{"ID":2876507,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00531","arxiv_id":"2509.00531","title":"MobiAgent: A Systematic Framework for Customizable Mobile Agents","abstract":"With the rapid advancement of Vision-Language Models (VLMs), GUI-based mobile agents have emerged as a key development direction for intelligent mobile systems. However, existing agent models continue to face significant challenges in real-world task execution, particularly in terms of accuracy and efficiency. To address these limitations, we propose MobiAgent, a comprehensive mobile agent system comprising three core components: the MobiMind-series agent models, the AgentRR acceleration framework, and the MobiFlow benchmarking suite. Furthermore, recognizing that the capabilities of current mobile agents are still limited by the availability of high-quality data, we have developed an AI-assisted agile data collection pipeline that significantly reduces the cost of manual annotation. Compared to both general-purpose LLMs and specialized GUI agent models, MobiAgent achieves state-of-the-art performance in real-world mobile scenarios.","short_abstract":"With the rapid advancement of Vision-Language Models (VLMs), GUI-based mobile agents have emerged as a key development direction for intelligent mobile systems. However, existing agent models continue to face significant challenges in real-world task execution, particularly in terms of accuracy and efficiency. To addre...","url_abs":"https://arxiv.org/abs/2509.00531","url_pdf":"https://arxiv.org/pdf/2509.00531v1","authors":"[\"Cheng Zhang\",\"Erhu Feng\",\"Xi Zhao\",\"Yisheng Zhao\",\"Wangbo Gong\",\"Jiahui Sun\",\"Dong Du\",\"Zhichao Hua\",\"Yubin Xia\",\"Haibo Chen\"]","published":"2025-08-30T15:24:47Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}