Mechanistic Interpretability

Mechanistic interpretability reverse-engineers what a neural network actually computes: the features it represents, the circuits that combine them, and how to test causal claims with interventions. It matters for safety and debugging because behavioral evals tell you what a model does, not why, and a model that passes every test can still harbor an unwanted internal mechanism. Applied AI interviews probe it to separate people who can reason about model internals and their current limits from people who only know prompts and benchmarks.