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A. M. Bronstein

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#machine learning Preprint Sep 2026

The Residual Stream's Effective Depth

We introduce \emph{effective depth} ($\Deff$), a scalar diagnostic that treats the layer-wise residual stream of a transformer as a discrete-time process, measures how representation similarity decays with layer distance, and aggregates that profile into one number. Across sixteen decoder-only language models, $\Deff$...

Barak Gahtan, Ido Galil, A. M. Bronstein · 0 citations
#machine learning Preprint Apr 2026

Invertible Query-Key Coupling Composes with Attention Mechanisms

Coupled query-key dynamics, a pre-scoring transformation that evolves each token's query and key jointly through a shared invertible coupling before standard scoring, is studied, finding that coupling is most useful in composition with a scoring-stage method rather than as a standalone change.

Barak Gahtan, A. M. Bronstein · 0 citations

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