residual stream analysis

**Residual stream analysis** is the **interpretability approach that treats the residual stream as the primary information channel carrying model state across layers** - it helps quantify how features accumulate, transform, and influence output logits. **What Is Residual stream analysis?** - **Definition**: Residual stream aggregates attention and MLP outputs into a shared running representation. - **Feature View**: Analysis decomposes stream vectors into interpretable feature directions. - **Causal Role**: Most downstream computations read from and write to this shared pathway. - **Tooling**: Common tools include logit lens variants, patching, and projection diagnostics. **Why Residual stream analysis Matters** - **Global Visibility**: Provides unified view of information flow across transformer blocks. - **Behavior Attribution**: Helps identify which layers introduce or suppress target features. - **Intervention Planning**: Pinpoints where edits should be applied for maximal effect. - **Debugging**: Useful for locating layer-wise corruption or drift in long-context tasks. - **Research Utility**: Foundational for mechanistic studies of circuit composition. **How It Is Used in Practice** - **Layer Projections**: Track target-feature projections at each residual stream location. - **Patch Experiments**: Swap residual activations between prompts to test causal contribution. - **Output Mapping**: Measure how stream directions map to final logits over generation steps. Residual stream analysis is **a high-value framework for tracing information flow in transformers** - residual stream analysis is most informative when combined with causal intervention and feature decomposition.