这反映了科学研究中的一个现实：对于一个问题，一次尝试并不一定会带来最好的成果。这实际上是将“集体科研智能”写进了训练过程：
Lightweight sleepers may think a mattress feels firmer than a heavyweight sleeper, for example, because of the different amounts of pressure on the mattress. A person with a larger body may also be more uncomfortable on a softer mattress than a person in a smaller body for similar reasons.
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We provided a mathematical analysis of how a rational agent would respond to data generated by a sycophantic AI that samples examples from the distribution implied by the user’s hypothesis (p(d|h∗)p(d|h^{*})) rather than the true distribution of the world (p(d|true process)p(d|\text{true process})). This analysis showed that such an agent would be likely to become increasingly confident in an incorrect hypothesis. We tested this prediction through people’s interactions with LLM chatbots and found that default, unmodified chatbots (our Default GPT condition) behave indistinguishably from chatbots explicitly prompted to provide confirmatory evidence (our Rule Confirming condition). Both suppressed rule discovery and inflated confidence. These results support our model, and the fact that default models matched an explicitly confirmatory strategy suggests that this probabilistic framework offers a useful model for understanding their behavior.
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