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

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.