Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices

2026-04-30

AI Summary

Root cause localization in microservice systems is challenged by complex dependencies, heterogeneous observability data, and irregular temporal dynamics. To address these issues, this work proposes HyperODE RCA, a novel framework that integrates differentiable hypergraph attention, latent-variable ordinary differential equations, and multimodal cross-attention to enable fine-grained root cause analysis. The approach models continuous anomaly evolution and employs context-aware multimodal routing, enhanced by variational information bottleneck, temporal causal regularization, and invariant risk constraints to improve robustness and generalization. Evaluated on the Tianchi AIOps benchmark, the method significantly outperforms strong baselines in both ranking and classification metrics while maintaining high interpretability through its hypergraph attention mechanism.

Abstract

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.

Problem

• root cause localization
• microservices
• service dependencies
• temporal dynamics
• heterogeneous observability data

Innovation

• hypergraph attention
• latent ODE
• multimodal fusion
• root cause localization
• microservices