PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction

Excerpt

arXiv:2605.06979v2 Announce Type: replace-cross Abstract: Causal abstraction offers a principled framework for mechanistic interpretability, aligning a high-level causal model with low-level neural computation through interchange intervention analysis. Finding such an alignment, however, often requires fitting and evaluating separate learned mappings across many candidate neural locations. We introduce PLOT, a gradient-free approach for joint correspondence discovery via a global matching of int