2024/07/03 by Yuxiang Chai, Chai, Yuxiang, Siyuan Huang +13 · 25 citations
Computer Science · #Artificial Intelligence (cs.AI) #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mobile Agent-Based Network Management #Multimedia (cs.MM) #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2407.17490
openalex publication_date 2024/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.The project is available at https://yxchai.com/AMEX/.