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Both attention and MLP follow a shared key-value template $\\phi(S)U$. We exploit this structure to develop Unpack, a backward recursion that decomposes credit through both sublayers, producing interaction strengths between any two components, named end-to-end paths with K/Q/V composition labels, and per-token attribution, all from a single forward pass, without intervention, gradients, or auxiliary training. The interaction scores are causally grounded: across the Pythia-deduped family from 160M to 6.9B parameters, a component's score predicts the perplexity increase when its communication is ablated (within-layer Spearman $\\rho = 0.72$ to $0.96$). The composition paths surface all three connections of the indirect-object-identification circuit of Wang e","title":"Every Component is a Lookup: Token Attribution and Composition from a Single Decomposition","url":"https://arxiv.org/abs/2605.23393","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.23393v2 Announce Type: replace-cross \nAbstract: Mechanistic interpretability of transformers requires identifying not just which components matter but how they compose into the computational route that produced a prediction. Both attention and MLP follow a shared key-value template $\\phi(S)U$. We exploit this structure to develop Unpack, a backward recursion that decomposes credit through both sublayers, producing interaction strengths between any two components, named end-to-end paths with K/Q/V composition labels, and per-token attribution, all from a single forward pass, without intervention, gradients, or auxiliary training. The interaction scores are causally grounded: across the Pythia-deduped family from 160M to 6.9B parameters, a component's score predicts the perplexity increase when its communication is ablated (within-layer Spearman $\\rho = 0.72$ to $0.96$). 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