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Decoupled Marginal Sharpening (DMS) is proposed, which separately controls how strongly the model favors particular reasoning paths and how strongly it favors final answers after aggregating support across those paths.
Which forms of test-time compute improve the predictions of strong pretrained tabular foundation models (TFMs)? We systematically study this along three axes: adaptation, aggregation, and context construction. Our evaluation spans modern TFMs across the TabArena benchmark, supplemented by experiments on wide and large-...
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