FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances

• URL: http://arxiv.org/abs/2605.07770v1
• Date: Fri, 08 May 2026 14:11:49 GMT
• Title: FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances
• Abstract summary: FAVOR is an efficient filter-agnostic vector ANNS that supports arbitrary filtering conditions while maintaining stable performance across varying selectivity levels.<n>Experiments on real-world datasets demonstrate that FAVOR achieves a 1.3-5$times$ higher QPS at $Recall@10 = 95%$ compared to state-of-the-art methods.
• Score: 17.58209161125891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern retrieval systems increasingly require integrating approximate nearest neighbor search (ANNS) with complex attribute filtering to handle hybrid queries in applications such as recommendation systems and retrieval-augmented generation (RAG). While HNSW-based inline-filtering methods show promise, existing approaches struggle to deliver high throughput under low-selectivity scenarios while balancing search efficiency, filtering generality, and index connectivity. To address these challenges, we propose FAVOR, an efficient filter-agnostic vector ANNS that supports arbitrary filtering conditions while maintaining stable performance across varying selectivity levels. FAVOR introduces three novel features: (1) an integrated architecture that unifies selectivity estimation and filtered ANNS execution, providing a cohesive solution for hybrid vector-attribute queries; (2) a HNSW-based inline-filtering algorithm that introduces an exclusion distance mechanism to dynamically reshape the vector distance distribution, pushing non-target vectors away from the query while promoting valid candidates toward the query, thus improving search efficiency without compromising generality or graph connectivity; and (3) a selectivity-driven search selector that estimates query selectivity and dynamically routes queries between a pre-filtering brute-force algorithm for low-selectivity cases and an optimized HNSW-based search algorithm for other scenarios, ensuring consistent performance. Extensive experiments on real-world datasets demonstrate that FAVOR achieves a 1.3-5$\times$ higher QPS at $Recall@10 = 95\%$ compared to state-of-the-art methods for arbitrary filtering conditions, while maintaining competitive performance even against tailored solutions in some filtering conditions.

