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CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements — arXiv:2611.03126

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03126
License (as archived): CC0-1.0
Archived: 2026-11-03T10:24:11.548Z
SHA-256 of the archived text: a683bc453f09e2369eab99c2bc2351fa0b2a86ecb019a054a1c7c13bfa44b72d
Stable URL of this page: https://metraipolis.com/documento/dc_f0636df5a809

How to cite

CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements — arXiv:2611.03126 — arXiv (en), https://arxiv.org/abs/2611.03126 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f0636df5a809 (Metraipolis archive dc_f0636df5a809, sha256 a683bc453f09e2369eab99c2bc2351fa0b2a86ecb019a054a1c7c13bfa44b72d)

BibTeX

@misc{dcf0636df5a809,
  title = {CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements — arXiv:2611.03126},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03126},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f0636df5a809; sha256 a683bc453f09e2369eab99c2bc2351fa0b2a86ecb019a054a1c7c13bfa44b72d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03126. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Despite recent advances in CUAs, their development remains constrained by the limited diversity, controllability, and reliability of interaction data. Moreover, conventional optimization pipelines lack iterative feedback, while existing recursive improvement approaches often rely on self-generated experiences, limiting ability expansion and yielding diminishing returns. To address these challenges, we introduce CUA-Master, a nested double-loop recursive improvement framework. At the system level, an outer world-data-model flywheel continuously incorporates open-world corpus and leverages evolving model weaknesses to guide subsequent data construction and optimization iterations. At the data level, an inner flywheel couples open-world seeding and autocatalytic propagation. The former mines authentic workflows as seed tasks from instructional videos, and employs visual-semantic dual-path trajectory reconstruction; The latter transforms these seeds into reusable primitives for controllable synthesis of diverse, complex, and verifiable tasks. To enable efficient and reliable trajectory collection, we further introduce a feedback-guided snapshot-rollback retry mechanism in various real environments. Extensive experiments demonstrate that CUA-Master-27B achieves competitive performance across GUI-only and GUI-CLI hybrid benchmarks, scoring 80.2 on OSWorld Verified, 36.9 on OSWorld 2.0, 43.9 on WeaveBench, and 25.0 on Agents' Last Exam. Our framework also substantially improves the quality and efficiency of data synthesis, and exhibits continuous gains across recursive iterations.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.03126 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:19:49.936Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).