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.