Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation Article Swipe

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Yongkun Du ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2507.05933

Vector retrieval systems exhibit significant performance variance across queries due to heterogeneous embedding quality. We propose a lightweight framework for predicting retrieval performance at the query level by combining quantization robustness and neighborhood density metrics. Our approach is motivated by the observation that high-quality embeddings occupy geometrically stable regions in the embedding space and exhibit consistent neighborhood structures. We evaluate our method on 4 standard retrieval datasets, showing consistent improvements of 9.4$\pm$1.2\% in Recall@10 over competitive baselines. The framework requires minimal computational overhead (less than 5\% of retrieval time) and enables adaptive retrieval strategies. Our analysis reveals systematic patterns in embedding quality across different query types, providing insights for targeted training data augmentation.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2507.05933
PDF: https://arxiv.org/pdf/2507.05933
OA Status: green
OpenAlex ID: https://openalex.org/W4416061687

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OpenAlex ID: https://openalex.org/W4416061687

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DOI: https://doi.org/10.48550/arxiv.2507.05933

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Title: Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-07-08

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Authors: Yongkun Du

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Landing page: https://arxiv.org/abs/2507.05933

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PDF URL: https://arxiv.org/pdf/2507.05933

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OA status: green

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Cited by: 0

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