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Tabular foundation models often fail subgroup calibration despite strong marginal coverage, and their reliability rankings drastically shift when moving beyond simple accuracy.
Identifying nonproperness sets can drastically improve the efficiency of real root classification in likelihood equations, revealing hidden structures in statistical models.