外观
论文arXiv 2610.1391219 天前
RSI-Anything：用世界模型解锁递归自我改进
原文链接：RSI-Anything: Unlocking Recursive Self-Improvement with World Models（arXiv 2610.13912 · 发布日期：2026-10-09）
RSI-Anything: Unlocking Recursive Self-Improvement with World Models
提出基于JEPA-Anything的RSI-Anything，用预测世界模型指导行动与学习数据选择，并在多类计算和科学领域验证递归改进。
核心要点
- 递归自我改进需将交互经验转化为持续提升，但外部反馈昂贵且世界模型可能随策略演化失准。
- 框架用预测世界模型指导行动和学习数据选择，并依据观测反馈改进策略或工作流及适配世界模型。
- 实验显示代码修复成功率提升，GUI任务完成率更高且平均动作减少39.4%。
- 实验室规划分数翻倍，反应优化平均最佳产率达96.15%，并连接免疫科学假设生成。
验证范围与局限：验证范围限于摘要所述的代码修复、GUI、实验室规划、反应优化和免疫科学等计算与科学领域。
英文摘要 (Abstract)
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.
作者：Taoyong Cui, Pinxian Zeng, Wanghan Xu, Hao Xiao, Shuhan Xue, Qiang Gao, Wanli Ouyang, Pheng Ann Heng, Zhenfei Yin, Yingcheng Wu
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