AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents
Yuxiang Chai, Siyuan Huang, Yazhe Niu, Han Xiao, Liang Liu, Guozhi Wang, Dingyu Zhang, Shuai Ren
Abstract
AI agents have drawn increasing attention mostly on their ability to perceive environments, understand tasks, and autonomously achieve goals. To advance research on AI agents in mobile scenarios, we introduce the Android Multi-annotation EXpo (AMEX), a comprehensive, large-scale dataset designed for generalist mobile GUI-control agents which are capable of completing tasks by directly interacting with the graphical user interface (GUI) on mobile devices. AMEX comprises over 104K high-resolution screenshots from popular mobile applications, which are annotated at multiple levels. Unlike existing GUI-related datasets, e.g., Rico, AitW, etc., AMEX includes three levels of annotations: GUI interactive element grounding, GUI screen and element functionality descriptions, and complex natural language instructions with stepwise GUI-action chains. We develop this dataset from a more instructive and detailed perspective, complementing the general settings of existing datasets. Additionally, we finetune a baseline model SPHINX Agent and illustrate the effectiveness of AMEX.
BibTeX
@inproceedings{chai-etal-2025-amex,
title = "{AMEX}: Android Multi-annotation Expo Dataset for Mobile {GUI} Agents",
author = "Chai, Yuxiang and
Huang, Siyuan and
Niu, Yazhe and
Xiao, Han and
Liu, Liang and
Wang, Guozhi and
Zhang, Dingyu and
Ren, Shuai and
Li, Hongsheng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.110/",
doi = "10.18653/v1/2025.findings-acl.110",
pages = "2138--2156",
ISBN = "979-8-89176-256-5"
}