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> LLMs can benefit from "thinking out loud" much as humans can.

The two processes aren't equivalent. An LLM that fills the thinking trace with a meaningless placeholder token will still exhibit improved performance. There are also regularly things in the thinking trace that don't match the final output if you look closely but on the surface they appear convincing.

It's largely a trained performance. If you go in with the erroneous expectation that it accurately reflects the underlying thought process then you're likely to come away with faulty conclusions.