Even if large language models grasp deep concepts like symmetry or linearity, their internal representations remain messy and entangled — opaque to human understanding. This lack of structure may limit their ability to generalise far beyond training data. Pairing them with well-organised, ontological frameworks offers a path toward clarity, verifiability, and deeper reasoning.