People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We argue that this sycophancy poses a unique epistemic risk to how individuals come to see the world: unlike hallucinations that introduce falsehoods, sycophancy distorts reality by returning responses that are biased to reinforce existing beliefs. We provide a rational analysis of this phenomenon, showing that when a Bayesian agent is provided with data that are sampled based on a current hypothesis the agent becomes increasingly confident about that hypothesis but does not make any progress towards the truth. We test this prediction using a modified Wason 2-4-6 rule discovery task where participants (N=557N=557) interacted with AI agents providing different types of feedback. Unmodified LLM behavior suppressed discovery and inflated confidence comparably to explicitly sycophantic prompting. By contrast, unbiased sampling from the true distribution yielded discovery rates five times higher. These results reveal how sycophantic AI distorts belief, manufacturing certainty where there should be doubt.
从当前看，要能支撑稳就业、稳市场、惠民生等目标。从长远看，则要与中长期目标和远景目标相衔接。
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Ранее прибывшая из Эмиратов россиянка описала эмоции словами «хочется целовать землю родную». По ее словам, находясь в Абу-Даби, она слышала лишь хлопки и сирены.
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AI memory crunch forces DRAM market into 'hourly pricing' model, report claims
京东：2025年全年收入13091亿元，同比增长13%，更多细节参见PDF资料