It would be really cool if they could expose this information to customers somehow. Imagine:

   - having a log of the most prominent J-space tokens during your customer support chatbot's interactions with a user, so you can have more introspection into why a particular outcome happened
   - being able to detect certain thoughts associated with undesirable behavior (hallucinations, overstepping authority, lying, etc.) and trigger some sort of remediation (e.g. upgrading to a better model, redirecting to a human, forcing tool calls)

Presumably the rationale for the decision to abridge the thinking traces will ensure that they don’t; if this is real (and there’s no good reason to trust that it is yet) then it is the secret sauce.

Anthropic aren't even willing to expose the CoT of their models. You will have to rely on them to build those sorts of things into dedicated signals.

Anthropic won't do it, but they published the j-lens to introspect the model- from what I understand it's roughly simply feeding a chosen layer straight into the final layers of the LLM for decoding into language:

https://github.com/anthropics/jacobian-lens

Looks like it should be easy to use on open weights models.