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.