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今天的主题是：Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks

Summary

This research introduces Mobile-Agent-E, an innovative mobile assistant designed to handle complex, real-world tasks on smartphones. The system employs a hierarchical multi-agent framework with a self-evolution module, enabling it to learn from past experiences and improve its performance over time. Mobile-Agent-E separates high-level planning from low-level action execution, utilizing specialized agents for perception, operation, reflection, and note-taking. A novel benchmark, Mobile-Eval-E, is introduced to evaluate the agent's capabilities on challenging, multi-app tasks. Experimental results demonstrate significant improvements over existing approaches, showcasing the effectiveness of the hierarchical design and self-evolution mechanism. The study also analyzes the impact of self-generated tips and shortcuts, paving the way for more efficient and user-friendly mobile agents.

本研究介绍了 Mobile-Agent-E，一款创新的移动助手，专为在智能手机上处理复杂的现实世界任务而设计。该系统采用分层多智能体框架，并引入自进化模块，使其能够从过去的经验中学习，并随着时间推移不断提升性能。Mobile-Agent-E 将高层规划与低层动作执行分离，利用专门的智能体负责感知、操作、反思和笔记记录。此外，研究提出了一个新的基准测试 Mobile-Eval-E，用于评估智能体在复杂的多应用任务中的能力。实验结果表明，该方法相较于现有方案有显著提升，验证了分层设计与自进化机制的有效性。研究还分析了智能体自生成的提示和快捷方式的影响，为开发更高效、用户友好的移动智能体奠定了基础。

原文链接：https://arxiv.org/abs/2501.11733