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arXiv · 2412.04454

Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction

Yiheng Xu·Zekun Wang·Junli Wang·Dunjie Lu·Tianbao Xie·Amrita Saha·Doyen Sahoo·Tao Yu·Caiming Xiong
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Abstract

Automating GUI tasks remains challenging due to reliance on textual representations, platform-specific action spaces, and limited reasoning capabilities. We introduce Aguvis, a unified vision-based framework for autonomous GUI agents that directly operates on screen images, standardizes cross-platform interactions and incorporates structured reasoning via inner monologue. To enable this, we construct Aguvis Data Collection, a large-scale dataset with multimodal grounding and reasoning annotations, and develop a two-stage training pipeline that separates GUI grounding from planning and reasoning. Experiments show that Aguvis achieves state-of-the-art performance across offline and real-world online benchmarks, marking the first fully autonomous vision-based GUI agent that operates without closed-source models. We open-source all datasets, models, and training recipes at https://aguvis-project.github.io to advance future research.

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Yiheng Xu, Zekun Wang, Junli Wang, Dunjie Lu, Tianbao Xie, Amrita Saha, Doyen Sahoo, Tao Yu, Caiming Xiong. 2024-12-05. Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction. https://arxiv.org/abs/2412.04454

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