The field of imbalanced classification is moving towards more adaptive and dynamic approaches to handling class imbalance. Researchers are exploring new methods that can adjust to changes in class-wise learning difficulty, allowing models to focus on underperforming classes and improve overall performance. Notably, innovative techniques such as adaptive resampling, group-aware threshold calibration, and quantum-inspired oversampling are being developed to address the challenges of class imbalance. These approaches have shown promising results in various benchmarks and datasets, demonstrating their potential to advance the state-of-the-art in imbalanced classification. Noteworthy papers in this area include: ART, which proposes an adaptive resampling-based training method that updates the distribution of the training data based on class-wise performance. Extrapolated Markov Chain Oversampling Method, which introduces a novel Markov chain-based text oversampling method that expands the minority feature space. Beyond Synthetic Augmentation, which demonstrates the effectiveness of group-aware threshold calibration in achieving robust balanced accuracy. QI-SMOTE, which leverages quantum principles to generate synthetic instances that preserve complex data structures. AxelSMOTE, which implements an agent-based approach that views data instances as autonomous agents engaging in complex interactions.