Update README.md

README.md

@@ -128,23 +128,35 @@ SVD information with the attention alignment spectrum.
 
 # What Needs To Work
 **Using MLP will reach fair accuracy and not use CONV or TRANSFORMERS.**
+
 I have seen **around 60% on cifar100** with no traditional encoders, but the system was crutching the M_path to fill the gaps after enough epochs of the SVD path.
 This structure is under the microscope now.
 
 Instability allows SGD optimization to heavily benefit some image tasks while it fails completely on text tasks.
 
 **Out Projection SUVt tokens are iffy**
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 The out projection is an MLP multiscale projection that took a while to set up, and it produces approximate transformer QKV with useful SUVt tokens downstream. 
 
 **Many activations corrupt geometry**
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 They are in there for experimentation. Feel free to experiment.
 
 **without the expanded triton core spectrum larger systems suffer with triton**
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 Claude code is having trouble with this one as a full task, I'll need to build it in pieces. I've had OpenClaw working on it but the outcome
 isn't looking good. The 4x4 and 5x4 won't converge, while the 6x6 crashes the system entirely instead of building it.
 
 I'll need to wait for a fix for claude code, this is a known issue apparently.
 
+**Magnitudes are WILDLY hard to control**
+
+High magnitudes and complexity associations with those must be controlled. The vocabulary spectrum of a token is widely diverse, so the noise generated
+from large and small magnitudes is very difficult to curate for. I'm debugging this task as I progress, and the dominant shape learning corruption is
+magnitude differentiation currently.
+
+I have some potentials and this is my current direction.
+
 
 ## Additionally