AI 自我改进终于有了能画出来的坐标系
「递归自我改进」这个词被喊了很多年，但没人能说清它到底指什么——是现象、机制，还是愿景？这篇论文给了第一张能画出来的地图：把 AI 系统放在两根轴上比较，一根看「改进自己的机制在系统内部还是外部」，另一根看「衡量它的标准是外部给的还是自己定的」。两根轴一交叉，经典 AI 和会自我进化的 AI 就不再是两种东西，而是同一个学习循环的两个极端。更关键的是，它让「自我改进会失控」这种模糊的担忧，变成了可以逐条指认的具体缺陷。这不是你明天能用的工具，但它是第一次有人把「AI 自我进化」从玄学变成可讨论的工程问题。
📄 原文摘要(英文)
When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these emerging instances exists. Its counterpart in the classical realm, iterative policy improvement, is characterized by generalized policy iteration (GPI), a framework of broad applicability with well-understood theoretical properties, but only where the update principle and the evaluation base lie outside the agent. In this paper, we propose Generalized Agent Iteration (GAI), a formal framework that describes iterative policy improvement and RSI as two cases of a single learning paradigm. GAI defines the agent as a configuration of modifiable components within a system and models the learning process as a cycle of agent evaluation and agent improvement. Two pivotal dials then distinguish the instances: whether the improving mechanism is part of the agent and whether the standard it is measured against is grounded outside it. The former dial delineates the boundary between GPI and RSI, and the latter determines a system's polarity as anchored, goal drift, or fully self-referential. Moreover, we use these coordinates to place existing systems on the same two axes and make the defects of recursive self-improvement statable one condition at a time. We see this paper as a first step toward exploring a formal characterization of RSI that rests on the classical account, makes existing systems comparable, and provides a principled basis for analyzing and designing new ones.