UModel provides an object-centric ontological model for observability data and U-SPL query interface, improving root cause localization precision by 8% on the AIOps 2025 Challenge dataset with production deployment at Alibaba Cloud.
A survey of AIOps in the era of large language models,
2 Pith papers cite this work. Polarity classification is still indexing.
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Graph Traversal Agent improves root-cause F1 from 0.6087 to 0.9130 on ITBench snapshots but the gain is benchmark-coupled to cases where the injected fault is already in the evidence graph.
citing papers explorer
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UModel: An Agent-Ready Observability Data Modeling Method at Scale
UModel provides an object-centric ontological model for observability data and U-SPL query interface, improving root cause localization precision by 8% on the AIOps 2025 Challenge dataset with production deployment at Alibaba Cloud.
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Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents
Graph Traversal Agent improves root-cause F1 from 0.6087 to 0.9130 on ITBench snapshots but the gain is benchmark-coupled to cases where the injected fault is already in the evidence graph.