Large Language Models (LLMs) for Telecom Root Cause... | AI Research

Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis
Modern 5G and emerging 6G networks are highly complex, making it difficult to identify the root causes of performance issues. While LLMs have the potential to assist in troubleshooting, they often struggle with "hallucinations"—making up incorrect explanations—or providing unstable reasoning when faced with technical network data. This paper introduces a new framework called SEKA-FT, which moves beyond simple prediction by forcing the AI to follow a structured, evidence-based reasoning path, ensuring that its final diagnosis is grounded in actual network telemetry and domain knowledge.

From Statistical Guessing to Structured Reasoning

Traditional methods for diagnosing network faults have evolved from rigid, rule-based expert systems to machine learning models that look for statistical patterns. While these methods can process large amounts of data, they often fail to explain why a fault occurred. LLMs offer a significant upgrade because they can integrate knowledge across different network layers and provide human-readable explanations. However, the authors note that simply using a standard LLM is insufficient because these models often rely on superficial correlations rather than the causal relationships required for reliable telecom operations.

How the SEKA-FT Framework Works

The proposed SEKA-FT framework transforms the diagnostic process into a three-step "evidence-to-path-to-decision" structure:

1. Canonical Context Structuring: The framework organizes messy, heterogeneous network data—such as user-plane KPIs, mobility statistics, and configuration parameters—into standardized, consistent blocks. This ensures the model always interprets specific metrics (like signal strength or scheduling data) in the same way, regardless of how the raw data was originally formatted. 2. Decision-Path Control: Instead of asking the model to jump straight to a conclusion, the framework forces it to perform staged diagnostic checks. The model must systematically rule out unsupported hypotheses—such as checking if a user's speed or handover frequency is the culprit—before arriving at a final decision. 3. Evidence-Anchored Explanations: The model is trained to generate explanations that explicitly link its final diagnosis back to the specific network indicators it analyzed. This makes the AI’s reasoning traceable and reduces the likelihood of it "guessing" a root cause without supporting evidence.

Performance and Reliability

The authors tested the SEKA-FT framework on two 5G-specific datasets, TeleLogs and TelecomTS. The results showed that this structured approach consistently outperformed baseline techniques in both diagnostic accuracy and decision consistency. By forcing the model to verify its reasoning against concrete telecom evidence, the framework successfully mitigated common issues like hallucination and unstable logic, proving that structured reasoning is essential for deploying LLMs in real-world, high-stakes network environments.

Key Takeaways for Future Implementation

The research highlights that the future of AI-driven telecom troubleshooting lies in "grounding"—connecting the language capabilities of LLMs to the physical reality of the network. The authors suggest that effective RCA systems should evolve through a progression of techniques: starting with structured reasoning (Chain-of-Thought), moving to external knowledge retrieval (RAG), utilizing autonomous agents for tool interaction, and eventually incorporating outcome-based feedback (RLVR) where the model learns from the actual impact of its diagnostic actions on the network.