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Thin Evidence, Thick Priors: How Language Models Substitute Identity for Missing Financial Facts
People increasingly ask large language models what to do with their money, yet seldom describe their finances in full. This paper asks what a model does with the gap. Holding finances fixed and changing only who the investor is said to be, we grade the financial evidence in the prompt from eight fac
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