Revisiting Simple Baselines for Relational Deep Learning

cs.LG

Submitted: 2026-10-14

Updated: 2026-10-14

Code: https://github.com/vietngth/revisit-baselines-rdl

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• Griffin: Towards a Graph-Centric Relational Database Foundation Model
• KumoRFM-2: Scaling Foundation Models for Relational Learning
• RelBench v2: A Large-Scale Benchmark and Repository for Relational Data
• No Need to Train Your RDB Foundation Model
• Parameter-Free Encoders Remain Viable for RDB Foundation Models

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks