Proposes a pretrained Universal Row Encoder using transformers and global statistics to generate table-width invariant row embeddings for modular relational graph models, claiming improved transfer, convergence, and memory on RelBench.
Predictive query language: A domain-specific language for predictive modeling on relational databases.arXiv preprint arXiv:2602.09572, 2026
3 Pith papers cite this work. Polarity classification is still indexing.
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OpenRFM combines a relational transformer backbone with a batch-level ICL layer and homophily-aware synthetic-plus-real pre-training to improve relational in-context learning by ~30% over prior open models and surpass KumoRFMv1.
KumoRFM-2 pre-trains on synthetic and real relational data across row, column, foreign-key and cross-sample axes, injects task information early, and achieves up to 8% gains over supervised baselines on 41 benchmarks in few-shot and fine-tuned regimes while handling billion-scale datasets.
citing papers explorer
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Universal Encoders for Modular Relational Deep Learning
Proposes a pretrained Universal Row Encoder using transformers and global statistics to generate table-width invariant row embeddings for modular relational graph models, claiming improved transfer, convergence, and memory on RelBench.
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OpenRFM: Dissecting Relational In-Context Learning
OpenRFM combines a relational transformer backbone with a batch-level ICL layer and homophily-aware synthetic-plus-real pre-training to improve relational in-context learning by ~30% over prior open models and surpass KumoRFMv1.
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KumoRFM-2: Scaling Foundation Models for Relational Learning
KumoRFM-2 pre-trains on synthetic and real relational data across row, column, foreign-key and cross-sample axes, injects task information early, and achieves up to 8% gains over supervised baselines on 41 benchmarks in few-shot and fine-tuned regimes while handling billion-scale datasets.