LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models
arXiv:2606.04485v1 Announce Type: new 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 feat
The continuous drive for more efficient and performant AI models necessitates advanced techniques to overcome limitations in current architectures, pushing research into areas like mitigating low-rank collapse.
This development addresses key computational inefficiencies in foundational models, potentially leading to more powerful and resource-efficient AI across various applications.
The proposed 'tokenize-and-route' framework introduces a new paradigm for building strong tabular foundation models, improving their value sensitivity and reducing redundant hidden states.
- · AI researchers and developers
- · Companies using tabular data for AI
- · Cloud computing providers
- · Inefficient tabular model architectures
- · Organizations heavily reliant on older compute-intensive methods
Improved performance and reduced resource consumption for AI models handling tabular data.
Accelerated development of more complex and robust AI systems benefiting from these foundational model improvements.
Broader adoption of AI in sectors previously limited by the computational cost or accuracy of tabular data processing.
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Read at arXiv cs.LG