混合智能体系统如何平衡云端与设备端模型的性能、成本与能耗。
When Cloud Agents Meet Device Agents: Lessons from Hybrid Multi-Agent Systems
- 将云端大模型与设备端小模型结合，构建混合多智能体架构。
- 不同任务下最优配置差异大，算力提升不保证性能提升。
- 为跨场景应用提供可复用的设计指导，适合资源受限部署者。
智能体式AI推理的设计空间介于两类极端之间：部署在云端的前沿大语言模型（LLMs），虽具备广泛任务处理能力但成本高昂；以及可在设备端运行、更经济的小语言模型（SLMs）。混合多智能体系统（MASs）融合云端与设备模型，提供了有前景的折中方案，但也带来了性能、成本与边缘能耗紧密耦合的复杂设计空间。由于缺乏通用设计原则，当前混合组件多依赖特定领域经验的临时决策。本文系统研究该设计空间，将两种代表性MAS架构改造为支持混合推理，并分析各项设计选择如何影响功耗、成本与性能的帕累托前沿。结果表明：虽然SLMs能从LLM辅助中获益，但最优架构高度依赖任务特性，且更高层级的算力投入并不总带来性能提升。
原文摘要 · Abstract (English)
The design space of agentic AI inference spans two extremes: frontier large language models (LLMs), typically hosted in the cloud and offering strong performance across a wide range of tasks at substantially high cost, and more cost-efficient small language models (SLMs), which are amenable to on-device inference. Hybrid multi-agent systems (MASs) combining on-device and cloud models offer a promising middle ground, but they also introduce a complex and poorly understood design space in which task accuracy, monetary cost, and edge energy consumption are tightly coupled; in the absence of general design principles, hybrid components, although not the most prevalent choice, are typically introduced through ad hoc decisions tailored to specific domains. In this work, we examine this design space more systematically. We adapt two representative MAS architectures to support hybrid inference and study how individual design choices shift the operating point along the Pareto frontier of power, cost, and performance. Our findings paint a nuanced picture of hybrid MAS design: while SLMs can effectively benefit from LLM assistance, the optimal architecture is highly task-dependent, and greater frontier-level compute does not consistently translate to better performance.
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