Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis
A telecom diagnosis pipeline structures telemetry before forcing a decision path and evidence-grounded explanation. The pattern is relevant to agents reasoning over heterogeneous operational data.
The framework turns heterogeneous telemetry into **canonical contexts**, enforces **decision-path reasoning**, and produces evidence-grounded fault explanations. It was evaluated on the TeleLogs and TelecomTS 5G datasets.
For diagnostic agents, normalize observations before inference and require each conclusion to follow an explicit path tied to evidence. This creates checkpoints for validation instead of relying on a vanilla model’s free-form answer.
The framework turns heterogeneous telemetry into **canonical contexts**, enforces **decision-path reasoning**, and produces evidence-grounded fault explanations. It was evaluated on the TeleLogs and TelecomTS 5G datasets. For diagnostic agents, normalize observations before inference and require each conclusion to follow an explicit path tied to evidence. This creates checkpoints for validation instead of relying on a vanilla model’s free-form answer. The paper reports improved accuracy and consistency across **two 5G datasets**, but the supplied abstract gives no scores. Telecom-specific evidence structures may not transfer directly to software incident diagnosis.
This turns structured context from a retrieval design into a diagnostic control: telemetry is normalized before inference, and every fault conclusion must traverse an evidence-linked decision path. It reinforces preserving relationships and provenance, while adding explicit reasoning checkpoints. The evidence is limited to two telecom datasets, so it supports the pattern without establishing transfer to software incidents.