GUI Agents with Foundation Models: A Comprehensive Survey

• URL: http://arxiv.org/abs/2411.04890v1
• Date: Thu, 07 Nov 2024 17:28:10 GMT
• Title: GUI Agents with Foundation Models: A Comprehensive Survey
• Authors: Shuai Wang, Weiwen Liu, Jingxuan Chen, Weinan Gan, Xingshan Zeng, Shuai Yu, Xinlong Hao, Kun Shao, Yasheng Wang, Ruiming Tang,
• Abstract summary: This survey consolidates recent research on (M)LLM-based GUI agents. We highlight key innovations in data, frameworks, and applications. We hope this paper will inspire further developments in the field of (M)LLM-based GUI agents.
• Score: 52.991688542729385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), facilitate intelligent agents being capable of performing complex tasks. By leveraging the ability of (M)LLMs to process and interpret Graphical User Interfaces (GUIs), these agents can autonomously execute user instructions by simulating human-like interactions such as clicking and typing. This survey consolidates recent research on (M)LLM-based GUI agents, highlighting key innovations in data, frameworks, and applications. We begin by discussing representative datasets and benchmarks. Next, we summarize a unified framework that captures the essential components used in prior research, accompanied by a taxonomy. Additionally, we explore commercial applications of (M)LLM-based GUI agents. Drawing from existing work, we identify several key challenges and propose future research directions. We hope this paper will inspire further developments in the field of (M)LLM-based GUI agents.

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