2024/06/12 by Luyuan Wang, Wang, Luyuan, Yongyu Deng +13 · 14 citations
Computer Science · #Artificial Intelligence (cs.AI) #Distributed systems and fault tolerance #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mobile Agent-Based Network Management #Peer-to-Peer Network Technologies
paper · pdf · doi:10.48550/arxiv.2406.08184
openalex publication_date 2024/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Large language model (LLM)-based mobile agents are increasingly popular due to their capability to interact directly with mobile phone Graphic User Interfaces (GUIs) and their potential to autonomously manage daily tasks. Despite their promising prospects in both academic and industrial sectors, little research has focused on benchmarking the performance of existing mobile agents, due to the inexhaustible states of apps and the vague definition of feasible action sequences. To address this challenge, we propose an efficient and user-friendly benchmark, MobileAgentBench, designed to alleviate the burden of extensive manual testing. We initially define 100 tasks across 10 open-source apps, categorized by multiple levels of difficulty. Subsequently, we evaluate several existing mobile agents, including AppAgent and MobileAgent, to thoroughly and systematically compare their performance. All materials are accessible on our project webpage: https://MobileAgentBench.github.io, contributing to the advancement of both academic and industrial fields.