title: Mobile-Agent-v2: Mobile Device Operation Assistant with Effective
    Navigation via Multi-Agent Collaboration
summary: Mobile device operation tasks are increasingly becoming a popular multi-modal
  AI application scenario. Current Multi-modal Large Language Models (MLLMs),
  constrained by their training data, lack the capability to function effectively
  as operation assistants. Instead, MLLM-based agents, which enhance capabilities
  through tool invocation, are gradually being applied to this scenario. However,
  the two major navigation challenges in mobile device operation tasks, task
  progress navigation and focus content navigation, are significantly complicated
  under the single-agent architecture of existing work. This is due to the overly
  long token sequences and the interleaved text-image data format, which limit
  performance. To address these navigation challenges effectively, we propose
  Mobile-Agent-v2, a multi-agent architecture for mobile device operation
  assistance. The architecture comprises three agents: planning agent, decision
  agent, and reflection agent. The planning agent generates task progress, making
  the navigation of history operations more efficient. To retain focus content,
  we design a memory unit that updates with task progress. Additionally, to
  correct erroneous operations, the reflection agent observes the outcomes of
  each operation and handles any mistakes accordingly. Experimental results
  indicate that Mobile-Agent-v2 achieves over a 30% improvement in task
  completion compared to the single-agent architecture of Mobile-Agent. The code
  is open-sourced at https://github.com/X-PLUG/MobileAgent.
paper_authors._id: 666113b372f344f198df601c
paper_authors.name: Junyang Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: Junyang Wang
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paper_authors.user: junyangwang0410
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paper_authors.name: Haiyang Xu
paper_authors.status: claimed_verified
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paper_authors.fullname: xuhaiyang
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paper_authors.user: xhyandwyy
paper_authors._id: 666113b372f344f198df601e
paper_authors.name: Haitao Jia
paper_authors._id: 666113b372f344f198df601f
paper_authors.name: Xi Zhang
paper_authors._id: 666113b372f344f198df6020
paper_authors.name: Ming Yan
paper_authors.status: admin_assigned
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paper_authors.fullname: Ming
paper_authors.type: user
paper_authors.user: MingYan123
paper_authors._id: 666113b372f344f198df6021
paper_authors.name: Weizhou Shen
paper_authors.status: admin_assigned
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paper_authors._id: 64777a346e6c7ac608c1e9bf
paper_authors.avatarUrl: /avatars/b0e65ba781c90c2560606eb5467101eb.svg
paper_authors.fullname: Weizhou Shen
paper_authors.type: user
paper_authors.user: shenwzh3
paper_authors._id: 666113b372f344f198df6022
paper_authors.name: Ji Zhang
paper_authors._id: 666113b372f344f198df6023
paper_authors.name: Fei Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-06T07:13:19.531Z
paper_authors._id: 635b8b6a37c6a2c12e2cce00
paper_authors.avatarUrl: /avatars/229fb72180529141515d1df797b33709.svg
paper_authors.fullname: Fei Huang
paper_authors.type: user
paper_authors.user: hzhwcmhf
paper_authors._id: 666113b372f344f198df6024
paper_authors.name: Jitao Sang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-04T09:47:26.638Z
paper_authors._id: 6494457c6339264dd78bcb95
paper_authors.avatarUrl: /avatars/d87842251f1a43f50cc827f0e2a995ee.svg
paper_authors.fullname: sdzy
paper_authors.type: user
paper_authors.user: sdzy
paper_title: Mobile-Agent-v2: Mobile Device Operation Assistant with Effective
    Navigation via Multi-Agent Collaboration
paper_summary: Mobile device operation tasks are increasingly becoming a popular multi-modal
  AI application scenario. Current Multi-modal Large Language Models (MLLMs),
  constrained by their training data, lack the capability to function effectively
  as operation assistants. Instead, MLLM-based agents, which enhance capabilities
  through tool invocation, are gradually being applied to this scenario. However,
  the two major navigation challenges in mobile device operation tasks, task
  progress navigation and focus content navigation, are significantly complicated
  under the single-agent architecture of existing work. This is due to the overly
  long token sequences and the interleaved text-image data format, which limit
  performance. To address these navigation challenges effectively, we propose
  Mobile-Agent-v2, a multi-agent architecture for mobile device operation
  assistance. The architecture comprises three agents: planning agent, decision
  agent, and reflection agent. The planning agent generates task progress, making
  the navigation of history operations more efficient. To retain focus content,
  we design a memory unit that updates with task progress. Additionally, to
  correct erroneous operations, the reflection agent observes the outcomes of
  each operation and handles any mistakes accordingly. Experimental results
  indicate that Mobile-Agent-v2 achieves over a 30% improvement in task
  completion compared to the single-agent architecture of Mobile-Agent. The code
  is open-sourced at https://github.com/X-PLUG/MobileAgent.
paper_ai_summary: Mobile-Agent-v2, a multi-agent system with planning, decision, and reflection components, improves task completion in mobile device operations by addressing navigation challenges and handling errors.
paper_ai_keywords: Multi-modal Large Language Models (MLLMs) Mobile-Agent-v2 multi-agent architecture planning agent decision agent reflection agent task progress navigation focus content navigation memory unit erroneous operations