SOURCE-LINKED INTELLIGENCE
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 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 rela
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- arXiv · AI, language, vision and robotics · 2026-08-31T22:59:57.000Z
First collected: 2026-09-21T06:21:59.299Z. This is not the publication date.