arXiv Machine Learning July 21, 2026 By Huanchi Wang, Zihang Huang, Yifang Tian, Kristina Dzeparoska, Hans-Arno Jacobsen, Alberto Leon-Garcia

FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection

Read the original on arXiv Machine Learning →

arXiv:2605. 22779v2 Announce Type: replace-cross Abstract: Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines rather than identifying the specific message responsible.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

NLLog: Lightweight, Explainable SOC Anomaly Detection via Log-to-Language Rewriting

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