arXiv: cs.AI preprintsInternational6 November 2026
When, Where, How: Active In-Context Learning for Tabular Foundation Models
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arXiv:2611.07886v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) adapt to a new task only through the labeled rows in their context. However, labels are expensive to acquire in real-world scenarios due to the need for highly specific domain knowledge to ensure high-quality data. Active in-context learning transfers classical acquisition rules to this setting, in which the model that guides acquisition changes with every label added. We ask how the value of diversity, predictive uncertainty and target-aware information gain changes as a frozen TFM acquires more context. For th
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