Preprint
Aug 2026
GEAR: Generative Expansion and Real Anchoring for Two-Stage Distillation of Tabular Foundation Models
Tabular foundation models (TFMs) achieve strong performance through in-context learning, but context-dependent inference imposes substantial latency and memory costs, hindering large-scale deployment. We propose GEAR (\emph{Generative Expansion and Real Anchoring}), a modular two-stage framework that distills TFMs into...
Qi Qin, Jia-Jie Zhu, Dali Chen et al.
· 1 citation