Related papers

• Deformable Wiener Filter for Future Video Coding [55.405068056883614]
  We propose a deformable Wiener Filter (DWF) for in-loop filters in Versatile Video Coding (VVC)<n>It combines the local and non-local characteristics and supervisedly trains the filter coefficients based on the Wiener Filter theory.<n>It achieves 1.16%, 1.92%, and 2.67% bit-rate savings on average compared to the VTM-11.0 for All Intra, Random Access, and Low-Delay B configurations.
  arXiv  Detail & Related papers  (2026-06-01T02:24:07Z)
• F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking [79.49893545611779]
  Large Language Models (LLMs) can generate a subset and order it within a single autoregressive pass.<n>This flexibility introduces a new optimization challenge: the model must search an output space while receiving utility feedback only after the full ranked list is generated.<n>This credit assignment gap makes end-to-end optimization unstable and sample-inefficient.<n>We propose a unified framework that performs both within a single autoregressive rollout and optimize them end-to-end via factorized group-relative policy optimization.
  arXiv  Detail & Related papers  (2026-05-13T04:52:33Z)
• Filtered Approximate Nearest Neighbor Search in Vector Databases: System Design and Performance Analysis [0.5249805590164902]
  Filtered Approximate Nearest Neighbor Search (FANNS) is used to combine semantic retrieval with metadata constraints.<n>In this work, we systematize the taxonomy of filtering strategies and evaluate their integration into vectors.<n>Our experiments reveal that algorithmic adaptations within the engine often override raw index performance.
  arXiv  Detail & Related papers  (2026-02-11T23:40:26Z)
• JAG: Joint Attribute Graphs for Filtered Nearest Neighbor Search [8.398505388767248]
  JAG (Joint Attribute Graphs) is a graph-based algorithm designed to deliver robust performance across the entire selectivity spectrum.<n>Our experimental results demonstrate that JAG significantly outperforms existing state-of-the-art baselines in both throughput and recall robustness.
  arXiv  Detail & Related papers  (2026-02-10T20:00:51Z)
• Adaptive Prefiltering for High-Dimensional Similarity Search: A Frequency-Aware Approach [0.0]
  We present an adaptive prefiltering framework that partitions the query space into frequency tiers following Zipfian distributions.<n>Experiments on ImageNet-1k using CLIP embeddings demonstrate that frequency-aware budget allocation achieves equivalent recall with 20.4% fewer distance computations.<n>The framework introduces minimal overhead through lightweight frequency tracking and provides graceful degradation for unseen queries through coherence-based fallback policies.
  arXiv  Detail & Related papers  (2025-12-03T10:11:35Z)
• Representation Quantization for Collaborative Filtering Augmentation [49.14087936092634]
  We propose a novel two-stage collaborative recommendation algorithm, DQRec.<n>It augments features and homogeneous linkages by extracting behavior characteristics jointly from interaction sequences and attributes.<n>By integrating these semantic ID patterns into the recommendation process through feature and linkage augmentation, the system enriches both latent and explicit user and item features.
  arXiv  Detail & Related papers  (2025-08-15T04:00:50Z)
• Billion-scale Similarity Search Using a Hybrid Indexing Approach with Advanced Filtering [49.1574468325115]
  This paper presents a novel approach for similarity search with complex filtering capabilities on billion-scale datasets, optimized for CPU inference.<n>Our method extends the classical IVF-Flat index structure to integrate multi-dimensional filters.<n>The proposed algorithm combines dense embeddings with discrete filtering attributes, enabling fast retrieval in high-dimensional spaces.
  arXiv  Detail & Related papers  (2025-01-23T07:47:00Z)
• Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization [52.80408805368928]
  We introduce a novel greedy-style subset selection algorithm for batch acquisition. Our experiments on the red fluorescent proteins show that our proposed method achieves the baseline performance in 1.69x fewer queries.
  arXiv  Detail & Related papers  (2024-06-21T05:57:08Z)
• Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement [19.277560848076984]
  Two-stage selection strategies result in scale bias and redundancy due to mismatch between selected queries and objects. We propose hierarchical salience filtering refinement, which performs transformer encoding only on filtered discriminative queries. The proposed Salience DETR achieves significant improvements of +4.0% AP, +0.2% AP, +4.4% AP on three challenging task-specific detection datasets.
  arXiv  Detail & Related papers  (2024-03-24T13:01:57Z)
• Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning [5.998027804346945]
  Filter pruning is a common method to achieve model compression and acceleration in deep neural networks (DNNs) We propose a Sub-network Multiobjective Evolutionary Algorithm (SMOEA) for filter pruning. Experiments on VGG-14 model for CIFAR-10 verify the effectiveness of the proposed SMOEA.
  arXiv  Detail & Related papers  (2022-10-22T13:34:14Z)
• Globally Convergent Policy Search over Dynamic Filters for Output Estimation [64.90951294952094]
  We introduce the first direct policy search algorithm convex which converges to the globally optimal $textitdynamic$ filter. We show that informativity overcomes the aforementioned degeneracy.
  arXiv  Detail & Related papers  (2022-02-23T18:06:20Z)
• Dependency Aware Filter Pruning [74.69495455411987]
  Pruning a proportion of unimportant filters is an efficient way to mitigate the inference cost. Previous work prunes filters according to their weight norms or the corresponding batch-norm scaling factors. We propose a novel mechanism to dynamically control the sparsity-inducing regularization so as to achieve the desired sparsity.
  arXiv  Detail & Related papers  (2020-05-06T07:41:22Z)
• Rethinking Differentiable Search for Mixed-Precision Neural Networks [83.55785779504868]
  Low-precision networks with weights and activations quantized to low bit-width are widely used to accelerate inference on edge devices. Current solutions are uniform, using identical bit-width for all filters. This fails to account for the different sensitivities of different filters and is suboptimal. Mixed-precision networks address this problem, by tuning the bit-width to individual filter requirements.
  arXiv  Detail & Related papers  (2020-04-13T07:02:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.