# RelBench v2: A Large-Scale Benchmark and Repository for Relational Data

arXiv: https://arxiv.org/abs/2602.12606 · HTML: https://mlsanity.com/paper/2602.12606/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 23 of 23)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [TabICL: A Tabular Foundation Model for In-Context Learning on Large Data](https://mlsanity.com/paper/2502.05564/) (2025)
- [Identity-aware Graph Neural Networks](https://mlsanity.com/paper/2101.10320/) (2021)
- [RelBench: A Benchmark for Deep Learning on Relational Databases](https://mlsanity.com/paper/2407.20060/) (2024)
- [CARTE: Pretraining and Transfer for Tabular Learning](https://mlsanity.com/paper/2402.16785/) (2024)
- [RelGNN: Composite Message Passing for Relational Deep Learning](https://mlsanity.com/paper/2502.06784/) (2025)
- [Griffin: Towards a Graph-Centric Relational Database Foundation Model](https://mlsanity.com/paper/2505.05568/) (2025)
- [Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data](https://mlsanity.com/paper/2510.06377/) (2025)
- [Relational Graph Transformer](https://mlsanity.com/paper/2505.10960/) (2025)
- [PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning](https://mlsanity.com/paper/2404.00776/) (2024)
- [Learning Efficient Positional Encodings with Graph Neural Networks](https://mlsanity.com/paper/2502.01122/) (2025)
- [Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures](https://mlsanity.com/paper/2506.16654/) (2025)
- [PORTAL: Scalable Tabular Foundation Models via Content-Specific Tokenization](https://mlsanity.com/paper/2410.13516/) (2024)
- [SALT: Sales Autocompletion Linked Business Tables Dataset](https://mlsanity.com/paper/2501.03413/) (2025)
- [4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs](https://mlsanity.com/paper/2404.18209/) (2024)
- [Transformers Meet Relational Databases](https://mlsanity.com/paper/2412.05218/) (2024)
- [Joint Relational Database Generation via Graph-Conditional Diffusion Models](https://mlsanity.com/paper/2505.16527/) (2025)
- [REDELEX: A Framework for Relational Deep Learning Exploration](https://mlsanity.com/paper/2506.22199/) (2025)
- [RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models](https://mlsanity.com/paper/2506.00710/) (2025)

## Built on by (most cited first, 20 of 20)

- [KumoRFM-2: Scaling Foundation Models for Relational Learning](https://mlsanity.com/paper/2604.12596/) (2026)
- [No Need to Train Your RDB Foundation Model](https://mlsanity.com/paper/2602.13697/) (2026)
- [Towards Anomaly Detection on Relational Data](https://mlsanity.com/paper/2606.18621/) (2026)
- [OpenRFM: Dissecting Relational In-Context Learning](https://mlsanity.com/paper/2606.04320/) (2026)
- [Advancing Open and Reproducible Relational Learning: RelArena-α, TabPFN-Rel and RPI](https://mlsanity.com/paper/2608.16319/) (2026)
- [RelAgent: LLM Agents as Data Scientists for Relational Learning](https://mlsanity.com/paper/2605.07840/) (2026)
- [NGDBench: Towards Neural Graph Data Management](https://mlsanity.com/paper/2603.05529/) (2026)
- [RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases](https://mlsanity.com/paper/2606.03040/) (2026)
- [Beyond Accuracy: Measuring Logical Compliance of Predictive Models](https://mlsanity.com/paper/2606.20208/) (2026)
- [Universal Encoders for Modular Relational Deep Learning](https://mlsanity.com/paper/2606.21434/) (2026)
- [InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs](https://mlsanity.com/paper/2607.05478/) (2026)
- [AutoGrable: What Is a Good Graph for a Table?](https://mlsanity.com/paper/2608.11431/) (2026)
- [Incremental Evaluation and Training in Relational Deep Learning](https://mlsanity.com/paper/2608.13023/) (2026)
- [Relational Task Generation Language: A Declarative Specification Framework for Relational Deep Learning](https://mlsanity.com/paper/2609.01292/) (2026)
- [QUARTET: Quad-branch cross-Attention and Random-walk Traces for Enhancing Transformers on Relational Graphs](https://mlsanity.com/paper/2609.26855/) (2026)
- [Support-Set Target Leakage in Relational Foundation Models during In-Context Learning: Model Dependence and Evaluation Reliability](https://mlsanity.com/paper/2609.36417/) (2026)
- [STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling](https://mlsanity.com/paper/2610.00907/) (2026)
- [When Does Synthetic Relational Data Teach Models to Use Relations? Tracing Predictive Structure from Pretraining Data to Model Behavior](https://mlsanity.com/paper/2610.03057/) (2026)
- [RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions](https://mlsanity.com/paper/2610.17867/) (2026)
- [Revisiting Simple Baselines for Relational Deep Learning](https://mlsanity.com/paper/2610.20534/) (2026)

## Abstract (arXiv)

Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relationships across multiple interconnected tables. As this paradigm evolves toward larger models and relational foundation models, scalable and realistic benchmarks are essential for enabling systematic evaluation and progress. In this paper, we introduce RelBench v2, a major expansion of the RelBench benchmark for RDL. RelBench v2 adds four large-scale relational datasets spanning scholarly publications, enterprise resource planning, consumer platforms, and clinical records, increasing the benchmark to 11 datasets comprising over 22 million rows across 29 tables. We further introduce autocomplete tasks, a new class of predictive objectives that require models to infer missing attribute values directly within relational tables while respecting temporal constraints, expanding beyond traditional forecasting tasks constructed via SQL queries. In addition, RelBench v2 expands beyond its native datasets by integrating external benchmarks and evaluation frameworks: we translate event streams from the Temporal Graph Benchmark into relational schemas for unified relational-temporal evaluation, interface with ReDeLEx to provide uniform access to 70+ real-world databases suitable for pretraining, and incorporate 4DBInfer datasets and tasks to broaden multi-table prediction coverage. Experimental results demonstrate that RDL models consistently outperform single-table baselines across autocomplete, forecasting, and recommendation tasks, highlighting the importance of modeling relational structure explicitly.