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These results provide initial evidence that PFN-style synthetic pretraining transfers to graph-based relational foundation models.
The support/query information boundary is identified as an important component of reliable relational ICL evaluation as well as a concern with native-schema leakage candidates from the original relational schema.
It is suggested that curriculum design and synthetic data diversity may matter more for relational PFN pretraining than the specific relational generator or raw synthetic scale alone.
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