Mechanistic interpretability targets algorithms in weights

Mechanistic interpretability is distinguished from approaches like saliency maps by aiming to reverse-engineer the actual algorithms running in a model: treating the weights like a compiled binary and activations like the running program, the goal is to figure out how the weights correspond to algorithms.