A Modular Architecture for Detecting Anomalous Data Trends in Research Systems

Modern AI and data-driven systems operate in dynamic environments where anomalous drift of interpreted/produced data (semantic, statistical, or calibration-based) can degrade reliability. This project presents a modular supervision architecture for real-time drift detection across diverse data streams. The framework integrates centroid-based embedding comparison, k-nearest neighbor classification, and calibration metrics to flag deviations from a learned baseline. Designed for flexibility, it allows researchers to configure layers, thresholds, and fusion strategies for domains ranging from language and signal processing to experimental instrumentation. By providing a transparent, adaptable foundation for detecting drift, this work highlights the importance of interpretability and reproducibility in research software.