RDBLearn: Simple In-Context Prediction Over Relational Databases

• URL: http://arxiv.org/abs/2602.18495v1
• Date: Sat, 14 Feb 2026 09:24:04 GMT
• Title: RDBLearn: Simple In-Context Prediction Over Relational Databases
• Authors: Yanlin Zhang, Linjie Xu, Quan Gan, David Wipf, Minjie Wang,
• Abstract summary: We show that a simple recipe can be extended to relational prediction with a simple recipe.<n>We package this approach in textitRDBLearn, an easy-to-use toolkit with a scikit-learn-style estimator interface.<n>Across a broad collection of RelBench and 4DBInfer datasets, RDBLearn is the best-performing foundation model approach we evaluate.
• Score: 21.996337463952255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in tabular in-context learning (ICL) show that a single pretrained model can adapt to new prediction tasks from a small set of labeled examples, avoiding per-task training and heavy tuning. However, many real-world tasks live in relational databases, where predictive signal is spread across multiple linked tables rather than a single flat table. We show that tabular ICL can be extended to relational prediction with a simple recipe: automatically featurize each target row using relational aggregations over its linked records, materialize the resulting augmented table, and run an off-the-shelf tabular foundation model on it. We package this approach in \textit{RDBLearn} (https://github.com/HKUSHXLab/rdblearn), an easy-to-use toolkit with a scikit-learn-style estimator interface that makes it straightforward to swap different tabular ICL backends; a complementary agent-specific interface is provided as well. Across a broad collection of RelBench and 4DBInfer datasets, RDBLearn is the best-performing foundation model approach we evaluate, at times even outperforming strong supervised baselines trained or fine-tuned on each dataset.

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