the chain of thought is not auditable. The best you can do is have another model which later maps the intermediate latent states at every thinking iteration to text describing that latent state, but it's going to be lossy.

But the chain of thought as text can be elusive, no? It might not be related with the actual "reasoning" of the model. There was a paper about this.

One of the core claims made in the paper is "To inspect any latent reasoning span, one can easily recover its semantics by mapping each latent step produced by HybridLink to its nearest token embedding, as in Eq. 5". They show some charts to support this.

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.

Isn't this where everything is converging? Binary is more efficient than text for many system, including language.