arXiv: cs.AI preprintsInternational6 November 2026
When Attribution Patching Lies: Diagnosis and a Second-Order Correction
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arXiv:2606.09899v2 Announce Type: replace-cross Abstract: A central goal of mechanistic interpretability is to identify which internal components causally drive a language model's behavior. Because these importance estimates serve as the evidence for identifying circuits, systematic errors can lead to the misidentification of the underlying mechanisms. While activation patching provides a gold-standard causal metric, its computational cost is prohibitive at scale. Practitioners instead rely on attribution patching, a gradient-based, first-order approximation whose reliability remains poorly un
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