This episode explores KumoRFM-2, a relational foundation model designed to learn directly from connected database tables instead of flattening customers, orders, products, and tickets into a single feature table. It explains why relational learning matters for enterprise tasks such as churn, fraud, and demand prediction, arguing that flattening often erases multi-hop relationships, repeated interactions, and temporal patterns that carry the real signal. The discussion centers on KumoRFM-2’s main technical claim: a two-stage, task-conditioned attention pipeline that first selects relevant information within each table and then aggregates evidence across foreign-key neighborhoods and labeled in-context examples derived from predictive queries. Listeners would find it interesting because it connects a very practical data-engineering pain point to a broader question about whether pretrained, database-native models can beat hand-built tabular pipelines without cheating on time-aware prediction.

Sources:

1. KumoRFM-2: Scaling Foundation Models for Relational Learning — Valter Hudovernik, Federico López, Vid Kocijan, Akihiro Nitta, Jan Eric Lenssen, Jure Leskovec, Matthias Fey, 2026

http://arxiv.org/abs/2604.12596

2. Learning Probabilistic Relational Models — Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer, 1999

https://scholar.google.com/scholar?q=Learning+Probabilistic+Relational+Models

3. Relational Inductive Biases, Deep Learning, and Graph Networks — Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Yujia Li, Razvan Pascanu, et al., 2018

https://scholar.google.com/scholar?q=Relational+Inductive+Biases%2C+Deep+Learning%2C+and+Graph+Networks

4. Relational Deep Learning: Graph Representation Learning on Relational Databases — Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec, 2023

https://scholar.google.com/scholar?q=Relational+Deep+Learning%3A+Graph+Representation+Learning+on+Relational+Databases

5. RelBench: A Benchmark for Deep Learning on Relational Databases — Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan E. Lenssen, Jure Leskovec, et al., 2024

https://scholar.google.com/scholar?q=RelBench%3A+A+Benchmark+for+Deep+Learning+on+Relational+Databases

6. Position: Why Tabular Foundation Models Should Be a Research Priority — Boris Van Breugel, Mihaela Van Der Schaar, 2024

https://scholar.google.com/scholar?q=Position%3A+Why+Tabular+Foundation+Models+Should+Be+a+Research+Priority

7. Accurate Predictions on Small Data with a Tabular Foundation Model — Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter, et al., 2025

https://scholar.google.com/scholar?q=Accurate+Predictions+on+Small+Data+with+a+Tabular+Foundation+Model

8. TabICL: A Tabular Foundation Model for In-Context Learning on Large Data — Jingang Qu, David Holzmüller, Gaël Varoquaux, Marine Le Morvan, 2025

https://scholar.google.com/scholar?q=TabICL%3A+A+Tabular+Foundation+Model+for+In-Context+Learning+on+Large+Data

9. KumoRFM: A Foundation Model for In-Context Learning on Relational Data — Matthias Fey, Vid Kocijan, Federico Lopez, Jan Eric Lenssen, Jure Leskovec, 2025

https://scholar.google.com/scholar?q=KumoRFM%3A+A+Foundation+Model+for+In-Context+Learning+on+Relational+Data

10. PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models — Vignesh Kothapalli et al., 2026

https://scholar.google.com/scholar?q=PluRel%3A+Synthetic+Data+unlocks+Scaling+Laws+for+Relational+Foundation+Models

11. Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data — Rishabh Ranjan et al., 2026

https://scholar.google.com/scholar?q=Relational+Transformer%3A+Toward+Zero-Shot+Foundation+Models+for+Relational+Data

12. No Need to Train Your RDB Foundation Model — Linjie Xu, Yanlin Zhang, Quan Gan, Minjie Wang, and David Wipf, 2026

https://scholar.google.com/scholar?q=No+Need+to+Train+Your+RDB+Foundation+Model

13. TabICLv2: A better, faster, scalable, and open tabular foundation model — Jingang Qu, David Holzmuller, Gael Varoquaux, and Marine Le Morvan, 2026

https://scholar.google.com/scholar?q=TabICLv2%3A+A+better%2C+faster%2C+scalable%2C+and+open+tabular+foundation+model

14. Griffin: Towards a Graph-Centric Relational Database Foundation Model — Yanbo Wang, Xiyuan Wang, Quan Gan, Minjie Wang, Qibin Yang, David Wipf, and Muhan Zhang, 2025

https://scholar.google.com/scholar?q=Griffin%3A+Towards+a+Graph-Centric+Relational+Database+Foundation+Model

15. Graph Machine Learning Meets Multi-Table Relational Data — Quan Gan, Minjie Wang, David Wipf, Christos Faloutsos, 2024

https://scholar.google.com/scholar?q=Graph+Machine+Learning+Meets+Multi-Table+Relational+Data

16. Large Scale Transfer Learning for Tabular Data via Language Modeling — Josh Gardner, Juan C. Perdomo, Ludwig Schmidt, 2024

https://scholar.google.com/scholar?q=Large+Scale+Transfer+Learning+for+Tabular+Data+via+Language+Modeling

17. Towards Synthetic Data for Fine-tuning Tabular Foundation Models — Magnus Buhler, Lennart Purucker, Frank Hutter, 2025

https://scholar.google.com/scholar?q=Towards+Synthetic+Data+for+Fine-tuning+Tabular+Foundation+Models

18. Range-limited Augmentation for Few-shot Learning in Tabular Data with Comprehensive Benchmark — Kyungeun Lee et al., 2025

https://scholar.google.com/scholar?q=Range-limited+Augmentation+for+Few-shot+Learning+in+Tabular+Data+with+Comprehensive+Benchmark

19. AI Post Transformers: Muon Is Scalable for LLM Training — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-25-muon-is-scalable-for-llm-training-587ed8.mp3

20. AI Post Transformers: Scaling Laws for Multilingual Code Pretraining — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-15-scaling-laws-for-multilingual-code-pretr-7d220e.mp3