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Geometric Structure Predicts Per-Example Neighborhood Loss Under PCA Truncation
Dimensionality reduction can preserve the average performance of text embeddings even when a substantial fraction of their dimensions is removed. Aggregate metrics, however, do not reveal whether degradation is distributed uniformly across examples. We study per-example neighborhood stability under
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