要

自我改进自主代理正从研究原型走向部署系统。其核心目标是通过经验实现可控演化或适应，且所需人工干预极少甚至为零。本综述将现代自我改进代理视为自适应系统，这类系统能将经验转化为持续累积的能力提升。我们提出一个系统级框架，将现代代理表征为一种将基础模型与操作支架（包含提示、记忆、工具及控制逻辑）相耦合的配置。在此框架下，自我改进被形式化为一种自诱导更新算子，该算子能够获取并提交对模型参数或支架组件的更新。我们按更新目标及驱动变化的信号对既有研究进行归类，随后回顾应用场景并讨论评估方法，最后以开放问题与未来方向作结。为便于追踪，相关技术更新可查阅 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.