The Anatomy and Boundary of Adaptation under Temporal Tabular Shift

2026-09-10

AI Summary

研究在时间漂移下预测适应性问题，通过诊断分析四种机制来理解收益来源及限制，并探讨了部分识别目标条件下的边界。

Abstract

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 representation cannot express. Two canonical mechanism priors collapse the first wall. Under stated nuisance-rate conditions, the wall can be estimated from labeled historical windows at a $\sqrt N$ rate above the margin threshold $γ^\star=d_0/(2α_s)$. At $γ=0$, the conditional lower-bound program depends on an open affinity estimate; the positive-margin lower branch also remains open. Semi-synthetic data illustrate the finite-sample mechanism with calibrated exponents. Stream-level proxies on eight industrial streams fall on the difficult side under a stated roughness bound, while the equality case $γ=γ^\star$ remains unresolved.

Problem

• Temporal Tabular Shift
• Prequential Adaptation
• Frozen Tabular Foundation Models
• Streaming Protocol
• Total-Variation Drift

Innovation

• Prequential Adaptation
• Temporal Drift
• Frozen Tabular Models
• Streaming Protocol
• Wall Concept