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.