A whole table can become one vector: frozen tabular foundation models already carry dataset-level structure, and contrastive post-training makes retrieval robust
Repurposing pretrained tabular foundation models as encoders of entire supervised datasets maps an unseen dataset to a fixed-dimensional vector in a single forward pass, reaching the strongest aggregate rank across six dataset-level tasks.