Update README.md

README.md

@@ -4,6 +4,20 @@ datasets:
 - AbstractPhil/geometric-vocab
 pipeline_tag: zero-shot-classification
 ---
+# I've had an epiphany. We don't NEED transformer layers in their current form.
+
+David's architecture already solved this need with high-efficiency multi-stage geometric mathematics.
+
+David's classification structure houses a series of dimensional projection sub-systems tasked with learning mastery based on each pentachoron structure.
+
+Each of those 5d representations ends up learning thousands of representative features. David is already capable of feature generation just not robust enough to fully manifest an enriched ViT-grade dimensional feature... yet.
+
+David's architecture can handle ImageNet's classifier count and features leveraging 1000 classes with ease, sitting on a floppy disk at over 70% accuracy because David sees Clip-Vit-Base-Patch16 features.
+
+I believe I've figured out a way to fundamentally represent those features in a meaningful way that can replace transformer layers in their methodology with a different form of feedforward trajectory, edge, point, deviation, jitter, helix, theta, and similarity assessment that should house the needed information to teach the experts how to behave like David did.
+
+This should allow the much larger networks to retain mathematical precision, learn the features in a different form of patch than is currently expected to be a patch, and to create legitimate high-density geometic features.
+
 # Better rope incoming with actual meaningful learning
 
 The last one wasn't meaningfully learning representations, the next should be more correctly curated and inferenced to impact representative outcome. Should be a bit more accurate than the last but no guarantees.