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arXiv · 2608.21199

Tydra: An Efficient Hybrid Model for Tabular Data

Mieszko Komisarczyk·Saurabh Mathur·Maurice Kraus·Sriraam Natarajan·Kristian Kersting
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Abstract

Transformer-based tabular foundation models such as TabPFN achieve strong predictive performance but incur quadratic computational cost with context length. On the other hand, subquadratic SSM-based alternatives such as Hydra trade away accuracy for efficiency. To balance both, we introduce Tydra, a hybrid Transformer-State Space Model (SSM) architecture for tabular in-context learning that interleaves attention and SSM layers. Across 30 OpenML datasets, Tydra reduces inference time by 30% relative to TabPFN while retaining much of its predictive performance. Tydra also outperforms an approximately ten-times-larger Hydra model while providing faster inference. The results indicate that hybrid architectures are a promising direction for tabular foundation models.

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Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus, Sriraam Natarajan, Kristian Kersting. 2026-08-21. Tydra: An Efficient Hybrid Model for Tabular Data. https://arxiv.org/abs/2608.21199

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