OpenGCUA 致力打造通用電腦操作的全開放技術棧
OpenGCUA: Toward a Fully Open Stack for Generalist Computer Use
OpenGCUA 論文介紹一套面向通用電腦操作的開放技術棧，整合視覺與程式化介面、程式碼執行，並將部分觀察及任務狀態決策由固定執行框架交給模型。在 OSWorld-v2 的八個模型評測中，完整配置相較批次式純 GUI 執行，平均部分得分提高 19.6 分，每項任務報告步數平均減少 53.0%。團隊同時公開實作、提示詞、八個模型的評測軌跡，以及初步的真實裝置執行框架和跨裝置 SDK。
Published on Oct 15
Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
Abstract
OpenGCUA introduces an open generalist computer-use agent that integrates visual and programmatic interfaces, harness-to-model decision transfer, and code execution to improve task performance and efficiency across models.
Computer use has reached a turning point: the focus is expanding from GUI-only interaction to systems that complete tasks on users' computers through visual and programmatic interfaces. We define a generalist computer-use agent (GCUA) as a system in which the model chooses how to observe, act, and organize its work across visual and programmatic interfaces. Yet frontier implementations, such as GPT-6 Astra's computer-use system, remain largely closed, limiting systematic study of their design. We introduce Open Generalist CUA (OpenGCUA), an ongoing project to develop and open the full stack for GCUAs with the community. This report presents the first release of OpenGCUA. Code execution is a key feature of our agent design: it lets intermediate results determine subsequent operations and enables models to apply their existing programming capabilities directly to computer interaction. We also propose harness-to-model decision transfer, moving selected observations and task-state decisions from fixed harness rules to the model. On OSWorld-v2, our full configuration improves task scores across all eight evaluated models, including GPT-6 Astra and Claude Opus 5, raising mean partial score by +19.6 points and reducing reported steps per task by an average of 53.0% relative to batched GUI-only execution. We release our implementation and prompts, evaluation trajectories from all eight models, and preliminary versions of a real-device harness and a cross-device SDK. The harness addresses real-device challenges not fully captured by benchmarks, while the SDK provides a common programming interface across computers, mobiles and browsers. Across four real macOS tasks under our real-device harness, selected Qwen3.8-Max runs use 19.4 times as many reported reasoning tokens as GPT-6 Astra, highlighting substantial differences between models in real-device execution efficiency.
View arXiv page View PDF Project page GitHub Add to collection
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2610.22200 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2610.22200 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2610.22200 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.
來源：HuggingFace Daily Papers（社區熱門論文） · huggingface.co