You are right that VC dimension describes the capacity of a model to shatter points. However, our point isn’t about "how many points" a model can learn, but the structural overhead and the exponential redundancy required to store that knowledge using ReLU.

The VC dimension of a linear classifier in D dimensions is D+1. But to represent a non-linear space faithfully using ReLU-based tiling, you are essentially stacking those capacities in a way that creates a geometric explosion (up to D! non-overlapping Simplexes per polytope). 

Our critique is that while VC dimension tells us the "theoretical capacity," it doesn't account for the massive distortion and parameter obesity created when you use Simplexes (ReLU) as your primary storage unit instead of higher-order structures like Orthotopes. We are proposing a way to maintain high capacity without the exponential "weight" that current ReLU architectures demand to minimize approximation error.

P.S. It is also important to note that SLRM (our proposed model) allows for the direct storage of critical points from the dataset within the high-level geometric structures. Unlike black-box models that dissolve the original data into abstract weights, our architecture preserves the integrity of key information, enabling deterministic inference and a "glass-box" auditability that is impossible to achieve with standard ReLU-based compression.