activation patching

Activation patching edits internal activations to understand the causal role of specific neurons, layers, or circuits. **Technique**: Run model on two inputs (clean and corrupted), at specific layer/position swap activations from clean run into corrupted run, measure if output changes. **Causal interpretation**: If patching activations restores correct behavior, those activations causally encode the relevant information. **Path patching variant**: Patch specific edge between components rather than full activation. **Use cases**: Identify which layer encodes specific features, find circuits responsible for behaviors, understand information flow, validate mechanistic hypotheses. **Example**: Patch subject token activations to see if model uses name information from those positions for next prediction. **Tools**: TransformerLens activation patching, custom PyTorch hooks. **Relationship to interventions**: Generalizes ablation studies to continuous interventions. **Limitations**: Computationally expensive (many patch combinations), interpretation requires expertise, may miss distributed representations. **Key research**: Used extensively in Anthropic's circuit analysis, IOI paper. Central technique in mechanistic interpretability research.