# Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis

arXiv: https://arxiv.org/abs/2609.02805 · HTML: https://mlsanity.com/paper/2609.02805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 12 of its 14 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 12 of 12)

- [Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs](https://mlsanity.com/paper/2506.14245/) (2025)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models](https://mlsanity.com/paper/2407.09424/) (2024)
- [Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks](https://mlsanity.com/paper/2507.21974/) (2025)
- [Large Language Models for Wireless Networks: An Overview from the Prompt Engineering Perspective](https://mlsanity.com/paper/2411.04136/) (2024)
- [Self-Refined Generative Foundation Models for Wireless Traffic Prediction](https://mlsanity.com/paper/2408.10390/) (2024)
- [Prompting Wireless Networks: Reinforced In-Context Learning for Power Control](https://mlsanity.com/paper/2506.06526/) (2025)
- [Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation](https://mlsanity.com/paper/2602.17529/) (2026)
- [AI Empowered Net-RCA for 6G](https://mlsanity.com/paper/2212.00331/) (2022)
- [TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks](https://mlsanity.com/paper/2507.18190/) (2025)
- [Leveraging Multi-Agent System (MAS) and Fine-Tuned Small Language Models (SLMs) for Automated Telecom Network Troubleshooting](https://mlsanity.com/paper/2511.00651/) (2025)
- [Understanding 6G through Language Models: A Case Study on LLM-aided Structured Entity Extraction in Telecom Domain](https://mlsanity.com/paper/2505.14906/) (2025)

## Abstract (arXiv)

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.