Note that this is not relevant for reasoning models, since they will think about the problem in whatever order it wants to before outputting the answer. Since it can “refer” back to its thinking when outputting the final answer, the output order is less relevant to the correctness. The relative robustness is likely why openai is trying to force reasoning onto everyone.
I think you are misleading as well. Thinking models do recursively generate the final “best” prompt to get the most accurate output. Unless you are genuinely giving new useful information in the prompt, it is kind of useless to structure the prompt in one way or another because reasoning models can generate intermediate steps that give best output. The evidence on this is clear - benchmarks reveal that thinking models are way more performant.
You're both kind of right. The order is less important for reasoning models, but if you carefully read thinking traces you'll find that the final answer is sometimes not the same as the last intermediary result. On slightly more challenging problems LLMs flip flop quite a bit and ordering the output cleverly can uplift the result. That might stop being true for newer or future models but I iterated quite a bit in this for sonnet 4.
This is misleading if not wrong. A thinking model doesn’t fundamentally work any different from a non-thinking model. It is still next token prediction, with the same position independence, and still suffers from the same context poisoning issues. It’s just that the “thinking” step injects this instruction to take a moment and consider the situation before acting, as a core system behavior.
But specialized instructions to weigh alternatives still works better as it ends up thinking about thinking, thinking, then making a choice.