Update README.md

README.md

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 license: mit
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+Last remembered highest accuracy; 66% accuracy, and it had a bunch of other stuff too that apparently didn't get pushed from the logger.
+
+Readme is busted, it uploaded a bad readme. I'll run test sets on all the models and accumulate a proper model list with accuracies asap.
+
+These currently defeat the standard vit-beatrix in terms of pure classification accuracy, while leaving both blocks nearly independent. 
+
+This enables efficient transfer learning without high-decay processes, but the system is a bit jank.
+
+Today I plan to shore up the actual repo's capacity to ensure this sort of fault doesn't happen again, where I run something and lose tracking information. 
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+Additionally the train manifest from all models will likely be stored in an independent repo elsewhere for automated connection and linkage with the huggingface systems.
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+
 # ViT-Beatrix Dual-Stream with Geometric Diversity
 
+This system is a dual-block transformer model inspired by Flux's dual-block structure. 
+
+## Experimental Tests
+One set of blocks is devoted to the geometry while the other set is devoted to the images ingested. 
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+The accuracy of the geometry can be completely decoupled and the image portion zeroed to retrain if systems start to decay. 
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+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%.
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 ## Current Experiment: beatrix-dualstream-base
 
 **Model Path**: `weights/beatrix-dualstream-base/20251009_030219/`
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 ## Performance
 
-- **Best Accuracy**: 0.0395
-- **Current Epoch**: 0
+- **Best Accuracy**: 66.000%~ from memory
+- **Current Epoch**: 100 give or take required, sorry about this I'll get real data here asap.
 - **Dataset**: CIFAR-100
 
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-*Last updated: Epoch 0 | Best Accuracy: 0.0395*