title: Mobile-Agent-V: Learning Mobile Device Operation Through Video-Guided
    Multi-Agent Collaboration
summary: 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.
paper_authors._id: 67bd3936daef22cbce6d7ef2
paper_authors.name: Junyang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:25:28.473Z
paper_authors._id: 6438f6415aa69077ffb16942
paper_authors.avatarUrl: /avatars/c83dbd3e10e88db97c2a86092bad5917.svg
paper_authors.fullname: Junyang Wang
paper_authors.type: user
paper_authors.user: junyangwang0410
paper_authors._id: 67bd3936daef22cbce6d7ef3
paper_authors.name: Haiyang Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T09:39:41.528Z
paper_authors._id: 645b10e80c73ea27d13f7aca
paper_authors.avatarUrl: /avatars/95e565306472a15067440b5b43e07a6f.svg
paper_authors.fullname: xuhaiyang
paper_authors.type: user
paper_authors.user: xhyandwyy
paper_authors._id: 67bd3936daef22cbce6d7ef4
paper_authors.name: Xi Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:25:39.626Z
paper_authors._id: 66b1762e023357106d7e1d50
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/knNwe9xeQmIHUT7hQOrvN.png
paper_authors.fullname: Xi Zhang
paper_authors.type: user
paper_authors.user: XiZhang
paper_authors._id: 67bd3936daef22cbce6d7ef5
paper_authors.name: Ming Yan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T16:29:39.149Z
paper_authors._id: 64771cfdd7cf39f2e9381aa9
paper_authors.avatarUrl: /avatars/48adf00c3b653df02628f80511639e19.svg
paper_authors.fullname: Ming
paper_authors.type: user
paper_authors.user: MingYan123
paper_authors._id: 67bd3936daef22cbce6d7ef6
paper_authors.name: Ji Zhang
paper_authors._id: 67bd3936daef22cbce6d7ef7
paper_authors.name: Fei Huang
paper_authors._id: 67bd3936daef22cbce6d7ef8
paper_authors.name: Jitao Sang
paper_title: Mobile-Agent-V: Learning Mobile Device Operation Through Video-Guided
    Multi-Agent Collaboration
paper_summary: 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.
paper_ai_summary: Mobile-Agent-V enhances mobile automation by leveraging video guidance and deep-reflection to autonomously learn and execute tasks efficiently.
paper_ai_keywords: video guidance sliding window strategy video agent deep-reflection agent