Update README.md

README.md

@@ -16,6 +16,14 @@ It seems only one loss is necessary, and that one loss is a combination of rose
 
 If my hunch is correct, centroid may be much weaker than rose5 with all the losses except geometric rose turned off.
 
+In any case, the model's objective needs to be restructured based on how this data responsed to the variants. Primarily the geometry and the patches need to be directly interdependent. The geometric pentachora need to learn and be an embedding gateway for additional information, as the frozen variants have shown they only provide meaning up to a certain point - and then that meaning is exhausted for small models. The meaning may be better than placbo but not by much. In fact; when cross entropy is introduce at high values, the meaning collapses into hinderance and the model simply learns to ignore the pentas, which is definitely not the goal.
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+The next version of this vit model will have gradient updates enabled with learning specifically allocated to the pentas in a few test methodologies. Some standard, some less standard and more geometric, some ccompletley bananas that probably won't work but I want to try them anyway.
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+I have quite a few formulas I need to try out, so as one chapter closes the next opens up - direct penta + patch learning using geometric classification only with feature analysis after rather than classification analysis. 
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+This will enable a much larger pool of pentachoron; as it's going to build on a similar system as bert to create a similar abstraction as clip-vit with some variant changes and requirements that geometry needs.
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 # Surgery again
 
 Alright. This initial cycle is concluded, I've determined that the frozen pentachoron are in fact utilizable by about 50% most of the time and will eventually cap at 60% if you stick to standard cross-entropy with tons of geometric regularization. So roughly 3/5ths of the pentas are covered and the other two completely discarded.