That's where we disagree I guess. I can very much distinguish between a human reflecting on e.g. how they don't know something, and an AI blindly saying "oh you're right I was mistaken, <profuse apology>, it was actually <additional bullshit>".
Reasoning models didn't really improve that much IMO. A stochastic representation of metacognition is just as metacognition as a stochastic representation of an answer is an answer, i.e. it's not. LLMs are just very good at giving the impression of metacognition just like they're good at giving the impression of an answer.
It might help bias the answers into different local minimas--because it resembles when people exteriorize metacognition, also because it dumps more info into its context instead of the first bullshit that it chose statistically--but it's still nowhere close to higher-order thinking and other metacognition phenomena that humans are capable of.