Mobile agents are essential for automating tasks in complex and dynamic mobile environments. As foundation models evolve, the demands for agents that can adapt in real-time and process multimodal data have grown. This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction. Recent evaluation benchmarks have been developed better to capture the static and interactive environments of mobile tasks, offering more accurate assessments of agents' performance. We then categorize these advancements into two main approaches: prompt-based methods, which utilize large language models (LLMs) for instruction-based task execution, and training-based methods, which fine-tune multimodal models for mobile-specific applications. Additionally, we explore complementary technologies that augment agent performance. By discussing key challenges and outlining future research directions, this survey offers valuable insights for advancing mobile agent technologies. A comprehensive resource list is available at https://github.com/aialt/awesome-mobile-agents

本研究解决了移动代理在复杂动态环境下自动化任务的重要性与实时适应性的需求缺口。通过对移动代理技术的最新进展进行全面评估，本论文提出了基于提示的方法和训练方法两种主要策略，提高了多模态交互和适应能力。研究的关键发现为提升移动代理技术提供了重要的见解，并指明了未来研究的方向。

多模态移动代理的基础与近期趋势：一项综述