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From Behavior to Provenance: Attributing Tabular Foundation Models to Synthetic Pretraining Data
Training-data attribution aims to identify which training examples shape model behavior, yet validating such claims is difficult because causal training influence is rarely observable. We argue that controlled synthetic pretraining makes attribution experimentally testable. Using O'PRIOR, a provenan
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