Learning How to Think with Meta Chain-of-Thought

I find their critique compelling, particularly their emphasis on the disconnect between CoT’s algorithmic mimicry and true cognitive exploration. The authors illustrate this with examples from advanced mathematics, such as the "windmill problem" from the International Mathematics Olympiad, a puzzle whose solution eludes brute-force sequential thinking. These cases underscore the limits of a framework that relies on static datasets and rigid generative processes. CoT, as they demonstrate, falters not because it cannot generate solutions, but because it cannot conceive of them in ways that mirror human ingenuity.

As they say - "Superintelligence isn't about discovering new things; it's about discovering new ways to discover."

> That is, language models learn the implicit meaning in text, as opposed to the early belief some researchers held that sequence-to-sequence models (including transformers) simply fit correlations between sequential words.

Is this so, that the research community is agreed? Are there papers discussing this topic?

Is Meta the company here or are they using meta the word? or both?

Meta's recently released Large Concept Models + this Meta Chain of Thought sounds very promising for AGI. The timeline of 2030 sounds increasingly plausible IMO.