# Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphs

arXiv: https://arxiv.org/abs/2511.17566 · HTML: https://mlsanity.com/paper/2511.17566/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 14 Nov 2025.

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

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [Efficiently Identifying Task Groupings for Multi-Task Learning](https://mlsanity.com/paper/2109.04617/) (2021)
- [UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks](https://mlsanity.com/paper/2105.00956/) (2021)
- [Exploring LLM-based Agents for Root Cause Analysis](https://mlsanity.com/paper/2403.04123/) (2024)
- [HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs](https://mlsanity.com/paper/2010.04558/) (2020)
- [Robust Failure Diagnosis of Microservice System through Multimodal Data](https://mlsanity.com/paper/2302.10512/) (2023)
- [BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection](https://mlsanity.com/paper/2405.09330/) (2024)
- [Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data](https://mlsanity.com/paper/2302.05092/) (2023)
- [Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems](https://mlsanity.com/paper/2207.09021/) (2022)
- [A Survey of AIOps for Failure Management in the Era of Large Language Models](https://mlsanity.com/paper/2406.11213/) (2024)
- [Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis](https://mlsanity.com/paper/2407.01710/) (2024)
- [Cross-System Software Log-based Anomaly Detection Using Meta-Learning](https://mlsanity.com/paper/2412.15445/) (2024)
- [TVDiag: A Task-oriented and View-invariant Failure Diagnosis Framework with Multimodal Data](https://mlsanity.com/paper/2407.19711/) (2024)
- [FaaSRCA: Full Lifecycle Root Cause Analysis for Serverless Applications](https://mlsanity.com/paper/2412.02239/) (2024)

## Built on by (most cited first, 2 of 2)

- [Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems](https://mlsanity.com/paper/2605.14259/) (2026)
- [How Far Can Root Cause Analysis Go on Real-World Telemetry Data?](https://mlsanity.com/paper/2607.13548/) (2026)

## Abstract (arXiv)

Root cause analysis in microservice systems typically involves two core tasks: root cause localization (RCL) and failure type identification (FTI). Despite substantial research efforts, conventional diagnostic approaches still face two key challenges. First, these methods predominantly adopt a joint learning paradigm for RCL and FTI to exploit shared information and reduce training time. However, this simplistic integration neglects the causal dependencies between tasks, thereby impeding inter-task collaboration and information transfer. Second, these existing methods primarily focus on point-to-point relationships between instances, overlooking the group nature of inter-instance influences induced by deployment configurations and load balancing. To overcome these limitations, we propose CCLH, a novel root cause analysis framework that orchestrates diagnostic tasks based on cascaded conditional learning. CCLH provides a three-level taxonomy for group influences between instances and incorporates a heterogeneous hypergraph to model these relationships, facilitating the simulation of failure propagation. Extensive experiments conducted on datasets from three microservice benchmarks demonstrate that CCLH outperforms state-of-the-art methods in both RCL and FTI.