Adaptive Matrix Validation calibrates AI-mapped survey responses using sparse randomized validation questions from other respondents then corrects with the target's own answers, with estimators and planning formulas for means, subgroups, and regressions.
arXiv preprint arXiv:2512.05456 , year=
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Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.
Mechanistic learning from ML is generically underdetermined in high-dimensional proxy regimes, with LLMs worsening the problem by collapsing many possible explanations into one fluent narrative.
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Adaptive Matrix Validation calibrates AI-mapped survey responses using sparse randomized validation questions from other respondents then corrects with the target's own answers, with estimators and planning formulas for means, subgroups, and regressions.
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Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.
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Mechanistic learning from ML is generically underdetermined in high-dimensional proxy regimes, with LLMs worsening the problem by collapsing many possible explanations into one fluent narrative.