Autonomous graphical user interface (GUI) agents aim to facilitate task automation by interacting with the user interface without manual intervention. Recent studies have investigated eliciting the capabilities of large language models (LLMs) for effective engagement in diverse environments. To align with the input-output requirement of LLMs, most existing approaches are developed under a sandbox setting where they rely on external tools and application-specific APIs to parse the environment into textual elements and interpret the predicted actions. Consequently, those approaches often grapple with inference inefficiency and error propagation risks. To mitigate the challenges, we introduce Auto-GUI, a multimodal solution that directly interacts with the interface, bypassing the need for environment parsing or reliance on application-dependent APIs. Moreover, we propose a chain-of-action technique -- leveraging a series of intermediate previous action histories and future action plans -- to help the agent decide what action to execute. We evaluate our approach on a new device-control benchmark AITW with 30$K$ unique instructions, spanning multi-step tasks such as application operation, web searching, and web shopping. Experimental results show that Auto-GUI achieves state-of-the-art performance with an action type prediction accuracy of 90\% and an overall action success rate of 74\%. Code is publicly available at https://github.com/cooelf/Auto-GUI.

翻译：自主图形用户界面（GUI）智能体旨在无需人工干预即可与用户界面交互，实现任务自动化。近期研究探索了激发大型语言模型（LLMs）的能力，以有效参与多种环境。为匹配LLMs的输入输出要求，现有方法多基于沙箱环境开发，依赖外部工具和特定应用API将环境解析为文本元素并解释预测动作。然而，此类方法常面临推理效率低下和错误传播风险。为缓解这些挑战，我们提出Auto-GUI——一种直接与界面交互的多模态解决方案，无需环境解析或依赖应用相关API。此外，我们提出动作链技术（chain-of-action）——利用一系列中间历史动作序列与未来动作计划——帮助智能体决策待执行动作。我们在包含30K条独特指令的新设备控制基准AITW上评估所提方法，涵盖应用操作、网络搜索与网络购物等多步任务。实验结果表明，Auto-GUI在动作类型预测准确率达90%，整体动作成功率达74%，实现了最先进性能。代码已开源至https://github.com/cooelf/Auto-GUI。