🤖 AI Summary
In vector retrieval, inconsistent embedding quality causes significant query-level performance fluctuations, while existing methods lack lightweight, training-free capabilities for per-query performance prediction. To address this, we propose Q-Robust—a fine-tuning-free, lightweight framework that jointly models geometric robustness (quantifying local stability) and neighborhood density (capturing semantic compactness) in the embedding space to accurately predict retrieval effectiveness for individual queries. Our analysis reveals systematic patterns in query-specific embedding quality distributions, enabling dynamic, adaptive retrieval strategies. Evaluated on four standard benchmarks, Q-Robust achieves an average 9.4±1.2% improvement in Recall@10 over strong baselines, with prediction overhead accounting for less than 5% of retrieval latency—demonstrating superior accuracy, efficiency, and practicality.
📝 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.