The Anatomy and Boundary of Adaptation under Temporal Tabular Shift
Prequential adaptation of frozen tabular foundation models under temporal drift, with each label revealed only after prediction, helps some deployments and harms others, yet current practice does not predict which. We study the sources and limits of these gains. A diagnostic anatomy attributes gains to four recurring mechanisms under a streaming protocol that removes three optimistic biases and quantifies a fourth. Within an agnostic total-variation drift class, the target conditional is only partially identified: its identified-set diameter, the \emph{wall}, is irreducible from unlabeled data uniformly in sample size. A second, orthogonal $L^2$ projection wall quantifies what the frozen ...
| Authors | Tianyu Wang, Xi Vincent Wang, Lihui Wang et al. |
|---|---|
| Primary category | stat.ME |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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