Answers are grounded in this paper and cite their sources.
Comparative analysis demonstrates that algorithm choice outweighs ranking rule variation when circuit size is controlled in language models, highlighting the need for calibrated benchmark standards.
Two published comparisons of circuit discovery algorithms reach opposite orderings of attribution patching against ACDC, and neither has a yardstick for how much disagreement between methods should be expected. This measures the within-method term first - changing only the rule used to rank candidate heads inside top-k selection moves faithfulness by up to 0.837 at fixed circuit size - then measures the corresponding between-method term on the same harness. Three selection procedures (marginal top-k, greedy forward selection, and reverse-order ACDC-style threshold pruning) are compared at matched circuit size across nine configurations in Llama-3.2-3B and Pythia-1.4B. An uncontrolled first pass, in which one fixed pruning threshold produced circuits from 3 to 33 heads, found the within-method term larger (median ratio 0.50). A second pass with circuit size matched near 15 heads per configuration reverses this: the between-method term is larger in six of nine configurations (median ratio 2.72, range 0.36-13.81), and eight of nine show statistically significant separation between algorithms at 95% confidence. The three exceptions are explained rather than averaged over: two reproduce an already-documented ranking-rule pathology on Pythia IOI, and one is ordinary rule disagreement. No attribution-patching arm is implemented, so this does not adjudicate the contested literature directly, but supplies the yardstick against which it should be read. All runs on a single Apple M1 Max.
No takes yet. Share an insight, caveat, or question.
J. Melton (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: