Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis
A research paper proposes a structured reasoning framework for LLM-enabled telecom root cause analysis. The framework organizes heterogeneous network telemetry into canonical contexts, enforces decision-path reasoning during diagnosis, and generates evidence-grounded explanations. The paper reviews the evolution of telecom RCA from rule-based and ML approaches to LLM-enabled techniques, including structured reasoning, retrieval-augmented knowledge grounding, agentic orchestration, and verifiable reasoning.
The paper addresses challenges in telecom root cause analysis for 5G and emerging 6G networks, where vanilla LLMs often hallucinate or reason unstably. It proposes a structured reasoning framework that aligns diagnostic reasoning with telecom-specific evidence and domain knowledge. The approach first organizes heterogeneous network telemetry into canonical contexts, then enforces decision-path reasoning, and finally generates evidence-grounded explanations for reliable fault identification.
The framework introduces a three-stage pipeline: canonical context organization, enforced decision-path reasoning, and evidence-grounded explanation generation. This design aims to mitigate hallucination and unstable reasoning by constraining LLM outputs to structured evidence and explicit reasoning paths, potentially improving reliability in complex cross-layer network diagnostics.
Telecom operators may adopt LLM-based RCA tools that integrate structured reasoning and retrieval-augmented grounding to reduce manual troubleshooting time and improve network reliability. The approach could lower operational costs and accelerate fault resolution in 5G/6G networks, but requires validation on real-world telemetry data.
Improved RCA accuracy and explainability could reduce network downtime, lower operational expenses, and enhance service quality for telecom providers. The framework may also create opportunities for AIOps vendors targeting telecom verticals.
Next signals include empirical evaluations of the proposed framework on telecom datasets, comparisons with baseline LLM and ML methods, and potential integration with agentic orchestration or verifiable reasoning systems. Adoption by network equipment vendors or operators would indicate commercial traction.