A transformer foundation model is trained on synthetic data from a novel prior over continuous-treatment data-generating processes to predict treatment-response curves via in-context learning without task-specific fine-tuning.
hub Baseline reference
TabICLv2: A better, faster, scalable, and open tabular foundation model.arXiv preprint arXiv:2602.11139
Baseline reference. 57% of citing Pith papers use this work as a benchmark or comparison.
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2026 29representative citing papers
A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 11 models and 142 datasets in the new BeyondArena benchmark.
CalArena is a large-scale benchmark that evaluates dozens of post-hoc calibration methods using Post-Hoc Improvement (PHI) in proper scoring rules and finds that smooth functions outperform binning while dedicated multiclass methods are required in high-dimensional settings.
SurvivalPFN amortizes Bayesian survival analysis for right-censored data by pretraining a prior-data fitted network on synthetic identifiable DGPs and then performing in-context inference, achieving competitive results on 61 real datasets.
MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.
PFN-TS converts PFN posterior predictives into mean-reward samples for Thompson sampling using a subsampled predictive CLT, with consistency proofs, regret bounds, and strong empirical performance on synthetic and real bandit benchmarks.
Schema-1 is the first Data Language Model that natively understands raw tabular data and outperforms gradient-boosted ensembles, AutoML, and prior tabular foundation models on row-level prediction and imputation tasks.
TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
RamanBench unifies 74 datasets into the first large-scale reproducible benchmark for ML on Raman spectra, finding tabular foundation models outperform baselines but no method generalizes across datasets.
TabPATE applies a PATE-style private aggregation to synthetic tabular queries generated from feature ranges, enabling private in-context learning with near-random membership inference success while keeping competitive utility.
LUCoS replaces raw tabular geometry with unsupervised PFN latent embeddings for medoid-based context selection and ranks first on mean AUC, ACC, and F1 across 67 datasets and six budgets.
O'Prior, a compositional synthetic prior with hierarchical SCMs, realism engines, stress modules, and curriculum protocols, improves tabular foundation model accuracy and robustness on real benchmarks when architecture and compute are held fixed.
Distilling TabICLv2 into XGBoost via stratified OOF labeling yields 0.882 macro-mean AUC (96.5% of teacher) at 1.9 ms CPU across 153 datasets, with significant gains over tuned CatBoost on low-dimensional data.
KGPFN pretrains on multiple KGs to learn relation patterns, then performs query-specific reasoning by encoding local context with NBFNet and global context via retrieved instances aggregated in a PFN with feature- and sample-level attention.
OSCBO adaptively balances Gaussian process sharpness and calibration in Bayesian optimization by casting hyperparameter selection as constrained online learning, while preserving sublinear regret bounds.
FICBO pretrains a feedback-aware transformer with a structured prior on feedback distortion to adaptively exploit or ignore unreliable auxiliary signals during in-context black-box optimization.
Tabular foundation models achieve high accuracy in molecular property prediction through in-context learning, with up to 100% win rates on MoleculeACE tasks when paired with CheMeleon embeddings.
Muon optimizer outperforms AdamW across 17 tabular datasets when training MLPs under a shared protocol.
KumoRFM-2 pre-trains on synthetic and real relational data across row, column, foreign-key and cross-sample axes, injects task information early, and achieves up to 8% gains over supervised baselines on 41 benchmarks in few-shot and fine-tuned regimes while handling billion-scale datasets.
Enterprise tabular data differs from public benchmarks in ways that prevent good generalization of models like TabPFN, TabICL, and ConTextTab between the two domains.
A pose-estimation plus tabular foundation model pipeline trained on 25 adults transfers to 12 pediatric hyperkinetic movement disorder cases with lightweight final-layer calibration, raising Hamming accuracy from 0.804 to 0.839 and Jaccard index from 0.548 to 0.633 on held-out patients.
Oracle Markov boundaries improve prediction on high-dimensional sparse tabular data but causal discovery pipelines rarely recover boundaries that beat using all features.
CoMET achieves strong multimodal classification performance by composing frozen modality encoders, PCA compression, and tabular foundation models without any training, reaching state-of-the-art on diverse benchmarks including large-scale hierarchical tasks.
citing papers explorer
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MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.