arXiv AI
Aug 12
TACTICL: Task-Aware Compression of Tabular ICL Models
arXiv:2608. 10837v1 Announce Type: cross Abstract: The strong performance of foundation models for tabular tasks comes at substantial inference costs.
OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning
OrionMoE-v1.0 is a compact tabular foundation model that uses a dual‑axis Transformer and a sparse mixture‑of‑experts architecture to perform in‑context learning on labeled support tables. It selectively activates experts along the feature axis, guided by both individual feature representations and overall dataset context, enabling adaptive computation across diverse tabular problems. Pretrained on synthetic classification tasks, it achieves competitive performance on multiple benchmarks, especially on small‑context, high‑dimensional, and class‑imbalanced datasets.
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arXiv Machine Learning
Oct 12
TabMF: Learning Dataset Representations with Tabular Foundation Models
TabMF is a framework that repurposes pretrained tabular foundation models (TFMs) as encoders for supervised datasets, adding a dataset-level readout and fine‑tuning via contrastive post‑training on synthetic views. The authors instantiate TabMF on TabICLv2 and TabPFN‑2.5 and evaluate the resulting representations on six tasks involving unseen real datasets. The approach achieves the best aggregate performance among both learned and classical dataset representations, excelling in dataset retrieval, compact benchmark construction, and distribution‑shift severity assessment.
arXiv Machine Learning
Sep 30
TabFM: A Zero-Shot Foundation Model for Tabular Data
arXiv:2609.37959v1 Announce Type: new Abstract: Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We p...
arXiv AI
Jun 30
Beyond IID: How General Are Tabular Foundation Models, Really?
arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.
arXiv Machine Learning
Oct 2
VIP-COP: Context Optimization for Tabular Foundation Models
arXiv:2605.12904v2 Announce Type: replace Abstract: Tabular foundation models (TFMs) have emerged as a powerful paradigm for in-context learning on structured data, enabling direct prediction on new...
arXiv Machine Learning
4d ago
TabFORGE: A Tabular Foundation Model for Generative Modelling
arXiv:2605.09424v2 Announce Type: replace Abstract: Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modali...
arXiv Machine Learning
Aug 24
Tydra: An Efficient Hybrid Model for Tabular Data
Tydra is a hybrid Transformer‑State Space Model that interleaves attention and SSM layers for tabular in‑context learning. It achieves a 30% reduction in inference time compared to the Transformer‑only TabPFN while preserving most of its predictive performance. On 30 OpenML datasets, Tydra also outperforms a Hydra model that is roughly ten times larger, demonstrating that hybrid architectures can balance accuracy and efficiency for tabular foundation models.
arXiv Machine Learning
Aug 19
TabNSM: Neural Sparse Mixer for Tabular Regression
TabNSM is a scalable regression framework for large-scale, high-dimensional tabular data that builds on sparse-attention and mixer architectures. Its core component, the Adaptive Sparse Interaction Module (ASIM), combines foreground feature discovery, sparse local interaction encoding, and Feature-Token Mixing to achieve near-linear complexity. For regression, TabNSM adds a Multi-Stage Regression Head, GridLoss (an ordinal-aware soft-binning objective), and RISE (a difficulty-aware sampling strategy), achieving strong predictive performance and practical scalability across nine real-world benchmarks, especially on high-dimensional and heterogeneous datasets.
arXiv Machine Learning
Sep 17
TabICLv2: A better, faster, scalable, and open tabular foundation model
TabICLv2 is a new state‑of‑the‑art tabular foundation model that outperforms existing methods on regression and classification tasks. It relies on a synthetic data generation engine for diverse pretraining, architectural innovations such as a scalable softmax attention, and optimized training protocols that replace AdamW with the Muon optimizer. On the TabArena and TALENT benchmarks, TabICLv2 surpasses the current best model, RealTabPFN‑2.5, without any tuning, while also being faster and capable of handling million‑scale datasets with limited GPU memory.
arXiv AI
Sep 30
LoopICL: Looping a single transformer block to solve tabular tasks
LoopICL is a transformer architecture that loops a single block to address tabular tasks. It separates parameter count from computational depth by using a cell stream for per‑cell features and a row stream for in‑context examples, refined via within‑column and cross‑column attention. During pre‑training, varying loop counts and a learned exit‑gate allow the model to adjust inference depth at test time, achieving competitive performance with TabICLv2 while using about 90% fewer parameters.
arXiv AI
Sep 4
Xiaomi-TabLDM: A Tabular Foundation Model Technical Report
Xiaomi-TabLDM is a tabular foundation model that performs classification and regression via in-context learning without task‑specific fine‑tuning. It is pretrained solely on synthetic data from structural causal models, achieving top‑ranked regression results on multiple benchmarks while reducing training and prediction time compared to leading models. The architecture incorporates a three‑stage training strategy, dual‑stream feature grouping, lightweight attention residuals, and sparse mixture‑of‑experts, and it can further improve accuracy through test‑time compute scaling.
arXiv AI
Aug 11
iLTM: Integrated Large Tabular Model
arXiv:2511. 15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services.