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Forget scraping private databases: RDB-PFN shows you can pre-train a relational foundation model from scratch using 2 million synthetically generated relational databases and achieve strong few-shot performance.
Generating realistic tabular data with both numbers and free-form text just got easier: TabDLM bridges the gap between diffusion models and LLMs for superior joint modeling.
Ditch the equivariant constraints: canonicalization lets you train simpler, faster diffusion models that actually *outperform* equivariant architectures for symmetric generative tasks like 3D molecule design.
