The substack isnt what was supposed to be evaluated, it was the repo. That's creative writing and the repo is sciencetific. Two different things. One has nothing to do with the other. The technical direction here is straightforward, almost boring in a sense: freeze the teacher, extract intermediate activations, compress, then train a student to match the compressed fields. Sometimes when people aren't able to evaluate the work, they dig for something else online that they can comment on or bring down. The only thing I can offer in response is the simplest one: look at the code and the experiments themselves, not the narrative around them. Everything in the paper is fully reproducible from the reference implementation, and every number in the results section came from running those scripts, not from a model filling in blanks. The surprise is not in the prose, but in how much structure those early-layer fields ended up carrying.

If you think something in the repo looks wrong or inflated, I’m happy to walk through it point by point. I have no problem with hard questions. What matters to me is whether the experiments hold when someone else runs them, not whether the story around them fits a certain aesthetic.