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Per-Arne Andersen

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Open access Aug 2026

DESS: A Robust Uncertainty Layer for Embedding-Space Models

DESS is introduced, a lightweight uncertainty layer that augments an existing embedding model with a predicted mean vector and an independent per-dimension spread vector that provides a modular, geometry-aware uncertainty layer for embedding-space models, provided its spread is calibrated to local embedding geometry.

Morten Grundetjern, J. Voigt, Per-Arne Andersen et al. · 0 citations