Root Cause Analysis Method Based on Large Language Models with Residual Connection Structures

• URL: http://arxiv.org/abs/2602.08804v1
• Date: Mon, 09 Feb 2026 15:41:55 GMT
• Title: Root Cause Analysis Method Based on Large Language Models with Residual Connection Structures
• Authors: Liming Zhou, Ailing Liu, Hongwei Liu, Min He, Heng Zhang,
• Abstract summary: This paper proposes a residual-connection-based root cause analysis method using large language model (LLM)<n> Experimental results on CCF-AIOps microservice datasets demonstrate that RC-LLM achieves strong accuracy and efficiency in root cause analysis.
• Score: 13.802178274787948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Root cause localization remain challenging in complex and large-scale microservice architectures. The complex fault propagation among microservices and the high dimensionality of telemetry data, including metrics, logs, and traces, limit the effectiveness of existing root cause analysis (RCA) methods. In this paper, a residual-connection-based RCA method using large language model (LLM), named RC-LLM, is proposed. A residual-like hierarchical fusion structure is designed to integrate multi-source telemetry data, while the contextual reasoning capability of large language models is leveraged to model temporal and cross-microservice causal dependencies. Experimental results on CCF-AIOps microservice datasets demonstrate that RC-LLM achieves strong accuracy and efficiency in root cause analysis.

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