We propose sycophancy leads to less discovery and overconfidence through a simple mechanism: When AI systems generate responses that tend toward agreement, they sample examples that coincide with users’ stated hypotheses rather than from the true distribution of possibilities. If users treat this biased sample as new evidence, each subsequent example increases confidence, even though the examples provide no new information about reality. Critically, this account requires no confirmation bias or motivated reasoning on the user’s part. A rational Bayesian reasoner will be misled if they assume the AI is sampling from the true distribution when it is not. This insight distinguishes our mechanism from the existing literature on humans’ tendency to seek confirming evidence; sycophantic AI can distort belief through its sampling strategy, independent of users’ bias. We formalize this mechanism and test it experimentally using a rule discovery task.
我们说的 “代际差”，不只是���一指标的差距，更关键是有没有切换整套做事思路，迭代速度有没有质变。我们现在追求的是不仅跑得快，加速度还在持续变大，因为我们在构建底层通用能力体系，这才是真正的代际差，而非单点指标领先。
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Ранее Армия обороны Израиля (ЦАХАЛ) нанесла удар по секретному центру «Минзадехей» в Иране, который занимается разработкой необходимых для ядерного оружия компонентов. Уточнялось, что инфраструктура была перемещена на подземный объект, защищенный от воздушных ударов.