RCLAgent uses multi-agent recursion-of-thought with parallel reasoning on trace graphs to outperform prior methods in root cause localization accuracy and efficiency for microservice systems.
Simplifying root cause analysis in Kubernetes with StateGraph and LLM
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
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.
A catalog-driven framework translates natural language into PromQL queries with dynamic temporal resolution for cloud-native observability.
citing papers explorer
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Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought
RCLAgent uses multi-agent recursion-of-thought with parallel reasoning on trace graphs to outperform prior methods in root cause localization accuracy and efficiency for microservice systems.
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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.
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From Natural Language to PromQL: A Catalog-Driven Framework with Dynamic Temporal Resolution for Cloud-Native Observability
A catalog-driven framework translates natural language into PromQL queries with dynamic temporal resolution for cloud-native observability.