A Knowledge-Enhanced Iterative Reasoning Framework for Accurate and Traceable Fault Diagnosis in Distributed Service Systems

Abstract

Fault diagnosis in distributed systems is challenged by complex service dependencies, cascading anomaly propagation, and similar symptom patterns. This paper proposes a knowledge-enhanced iterative reasoning framework that integrates large language models (LLMs) with a numerical domain knowledge graph (KG). The KG encodes fault–symptom relations, anomaly directions, and training-derived mean and standard-deviation intervals. Structured prompting first generates candidate faults; interval verification then rejects numerically inconsistent candidates. For retained candidates, counterfactual reasoning constructs hierarchical causal chains, KG traversal refines missing or inconsistent links, and a deterministic evidence score supports acceptance, exclusion, early stopping, and fallback across at most five iterations. Under the common 68-case evaluation protocol for eight known single-root-cause faults in the controlled Redis-based testbed, the complete framework achieved 100.00% Accuracy, Macro-F1, and Balanced Accuracy with GPT-4o and GPT-5.2, compared with 91.18% accuracy for KG-only reasoning and 85.29–89.71% for Random Forest, XGBoost, and Transformer baselines. GPT-3.5 reached 98.53%, whereas LLaMA-3.1-8B reached 80.88%, showing that the incremental KG–LLM gain is backbone-dependent. Five GPT-4o repetitions and three GPT-5.2 repetitions yielded 100.00% ± 0.00, and all three metrics remained at 100.00% across the evaluated Z-score thresholds, iteration limits, and interval tolerances. The framework therefore provides highly accurate, stable, and traceable diagnoses within the evaluated Redis-based distributed-service protocol, while providing explicit intermediate reasoning and solution retrieval.