SOMTab: Set-Order Mamba for Efficient Tabular In-Context Learning
Tabular foundation models based on in-context learning have recently emerged as strong alternatives to task-specific model fitting. However, the current performance frontier remains dominated by attention-heavy architectures, where attention is used throughout the modeling pipeline. This raises a natural question: is attention necessary at every stage of tabular in-context learning? We introduce SOMTab, a Set-Order Mamba architecture for efficient tabular in-context learning. SOMTab separates representation construction from query-conditioned retrieval. For row and column representations, it maps unordered table tokens into stable latent slots and applies Mamba-based state-space mixing to...
| Authors | Hao Wang, Siyu Zhang, Wei Ma |
|---|---|
| Primary category | cs.LG |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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