发表机构
i2CAT Foundation; University of Oulu; Research Institute for Digital Future, Khalifa University（i2CAT基金会; 奥卢大学; 哈利法大学数字未来研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对6G网络中LLM智能体管理RAN时的幻觉级联问题，提出基于cellular sheaf的心智理论框架及五条设计原则，经10亿参数电信语言模型验证有效。
AI 中文摘要
未来6G网络将依赖大语言模型（LLM）智能体来管理无线接入网（RAN）。然而，当前架构假设智能体间的消息传递客观事实，但实际上消息是发送者推理的“轨迹”：它承载主观结论，因此语法有效的报告可能传播AI幻觉并触发协议验证无法察觉的级联中断。读取此类轨迹需要心智理论（ToM）——接收者在行动前必须建模对等体的信念，以及处于该位置的对等体应有的信念。我们将这些交互建模为 cellular sheaf 上的认知信道，得到了弹性多智能体系统的统一框架，从中衍生出五条设计原则：（i）消息是发送者隐藏推理的证据；（ii）信任是连续的认知信噪比（SNR）——断言精度与建模对等体信念偏差的比值；（iii）全网一致性和抗幻觉传播性可通过 sheaf 的拉普拉斯算子计算；（iv）对等体建模必须恰好在两层停止，以节省计算并应对互信息衰减；（v）可信容量受操作目标一致性而非链路带宽限制。对本地部署的10亿参数电信语言模型的信令风暴研究验证了该框架：认知信噪比可隔离出4个邻居中有3个认同的产生幻觉的对等体，其中分歧门将每个错误对等体排在正确对等体之上；仅两层ToM能恢复正确行动；谱间隙决定拓扑是否在近实时预算内达到一致性。
英文摘要
Future 6G networks will rely on Large Language Model (LLM) agents to manage the Radio Access Network (RAN). However, current architectures assume inter-agent messages convey objective facts. A message is instead a \emph{trace} of the sender's reasoning: it carries a subjective conclusion, so a syntactically valid report can propagate an AI hallucination and trigger a cascading outage invisible to protocol validation. Reading such a trace requires a Theory of Mind (ToM)---before acting, the receiver must model what the peer believes, and what a peer in that position should have believed. Modeling these interactions as cognitive channels on a cellular sheaf, we obtain a unified framework for resilient multi-agent systems, from which five design principles emerge: (i) a message is evidence of the sender's hidden reasoning; (ii) trust is a continuous cognitive Signal-to-Noise Ratio (SNR)---asserted precision over deviation from the modeled peer belief; (iii) network-wide consistency and resistance to hallucination contagion are computable via the sheaf's Laplacian; (iv) peer-modeling must halt at exactly two levels to conserve compute and survive mutual information decay; and (v) credible capacity is bounded by operational goal alignment, not link bandwidth. A signaling-storm study on locally deployed 1B-parameter telecom language models validates it: cognitive SNR isolates a hallucinating peer that three of its four neighbors agree with, where a divergence gate ranks every wrong peer above the right one; only depth two ToM recovers the correct action; and the spectral gap decides whether a topology reaches consistency inside the near-real-time budget.
Comments8 pages, 3 figures, 3 tables
