Graphical User Interfaces (GUIs) are critical to human-computer interaction, yet automating GUI tasks remains challenging due to the complexity and variability of visual environments. Existing approaches often rely on textual representations of GUIs, which introduce limitations in generalization, efficiency, and scalability. In this paper, we introduce Aguvis, a unified pure vision-based framework for autonomous GUI agents that operates across various platforms. Our approach leverages image-based observations, and grounding instructions in natural language to visual elements, and employs a consistent action space to ensure cross-platform generalization. To address the limitations of previous work, we integrate explicit planning and reasoning within the model, enhancing its ability to autonomously navigate and interact with complex digital environments. We construct a large-scale dataset of GUI agent trajectories, incorporating multimodal reasoning and grounding, and employ a two-stage training pipeline that first focuses on general GUI grounding, followed by planning and reasoning. Through comprehensive experiments, we demonstrate that Aguvis surpasses previous state-of-the-art methods in both offline and real-world online scenarios, achieving, to our knowledge, the first fully autonomous pure vision GUI agent capable of performing tasks independently without collaboration with external closed-source models. We open-sourced all datasets, models, and training recipes to facilitate future research at https://aguvis-project.github.io/.

翻译：图形用户界面（GUI）是人机交互的关键组成部分，然而由于视觉环境的复杂性和多变性，实现GUI任务的自动化仍面临挑战。现有方法通常依赖于GUI的文本化表示，这导致其在泛化性、效率和可扩展性方面存在局限。本文提出Aguvis，一个跨平台运行的统一纯视觉自主GUI智能体框架。我们的方法利用基于图像的观测，将自然语言指令与视觉元素进行对齐，并采用一致的动作空间以确保跨平台泛化能力。为克服先前工作的局限，我们在模型中整合了显式规划与推理机制，从而增强其在复杂数字环境中自主导航与交互的能力。我们构建了一个大规模GUI智能体轨迹数据集，融合了多模态推理与对齐信息，并采用两阶段训练流程：首先专注于通用GUI对齐，随后进行规划与推理训练。通过全面实验，我们证明Aguvis在离线与真实在线场景中均超越现有最优方法，据我们所知，首次实现了完全自主的纯视觉GUI智能体，能够独立执行任务而无需依赖外部闭源模型。我们已开源全部数据集、模型及训练方案以促进后续研究，项目地址：https://aguvis-project.github.io/。