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arXiv Computation and Language By Taoyong Cui, Pinxian Zeng, Wanghan Xu, Hao Xiao, Shuhan Xue, Qiang Gao, Wanli Ouyang, Pheng Ann Heng, Zhenfei Yin, Yingcheng Wu, Ling Yang

RSI-Anything: Unlocking Recursive Self-Improvement with World Models

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RSI-Anything is a domain-general framework that leverages predictive world models to guide action and learning-data selection for recursive self-improvement. It uses observed feedback to improve policies or workflows and adapts world models conditionally, while component and compatibility checks control the retention of updated policy-model pairs. Across multiple domains—including code repair, GUI tasks, laboratory planning, reaction optimization, and immune‑science applications—RSI-Anything shows substantial performance gains, such as higher success rates, reduced actions, doubled planning scores, and superior yield compared to baseline methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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