MobA: A Two-Level Agent System for Efficient Mobile Task Automation
Authors: name: Zichen Zhu; name: Hao Tang; name: Yansi Li; name: Kunyao Lan; name: Yixuan Jiang; name: Hao Zhou; name: Yixiao Wang; name: Situo Zhang; name: Liangtai Sun; name: Lu Chen; name: Kai Yu
Published: 2024-10-17 16:53:50

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.