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

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 tokenize-and-route framework for strong TFMs: 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 https://github.com/limix-ldm-ai/LimiX.

Many real-world decisions rely on tables, such as spreadsheets about customers, companies, products, or medical records. Modern AI models can learn from such tables, but they often require large amounts of computation to work well. This paper studies why some existing models use computation inefficiently and proposes a more compact design that helps the model better understand numerical values and combine information across a table. Our resulting model is much smaller than several strong alternatives, but still achieves better performance on widely used tabular prediction benchmarks. This suggests that tabular AI systems can become more accurate, faster, and cheaper to use in practical applications.