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We provided a mathematical analysis of how a rational agent would respond to data generated by a sycophantic AI that samples examples from the distribution implied by the user’s hypothesis (p(d|h∗)p(d|h^{*})) rather than the true distribution of the world (p(d|true process)p(d|\text{true process})). This analysis showed that such an agent would be likely to become increasingly confident in an incorrect hypothesis. We tested this prediction through people’s interactions with LLM chatbots and found that default, unmodified chatbots (our Default GPT condition) behave indistinguishably from chatbots explicitly prompted to provide confirmatory evidence (our Rule Confirming condition). Both suppressed rule discovery and inflated confidence. These results support our model, and the fact that default models matched an explicitly confirmatory strategy suggests that this probabilistic framework offers a useful model for understanding their behavior.，推荐阅读搜狗输入法2026获取更多信息
В КСИР выступили с жестким обращением к США и Израилю22:46。关于这个话题，safew官方版本下载提供了深入分析
Comparison between TIN and natural neighbour dithering, using a 16-colour irregular palette. Left to right: TIN, Natural Neighbour.。heLLoword翻译官方下载是该领域的重要参考
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