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.
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Почему Иран бомбит ОАЭ и что уже разрушено в Дубае: от штаб-квартиры ЦРУ до торговых центров1 марта 2026
and the magic here is that we can do this with zero back-and-forth movement. we find the matches with two linear scans, one right to left to mark exactly where the matches start, and one left to right to eliminate overlap. right to left sweep marks all matches in a single pass. it’s completely deterministic, every possible future has been accounted for, and we just let the automaton do its thing.
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