Circuit discovery is the process of identifying interacting model components that jointly implement a specific behavior in a language model - it aims to map behavior from outputs back to causal internal computation.

What Is Circuit discovery?

• Definition: Treats groups of heads, neurons, and residual pathways as functional subcircuits.
• Target Behaviors: Common targets include induction, factual retrieval, and arithmetic-style reasoning.
• Method Stack: Uses activation patching, ablation, attribution, and feature analysis together.
• Output Form: Produces mechanistic hypotheses that can be tested with interventions.

Why Circuit discovery Matters

• Causal Understanding: Moves beyond correlation to identify which components are necessary.
• Safety Utility: Helps locate pathways linked to harmful outputs or policy failures.
• Model Editing: Enables targeted interventions instead of broad retraining.
• Debug Speed: Narrows failure investigation to small internal regions.
• Research Progress: Builds reusable knowledge about transformer computation patterns.

How It Is Used in Practice

• Behavior Spec: Define narrow behavior tests before searching for candidate circuits.
• Intervention Tests: Validate circuit necessity with controlled patching and ablation experiments.
• Replication: Check discovered circuits across prompts, seeds, and nearby checkpoints.

Circuit discovery is a core workflow for mechanistic transformer analysis - circuit discovery is most useful when hypotheses are validated with explicit causal interventions.