O'Prior, a compositional synthetic prior with hierarchical SCMs, realism engines, stress modules, and curriculum protocols, improves tabular foundation model accuracy and robustness on real benchmarks when architecture and compute are held fixed.
Orion-bix: Bi-axial attention for tabular in-context learning.CoRR, abs/2512.00181, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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Context construction strategies such as balanced sampling improve AUC-ROC by 3-4 points over uniform sampling in tabular foundation models for credit risk, exceeding differences between model families and matching classical baselines.
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Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality
O'Prior, a compositional synthetic prior with hierarchical SCMs, realism engines, stress modules, and curriculum protocols, improves tabular foundation model accuracy and robustness on real benchmarks when architecture and compute are held fixed.
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Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models
Context construction strategies such as balanced sampling improve AUC-ROC by 3-4 points over uniform sampling in tabular foundation models for credit risk, exceeding differences between model families and matching classical baselines.