iLTM: Integrated Large Tabular Model

• URL: http://arxiv.org/abs/2511.15941v1
• Date: Thu, 20 Nov 2025 00:20:16 GMT
• Title: iLTM: Integrated Large Tabular Model
• Authors: David Bonet, Marçal Comajoan Cara, Alvaro Calafell, Daniel Mas Montserrat, Alexander G. Ioannidis,
• Abstract summary: iLTM is an integrated Large Tabular Model that unifies tree-derived embeddings, dimensionality-agnostic representations, a meta-trained hypernetwork, multilayer perceptrons, and retrieval within a single architecture.
• Score: 41.81329403540607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tabular data underpins decisions across science, industry, and public services. Despite rapid progress, advances in deep learning have not fully carried over to the tabular domain, where gradient-boosted decision trees (GBDTs) remain a default choice in practice. We present iLTM, an integrated Large Tabular Model that unifies tree-derived embeddings, dimensionality-agnostic representations, a meta-trained hypernetwork, multilayer perceptrons (MLPs), and retrieval within a single architecture. Pretrained on more than 1,800 heterogeneous classification datasets, iLTM achieves consistently superior performance across tabular classification and regression tasks, from small datasets to large and high-dimensional tasks. After light fine-tuning, the meta-trained hypernetwork transfers to regression targets, matching or surpassing strong baselines. Extensive experiments show that iLTM outperforms well-tuned GBDTs and leading deep tabular models while requiring less task-specific tuning. By bridging the gap between tree-based and neural methods, iLTM offers a new framework for tabular foundation models for robust, adaptable, and scalable tabular learning.

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