Gen-Verse/RSI-Anything

RSI-Anything Unlocking Recursive Self-Improvement with World Models Predict outcomes. Learn from interaction. Improve the next decision. Framework · Domains · Results · Quick Start · Citation · 简体中文 RSI-Anything is a domain-general framework that turns world-model predictions and interaction feedback into recursive self-improvement. Built on JEPA-Anything, it connects candidate generation, prediction-guided selection, external evaluation and learning in a shared process. The paper applies this process to code repair, GUI interaction, laboratory planning, reaction optimization and immune science. Each domain supplies its own state, actions, prediction targets and feedback, while using the same cycle of proposing candidates, anticipating consequences, evaluating selected actions and retaining improvements for subsequent rounds. From the predictive foundation of JEPA-Anything to recursive self-improvement across domains. Propose → Predict → Select → Evaluate → Learn → Retain. A policy or workflow proposes actions; the world model predicts their consequences; selected actions receive external feedback. The resulting experience supports…