SDVDiag integrates RLHF and context pruning to raise causal edge detection precision from 14% to 100% in an automated valet parking test, outperforming purely data-driven methods.
A comprehensive survey on root cause analysis in (micro) services: Methodologies, challenges, and trends
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices that uses multimodal alignment, vector-quantized symptom tokens, and a hypothesis-evidence-test workflow to separate root causes from cascading symptoms.
LATS-RCA applies multi-agent Language Agent Tree Search to automate root cause analysis in microservices, reporting high accuracy on a small open-source Java system but lower accuracy in a complex production environment.
citing papers explorer
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SDVDiag: Using Context-Aware Causality Mining for the Diagnosis of Connected Vehicle Functions
SDVDiag integrates RLHF and context pruning to raise causal edge detection precision from 14% to 100% in an automated valet parking test, outperforming purely data-driven methods.
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TopoEvo: A Topology-Aware Self-Evolving Multi-Agent Framework for Root Cause Analysis in Microservices
TopoEvo is a topology-aware self-evolving multi-agent framework for root cause analysis in microservices that uses multimodal alignment, vector-quantized symptom tokens, and a hypothesis-evidence-test workflow to separate root causes from cascading symptoms.
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Multi-Agent Systems for Root Cause Analysis in Microservices
LATS-RCA applies multi-agent Language Agent Tree Search to automate root cause analysis in microservices, reporting high accuracy on a small open-source Java system but lower accuracy in a complex production environment.