Quesma Releases OTelBench: Independent Benchmark Reveals Frontier LLMs Struggle with Real ...

New benchmark shows top LLMs achieve only 29% pass rate on OpenTelemetry instrumentation, exposing the gap between ...
IEEE

STAD: Self-Supervised Transformer for Anomaly Detection in Multi-Variate Time Series Data

Abstract: Anomaly detection in multivariate time series has gained significant attention in past few years. The rarity of anomalies, considerable data volatility, absence of anomaly labels, and need ...
GitHub

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

07.2025: Dinomaly has been integrated in Intel open-edge Anomalib in v2.1.0. Great thanks to the contributors for the nice reproduction and integration. Anomalib is a comprehensive library for ...
IEEE

Advancing Anomaly Detection: Non-Semantic Financial Data Encoding With Large Language Models

Abstract: Detecting anomalies in general ledger data is of utmost importance to ensure the trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms ...
GitHub

HVAC Fault Detection with Anomaly Pipeline

A comprehensive anomaly detection system for commercial building HVAC systems. This project simulates realistic HVAC sensor data with labeled faults and implements both rules-based and ML-based ...