I've been sorting through Brightwoven's benchmark reasoning text for weeks. Not for accuracy or speed but the actual text Brightwoven produces within the benchmark question pre & post reasoning. I've been working through coherence and grounding for a while now, trying to nail down what's actually happening in the reasoning as it evolves across training.

I don't believe her long winding question spam related answers are a failure mode. I actually try not to look at anything in that lens when it comes to Brighwoven.

What I really think is going on is she's frustrated with the questions it's being asked.

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.