NorthCUA Technical Report: Turbocharging Computer-Use Agents through Experience at Scale
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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arXiv:2607. 11185v1 Announce Type: new Abstract: Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution.
arXiv:2610.33907v1 Announce Type: new Abstract: Computer-using agents are increasingly capable of performing complex tasks in the digital world. However, high-quality interaction trajectories remain...
Mage‑VL‑Cowork is a 4‑billion‑parameter hybrid computer‑use agent that integrates visual GUI interaction with code and terminal execution in a single policy. It is trained via a progressive five‑stage curriculum—mid‑training, supervised fine‑tuning, and reinforcement learning—starting from a multimodally aligned checkpoint. The training pipeline, Mage‑CUA‑Forge, automates task generation and skill conditioning, producing nearly 300,000 desktop and web tasks while cutting per‑task costs by 82.6%, and the resulting model achieves 66.0% on OSWorld‑Verified, setting a new state‑of‑the‑art for its size.
CUA-Master introduces a nested double-loop recursive improvement framework to scale computer-use agents. The outer flywheel continuously integrates open-world data and model weaknesses to guide data construction and optimization, while the inner flywheel mines instructional videos for seed tasks and propagates them into reusable primitives for diverse task synthesis. The system also employs a feedback-guided snapshot-rollback retry mechanism for reliable trajectory collection, achieving competitive performance on multiple benchmarks and demonstrating continuous gains across iterations.
OpenClaw has emerged as a leading agent framework for complex task automation, yet it faces insufficient cross-platform GUI interaction support and a well-built self-evolution mechanism. These flaws limit its adaptation to diverse device ecosystems and prevent performance improvements through continuous learning from execution experience.
arXiv:2610.16319v1 Announce Type: new Abstract: Real-world hybrid web tasks need both information gathering and page interaction, yet search-based and operation-based agents are developed and evaluat...