The recently introduced GAFD-CC (Global-Aware Feature Decoupling with Confidence Calibration) method offers a significant advancement in out-of-distribution (OOD) detection by explicitly addressing the correlation between features and logits within learning models. This approach enhances the reliability of models when deployed in real-world applications, where distinguishing between in-distribution and OOD data is critical. By decoupling features globally and calibrating confidence scores, GAFD-CC improves the accuracy and robustness of OOD detection systems. Confirmed evaluations indicate that this method outperforms previous techniques, providing more dependable detection outcomes. The development aligns with ongoing research efforts documented on arXiv, reflecting a growing focus on improving model trustworthiness through refined feature analysis and confidence calibration. As such, GAFD-CC represents a promising direction for enhancing AI safety and performance in diverse operational contexts.