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 ·
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## Overview
**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.
## Framework
 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 learning and evaluation, and the retained system guides the next round.
 Explore the full framework and domain applications 
The framework connects prediction-guided interaction with persistent improvements in policies and scientific workflows. View the framework PDF.
## Domains
| Domain | Tasks and environments | Role of RSI-Anything | Hugging Face resources |
| :--- | :--- | :--- | :--- |
| **Code repair** | CodeContests, APPS Introductory, LiveCodeBench | Rank candidate programs using predicted execution outcomes and refine solutions with test feedback. | Resources |
| **GUI interaction** | MiniWoB++ and OSWorld | Connect observations, action proposals and interaction feedback to improve task completion. | Resources |
| **Laboratory planning** | LabHorizon F200 | Predict candidate plan quality and risk to select protocol-aligned laboratory action sequences. | Resources |
| **Reaction optimization** | Suzuki–Miyaura reaction screens | Rank catalyst, ligand, base and solvent choices using yield predictions, then use measured yields to guide subsequent experiments. | Resources |
| **Immune science** | Scientific-agent workflows and intervention analysis | Connect workflow evolution, immune-state forecasting and self-correction to intervention ranking and prospective hypothesis generation. | Resources |
Domain adapters connect the shared workflow to each task's states, action space and external feedback.
## Results
Results across domains. See benchmark settings for task definitions, comparison protocols and metric interpretation:
| Domain | Evaluation | RSI-Anything result |
| :--- | :--- | :--- |
| Code repair | 200 CodeContests tasks, three seeds | **31.2%** final hidden-test success |
| Code repair | APPS Introductory, 1,000 problems | **46.37%** final full-test s