Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911638325559296 |
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| author | Liu, Xin He, Yuhang Zhao, Sichen Tong, Kejian Zhang, Xingyu |
| author_facet | Liu, Xin He, Yuhang Zhao, Sichen Tong, Kejian Zhang, Xingyu |
| contents | Root cause localization in cloud native microservice systems requires modeling complex service dependencies, irregular temporal dynamics, and heterogeneous observability data. We present HyperODE RCA, a unified framework that combines hypergraph attention learning, latent ordinary differential equations, and multimodal cross attention fusion for fine grained root cause analysis. The method learns higher order service interactions through differentiable hyperedge construction, captures continuous anomaly evolution from irregular observations with an ODE RNN encoder, and adaptively fuses logs, traces, metrics, entities, and events using context aware modality routing. We further improve robustness with a variational information bottleneck, temporal causal regularization, and invariant risk constraints. Experiments on the Tianchi AIOps benchmark show clear gains over strong baselines in ranking and classification performance, while preserving interpretability through learned hypergraph attention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00351 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices Liu, Xin He, Yuhang Zhao, Sichen Tong, Kejian Zhang, Xingyu Machine Learning Artificial Intelligence Root cause localization in cloud native microservice systems requires modeling complex service dependencies, irregular temporal dynamics, and heterogeneous observability data. We present HyperODE RCA, a unified framework that combines hypergraph attention learning, latent ordinary differential equations, and multimodal cross attention fusion for fine grained root cause analysis. The method learns higher order service interactions through differentiable hyperedge construction, captures continuous anomaly evolution from irregular observations with an ODE RNN encoder, and adaptively fuses logs, traces, metrics, entities, and events using context aware modality routing. We further improve robustness with a variational information bottleneck, temporal causal regularization, and invariant risk constraints. Experiments on the Tianchi AIOps benchmark show clear gains over strong baselines in ranking and classification performance, while preserving interpretability through learned hypergraph attention. |
| title | Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.00351 |