LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence https://arxiv.org/abs/2609.17488
RelBench: Relational Deep Learning Benchmark
TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
Minimal, fast + educational reimplementation of the TabICLv2 architecture + prior
Pretraining, fine-tuning, inference and test-time scaling of relational transformer (RT) models, including RT-J and RT-PluRel
A synthetic tabular and relational data generation framework
(ICML 2026) GraphPFN: A Prior-Data Fitted Graph Foundation Model
🚀 Awesome TabPFN use-cases from around the world
Turning Tabular Foundation Models into Graph Foundation Models
TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery
A curated collection of research on tabular foundation models: models pretrained on large-scale (synthetic) data that generalize to unseen tabular tasks at inference time.
Tabular Foundation Models Adaptation for Survival Analysis
🔨 [NeurIPS 2026] Official implementation of “TabFORGE: A Tabular Foundation Model for Generative Modelling”
Closing the Context Gap: Activation Alignment for Tabular In-Context Learning
Discovering association rules from high dimensional small tabular data via neurosymbolic rule mining supported by fine-tuning using tabular foundation models
The official implementation for the paper, “Clustering Method for Tabular Data Based on Pretrained Foundation Models with Synthetic Data.”
Research benchmark comparing tabular foundation models, classical methods and deep learners on credit default and loss given default.
[QDB'25] Out in the wild of modern data science, good tabular AI meets bad data. Who wins?
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