Beyond IID: How General Are Tabular Foundation Models, Really?

Title:Beyond IID: How General Are Tabular Foundation Models, Really?

Abstract:Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry. Research communities across disciplines are increasingly evaluating tabular foundation models on diverse datasets and tasks. However, these task- and discipline-specific evaluations remain largely inaccessible to model researchers because benchmark software and evaluation protocols are fragmented. As a result, model researchers rely on standard benchmarks, which are mostly defined for tasks where tabular foundation models already excel. The most challenging scenarios are excluded, limiting meaningful progress in the field by focusing on marginal improvements on IID data rather than on broader, more demanding challenges. To overcome this, we introduce BeyondArena, the first unified holistic benchmark for tabular data that supports diverse task types (IID, temporal, grouped), across sample size and feature dimensionality scales, with diverse feature types (with text, with high cardinality) from a broad range of disciplines. To enable unified benchmarking beyond standard benchmarks, we introduce DataFoundry, a Python framework and metadata schema for curating tabular datasets for predictive machine learning. Our results across 11 models and 142 curated datasets show that current open-source tabular foundation models are competitive or excel on datasets up to 100k rows, while traditional tree-based and deep learning models still dominate on grouped or large data. BeyondArena guides model research for the most demanding challenges in tabular data, enabling progress towards truly foundational tabular models.

Accepted at NeurIPS 2026 Datasets and Benchmarks Track. v2: NeurIPS camera-ready version

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2606.30410 [cs.LG]

(or arXiv:2606.30410v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.30410 Focus to learn more arXiv-issued DOI via DataCite

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