8 papers
Parameter-Free Encoders Remain Viable for RDB Foundation Models
Linjie Xu, David Wipf
Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or future) values in some target column of interest? As the space of potent…
No Need to Train Your RDB Foundation Model
Linjie Xu, Yanlin Zhang, Quan Gan +2
Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes. But since the space of potential targe…
RDBLearn: Simple In-Context Prediction Over Relational Databases
Yanlin Zhang, Linjie Xu, Quan Gan +2
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 t…
TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning
Jiaru Zou, Soumya Roy, Vinay Kumar Verma +6
Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of…
Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases
Ning Li, Kounianhua Du, Han Zhang +4
Relational databases (RDBs) have become the industry standard for storing massive and heterogeneous data. However, despite the widespread use of RDBs across various fields, the inh…
Griffin: Towards a Graph-Centric Relational Database Foundation Model
Yanbo Wang, Xiyuan Wang, Quan Gan +4
We introduce Griffin, the first foundation model attemptation designed specifically for Relational Databases (RDBs). Unlike previous smaller models focused on single RDB tasks, Gri…