Matrix spillover quantification represents a crucial challenge in complex learning. AI-driven approaches offer a promising solution by leveraging sophisticated algorithms to interpret the magnitude of spillover effects between distinct matrix elements. This process boosts our understanding of how information flows within neural networks, leading to