基于无线网络的LLM多智能体系统：智能体-网络联合设计视角
LLM-Based Multi-Agent Systems over Wireless Networks: A Joint Agent--Network Design Perspective
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中文总结 AI 辅助
本文提出智能体-网络联合设计视角，通过协调智能体交互调度、消息选择传输、拓扑设计与资源分配，解决无线网络中LLM多智能体系统的耦合挑战，提升任务完成率。
中文摘要 AI 辅助
随着大型语言模型（LLM）从独立模型演变为嵌入物理系统的协作智能体，其推理和执行过程日益分布在无线边缘节点上。在这种背景下，无线网络正经历从仅提供数据连接到支持多智能体推理工作流本身的范式转变。此类受网络约束的基于LLM的多智能体系统（MASs）的任务性能受到多智能体推理依赖关系以及底层网络连接和边缘资源的共同影响。这种耦合引发了各种技术挑战，包括度量不匹配和消息冗余、状态不一致和拓扑不匹配，以及资源限制和信任中断。为应对这些挑战，本文提出了一种新颖的智能体-网络联合设计视角，协调系统两侧的决策。具体而言，我们提出了智能体交互调度与资源分配的联合设计、消息选择-传输协同设计，以及智能体-网络拓扑设计与工作负载-资源分配的联合设计。此外，我们考虑了与智能体侧信息流控制相关联的网络验证溯源，以约束接收信息对后续操作的影响。一个示例性的车联网（V2X）案例研究表明，联合调整智能体侧交互决策和网络操作可在通信和边缘资源约束下提高任务完成率，优于传统的仅智能体设计和仅无线设计。
英文摘要
As large language models (LLMs) evolve from standalone models into collaborative agents embedded in physical systems, their reasoning and execution are increasingly distributed across wireless edge nodes. In this setting, wireless networks are experiencing a paradigm shift from only providing data connectivity to supporting the multi-agent reasoning workflow itself. The task performance of such network-constrained LLM-based multi-agent systems (MASs) is jointly affected by the multi-agent reasoning dependencies as well as the underlying network connectivity and edge resources. This coupling gives rise to various technical challenges, including the metric misalignment and message redundancy, state inconsistency and topology mismatch, as well as resource limitation and trust discontinuity. To address these challenges, this article develops a novel joint agent--network design perspective that coordinates decisions on both sides of the system. Specifically, we present the joint design of agent--interaction scheduling and resource allocation, the message selection-transmission co-design, as well as the joint agent--network topology design and workload--resource allocation. Furthermore, we consider the network-verified provenance that is linked with agent-side information-flow control to constrain how received information affects subsequent operations. An illustrative vehicle-to-everything (V2X) case study shows that jointly adapting agent-side interaction decisions and network operations improves task completion under communication and edge-resource constraints, outperforming the conventional agent-only and wireless-only separate designs.
发表机构
- Shenzhen University（深圳大学）
- The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
- Beijing Institute of Technology（北京理工大学）
机构由 AI 辅助整理，请以论文原文为准。
