A newly published arXiv paper posits that a rigorous scientific theory of deep learning will inevitably emerge to fully explain neural network behavior. The authors argue that transitioning from purely empirical observations to a unified mathematical framework is essential for the field's continued advancement. This foundational theory aims to demystify exactly why deep learning models generalize so effectively despite their immense architectural complexity.