# Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks

arXiv: https://arxiv.org/abs/2507.21974 · HTML: https://mlsanity.com/paper/2507.21974/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## 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, 11 of 11)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training](https://mlsanity.com/paper/2501.17161/) (2025)
- [Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs](https://mlsanity.com/paper/2412.21187/) (2024)
- [RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models](https://mlsanity.com/paper/2310.16340/) (2023)
- [Exploring LLM-based Agents for Root Cause Analysis](https://mlsanity.com/paper/2403.04123/) (2024)
- [Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis](https://mlsanity.com/paper/2502.08224/) (2025)
- [Survey on Models and Techniques for Root-Cause Analysis](https://mlsanity.com/paper/1701.08546/) (2017)
- [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)

## Built on by (most cited first, 13 of 13)

- [TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications](https://mlsanity.com/paper/2605.09929/) (2026)
- [TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?](https://mlsanity.com/paper/2605.18025/) (2026)
- [PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis](https://mlsanity.com/paper/2606.00582/) (2026)
- [TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Tasks](https://mlsanity.com/paper/2609.25356/) (2026)
- [Automated Fault Detection in 5G Core Networks Using Large Language Models](https://mlsanity.com/paper/2512.19697/) (2025)
- [Communications-Incentivized Collaborative Reasoning in NetGPT through Agentic Reinforcement Learning](https://mlsanity.com/paper/2602.00766/) (2026)
- [HPO: Hysteretic Policy Optimization for Stable and Efficient Training under Sparse-Reward Regime](https://mlsanity.com/paper/2605.30201/) (2026)
- [CTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations](https://mlsanity.com/paper/2608.12002/) (2026)
- [Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge](https://mlsanity.com/paper/2608.21021/) (2026)
- [TelecomGPT-R1: A Unified Open-Source Reasoner for the Telecom Stack](https://mlsanity.com/paper/2608.26126/) (2026)
- [Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis](https://mlsanity.com/paper/2609.02805/) (2026)
- [EdgeCraft: Automated Model Crafting for Edge IoT](https://mlsanity.com/paper/2609.35167/) (2026)
- [OTel: Open Telco AI Datasets, Benchmarks, and Models](https://mlsanity.com/paper/2610.07766/) (2026)

## Abstract (arXiv)

Root Cause Analysis (RCA) in mobile networks remains a challenging task due to the need for interpretability, domain expertise, and causal reasoning. In this work, we propose a lightweight framework that leverages Large Language Models (LLMs) for RCA. To do so, we introduce TeleLogs, a curated dataset of annotated troubleshooting problems designed to benchmark RCA capabilities. Our evaluation reveals that existing open-source reasoning LLMs struggle with these problems, underscoring the need for domain-specific adaptation. To address this issue, we propose a two-stage training methodology that combines supervised fine-tuning with reinforcement learning to improve the accuracy and reasoning quality of LLMs. The proposed approach fine-tunes a series of RCA models to integrate domain knowledge and generate structured, multi-step diagnostic explanations, improving both interpretability and effectiveness. Extensive experiments across multiple LLM sizes show significant performance gains over state-of-the-art reasoning and non-reasoning models, including strong generalization to randomized test variants. These results demonstrate the promise of domain-adapted, reasoning-enhanced LLMs for practical and explainable RCA in network operation and management.