Temporal-semantic-drift

Machine learning systems deployed in production environments face a fundamental architectural tension: they are traditionally trained under the assumption of stationary data distributions, yet real-world text streams are inherently non-stationary. As language evolves, cultural references shift, and breaking news cycles introduce novel token combinations, static classifiers suffer from silent performance decay over time. In this paper, we present a reproducible, lightweight research framework designed to quantify semantic space divergence in continuous text streams. By leveraging dense transformer embeddings (allMiniLM-L6-v2) and tracking geometric centroid trajectories via cosine distance, our pipeline detects distribution shifts and drives an automated, threshold-triggered adaptive retraining mechanism. Empirical evaluations across multiple classification architectures—Logistic Regression, Linear Support Vector Machines (SVM), and Random Forest—reveal that while unmitigated static models experience sharp F1-score degradation and prediction confidence erosion, automated adaptation successfully restores predictive performance without requiring continuous, computationally prohibitive full-dataset retraining.