Every Component Is a Lookup: One Linear Graph for Interaction, Composition and Attribution
Interpretability methods for transformers are typically built around separate questions: which components interact, how information routes to the output, and which input tokens contribute. Because these methods rely on different assumptions, their answers are difficult to relate. We argue that two architecturally motivated assumptions suffice to address all three questions: attention and MLPs share a key-value form, $φ(S)\,U$, in which $φ(S)$ selects over values $U$, and components read from an additive residual stream, the sum of component outputs. Holding these selections at their forward-pass values turns the model into a computational graph, of which component interactions, compositio...
| Authors | Po-Kai Chen, Aske Plaat, Niki van Stein |
|---|---|
| Primary category | cs.LG |
| Published | 22 May 2026 |
| Last updated at source | 24 September 2026 |
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