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.
国务院国资委主任张玉卓：去年战略性新兴产业营收超12万亿
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We should not get ahead of ourselves. Mupita says that 40% of MTN’s 300 million customers are “still in the voice era” and “have not yet experienced the internet”. 6G is a conversation for another time, as carriers focus on 4G and 5G capabilities. “How does the Global South not get left behind?” he asks, saying that the growth story for Africa relies on low-cost product and service options, global openness to partnerships and common standards. “It is a great opportunity.”，更多细节参见谷歌浏览器下载
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