Раскрыты подробности о договорных матчах в российском футболе18:01
социально-экономическая, экологическая и гуманитарная обстановка из-за пятилетней засухи;
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19:01, 3 марта 2026Мир。关于这个话题，体育直播提供了深入分析
对此，Tabbit官方回应称，当时read-frog项目中仓库中并未包含任何开源协议声明。团队经评估进行了项目fork，以独立项目的方式进行开发。虽然我们的fork代码行为发生在该项目添加明确的开源协议之前，但我们充分尊重和理解原作者对项目的所有权及其协议选择。将从Tabbit浏览器新版中移除此翻译项目，并已将此项目完整开源。
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.