How to turn circuit diagrams into testable scientific theories

Neural network circuits are discovered in isolation with no shared language to compare findings or test if two discoveries describe the same mechanism. This work creates that infrastructure by representing each circuit through two signatures: one capturing what components causally do (grounded in evidence), another learned via logic programming from structural patterns. The approach reveals distinct computational strategies across tasks and transfers circuit descriptions reliably across model sizes and architectures.