Earlier quoted context omitted.
Humans can break things down and work through them step by step. The LLMs one-shot pattern match. Even the reasoning models have been shown to do just that. Anthropic even showed that the reasoning models tended to work backwards: one shotting an answer and then matching a chain of thought to it after the fact. If a human is capable of multiplying double digit numbers, they can also multiple those large ones. The ste…
>Humans can break things down and work through them step by step. The LLMs one-shot pattern match. I've had LLMs break down problems and work through them, pivot when errors arise and all that jazz. They're not perfect at it and they're worse than humans but it happens. >Anthropic even showed that the reasoning models tended to work backwards: one shotting an answer and then matching a chain of thought to it after th…
- LLMs don’t “reason” in the symbolic, step‑by‑step sense that humans or logic engines do. They don’t manipulate abstract symbols with guaranteed consistency.
- What they do have is a statistical prior over reasoning traces: they’ve seen millions of examples of humans doing step‑by‑step reasoning (math proofs, code walkthroughs, planning text, etc.).
- So when you ask them to “think step by step,” they’re not deriving logic — they’re imitating the distribution of reasoning traces they’ve seen.
This means:
- They can often simulate reasoning well enough to be useful.
- But they’re not guaranteed to be correct or consistent.
That at least sounds consistent with what I’ve been trying to say and what I’ve observed.