Update README.md

README.md

@@ -16,7 +16,8 @@ Multi-modal Variational Autoencoder for text embedding transformation using geom
 This first version is essentialy clip_l + t5-base. Similar to those shunt prototypes in concept but entirely divergent in this implementation. This variation is formatted and trained specifically as a VAE to encode/decode pairs of encodings together.
 Cantor cross-attention allows a form of high-density sparse containment, which when implemented correctly is a highly efficient global attention mechanism to ensure solidity.
 Fractal modalities make this possible. This is due to sparsity gaps in combinatory route pathologies to learned encoding pattern point encodings,
-thus this allows the matching of a series of potentials that can be viewed only when necessary in the otherwise empty space. Fractal gaps that are filled with purpose.
+thus this allows the matching of a series of potentials that can be viewed only when necessary in the otherwise empty cantor stair space. Fractal gaps that are filled with purpose occupy this space based on fingerprint routes, 
+allowing emergent fractal mathematics that otherwise could not assist each-other to understand the rules of those topologies.
 
 The current implementation is trained with only a handful of token sequences, so it's essentially front-loaded. Expect short sequences to work along with many longer squences.
 Full-sequence pretraining will begin soon with a uniform vocabulary that takes both tokens in for a representative uniform token based on the position.