Mechanistic Interpretability

We present the first case study in rigorously compressing nonlinear feature-maps, discovering that the MLP in modular addition models can be understood as evaluating a quadrature scheme.

We introduce a more principled approach for evaluating the quality of mechanistic interpretations via behavior-preserving resampling ablations—converting hypotheses into classes of activations inside a neural network can be resampled without affecting behavior.