  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. Their capabilities of completing complex
tasks by directly interacting with the graphical user interface (GUI) on mobile
devices are trained and evaluated with the proposed dataset. AMEX comprises
over 104K high-resolution screenshots from 110 popular mobile applications,
which are annotated at multiple levels. Unlike existing mobile device-control
datasets, e.g., MoTIF, AitW, etc., AMEX includes three levels of annotations:
GUI interactive element grounding, GUI screen and element functionality
descriptions, and complex natural language instructions, each averaging 13
steps 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 develop a baseline model SPHINX Agent and
compare its performance across state-of-the-art agents trained on other
datasets. To facilitate further research, we open-source our dataset, models,
and relevant evaluation tools. The project is available at
https://yuxiangchai.github.io/AMEX/
