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

Paper recorded by Signals 4 on 2026-09-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.AI · 人工智能 · first seen 2026-09-03

Abstract

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, unst

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#205 most recent of 300 cs.AI papers we have recorded · ↑ newer: AI Contextual Measurement for Recovering Individual and Group-Level Ef · ↓ older: frb100-40 After Two Decades: An Optimality Certificate and a Preregist
Cite this page: Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis: the #205 most recent of 300 cs.AI papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/large-language-models-llms-for-telecom-root-cause-analysis-rca-a-structured-reas.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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