Cloud-based mobile agents powered by (multimodal) large language models ((M)LLMs) offer strong reasoning abilities but suffer from high latency and cost. While fine-tuned (M)SLMs enable edge deployment, they often lose general capabilities and struggle with complex tasks. To address this, we propose \textbf{EcoAgent}, an \textbf{E}dge-\textbf{C}loud c\textbf{O}llaborative multi-agent framework for mobile automation. EcoAgent features a closed-loop collaboration among a cloud-based Planning Agent and two edge-based agents: the Execution Agent for action execution and the Observation Agent for verifying outcomes. The Observation Agent uses a Pre-Understanding Module to compress screen images into concise text, reducing token usage and communication overhead. In case of failure, the Planning Agent retrieves screen history through a Memory Module and replans via a Reflection Module. Experiments on AndroidWorld show that EcoAgent achieves task success rates comparable to cloud-based mobile agents while significantly reducing MLLM token consumption, enabling efficient and practical mobile automation.

本文针对云端移动代理的高延迟和成本问题，提出了一种新的边缘-云协同多智能体框架EcoAgent，旨在提高移动自动化效率。EcoAgent通过云端规划代理和边缘执行代理与观察代理的闭环合作，显著降低了令牌使用量和通信开销，同时在任务成功率上与云端代理相当，展示了其在移动自动化领域的潜在价值。

EcoAgent：一种高效的边缘-云协同多智能体框架用于移动自动化