arXiv — Machine Learning · · 3 min read

An Analysis of Residual-Stream Geometry Across Transformer Depth

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Computer Science > Machine Learning

arXiv:2607.18348 (cs)
[Submitted on 20 Jul 2026]

Title:An Analysis of Residual-Stream Geometry Across Transformer Depth

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Abstract:We propose a transition-centred geometric analysis of transformer residual streams. Relative displacement measures how \emph{far} representations move between consecutive layers, and orthogonal Procrustes analysis separates each transition into a rigid rotation and a non-rigid residual. Across six instruction-tuned models, on code generation and cross-lingual translation, these measurements reveal reproducible depth regularities. Relative displacement is strongly layer-dependent; typically larger early and late, with a quieter middle third; and nearly invariant across conditions within each model. Rotation magnitude is nearly constant across depth, while Procrustes residual and angle concentration remain depth-modulated, with residual peaking at the final transition. During generation, non-English targets show larger final-layer displacement and residual than English targets. We present these as descriptive geometric regularities, not as measures of computational effort or causal explanations. The contribution is a measurement framework for residual-stream transitions and evidence that, in the settings studied here, depth curves are model-dependent and largely condition-stable.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.18348 [cs.LG]
  (or arXiv:2607.18348v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2607.18348
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Sunit Bhattacharya [view email]
[v1] Mon, 20 Jul 2026 08:43:22 UTC (5,658 KB)
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