Mobile-Agent-V: Learning Mobile Device Operation Through Video-Guided Multi-Agent Collaboration
Junyang Wang, Haiyang Xu, Xi Zhang, Ming Yan, Ji Zhang, Fei Huang, Jitao Sang
2025-02-25
Summary
This paper talks about Mobile-Agent-V, a new AI system that learns how to use mobile devices by watching instructional videos, making it easier for phones and tablets to do tasks automatically
What's the problem?
As more people use smartphones and tablets, we need better ways for these devices to do tasks on their own. Current AI systems aren't great at this because they don't know enough about how to use mobile devices. Writing instructions for AI is time-consuming and not very efficient
What's the solution?
The researchers created Mobile-Agent-V, which learns by watching videos of people using mobile devices. It uses a 'video agent' to understand what's happening in the video and a 'deep-reflection agent' to think about how to apply what it sees. Users can record themselves doing tasks, and Mobile-Agent-V learns from these videos to do the tasks on its own. It doesn't need special video processing, making it easier to use
Why it matters?
This matters because it could make our phones and tablets much smarter and more helpful. Imagine your phone being able to learn new tasks just by watching you do them once. This could save people time and make mobile devices more useful, especially for complex tasks. The system is 30% better than other similar AI systems, which is a big improvement in the world of technology
Abstract
The rapid increase in mobile device usage necessitates improved automation for seamless task management. However, many AI-driven frameworks struggle due to insufficient operational knowledge. Manually written knowledge helps but is labor-intensive and inefficient. To address these challenges, we introduce Mobile-Agent-V, a framework that leverages video guidance to provide rich and cost-effective operational knowledge for mobile automation. Mobile-Agent-V enhances task execution capabilities by leveraging video inputs without requiring specialized sampling or preprocessing. Mobile-Agent-V integrates a sliding window strategy and incorporates a video agent and deep-reflection agent to ensure that actions align with user instructions. Through this innovative approach, users can record task processes with guidance, enabling the system to autonomously learn and execute tasks efficiently. Experimental results show that Mobile-Agent-V achieves a 30% performance improvement compared to existing frameworks.