# ReLaG: A Scalable Framework Generalizing Random Splits to Data with Latent Relations

arXiv: https://arxiv.org/abs/2609.38538 · HTML: https://mlsanity.com/paper/2609.38538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding](https://mlsanity.com/paper/2206.02096/) (2022)
- [Scaffold Splits Overestimate Virtual Screening Performance](https://mlsanity.com/paper/2406.00873/) (2024)
- [Challenging reaction prediction models to generalize to novel chemistry](https://mlsanity.com/paper/2501.06669/) (2025)
- [SpanSeq: Similarity-based sequence data splitting method for improved development and assessment of deep learning projects](https://mlsanity.com/paper/2402.14482/) (2024)
- [Revealing data leakage in protein interaction benchmarks](https://mlsanity.com/paper/2404.10457/) (2024)
- [Beyond the Performance Illusion: Structure-Aware Stratified Partitioning and Curriculum Distributionally Robust Optimization for Spatially Correlated Domains](https://mlsanity.com/paper/2607.02055/) (2026)

## Abstract (arXiv)

Random splitting can yield non-independent train--test subsets when a dataset contains related samples, as is common in certain applications such as biochemical studies. This leads to overly optimistic generalization estimates. Here, we introduce ReLaG, a modality-agnostic framework that models sample relatedness through a hierarchical latent-variable process and infers groups of related samples using proximity graphs and community detection to produce independent train--test subsets. Across molecular and protein datasets, ReLaG matches existing relation-aware methods while scaling substantially better, enabling splits at previously impractical dataset sizes. We further introduce a label-free procedure that adapts the splitting resolution to production data, aligning evaluation with the intended deployment setting. ReLaG's inferred groups provide a cheap estimate of effective dataset size, enabling diversity-aware dataset scaling. ReLaG is open source and can be installed with pip install relag.