AI 开始自己改自己了，人类只负责造第一台
「递归自我改进」不再是科幻设定，而是正在被拆成一张可执行的路由图。研究者提出一个叫 HCI 的指标，先指出当前大模型的问题：它们能学，但学到的改进不会沉淀成「改进能力本身」——你让它解数学题，它变强的是解题，不是「下次学得更快」。于是他们画了条路线：从「能自己执行改进」，到「能自己定改进策略」，再到「能自己找经验、自己适应环境」，最后是「改进改进本身」。不同领域进度不一样：软件工程里 AI 已经能自己写测试、修 bug，离自我改进最近；科学发现和具身智能还早。这不是你明天能用上的功能，但它划出了 AI 从工具变成「会进化的物种」的临界点——人类造出第一台能自我改进的 AI 后，剩下的版本号可能就不再由我们写了。
📄 原文摘要(英文)
Recursive self-improvement (RSI) enables AI systems to turn experience and feedback into persistent changes that improve both their capabilities and the process of future improvement. We first use the Headroom-Closed Index (HCI) to reveal the problems of existing LLMs, then introduce the RSI concept and its development roadmap: from improvement-execution autonomy, improvement-strategy autonomy, experience-acquisition autonomy, and environment-adaptation autonomy, to recursive meta-improvement. Next we examine RSI across scenarios (e.g., scientific discovery, embodied intelligence, software engineering), highlighting their distinct requirements and development speeds. Drawing on diverse industry practices and preliminary empirical evidence, we connect RSI research with practical systems and identify key challenges to achieving genuine RSI.