用视频自动注入操作知识，让手机自动化更高效。
Mobile-Agent-V: A Video-Guided Approach for Effortless and Efficient Operational Knowledge Injection in Mobile Automation
- 用视频直接提取操作知识，无需手动编写
- 相比现有方法性能提升36%
- 适合需要快速部署手机自动化的人
移动设备使用量激增，亟需高效自动化来管理任务，但多数AI框架因缺乏操作经验而表现不佳。虽可人工编写知识弥补，但过程繁琐低效。我们提出Mobile-Agent-V，利用视频作为引导，直接从视频内容中提取操作知识，实现无手动干预的知识注入，显著降低知识获取的时间与精力成本。为严格评估该方法，我们构建了专门用于衡量外部知识对移动智能体影响的基准Mobile-Knowledge。实验结果表明，Mobile-Agent-V相较现有方法性能提升36%，充分验证了其在移动自动化中的高效性与便捷性。
原文摘要 · Abstract (English)
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.
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