arXiv Machine Learning By Chaoyun Zhang, Bingxuan Li, Pinxue Guo, Yangfan Gao, Zikun Cai, Tianci Xie, Hui Li, Yuqi Huang, Yifei Cao, Bo Mao, Tian Tang, Qichang Zheng, Tongtong Liu, Xiaoxing Wang, Jinxiu Liu, Lizhi Lin, Zhou Shao, Maosen Zhou, Miao Zheng, Shuo Zhang, Yangji He, Kepeng Lei, Ning Zhao, Wei Shen, Zhenjiang Jin, Xinyue Zhang, Peng Sun, Zhenhua Han, Rui Zheng, Hang Yan

NorthCUA Technical Report: Turbocharging Computer-Use Agents through Experience at Scale

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arXiv AI
Jul 14

SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

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.

By Bowen Lv, Xiao Liu, Yanyu Ren, Hanyu Lai, Bohao Jing, Hanchen Zhang, Yanxiao Zhao, Shuntian Yao, Jie Tang, Yuxiao Dong
arXiv Machine Learning
4d ago

Mage-VL-Cowork: A Full Training Recipe to Learn Hybrid Computer Use From Evolved Harnesses

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.

By Xiaoyi Zhang, Haoqing Wang, Xinjie Zhang, Xiang Ming, Xing Wang, Senqiao Yang, Kaichen Zhang, Zhaoyang Jia, Andong Tan, Xinjiang Wang, Jinghao Guo, Yifei Shen, Xiao Li, Peng Zhang, Xun Guo, Jiahao Li, Shicheng Zheng, Jinglu Wang, Zongyu Guo, Wenxuan Xie, Bin Li, Yan Lu
Hugging Face Trending Papers
4d ago

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

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.

Hugging Face Trending Papers
Jul 14

KnowAct-GUIClaw: Know Deeply, Act Perfectly, Personal GUI Assistant with Self-Evolving Memory and Skill

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.