# Adaptive Prefiltering for High-Dimensional Similarity Search: A Frequency-Aware Approach Source: [Adaptive Prefiltering for High-Dimensional Similarity Search: A Frequency-Aware Approach]() Collection index: [llms.txt]() Data updated: 2026-02-28T05:00:04.461Z Authors: Teodor-Ioan Calin Published: 2025-12-03T10:11:35.000Z ## Abstract High-dimensional similarity search underpins modern retrieval systems, yet uniform search strategies fail to exploit the heterogeneous nature of real-world query distributions. We present an adaptive prefiltering framework that leverages query frequency patterns and cluster coherence metrics to dynamically allocate computational budgets. Our approach partitions the query space into frequency tiers following Zipfian distributions and assigns differentiated search policies based on historical access patterns and local density characteristics. Experiments on ImageNet-1k using CLIP embeddings demonstrate that frequency-aware budget allocation achieves equivalent recall with 20.4% fewer distance computations compared to static nprobe selection, while maintaining sub-millisecond latency on GPU-accelerated FAISS indices. The framework introduces minimal overhead through lightweight frequency tracking and provides graceful degradation for unseen queries through coherence-based fallback policies. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()