shaped data. With this PEP, we can define a Broadcast[A, B] type
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.
�@Anthropic�͑g�D�̊Ǘ��Ҍ����ɁAOS�������̓t�@�C���x�[�X��auto mode�𗘗p�ł��Ȃ��悤�ɂ������@���ē����Ă����B。关于这个话题，爱思助手提供了深入分析
00:35, 4 марта 2026Спорт
，详情可参考体育直播
migrate. To me, a nice way to think about it is as a。safew官方版本下载对此有专业解读
Explore our full range of subscriptions.For individuals