Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction
On the map: LLM reasoning → Multimodal reasoning
Abstract (arXiv)
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.
Key facts. Cited by 271 papers in the map (data through 7 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 44 of its 55 references that are in the map. First posted on arXiv 5 Dec 2024.
Builds on
44 of 55 references.
Closest ancestors · 1
Same direction, newest first
Other references · 43
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Citing papers per year: 2024: 1; 2025: 153; 2026: 117.
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