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Symbolic Mechanistic Data Attribution: Tracing Training Influence to Learned Behavioral Policies
While existing data attribution methods can identify which training examples build specific mechanistic circuits, they cannot explain how training data shapes the behavioral decisions a model learns. We introduce Symbolic Mechanistic Data Attribution (SMDA), a framework that attributes training pair
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