Relational In-Context Learning via Synthetic Pre-training with Structural Prior

• URL: http://arxiv.org/abs/2603.03805v1
• Date: Wed, 04 Mar 2026 07:30:54 GMT
• Title: Relational In-Context Learning via Synthetic Pre-training with Structural Prior
• Authors: Yanbo Wang, Jiaxuan You, Chuan Shi, Muhan Zhang,
• Abstract summary: RDB-PFN is the first relational foundation model trained purely via $textbfsynthetic$.<n>Inspired by Prior-Data Fitted Networks (PFNs) where synthetic data generated from Structural Causal Models (SCMs) enables reasoning on single tables.<n>Experiments verify RDB-PFN achieves strong few-shot performance on 19 real-world prediction tasks.
• Score: 60.404256960057545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are private, scarce and structurally heterogeneous, making internet-scale pre-training infeasible. To overcome this data scarcity, We introduce $\textbf{RDB-PFN}$, the first relational foundation model trained purely via $\textbf{synthetic data}$. Inspired by Prior-Data Fitted Networks (PFNs) where synthetic data generated from Structural Causal Models (SCMs) enables reasoning on single tables, we design a $\textbf{Relational Prior Generator}$ to create an infinite stream of diverse RDBs from scratch. Pre-training on $\textbf{over 2 million}$ synthetic single-table and relational tasks, RDB-PFN learns to adapt to any new database instantly via genuine $\textbf{in-context learning}$. Experiments verify RDB-PFN achieves strong few-shot performance on 19 real-world relational prediction tasks, outperforming graph-based and single-table foundation-model baselines (given the same DFS-linearized inputs), while using a lightweight architecture and fast inference. The code is available at https://github.com/MuLabPKU/RDBPFN

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