Take you and me as an example. When we work, we might start with A, suddenly think of C, briefly jump to D, then B, revise the premise of A, and finally reach a

Take you and me as an example. When we work, we might start with A, suddenly think of C, briefly jump to D, then B, revise the premise of A, and finally reach a conclusion. Humans think by constantly shifting between association, working memory, emotion, and social judgment. However, when we write, we organize these scattered results into a coherent structure. In other words, our writing is not a raw dump of human thought, but rather a normalized output of human thought arranged in a logical sequence. I believe that in this specific process, LLMs actually have an advantage over humans. Because it operates by continuously appending tokens conditioned on the sequence generated so far: What was just said -> The most natural logical next step -> The most natural logical next step after that. In short, when it comes to unfolding an already structured logic in a sequential order, I think LLMs are superior to humans. Of course, due to this very nature, they tend to obsess over local context... You might disagree with me. But if what you say is entirely true, then are the claims that current LLMs are eliminating practice problems for PhD-level mathematicians just a scam?