---
format: "aidr-story-markdown/v1"
id: "5c7b003776a9d552fa404d2ce38e2f2613f30f7bf2eae0bd5fdb0f7c98c8344a"
canonical_url: "https://aidr.today/5c7b0037?lang=en"
title: "RSI-Anything: Unlocking Recursive Self-Improvement with World Models"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-10-12T00:00:00.000Z"
category: "Frameworks"
topics: ["agent","framework"]
source_urls: ["https://huggingface.co/papers/2610.13912"]
summary: "RSI-Anything is a domain-general framework that uses predictive world models to guide action and learning-data selection for recursive self-improvement. In code repair, success increased from 26.3% with a static verifier to 31.2% after world-model adaptation."
---

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

> [Open the canonical story](<https://aidr.today/5c7b0037?lang=en>)

**Published:** 2026-10-12T00:00:00.000Z
**Category:** Frameworks
**Topics:** agent, framework

## Summary

RSI\-Anything is a domain\-general framework that uses predictive world models to guide action and learning\-data selection for recursive self\-improvement\. In code repair, success increased from 26\.3% with a static verifier to 31\.2% after world\-model adaptation\.

## Sources

- [Story source](<https://huggingface.co/papers/2610.13912>)