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arXiv · 2603.29914

Task Scarcity and Label Leakage in Relational Transfer Learning

Francisco Galuppo Azevedo·Clarissa Lima Loures·Denis Oliveira Correa
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Abstract

Training relational foundation models requires learning representations that transfer across tasks, yet available supervision is typically limited to a small number of prediction targets per database. This task scarcity causes learned representations to encode task-specific shortcuts that degrade transfer even within the same schema, a problem we call label leakage. We study this using K-Space, a modular architecture combining frozen pretrained tabular encoders with a lightweight message-passing core. To suppress leakage, we introduce a gradient projection method that removes label-predictive directions from representation updates. On RelBench, this improves within-dataset transfer by +0.145 AUROC on average, often recovering near single-task performance. Our results suggest that limited task diversity, not just limited data, constrains relational foundation models.

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Francisco Galuppo Azevedo, Clarissa Lima Loures, Denis Oliveira Correa. 2026-03-31. Task Scarcity and Label Leakage in Relational Transfer Learning. https://arxiv.org/abs/2603.29914

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