I'm not sure how to test this but I think there's an interesting possibility where the "reasoning" tokens are actually both an accurate reflection of a line of reasoning, but also, that there can be changes in the weights as the computation proceeds onward that may not be reflected in the apparently nominal meaning of the human language the tokens are output as for our consumption.

Some modest evidence is my own subjective experience of the many times I've explained why I'm doing something, and it is a true explanation in the sense that it is certainly not a lie, but it is also incomplete and there are entire strands of thought that went into my decision that are not being articulated. Though human speech is not equivalent to an LLM's output since we can trivially think without literally speaking whereas they can not. (No need to nitpick on the definitions there; all I'm observing here is that they are forced to emit an externally-visible artifact whereas I can sit in silence, thinking, with no externally-visible artifact being produced. Not trying to make any grand claims about what is "real" cognition or anything.)

It is conceivable how to create a test of whether the tokens correspond to the "real" thought process, and papers and work on that have been done, such as [1]. It is difficult for me to imagine how to scramble the nominal tokens without also completely trashing any implicit calculations that may be occurring too.

[1]: https://transformer-circuits.pub/2025/attribution-graphs/bio...