Graphical User Interface (GUI) automation holds significant promise for
assisting users with complex tasks, thereby boosting human productivity.
Existing works leveraging Large Language Model (LLM) or LLM-based AI agents
have shown capabilities in automating tasks on Android and Web platforms.
However, these tasks are primarily aimed at simple device usage and
entertainment operations. This paper presents a novel benchmark, AssistGUI, to
evaluate whether models are capable of manipulating the mouse and keyboard on
the Windows platform in response to user-requested tasks. We carefully
collected a set of 100 tasks from nine widely-used software applications, such
as, After Effects and MS Word, each accompanied by the necessary project files
for better evaluation. Moreover, we propose an advanced Actor-Critic Embodied
Agent framework, which incorporates a sophisticated GUI parser driven by an
LLM-agent and an enhanced reasoning mechanism adept at handling lengthy
procedural tasks. Our experimental results reveal that our GUI Parser and
Reasoning mechanism outshine existing methods in performance. Nevertheless, the
potential remains substantial, with the best model attaining only a 46% success
rate on our benchmark. We conclude with a thorough analysis of the current
methods' limitations, setting the stage for future breakthroughs in this
domain.

使用大型语言模型的图形用户界面（GUI）自动化在处理复杂任务方面有很大潜力。本论文介绍了一个新的基准测试框架 AssistGUI，评估模型是否能够响应用户请求的任务，在 Windows 平台上操纵鼠标和键盘。实验结果显示，我们的 GUI Parser 和 Reasoning 机制在性能上优于现有方法，但在基准测试中，最佳模型的成功率仅为 46％。通过对当前方法的限制进行彻底分析，为未来在该领域取得突破奠定基础。