In the context of AI interpretability, Circuit Analysis is a methodology for reverse-engineering the internal computations of a neural network by identifying minimal subgraphs—"circuits"—of neurons and Attention heads that are jointly responsible for a specific behavior. Adapted from classical electrical-circuit analysis, the approach treats each layer's activations as signals flowing through interconnected nodes, then uses activation patching, ablation, and causal tracing to isolate which components are necessary and sufficient for a given capability. Anthropic's mechanistic interpretability research and the "A Mathematical Framework for Transformer Circuits" paper are primary references.

Think of it like tracing exactly which wires and switches inside a radio are responsible for tuning to a particular station: you disconnect components one by one, observe when the station disappears, and map the minimal path that keeps it playing.

A well-known circuit example is the "induction head" circuit in Transformers: two attention heads cooperate so that, after seeing the pattern [A][B]...[A], the model predicts [B]. Researchers identified this by patching activations between model copies and measuring the causal effect on output probability. This type of analysis has been extended to factual recall, indirect object identification, and mathematical operations.

Circuit Analysis sits at the intersection of Self-Attention head behavior, Activation Function interpretation, Embedding geometry, and the study of Attention patterns more broadly. It is a core tool for Transformer interpretability alongside probing classifiers and sparse autoencoders. Understanding circuits informs Hallucination diagnosis and helps researchers predict how Fine-tuning will alter specific capabilities.

You will encounter Circuit Analysis in mechanistic interpretability papers, AI safety research on model internals, and increasingly in model auditing workflows aimed at understanding unexpected LLM behaviors.