用大模型驱动的对话式框架，实现6G网络轻量自治编排。
Conversational Orchestration for Organic 6G
- 基于大模型的域代理通过对话协商实现去中心化编排。
- 支持动态加入/退出域，控制面开销近线性增长且决策质量稳定。
- 适合需要弹性扩展与低耦合的未来6G网络研究者。
有机6G愿景旨在构建跨越边缘-云连续体并融合非地面资源的网络之网，需实现易操作、跨独立管理域可扩展、对域动态变化敏捷的服务部署。尽管已有跨域编排进展，但多数方案依赖重型集成架构、多层协调器和深度遥测管道，阻碍部署并增加协调开销。本文提出一种轻量级、去中心化的对话式编排框架，基于大语言模型（LLM）驱动的域代理。各域保持自治：代理通过工具观察本地状态，闭环推理，并在与数据平面耦合的代理间（A2A）覆盖网络上交换摘要信息。通过周期性、类似路由的可达性广告（延迟、瓶颈带宽、计算能力）实现快速可行部署；通过事件驱动请求与协商处理安全重优化、伸缩与迁移。为满足实时性要求，采用经验证器自验证训练的小型推理模型，并通过影子更新持续在线优化。仿真显示，随着域规模扩展及域动态加入，控制面开销可控且近线性增长，决策质量稳健，包括在目标变更后的恢复能力。最后，展望了有机6G中可证明性、安全性与不确定性感知的智能体编排未来方向。
原文摘要 · Abstract (English)
The Organic 6G vision of a network of networks spanning an edge-cloud continuum complemented by non-terrestrial resources requires, to realize its promise, service provisioning that is simple to operate, scalable across independently administered domains, and agile under domain churn (i.e., domains dynamically joining and leaving). Despite advances in cross-domain orchestration, many proposals rely on heavy integration fabrics, multi-layer coordinators, and deep telemetry pipelines that hinder deployability and amplify coordination overhead. We propose a lightweight, decentralized conversational orchestration framework based on Large Language Model (LLM)-driven domain agents. Each domain remains autonomous: an agent observes local state via tools, reasons in a closed loop, and exchanges summaries with neighboring agents over an Agent-to-Agent (A2A) overlay aligned with data-plane coupling. Fast feasible placement is enabled by periodic, routing-like dissemination of reachability advertisements (latency, bottleneck bandwidth, and compute capacity), while safe re-optimization, scaling, and migration are handled through event-driven requests and negotiation. To meet real-time constraints, we deploy a compact reasoning model trained with verifier-based self-verification and periodically refined online via shadow updates. Simulations show manageable, near-linear control-plane overhead as domains scale and during domain joins, and robust decision quality, including recovery after objective changes. We close by outlining future research directions for principled, secure, and uncertainty-aware agentic orchestration in Organic 6G.
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。