๐ค AI Summary
็ ็ฉถ้ๅฏนๅ
ณ็ณปๅๅบ็กๆจกๅๅค็้ซๅบๆฐๆฐๆฎๆถ็็ชๅฃ้ๅถ้ฎ้ข๏ผ้่ฟๅผๅ
ฅๅๆ้่ๆฐๆฎ้Animusๅนถ้็จๆถ้ด้ข่ๅๆนๆณ๏ผๆพ่ๆ้ซไบ้ขๆตๆง่ฝใ
๐ 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.