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AI CURATED HOT STORY · 10/12/2026

PhAI Labs Unveils RSI-Anything: World Models Drive Cross-Domain Recursive Self-Improvement

1 reports archived0 independent sourcesupdated 10/12/2026, 12:30:00
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PhAI Labs introduces RSI-Anything, a framework leveraging world models to predict action consequences and refine strategies via real feedback, extending Recursive Self-Improvement beyond digital tasks. The method boosts success rates in code repair, GUI interaction, and chemical planning, with a 4B-parameter model doubling its planning score after five iterations.

LATEST/PhAI Labs introduces RSI-Anything, a framework leveraging world models to predict action consequences and refine strategies via real feedback, extending Recursive Self-Improvement beyond digital tasks. The method boosts success rates in code repair, GUI interaction, and chemical planning, with a 4B-parameter model doubling its planning score after five iterations.

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Total 1 reports · Latest first
  1. 机器之心 (微信公众号)T2·78 pts
    • RSI-Anything uses world models to predict outcomes before acting and refines predictors with real feedback for cross-domain evolution.
    • Code repair success rates rose from 26.3% to 31.2% solely by using world models to select solutions, without changing the base model.
    • A small 4B-parameter model outperformed larger reference systems in lab planning after five rounds of self-improvement.