arXiv Machine Learning July 8, 2026 By Linjie Xu, David Wipf

Parameter-Free Encoders Remain Viable for RDB Foundation Models

Read the original on arXiv Machine Learning →

arXiv:2607. 05476v1 Announce Type: new Abstract: Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or future) values in some target column of interest?

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benchmarks

arXiv Machine Learning

Jun 5

No Need to Train Your RDB Foundation Model

arXiv:2602. 13697v2 Announce Type: replace-cross Abstract: Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes.

By Linjie Xu, Yanlin Zhang, Quan Gan, Minjie Wang, David Wipf

llms fine-tuning data-engineering

arXiv Machine Learning

Aug 14

Incremental Evaluation and Training in Relational Deep Learning

arXiv:2608. 13023v1 Announce Type: new Abstract: Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning.

By Jakub Pele\v{s}ka, Gustav \v{S}\'ir

fine-tuning benchmarks

arXiv AI

Jul 7

A Fair Benchmarking of Deep Relational Database Learning Models

arXiv:2607. 03659v1 Announce Type: cross Abstract: Relational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult.

By Kazi F. Akhter, Bharath Ajendla, Manar D. Samad

llms benchmarks

arXiv Machine Learning

Jun 30

FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks

arXiv:2606. 30336v1 Announce Type: new Abstract: We introduce FlexTab, a flexible encoder-decoder architecture for in-context learning on tabular data that pairs a single, task-agnostic encoder with a suite of task-specific decoders.

By Marek Polewczyk, Maximilian Schambach, Marco Spinaci, Sam Thelin, Johannes H\"ohne

llms rag benchmarks

arXiv AI

Jun 3

RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases

arXiv:2606. 03040v1 Announce Type: new Abstract: Relational databases underpin modern enterprise, scientific, and healthcare systems, yet predictive machine learning on such data remains challenging due to their multi-table, heterogeneous, and temporal structure.

By Phillip Jiang

llms

arXiv Machine Learning

Sep 2

Relational Task Generation Language: A Declarative Specification Framework for Relational Deep Learning

arXiv:2609.01292v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) has become a powerful paradigm for learning from multi-tabular data. However, manually defining RDL prediction tasks i...

By Oleksii Kolesnichenko, Jakub Pele\v{s}ka, Gustav \v{S}\'{\i}r

benchmarks data-engineering

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