Global Information Thresholding for Sufficient and Necessary Circuits

摘要

Mechanistic interpretability seeks circuits that are both sufficient on their own and necessary to a model's behavior under intervention, yet many circuit-discovery pipelines still rely on manually fixed circuit budgets. We propose a procedure that scores edges with sign-preserving integrated gradients and then chooses a single global cutoff by a performance-retention criterion for circuit selection. This makes circuit size a consequence of retained behavior rather than a manually selected budget. On the Mechanistic Interpretability Benchmark, this retention-calibrated thresholding yields circuits that are competitive on CPR/CMD across multiple tasks and models. On our GPT-2 IOI probability-space diagnostic, it improves sufficiency relative to the EAP-IG baseline while also improving the reported necessity diagnostics. Threshold ablations and stability checks further show that the selected operating point yields the best overall balance between self-sufficiency and necessity-oriented diagnostics.