Semantic Certainty Assessment In Vector Retrieval Systems: A Novel Framework For Embedding Quality Evaluation

Abstract

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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• arxiv keydu2025semantic

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