Rapid advancements in sixth-generation (6G) networks and large language models (LLMs) have paved the way for ubiquitous intelligence, wherein seamless connectivity and distributed artificial intelligence (AI) have revolutionized various aspects of our lives.However, realizing this vision faces significant challenges owing to the fragmented and heterogeneous computing resources across hierarchical networks, which are insufficient for individual LLM agents to perform complex reasoning tasks.To address this issue, we propose Collaborative Orchestration Role at Edge (CORE), an innovative framework that employs a collaborative learning system in which multiple LLMs, each assigned a distinct functional role, are distributed across mobile devices and tiered edge servers. The system integrates three optimization modules, encompassing real-time perception,dynamic role orchestration, and pipeline-parallel execution, to facilitate efficient and rapid collaboration among distributed agents. Furthermore, we introduce a novel role affinity scheduling algorithm for dynamically orchestrating LLM role assignments across the hierarchical edge infrastructure, intelligently matching computational demands with available dispersed resources.Finally, comprehensive case studies and performance evaluations across various 6G application scenarios demonstrated the efficacy of CORE, revealing significant enhancements in the system efficiency and task completion rates. Building on these promising outcomes, we further validated the practical applicability of CORE by deploying it on a real-world edge-computing platform,that exhibits robust performance in operational environments.

翻译：第六代（6G）网络与大型语言模型（LLM）的快速发展为泛在智能的实现铺平了道路，其中无缝连接与分布式人工智能（AI）已深刻改变我们生活的诸多方面。然而，由于分层网络中计算资源呈现碎片化与异构性，单个LLM智能体难以独立执行复杂推理任务，这为实现该愿景带来了重大挑战。为应对此问题，我们提出一种创新的边缘协同编排角色（CORE）框架。该框架采用一种协同学习系统，将多个被赋予不同功能角色的LLM分布式部署于移动设备与分层边缘服务器之上。该系统集成了三个优化模块，涵盖实时感知、动态角色编排与流水线并行执行，以促进分布式智能体间高效、快速的协作。此外，我们引入一种新颖的角色亲和度调度算法，用于在分层边缘基础设施上动态编排LLM的角色分配，从而智能地将计算需求与可用的分散资源相匹配。最后，通过对多种6G应用场景的综合案例研究与性能评估，我们验证了CORE框架的有效性，结果表明其在系统效率与任务完成率方面均有显著提升。基于这些积极成果，我们进一步将CORE部署于一个真实世界的边缘计算平台，其在运行环境中展现出稳健的性能，从而验证了该框架的实际适用性。