LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models

Abstract:Tabular foundation models (TFMs) increasingly rival tree ensembles, but their performance is often compute-inefficient: with standard affine scalar tokenization, each feature injects value variation through an essentially one-dimensional channel, and feature IDs/positional signals cannot increase within-feature value degrees of freedom, yielding weak early-layer value sensitivity and redundant hidden states. We present a unified \emph{tokenize-and-route} framework for strong TFMs: \textbf{RaBEL} expands each scalar into compact localized RBF features (optionally exponent-gated) to improve conditioning and shallow-layer effective rank, while a reordered bidirectional block \textbf{S$\rightarrow$N$\rightarrow$F} aligns computation with the readout by aggregating cross-sample context before feature mixing and using attention pooling. Together, these changes yield \textbf{LimiX-2M}, a 2M-parameter model that outperforms larger TabPFN-v2 and TabICL baselines on widely used tabular benchmarks while reducing training and inference costs. These results highlight value-aware tokenization and readout-aligned routing as key levers for improving the accuracy--efficiency trade-off in TFMs. Model checkpoints and inference code are available at this https URL.

Machine Learning (cs.LG)

arXiv:2606.04485 [cs.LG]

(or arXiv:2606.04485v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.04485 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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