---
format: "aidr-story-markdown/v1"
id: "70d09bc47f49ef0402b04c099b688c3973879af1b7dfb1d1b40a18423b3bba70"
canonical_url: "https://aidr.today/70d09bc4?lang=en"
title: "CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-11-03T00:00:00.000Z"
category: "Frameworks"
topics: ["agent","framework"]
source_urls: ["https://huggingface.co/papers/2611.03126"]
summary: "CUA-Master is a nested double-loop recursive improvement framework. An outer world-data-model flywheel incorporates open-world corpus to guide data construction and optimization iterations. An inner flywheel couples open-world seeding and autocatalytic propagation to create reusable primitives for task synthesis."
---

# CUA\-Master: Scaling Computer\-use Agents through Open\-World Recursive Improvements

> [Open the canonical story](<https://aidr.today/70d09bc4?lang=en>)

**Published:** 2026-11-03T00:00:00.000Z
**Category:** Frameworks
**Topics:** agent, framework

## Summary

CUA\-Master is a nested double\-loop recursive improvement framework\. An outer world\-data\-model flywheel incorporates open\-world corpus to guide data construction and optimization iterations\. An inner flywheel couples open\-world seeding and autocatalytic propagation to create reusable primitives for task synthesis\.

## Sources

- [Story source](<https://huggingface.co/papers/2611.03126>)