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