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

arXiv: https://arxiv.org/abs/2602.08804 · HTML: https://mlsanity.com/paper/2602.08804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Exploring LLM-based Agents for Root Cause Analysis](https://mlsanity.com/paper/2403.04123/) (2024)
- [Large Language Models in Fault Localisation](https://mlsanity.com/paper/2308.15276/) (2023)
- [CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications](https://mlsanity.com/paper/2209.02500/) (2022)
- [Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems](https://mlsanity.com/paper/2207.09021/) (2022)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings](https://mlsanity.com/paper/2108.00344/) (2021)
- [LLMs as Evaluators: A Novel Approach to Evaluate Bug Report Summarization](https://mlsanity.com/paper/2409.00630/) (2024)
- [The Multi-Agent Fault Localization System Based on Monte Carlo Tree Search Approach](https://mlsanity.com/paper/2507.22800/) (2025)

## Built on by (most cited first, 1 of 1)

- [Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition](https://mlsanity.com/paper/2609.36686/) (2026)

## Abstract (arXiv)

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.