Фото: Дмитрий Ермаков / «Лента.ру»
Блогеру Арсену Маркаряну дали срок14:50
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Иран назвал путь к прекращению войны14:05
执照、印章到手，张波忙了起来。“要是等节后上班才拿到执照，业务就得往后推。现在手续齐全，假期就是抢跑的最佳时机。”他说。
。关于这个话题，体育直播提供了深入分析
截至3月2日，半导体设备ETF易方达近1年净值上涨73.57%，排名可比基金前2，指数股票型基金排名147/3543，居于前4.15%。从收益能力看，截至2026年3月2日，半导体设备ETF易方达自成立以来，最高单月回报为25.48%，最长连涨月数为4个月，最长连涨涨幅为57.31%，涨跌月数比为13/7，上涨月份平均收益率为10.56%，年盈利百分比为100.00%，历史持有1年盈利概率为100.00%。截至2026年3月2日，半导体设备ETF易方达近1年超越基准年化收益为0.71%，排名可比基金前2/5。，更多细节参见下载安装汽水音乐
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.