Agent自我进化能力全景综述：从一次学会到持续成长（2023-2025）
This story is from 2026-09-04. It is preserved in the archive; the latest stories are on the live feed.
Agent 自我进化能力全景综述：从一次学会到持续成长（2023-2026） 摘要： 大语言模型（LLM）本质上是一套静态系统——训练完成，能力定格。但现实世界的任务从不会自我重复。当 Agent 开始具备"知道自己不知道什么"并主动改造自己的能力时，AI 的范式正在发生根本性转变。本文系统梳理 2023-2026 年 Agent 自我进化的核心方法，围绕" 进化什么 "" 何时进化 "" 如何进化 "三大维度展开，涵盖模型层、记忆层、工具层、架构层的进化机制，并介绍代表性系统（Voyager、MUSE、DGM、HyperAgents、RoboPhD 等）。最后讨论当前挑战与未来方向，为构建…
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- 2026-09-04 13:43 · DEV Community — Machine Learning
Agent自我进化能力全景综述：从一次学会到持续成长（2023-2025）
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