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.
│ ├── auth.go # Argon2id auth, sessions, middleware
You are friends with all the senior TLs, so can get them to review your code, but this is not a high-leverage use of time.。关于这个话题，PDF资料提供了深入分析
https://android.googlesource.com/platform/system/sepolicy/+/refs/heads/master/public/te_macros
，详情可参考电影
干部作风转变，各界勠力同心，体制机制创新，共同促成质变。哈尔滨马迭尔文化旅游投资集团有限公司董事会秘书李冬坚信：“冰雪文化积淀，政策护航，加上干部群众的坚定守护，是哈尔滨长红的坚实后盾。”，更多细节参见电影
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