Paper
Context Window Failures in Relational Foundation Models
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 rec · Authors: Denis Oliveira Correa, Francisco Galuppo Azevedo · Published 2026-08-31 · arXiv:2609.00460
- Published
- 2026-08-31
- Authors
- 2
- arXiv
- 2609.00460
- Category
- cs.LG
Abstract
arXiv:2609.00460
Denis Oliveira Correa, Francisco Galuppo Azevedo
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.