Probe
Mechanistic interpretability reverse-engineers neural network internals to understand circuits features and representations at a mechanistic level. Unlike black-box interpretability that correlates inputs with outputs mechanistic interpretability opens the black box to understand how models work. Research identifies circuits groups of neurons implementing specific algorithms like induction heads for in-context learning or curve detectors in vision models. Techniques include activation patching to test causal importance ablation studies removing components to measure impact feature visualization showing what neurons detect and circuit analysis tracing information flow. Anthropic and others use sparse autoencoders to find monocemantic features. Benefits include understanding failure modes detecting biases improving safety and enabling targeted interventions. Challenges include complexity of large models polysemantic neurons responding to multiple concepts and scaling analysis to billions of parameters. Mechanistic interpretability aims to fully understand model internals enabling safe AI through transparency. It represents a shift from treating models as black boxes to understanding them as engineered systems with discoverable mechanisms.