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.
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QTyBERT matches or exceeds BERT-based log anomaly detection effectiveness while reducing embedding generation time to near static word embedding levels.
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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.
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A Comparative Study of Semantic Log Representations for Software Log-based Anomaly Detection
QTyBERT matches or exceeds BERT-based log anomaly detection effectiveness while reducing embedding generation time to near static word embedding levels.