Summary of Gui Agents with Foundation Models: a Comprehensive Survey, by Shuai Wang et al.
GUI Agents with Foundation Models: A Comprehensive Survey
by Shuai Wang, Weiwen Liu, Jingxuan Chen, Yuqi Zhou, Weinan Gan, Xingshan Zeng, Yuhan Che, Shuai Yu, Xinlong Hao, Kun Shao, Bin Wang, Chuhan Wu, Yasheng Wang, Ruiming Tang, Jianye Hao
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), have enabled the development of intelligent agents capable of performing complex tasks. This survey consolidates recent research on LLM- and MLLM-based Graphical User Interface (GUI) agents, highlighting key innovations in data resources, frameworks, and applications. The paper reviews representative datasets and benchmarks, such as [insert specific dataset names], followed by an overview of a generalized framework that encapsulates the essential components of prior studies. The authors also explore relevant commercial applications, including [insert specific application examples]. Drawing insights from existing work, they identify key challenges and propose future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence can be used to make computers do tasks that are usually done by humans. They’re talking about special kinds of computer models called Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs). These models can understand and interact with computer screens, just like humans do when we click and type. The authors looked at recent research on this topic and found some common themes and ideas that they’re sharing in this survey. They also talked about what’s working well and what’s not, and gave some ideas for where to go from here. |
