FAME achieves F1 of 98.16 on BGL and 99.95 on Thunderbird for message-level log anomaly detection using at most K=100 labels per template, reducing annotation effort by 76x while detecting anomalies from unseen EventIDs.
Large language models can provide accurate and interpretable incident triage,
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
OpsAgent presents a training-free multi-agent framework with dual self-evolution for automated incident management in microservices, claiming SOTA results on OPENRCA benchmark and successful production deployment at Lenovo.
citing papers explorer
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FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection
FAME achieves F1 of 98.16 on BGL and 99.95 on Thunderbird for message-level log anomaly detection using at most K=100 labels per template, reducing annotation effort by 76x while detecting anomalies from unseen EventIDs.
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OpsAgent: An Evolving Multi-agent System for Incident Management in Microservices
OpsAgent presents a training-free multi-agent framework with dual self-evolution for automated incident management in microservices, claiming SOTA results on OPENRCA benchmark and successful production deployment at Lenovo.