Legacy Concept Lab
Automated Circuit Discovery: Patching, Attribution & Decomposition at Scale
Moves mechanistic interpretability from "one-off archaeology" to repeatable pipeline—essential for scaling to frontier models
Why It Matters for Modern Models
- Moves mechanistic interpretability from "one-off archaeology" to repeatable pipeline—essential for scaling to frontier models
- 2024-2025 trend: ACDC (slow patching) → EAP (faster approximation) → CD-T (decomposition in seconds)—speed is the bottleneck
- Enables target selection for steering, safety interventions, debugging—you need to find the circuit before you can edit it
- After SAEs (#27), this shows how to build feature-level circuits automatically, not just head/neuron circuits
- Bridges to automated red-teaming and interpretability evaluations at scale—necessary for AI safety pipelines
What Tutorials Skip
What is still poorly explained in textbooks and papers:
- Activation patching is causal; attribution patching is linearized causal—approximation can be useful even when unfaithful pointwise
- Metric choice is everything—some metrics create degenerate gradients (zero-gradient at optimum), automated methods inherit these failures
- Circuits are not unique—many sparse subgraphs can reproduce behavior, pruning reveals a mechanism not the mechanism
- Speed-faithfulness tradeoff is fundamental—slow patching is accurate, fast approximations trade correctness for scalability
- Circuit discovery is hypothesis testing—you're testing "does this subgraph explain behavior X" not discovering ground truth
Interactive Visualization
Core Math (Optional Deep Dive)
If you want intuition first, start with the key equation and the visualization. Come back here for the full walkthrough.
Automated circuit discovery turns mechanistic interpretability into a search/scoring/pruning problem over the model's computational graph, moving from hand-crafted case studies to scalable pipelines.
Activation patching as causal intervention (edge/node ablation):
This measures causal effect: replace edge with value from corrupted run, measure impact on loss.
Attribution patching (first-order/Taylor approximation):
Linearized causal estimate—faster than full patching, but approximation can be unfaithful.
Edge scoring + pruning (circuit extraction):
Rank edges by importance, prune to minimal circuit that preserves behavior.