Context Window Failures in Relational Foundation Models

Abstract:Recent Relational Deep Learning architectures have been proposed as foundation models for multi-table relational data, yet they impose constrained neighborhood budgets that force row truncation when an entity has many related records. We introduce Animus, a synthetic financial dataset in which predicting customer income requires aggregating up to tens of thousands of transactions. On the raw representation, three recently proposed models (RT, Griffin, RelGT) achieve $R^2 \le 0.18$; a single, routine, temporal pre-aggregation step recovers $R^2$ up to $0.65$. This questions whether current relational foundation models are ready for high-cardinality real-world data.

Accepted at the 2nd Foundation Models for Structured Data Workshop at ICML 2026, Seoul, South Korea. OpenReview: this https URL

Machine Learning (cs.LG)

arXiv:2609.00460 [cs.LG]

(or arXiv:2609.00460v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.00460 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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