让个性化AI代理在边缘设备间协作，提升任务完成率与响应速度。
PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence
- 基于原型的评分机制，动态匹配查询与远程代理。
- 平均准确率提升7.96%，延迟降低16.34%。
- 适合需要跨设备智能协作的边缘计算场景。
在边缘设备部署大语言模型可为不同用户提供个性化代理。随着多样化个性化代理的增多，用户间可通过点对点（P2P）协作，将本地代理无法处理的任务委托给更擅长的远程代理。本文提出首个个性化大模型代理互操作系统PPAI，支持基于代理专长的协同工作。然而，代理池动态变化且能力可替换，使查询与代理匹配及负载均衡面临新挑战。为此，我们设计一种基于原型的可扩展查询-代理对评分机制，以应对网络中代理频繁变更。同时，提出多代理互操作贝叶斯博弈模型，实现在远程代理负载变化过快无法及时观测时，平衡本地需求与全局效率。我们实现PPAI原型系统，验证其显著拓展了可执行任务范围，并保持负载均衡。平均而言，在多个任务上准确率提升达7.96%，相比基线延迟降低16.34%。
原文摘要 · Abstract (English)
Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized agents presents a unique opportunity for peer-to-peer (P2P) collaboration, wherein each user can delegate tasks beyond the local agent's expertise to remote agents more suited for the specific query. This paper introduces PPAI, the first personalized LLM agent interoperability system, which enables users to collaborate with each other based on agent specialization. However, the ever-changing pool of agents and their interchangeable capacity introduce new challenges when it comes to matching queries to agents and balancing loads, compared with existing P2P systems. Therefore, we propose a scalable query-agent pair scoring mechanism based on prototypes to identify suitable agents within a P2P network with churn. Moreover, we propose a multi-agent interoperability Bayesian game to balance local demand and global efficiency, when changes in remote agent load occur too quickly to be observed. Finally, we implement a prototype of PPAI and demonstrate that it substantially broadens the range of tasks that could be carried out while maintaining load balance. On average, it achieves an accuracy improvement of up to 7.96% across multiple tasks, while reducing latency by 16.34% compared to the baseline.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。