Mobile-Agent-V: A Video-Guided Approach for Effortless and Efficient Operational Knowledge Injection in Mobile Automation

TL;DR

Mobile-Agent-V leverages video content to automatically inject operational knowledge into mobile automation, significantly reducing manual effort and improving performance by 36%.

Contribution

It introduces a novel video-guided framework for knowledge injection in mobile automation, eliminating manual effort and establishing a new benchmark for evaluation.

Findings

Achieves 36% performance improvement over existing methods.

Reduces knowledge acquisition effort and time.

Introduces Mobile-Knowledge benchmark for assessment.

Abstract

The exponential rise in mobile device usage necessitates streamlined automation for effective task management, yet many AI frameworks fall short due to inadequate operational expertise. While manually written knowledge can bridge this gap, it is often burdensome and inefficient. We introduce Mobile-Agent-V, an innovative framework that utilizes video as a guiding tool to effortlessly and efficiently inject operational knowledge into mobile automation processes. By deriving knowledge directly from video content, Mobile-Agent-V eliminates manual intervention, significantly reducing the effort and time required for knowledge acquisition. To rigorously evaluate this approach, we propose Mobile-Knowledge, a benchmark tailored to assess the impact of external knowledge on mobile agent performance. Our experimental findings demonstrate that Mobile-Agent-V enhances performance by 36% compared to existing methods, underscoring its effortless and efficient advantages in mobile automation.