Running predictions in parallel is just doing prediction and we're back at square one. Why do things in parallel in that case? At that point, you are just training an "opportune injection model" with the existing token stream as it comes. Which is subject to exactly the limitation that I described.

These models do have an implicit model of thought, but it is only accessible through the token interface. You need more explicit access, which is not possible given the current architecture.

I'd like to be wrong here.