arXiv:2607. 29129v1 Announce Type: new Abstract: Relational Foundation Models (RFMs) require large-scale synthetic relational databases for pretraining, but existing approaches tightly couple data generation with the model training pipeline.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2607. 29120v1 Announce Type: new Abstract: Relational Prior-Data Fitted Networks (PFNs) such as RDB-PFN approximate Bayesian inference over multi-table relational databases by pretraining on millions of synthetic tasks.
arXiv:2602. 13697v2 Announce Type: replace-cross Abstract: Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes.
arXiv:2602. 04029v2 Announce Type: replace-cross Abstract: Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases.
arXiv:2608. 16319v1 Announce Type: new Abstract: This first release of Prior Labs in relational learning shows our continued commitment to open science.
This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate research in the field towards meaningful real-world impact.
arXiv:2606. 04320v1 Announce Type: cross Abstract: Relational Foundation Models (RFMs) promise a single pre-trained predictor that, given any relational database, returns predictions in one forward pass via relational in-context learning (ICL).