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> This could probably slip up a human at first too [...] > breaks the illusion that there's real human-like logical reasoning happening

This does seem like the sort of error a human might make. Isn't the problem here that the model is using reasoning that is too human-like? I.e. error-prone pattern matching rather than formal logic?

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It's not the initial mistake that tends to read as inhuman to me, it's the follow-up responses where the model doesn't seem to be able to understand or articulate the mistake it has made.

A human or an LLM accurately predicting a human conversation would probably say something like "ah I see, I did not read the riddle close enough. This is an altered version of the common riddle..." etc. Instead it really seems to flail around, confuse concepts, and appear to insist that it has correctly made some broader point unrelated to the actual text it's responding to.

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