发表机构
Samsung Research America（三星美国研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对5G/6G电信网络RCA难题，提出LLM驱动的结构化推理框架，经TeleLogs、TelecomTS数据集验证，可提升诊断准确率与决策一致性，为实用型LLM-RCA系统提供关键支撑。
AI 中文摘要
根本原因分析（RCA）是电信网络运营中的关键任务，但由于现代5G及新兴6G网络存在复杂的跨层依赖，其性能下降诊断仍具挑战性。尽管大语言模型（LLMs）具备推理与知识整合的潜力，但直接将普通LLMs应用于电信RCA常产生幻觉、推理不稳定且与结构化网络证据的适配性差等问题。本研究首先回顾了电信RCA从基于规则、机器学习（ML）方法到新兴LLM驱动技术的演进，概述了近期范式，包括结构化推理、检索增强知识接地、智能体编排及可验证推理。基于这些见解，我们提出一种用于LLM驱动电信RCA的结构化推理框架，该框架使诊断推理与电信特定证据及领域知识相适配。所提方法首先将异构网络遥测数据整理为规范上下文，随后在诊断过程中强制实施决策路径推理，最终生成证据支撑的解释以实现可靠的故障识别。在两个5G RCA数据集TeleLogs和TelecomTS上的实验结果表明，与基线技术相比，所提框架可持续提升诊断准确率与决策一致性。这些跨数据集结果凸显了结构化推理设计对下一代电信网络中实用型LLM驱动RCA系统的重要性。
英文摘要
Root cause analysis (RCA) is a critical task in telecom network operations, but diagnosing performance degradations in modern 5G and emerging 6G networks remains challenging due to complex cross-layer dependencies. While large language models (LLMs) offer promising capabilities for reasoning and knowledge integration, directly applying vanilla LLMs to telecom RCA often leads to hallucination, unstable reasoning, and poor alignment with structured network evidence. This work first reviews the evolution of telecom RCA from rule-based and machine learning (ML) approaches to emerging LLM-enabled techniques, and provides an overview of recent paradigms, including structured reasoning, retrieval-augmented knowledge grounding, agentic orchestration, and verifiable reasoning. Building upon these insights, we propose a structured reasoning framework for LLM-enabled telecom RCA that aligns diagnostic reasoning with telecom-specific evidence and domain knowledge. The proposed approach first organizes heterogeneous network telemetry into canonical contexts, and then enforces decision-path reasoning during diagnosis, and finally generates evidence-grounded explanations for reliable fault identification. Experimental results on two 5G RCA datasets, TeleLogs and TelecomTS, demonstrate that the proposed framework consistently improves diagnostic accuracy and decision consistency compared with baseline techniques. These cross-dataset results highlight the importance of structured reasoning design for practical LLM-based RCA systems in next-generation telecom networks.
