In doing this however, you lose meaning where the model is expressing combinations of tokens in a vector, and your logging/monitoring outputs just the closest one. You would need another model going through and outputting the full meaning being described (with more than one output token per reasoning token)
I wonder if their observability claims will hold up on a model trained from scratch to reason in latent space.