AI 开始自己给自己当老师了
AI 一直在靠人类反馈变聪明，但反馈又贵又慢。这篇让 AI 用「世界模型」自己预测结果、自己挑该学什么，再自己检查更新有没有用——相当于它开始自己给自己当老师。在代码修复上，成功率从 26.3% 提到 31.2%;在实验室规划上，五轮自我迭代后分数翻倍；在化学反应优化上，同样预算下最优产率从 82% 提到 96%。它还不是你明天能用上的东西，但「AI 自己改进自己」从概念变成了有数据支撑的路线。
📄 原文摘要(英文)
Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model predictions may become unreliable as policies evolve. We introduce RSI-Anything, a domain-general framework built on JEPA-Anything that uses predictive world models to guide action and learning-data selection. Observed feedback drives policy or workflow improvement and conditional world-model adaptation, while component and compatibility checks govern the retention of updated policy-model pairs. Across diverse computational and scientific domains, RSI-Anything demonstrates substantial gains. In code repair, with the coder fixed, success increases from 26.3% with a static verifier to 29.2% through predictive selection and 31.2% after world-model adaptation, at comparable test usage. Graphical user interface (GUI) evaluations showed higher task completion, while average actions decrease by 39.4%. Across five successive policy versions, laboratory-planning scores more than double from 21.80% to 46.01%. In reaction optimization, RSI-Anything achieves 96.15% mean best observed yield versus 82.16% for Bayesian optimization under the same 40-experiment budget. An end-to-end immune-science application further connects workflow evolution and predictive modeling with therapeutic hypothesis generation. These results demonstrate how predictive world models and retained system updates enable recursive improvement across diverse environments.