Circuit Condensation: Post-Training that Concentrates a Behavior's Causal Circuit
One approach in mechanistic interpretability explains model behavior through circuits: the components and connections that carry it. Frozen discovery often returns hundreds of edges, making them hard to inspect, compare, or verify exhaustively. We introduce Circuit Condensation, which post-trains models to concentrate behaviors into smaller causal graphs. Each round prunes low-attribution edges and trains a low-rank adapter to match the original through what remains. It keeps the cut only if task accuracy and held-out perplexity remain within tolerance. Across four behaviors and eight models, condensed circuits are smaller than the strongest published frozen-weight baseline in 30 of 32 se...
| Authors | Sai Adith Senthil Kumar |
|---|---|
| Primary category | cs.LG |
| Published | 27 August 2026 |
| Last updated at source | 11 October 2026 |
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