Relational Deep Learning

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Latest papers 44

9 papers in the last four weeks, up 29% on the four weeks before. 0.1% of all new papers.

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OpenRFM: Dissecting Relational In-Context Learning
Jun 3, 2026Zhikai Chen, Junyu Yin, Jialiang Gu +5Relational Foundation Models Synthetic Data Pretraining
Michigan State University · George Mason University · Georgia Institute of Technology +1
RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases
Jun 2, 2026Phillip JiangRelational Deep Learning Graph Transformer
Appsofa LLC
Incorporating Deep Learning Design in Database Queries
May 22, 2026Yuval Lev Lubarsky, Dean Light, Boaz Berger +2Relational Deep Learning Relational Databases
Technion · University of Washington · RelationalAI
RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases
May 22, 2026Jinyu Yang, Cheng Yang, Junze Chen +4Representation Learning Self-Supervised Pre-Training
Beijing University Of Posts and Telecommunications Beijing, China · Peng Cheng Laboratory Shenzhen, China · China Telecom Bestpay Beijing, China +1
Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
May 20, 2026Yi Huang, Qingyun Sun, Jia Li +2Graph Structure Learning Graph Neural Networks
SKLCCSE, School of Computer Science and Engineering, Beihang University, Beijing, China · Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, China
Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
May 15, 2026Jingcheng Wu, Ratan Bahadur Thapa, Mojtaba Nayyeri +4Relational Foundation Models Tabular Foundation Models
University of Stuttgart, Stuttgart, Germany · Web and Internet Science Research Group, University of Southampton, Southampton, United Kingdom
Gaussian Relational Graph Transformer
May 15, 2026Zezhong Ding, Jin Li, Xugang Wang +1Graph Attention Networks Graph Representation Learning
School of Artificial Intelligence and Data Science, University of Science and Technology of China (USTC) · Data Darkness Lab, Suzhou Institute for Advanced Research, USTC · School of Biomedical Engineering, USTC +1
KumoRFM-2: Scaling Foundation Models for Relational Learning
Apr 14, 2026Valter Hudovernik, Federico López, Vid Kocijan +4Relational Foundation Models Relational Deep Learning
Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
Mar 9, 2026Jun Yin, Peng Huo, Bangguo Zhu +4Relational Deep Learning Class-Imbalanced Learning
Department of Data Science and Artificial Intelligence, Hong Kong Polytechnic University, Hong Kong SAR, China · National Super Computing Center, Tianjin, China · School of Computer Science and Engineering, Central South University, Hunan, China +1
Relatron: Automating Relational Machine Learning over Relational Databases
Feb 26, 2026Zhikai Chen, Han Xie, Jian Zhang +3Relational Deep Learning Model Selection
Michigan State University · Amazon
A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning
Feb 19, 2026Aadi Joshi, Kavya BhandRelational Deep Learning Graph Neural Networks
Dept. of Computer Engineering, Vishwakarma Institute of Technology, Pune, India.
PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
Feb 3, 2026Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik +4Relational Foundation Models Synthetic Data Pretraining
1Stanford University · 2Kumo AI · 3SAP Labs LLC
Grables: When Graph Structure Helps Tabular Prediction
Feb 3, 2026Tamara Cucumides, Floris GeertsTabular ML Tabular Prediction
Department of Computer Science, University of Antwerp. Belgium.
FloydNet: A Learning Paradigm for Global Relational Reasoning
Jan 27, 2026Jingcheng Yu, Mingliang Zeng, Qiwei YeGraph Neural Networks Relational Reasoning
Beijing Academy of Artificial Intelligence

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OpenRFM: Dissecting Relational In-Context Learning

Zhikai Chen, Junyu Yin, Jialiang Gu, Siheng Xiong+4

Michigan State University · George Mason University · Georgia Institute of Technology+1

Relational Foundation Models Synthetic Data Pretraining Tabular Foundation Models Relational Reasoning In-Context Learning Relational Deep Learning+2

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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). Yet a substantial gap separates open RFMs from their commercial counterparts, and the origin of this gap has not been systematically understood. We dissect a representative framework, the Relational Transformer (RT), from two perspectives. Model side: we show that RT performs relation-level ICL, and a kernel regression view shows it fails when sparse label-cell coverage yields an underdetermined regression. Data side: we ablate RT's pre-training source and find that existing synthetic-only pre-training and in-distribution pre-training drive the same architecture into different regimes, lazy vs. feature-learning. Probing this gap reveals that the missing ingredient is a support-identifiable relational latent in the label-generation process. These two diagnoses translate into (1) a dual-stage ICL architecture that combines the relational backbone with a batch-level ICL layer lifted from a pre-trained tabular foundation model to overcome relation-level label scarcity, and (2) a homophily-aware synthetic plus continual real-data pre-training mixture, augmented with a prototype-based regularization. These choices define OpenRFM, a simple yet effective RFM that improves average task performance by approximately 30% over the RT backbone and surpasses the commercial model KumoRFMv1 on a large set of evaluation tasks.