arXiv:2606. 30258v1 Announce Type: cross Abstract: Tabular foundation models have advanced deep learning for tabular data by delivering strong default performance across many small and medium tasks.
arXiv:2608. 06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance, healthcare, and public services.
The paper investigates how synthetic pretraining priors used in tabular foundation models (TFMs) influence downstream performance. By reconstructing the synthetic data generators of four TFMs and comparing their generated tasks to two popular tabular benchmarks using structural descriptors, the authors measure structural coverage and normalized density. They find that some generators provide broader and denser support for benchmark tasks, and that stronger synthetic-to-benchmark support generally correlates with better model performance.
The paper demonstrates that a tabular foundation model can achieve strong generalization using only a single real table for self‑supervised pre‑training, challenging the belief that large synthetic or real datasets are necessary. By systematically pre‑training and evaluating across diverse benchmarks, the authors show that the number and quality of tasks that can be derived from a dataset are critical for downstream performance. This finding suggests that carefully constructed task sets from limited data can enable effective transfer learning in tabular models.
arXiv:2609.38744v1 Announce Type: new Abstract: Predicting molecular properties for compounds that differ structurally from labeled training molecules is important for drug discovery and materials de...
arXiv:2606. 31126v1 Announce Type: new Abstract: Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design.
arXiv:2506. 14126v2 Announce Type: replace-cross Abstract: Modern deep learning is increasingly characterized by the use of open-weight foundation models that can be fine-tuned on specialized datasets.
arXiv:2609.36883v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without...
arXiv:2606. 09664v1 Announce Type: new Abstract: Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects such as molecules and proteins.
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:2605. 19662v2 Announce Type: replace Abstract: Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are typically designed for \emph{non-strategic} settings where data distributions are independent of deployed classifiers.
arXiv:2603. 07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.
arXiv:2608. 09119v1 Announce Type: new Abstract: We introduce Motif 3, a decoder-only Mixture-of-Experts language model with 314 billion total parameters and 13.