OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning
Tabular foundation models enable in-context learning by predicting unseen observations from a labeled support table without task-specific parameter updates. Despite recent advances, most existing approaches rely on dense architectures that apply the same transformations across features and datasets. We introduce OrionMoE-Small, a compact tabular foundation model that combines distribution-aware representations with dataset-conditioned sparse mixture-of-experts computation. Built on a dual-axis Transformer architecture, OrionMoE-Small selectively activates experts along the feature axis, with routing decisions informed by both individual feature representations and the broader dataset cont...
| Authors | Mohamed Bouadi, Nassim Bouarour, Aditya Tanna et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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