Axiomatizing Neural Networks via Pursuit of Subspaces

ArXi:2605.20534v1 Announce Type: new While deep neural networks have achieved remarkable success across a wide range of domains, their underlying mechanisms remain poorly understood, and they are often regarded as black boxes. This gap between empirical performance and theoretical understanding poses a challenge analogous to the pre-axiomatic stage of classical geometry. In this work, we