让手机本地运行小模型多智能体协作，隐私安全还提速
CAMPHOR: Collaborative Agents for Multi-input Planning and High-Order Reasoning On Device
- 用分层智能体分工处理任务分解、上下文检索和动态规划
- 在本地完成任务，比闭源大模型高35%的完成率且零云端通信
- 适合注重隐私、追求低延迟的移动端应用开发
尽管服务器端大语言模型在函数调用和复杂推理上表现优异，但将小语言模型直接部署在设备上虽能提升延迟和隐私性，却带来准确率与内存的挑战。我们提出CAMPHOR，一种面向多输入与个人上下文推理的本地化小模型多智能体框架。其采用分层架构，由高阶推理智能体分解复杂任务，并协调负责个人上下文检索、工具交互和动态计划生成的专家智能体。通过智能体间参数共享与提示压缩技术，显著降低模型规模、延迟和内存占用。为验证方法，我们构建了聚焦个性化移动助手场景的新型多智能体任务轨迹数据集。实验表明，微调后的小模型智能体不仅在任务完成度F1上比闭源大模型高出约35%，还完全避免了设备与服务器间的通信，同时增强隐私保护。
原文摘要 · Abstract (English)
While server-side Large Language Models (LLMs) demonstrate proficiency in function calling and complex reasoning, deploying Small Language Models (SLMs) directly on devices brings opportunities to improve latency and privacy but also introduces unique challenges for accuracy and memory. We introduce CAMPHOR, an innovative on-device SLM multi-agent framework designed to handle multiple user inputs and reason over personal context locally, ensuring privacy is maintained. CAMPHOR employs a hierarchical architecture where a high-order reasoning agent decomposes complex tasks and coordinates expert agents responsible for personal context retrieval, tool interaction, and dynamic plan generation. By implementing parameter sharing across agents and leveraging prompt compression, we significantly reduce model size, latency, and memory usage. To validate our approach, we present a novel dataset capturing multi-agent task trajectories centered on personalized mobile assistant use-cases. Our experiments reveal that fine-tuned SLM agents not only surpass closed-source LLMs in task completion F1 by~35\% but also eliminate the need for server-device communication, all while enhancing privacy.
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