Is there any actual evidence that the reasoning tokens output by current models actually represents the computation happening in the hidden layers?

In both cases, the model is doing a ton of processing that you can't actually inspect, except here, you at least get some efficiency gains.

Even more importantly, you're also less likely to convince yourself that you know what the model is thinking.

No and we’ve observed evidence to the contrary

Do you have some reading material on this? How did they understand the difference between stated cot and "actual processing"

In the autoregressive decoding framework, the hidden layers' state for computation of token `t` is conditionally independent of all hidden states for `t-1`, `t-2` and so on given the observed tokens.

Put differently, the observed tokens are a bottleneck on the information that can be communicated across tokens. Any scheming performed by an LLM which requires more than one token to formulate must therefore pass through the visible tokens. With opaque vectors transferred across decoding steps, this is not the case.

The computation in the hidden layers, as far as we can tell, is not sufficient for scheming in a single decoding step. It looks like it requires O(10^2) or O(10^3) steps instead, judging from anecdotal evidence like the reports of scheming from o1 (https://cdn.openai.com/o1-system-card-20241205.pdf)

As far as your last point goes, I'd rather have a more transparent system, all other factors held constant.

That actually sounds like it'd be really helpful.

maybe let it reason in latent space but have a method to transform and output it to text for inspection.

The paper suggests that that is still possible with the proposed architecture if needed.

Chain of thought isn't exactly transparent either, you shouldn't fall into the pitfall of believing that the final sequence of tokens thinking about the task is the only processing the model actually performs during CoT.

There might me a lot of other hidden computations happening within the model's latents which may not immediately influence the predicted tokens but be relevant for the model's internal processing. And even disregarding that, the model is under no formal obligation to stick to the chain of thought it produced for its final decisions.