What I Found Inside Brightwoven's Layers

I trained sparse autoencoders alongside a small language model from step zero. When I looked at the feature co-occurrence graphs layer by layer, each one had a distinct geometric shape — and those shapes tell a story about how information organizes itself when you don't force it to converge.

The progression from dense to sparse across depth isn't noise. It looks like differentiation. And it maps onto a framework I've been developing about how embedding space should be structured: not as equidistant nodes on a hypersphere, but as sheets — layered surfaces with meaningful internal geometry.