Update README.md

README.md

@@ -31,7 +31,13 @@ One set of blocks is devoted to the geometry while the other set is devoted to t
 
 The accuracy of the geometry can be completely decoupled and the image portion zeroed to retrain if systems start to decay. 
 
-This has shown robust capability with multiple lineage trains; the geometry being left in a "frozen" state yeilds by far the worst outcomes - yet I froze everything including the geometric cross-attention and the subsystems while leaving the image-end of the cross-attention scrambled and learning, so more than likely it relearned incorrect math and got stuck at around 20%.
+This has shown robust capability with multiple lineage trains
+
+Imported geometry from another version showed that the geometry kept a cohesive shape, even when the image portion completely exploded. The model learned quickly and in non-shallow variance - presenting the potential of completely burning a model's shell with quick learning and then extracting the useful portions due to the stubbornness of the simplex and cayley-menger formulas.
+
+When the geometry being left in a "frozen" state yeilds by far the worst outcomes - yet I froze everything including the geometric cross-attention and the subsystems while leaving the image-end of the cross-attention scrambled and learning, so more than likely it relearned incorrect math and got stuck at around 20%.
+
+When the geometry is imported from the burned model and left learning, the outcome yielded some VERY interesting results. I'll need to rerun everything because NONE of the tensorboards got uploaded, which is annoying considering this was basically an afternoon - but it will be done.
 
 ## Current Experiment: beatrix-dualstream-base