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.

本研究解决了现有移动助手依赖系统API以及在复杂用户指令和多样接口下的理解和决策能力不足的问题。提出的MobA系统采用多模态大语言模型，通过双级代理架构提升了理解和规划能力。研究发现，MobA显著提高了任务执行效率和完成率，展示了其在处理复杂任务方面的潜力。

MobA：用于高效移动任务自动化的双级代理系统