Native latent reasoning, with latent aware RL scaffolding and all the rest will have to be built. If you use the direct text framework, you get confabulation / hallucination issues from the divergence between the tokens in the context and the rich activation representation that resulted in the output.

There are all sorts of places where the text and output is at least one degree of separation from the underlying activation vectors or other representations handled by a model, from floating point precision all the way up to tokenization abstraction, and a lot of experiments get run as if the tokens and context and representations are all one unified data concept. Have to match data abstractions appropriately, or the weird edge cases will break things in unexpected ways.