Reasoning Language Models for Root Cause Analysis in 5G Wireless
  Networks
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.
6889ded331e1218a089282ea
Mohamed Sana
claimed_verified
2025-09-15T15:09:51.965Z
6704fb61309c00c6ebfdd60f
/avatars/5b9d568850d85405659f20ee13d654fd.svg
Mohamed Sana
user
msana
6889ded331e1218a089282eb
Nicola Piovesan
claimed_verified
2025-08-06T19:32:40.719Z
650ab653cc02352e1e43e43e
https://cdn-avatars.huggingface.co/v1/production/uploads/650ab653cc02352e1e43e43e/Zl_nv3yFWhJYeu3EYKwIU.jpeg
Nicola Piovesan
user
nicopi
6889ded331e1218a089282ec
Antonio De Domenico
6889ded331e1218a089282ed
Yibin Kang
6889ded331e1218a089282ee
Haozhe Zhang
6889ded331e1218a089282ef
Merouane Debbah
6889ded331e1218a089282f0
Fadhel Ayed
Reasoning Language Models for Root Cause Analysis in 5G Wireless
  Networks
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.
A lightweight framework using Large Language Models (LLMs) with TeleLogs dataset and a two-stage training methodology improves Root Cause Analysis (RCA) in mobile networks by enhancing interpretability and reasoning quality.
Large Language Models TeleLogs RCA domain-specific adaptation supervised fine-tuning reinforcement learning diagnostic explanations reasoning-enhanced LLMs