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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.
二、中华人民共和国学位条例暂行实施办法（国务院学位委员会制定 1981年5月20日国务院批转）。体育直播是该领域的重要参考
不標準的發音破壞拍攝氛圍，周冠威只能補充道「想到香港就傷感」，調整現場狀態。他說，「這個例子我記到現在，我也很傷感，為什麼我要面對這些翻譯上的阻礙、這樣的艱苦艱難艱困要去到台灣？」
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