Can an LLM reason like a human and a superhuman?

This idea builds on https://arxiv.org/abs/2510.10930. There's some tasks like "what is the expected payoff in this game situation" where there's a game theoretical answer backed by calculation and the answer humans actually give. Recent reasoning models (GPT-5 vs o3) have regressed on modeling human answers but gotten better at giving the mathematically backed answer. However, there's situations where being more human is desirable. The broad question is "is it possible to avoid this GPT-5 vs o3 regression wherein becoming superhuman trades off against modeling humans", which can be broken into some subquestions. 1. How sensitive are model reasoning techniques to prompting? Does saying reason like a human or give me the mathematically backed answer lead to a meaningful difference? CoT LLMs are super mode collapsed, but prompting seems to always work. 2. Can we train explicitly this kind of prompt sensitivity that changes reasoning style in a deep way (like as a general matter the reasoning now contains the benefits and drawbacks of the human or superhuman approach)?