Mechanistic Interpretability Explainer
Mechanistic interpretability studies the internal computations that produce a neural network’s behavior. Instead of only describing input-output patterns, it tries to identify components, representations, and circuits that implement a capability.
Researchers inspect activations, attention patterns, neurons, features, and weight structure. They form hypotheses about a mechanism and test them by patching activations, ablating components, changing inputs, or measuring predicted effects.
The approach can reveal reusable circuits and help distinguish correlation from causal contribution. In large models, however, useful features may be distributed, polysemantic, redundant, or active only in particular contexts.
A compelling diagram is not enough. Strong evidence requires interventions, counterfactual predictions, replication across examples, and clear accounting of unexplained behavior. Scale remains a central challenge because a complete model may contain many interacting mechanisms.
Mechanistic interpretability studies how a neural network's internal components and computations produce its behavior.
Key terms include mechanism, circuit, activation, attention head, feature, ablation, activation patching, causal test and polysemanticity.
Researchers propose an internal circuit that should explain a behavior.
Ablation or activation patching tests whether changing that circuit changes the predicted result.
Redundancy and distributed features can preserve behavior after a component is removed.
Claims should state how much of the behavior remains unexplained.
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