让手机端智能体更高效、更协同，突破边缘设备的智能瓶颈
Scaling Mobile Agent Systems: From Capability Density to Collective Intelligence
- 通过轻量化模型设计与压缩提升单个智能体的能力密度
- 借助多智能体通信协作实现群体智能，克服设备碎片化局限
- 适合研究边缘智能、分布式AI及移动系统架构的开发者
移动智能体系统正成为边缘设备与AIoT生态中实现智能应用的关键范式。然而，其可扩展性受限于设备端计算能力不足以及智能分散于各设备的问题。本文提出一个统一的研究框架，从两个互补维度推进移动智能体系统的扩展：(1) 通过紧凑型基础模型设计与压缩技术提升单个智能体的能力密度；(2) 通过通信丰富的多智能体协作实现集体智能。基于近期模型与基础设施进展，该愿景旨在将孤立的移动智能体转化为高效、可扩展的分布式智能系统。
原文摘要 · Abstract (English)
Mobile agent systems are emerging as a key paradigm for enabling intelligent applications on edge devices and in AIoT ecosystems. However, their scalability is fundamentally constrained by limited on-device computation and fragmented intelligence across devices. In this work, we propose a unified research agenda for scaling mobile agent systems along two complementary dimensions: (1) improving capability density of individual agents through compact foundation model design and compression, and (2) enabling collective intelligence via communication-rich multi-agent collaboration. Building on recent model and infrastructure advances, this vision aims to transform isolated mobile agents into a distributed intelligent system that is efficient and scalable.
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