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CUA-Universe:混合 GUI+CLI 环境的论文发布

真·混合 GUI+CLI 环境缺货:上交/浙大 CUA-Universe,OSWorld SR +16.8

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可复现 VM 上练 hybrid GUI+CLI,效率与成功率双升。

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学术论文·arXiv:2609.05374

CUA-Universe (arXiv:2609.05374)

“View PDF HTML (experimental) Abstract: Computer-use agents have advanced on benchmarks like OSWorld and AndroidWorld, but still act mostly t”

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Abstract:Computer-use agents have advanced on benchmarks like OSWorld and AndroidWorld, but still act mostly through the GUI, often producing inefficient trajectories. Real-world computer work is hybrid, combining visual-state inspection with precise, high-throughput command-line operations, so capable agents must coordinate both modalities over shared application state. Yet scalable hybrid environments remain scarce because supporting both GUI and CLI over real applications typically requires substantial manual engineering for each application. Existing agents also struggle to use the two interfaces complementarily: CLI-native agents lack visual perception for tasks involving interface state or layout, while GUI-native agents are inefficient for operations better executed through commands. We introduce CUA-Universe, a scalable environment-to-data pipeline that turns real desktop software into hybrid GUI+CLI environments. App-Forge adapts applications into reproducible VMs and command-line surfaces it discovers, wraps, or generates, scaling to 16 applications; Task-Weave synthesizes diverse hybrid tasks of controllable difficulty from reusable operations over seed files; and Path-Steer steers rollouts along efficient hybrid paths and harvests verified trajectories for post-training. Training on this data shifts behavior from inefficient GUI interaction and brittle CLI scripting toward effective GUI+CLI orchestration. Our 9B model improves both success and efficiency on CUA-Verse (Score +39.3 pts; -37% steps, -60% tokens), OSWorld (SR +16.8 pts; -57% steps, -44% tokens), and OSWorld-MCP (Score +7.84 pts; -27% steps, -30% tokens). CUA-Universe provides a scalable path toward more capable and efficient computer-use agents.
Comments: 21 pages, 9 figures
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.05374 [cs.AI]
  (or arXiv:2609.05374v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2609.05374

arXiv-issued DOI via DataCite

Submission history

From: Haoting Shi [view email]
[v1] Fri, 4 Sep 2026 17:26:18 UTC (16,281 KB)

来源:Agent Control Flash News · arxiv.org

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