A recent paper published on arXiv introduces an adaptive sampling framework aimed at addressing the challenges of localized concept drift detection under conditions of label scarcity. This framework combines residual-based exploration with Exponentially Weighted Moving Average (EWMA) monitoring to enhance robustness in identifying shifts in data distributions. The approach is particularly relevant for dynamic industrial environments where data streams may evolve unpredictably and labeled data is limited. The proposed method has been positively evaluated for its effectiveness in managing concept drift, suggesting potential improvements over existing techniques. By integrating these monitoring and sampling strategies, the framework offers a practical solution for maintaining predictive model performance in changing conditions. This development aligns with ongoing research efforts focused on adaptive learning systems in real-world applications.