Papers
arxiv:2509.00531

MobiAgent: A Systematic Framework for Customizable Mobile Agents

Published on Aug 30
· Submitted by fengerhu on Sep 3
Authors:
,
,
,
,
,
,
,

Abstract

MobiAgent, a comprehensive mobile agent system, achieves state-of-the-art performance in real-world mobile scenarios through its MobiMind-series models, AgentRR framework, and MobiFlow benchmarking suite, while also reducing data annotation costs.

AI-generated summary

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.

Community

Paper author Paper submitter

We have fully open-sourced our on-device intelligent agent system: MobiAgent, 7B model outperforms GPT-5! Our open-sourced components include the data collection pipeline tool, agent models (MobiMind 7B/3B), the agent acceleration engine (AgentRR), the agent application, and the on-device agent benchmark (MobiFlow). In real-world mobile scenarios (such as shopping, entertainment, social networking, business trips, etc.), our system surpasses leading general-purpose large models like GPT-5 and Gemini-2.5-pro, as well as state-of-the-art open-source GUI agent models like UI-TARS-1.5. Additionally, our entire training and inference deployment is fully based on the Ascend 910B NPU.

Everyone is welcome to download our app (currently supports Chinese applications):

https://github.com/IPADS-SAI/MobiAgent/releases/download/v1.0/Mobiagent.apk

and experience it directly!

Sign up or log in to comment

Models citing this paper 2

Datasets citing this paper 0

No dataset linking this paper

Cite arxiv.org/abs/2509.00531 in a dataset README.md to link it from this page.

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2509.00531 in a Space README.md to link it from this page.

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.