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Computer Science > Multiagent Systems

arXiv:2410.13757 (cs)
[Submitted on 17 Oct 2024]

Title:MobA: A Two-Level Agent System for Efficient Mobile Task Automation

Authors:Zichen Zhu, Hao Tang, Yansi Li, Kunyao Lan, Yixuan Jiang, Hao Zhou, Yixiao Wang, Situo Zhang, Liangtai Sun, Lu Chen, Kai Yu
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Abstract: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.
Comments: 27 pages, 6 figures, and 5 tables. We will release our source code in a few days
Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2410.13757 [cs.MA]
  (or arXiv:2410.13757v1 [cs.MA] for this version)
  https://doi.org/10.48550/arXiv.2410.13757

Submission history

From: Zichen Zhu [view email]
[v1] Thu, 17 Oct 2024 16:53:50 UTC (2,904 KB)
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