Author

Bin Li

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#machine learning Preprint Sep 2026

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

A systematic empirical analysis across three public log datasets, examining different adaptation strategies, model architectures, parameter scales, and quantization settings reveals substantial performance differences across adaptation strategies, while model scaling yields varying detection gains across datasets.

Bin Li, Dongdong Wang, Si-Yang Lu · 0 citations

#artificial intelligence Preprint Aug 2026

Too Sure to Be Safe: Model Calibration for Reliable Log Anomaly Detection

Log Reconstruction and Distance (LoRD), a lightweight post-hoc calibration framework for reliable log anomaly detection, is proposed and demonstrates that LoRD consistently improves confidence reliability and substantially reduces overconfident anomaly-related errors without sacrificing anomaly detection performance.

Bin Li, Dongdong Wang, Si-Yang Lu · 0 citations