title: MobA: A Two-Level Agent System for Efficient Mobile Task Automation
summary: Current mobile assistants are limited by dependence on system APIs or
  struggle with complex user instructions and diverse interfaces due to
  restricted comprehension and decision-making abilities. To address these
  challenges, we propose MobA, a novel Mobile phone Agent powered by multimodal
  large language models that enhances comprehension and planning capabilities
  through a sophisticated two-level agent architecture. The high-level Global
  Agent (GA) is responsible for understanding user commands, tracking history
  memories, and planning tasks. The low-level Local Agent (LA) predicts detailed
  actions in the form of function calls, guided by sub-tasks and memory from the
  GA. Integrating a Reflection Module allows for efficient task completion and
  enables the system to handle previously unseen complex tasks. MobA demonstrates
  significant improvements in task execution efficiency and completion rate in
  real-life evaluations, underscoring the potential of MLLM-empowered mobile
  assistants.
paper_authors._id: 6711e2dd1133b5e4de846c00
paper_authors.name: Zichen Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-18T10:32:58.239Z
paper_authors._id: 64d4b50a8ebc404438d86961
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/dxdusVp085Lx9hZ-y9fMp.jpeg
paper_authors.fullname: Zichen Zhu
paper_authors.type: user
paper_authors.user: JamesZhutheThird
paper_authors._id: 6711e2dd1133b5e4de846c01
paper_authors.name: Hao Tang
paper_authors._id: 6711e2dd1133b5e4de846c02
paper_authors.name: Yansi Li
paper_authors._id: 6711e2dd1133b5e4de846c03
paper_authors.name: Kunyao Lan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-18T12:34:20.631Z
paper_authors._id: 62a430b7f4ec0e5e8760c776
paper_authors.avatarUrl: /avatars/708f7558ceeffdc41bb9c871c8f09409.svg
paper_authors.fullname: Kunyao Lan
paper_authors.type: user
paper_authors.user: lankunyao
paper_authors._id: 6711e2dd1133b5e4de846c04
paper_authors.name: Yixuan Jiang
paper_authors._id: 6711e2dd1133b5e4de846c05
paper_authors.name: Hao Zhou
paper_authors._id: 6711e2dd1133b5e4de846c06
paper_authors.name: Yixiao Wang
paper_authors._id: 6711e2dd1133b5e4de846c07
paper_authors.name: Situo Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-18T12:35:16.260Z
paper_authors._id: 64a6217e063f497473d7cab8
paper_authors.avatarUrl: /avatars/45e1a7ed953353a3602924374d2ac163.svg
paper_authors.fullname: Situo Zhang
paper_authors.type: user
paper_authors.user: Situo
paper_authors._id: 6711e2dd1133b5e4de846c08
paper_authors.name: Liangtai Sun
paper_authors._id: 6711e2dd1133b5e4de846c09
paper_authors.name: Lu Chen
paper_authors._id: 6711e2dd1133b5e4de846c0a
paper_authors.name: Kai Yu
paper_title: MobA: A Two-Level Agent System for Efficient Mobile Task Automation
paper_summary: Current mobile assistants are limited by dependence on system APIs or
  struggle with complex user instructions and diverse interfaces due to
  restricted comprehension and decision-making abilities. To address these
  challenges, we propose MobA, a novel Mobile phone Agent powered by multimodal
  large language models that enhances comprehension and planning capabilities
  through a sophisticated two-level agent architecture. The high-level Global
  Agent (GA) is responsible for understanding user commands, tracking history
  memories, and planning tasks. The low-level Local Agent (LA) predicts detailed
  actions in the form of function calls, guided by sub-tasks and memory from the
  GA. Integrating a Reflection Module allows for efficient task completion and
  enables the system to handle previously unseen complex tasks. MobA demonstrates
  significant improvements in task execution efficiency and completion rate in
  real-life evaluations, underscoring the potential of MLLM-empowered mobile
  assistants.
paper_ai_keywords: multimodal large language models two-level agent architecture Global Agent Local Agent high-level Global Agent low-level Local Agent function calls Reflection Module MLLM-empowered mobile assistants