Despite recent advances in CUAs, their development remains constrained by the limited diversity, controllability, and reliability of interaction data. Moreover, conventional optimization pipelines lac
CUA-Master uses nested recursive loops with open-world data and feedback-driven retries to continuously improve computer-use agents across diverse benchmarks.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
CUA-Master: Scaling Computer-use Agents through Open-World Recursive Improvements was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 11 checks on 2 different days. Its best position was #2 of 30, reached on Nov 3, 17:28 UTC. It dropped off the list after Nov 4, 03:28 UTC.
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Not at the moment. It was last on the list on Nov 4, 03:28 UTC, after 2 days of trending.
#2 on Hugging Face · Daily papers, on Nov 3, 17:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 2 days (11 checks) since.