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

arXiv: https://arxiv.org/abs/2605.07770 · HTML: https://mlsanity.com/paper/2605.07770/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Vector search and ANN indexes, one of 15 directions in Retrieval-augmented generation.
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 8 May 2026.

## Placed in

- Retrieval-augmented generation / [Vector search and ANN indexes](https://mlsanity.com/rag/vector-search-and-ann-indexes/)

## Builds on (most cited first, 24 of 24)

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- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Practical and Optimal LSH for Angular Distance](https://mlsanity.com/paper/1509.02897/) (2015)
- [A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2101.12631/) (2021)
- [Optimal Data-Dependent Hashing for Approximate Near Neighbors](https://mlsanity.com/paper/1501.01062/) (2015)
- [word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data](https://mlsanity.com/paper/2003.12590/) (2020)
- [ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data](https://mlsanity.com/paper/2403.04871/) (2024)
- [Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2409.09913/) (2024)
- [The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform](https://mlsanity.com/paper/1908.07389/) (2019)
- [Approximate Nearest Neighbor Search with Window Filters](https://mlsanity.com/paper/2402.00943/) (2024)
- [UNIFY: Unified Index for Range Filtered Approximate Nearest Neighbors Search](https://mlsanity.com/paper/2412.02448/) (2024)
- [HQANN: Efficient and Robust Similarity Search for Hybrid Queries with Structured and Unstructured Constraints](https://mlsanity.com/paper/2207.07940/) (2022)
- [CAPS: A Practical Partition Index for Filtered Similarity Search](https://mlsanity.com/paper/2308.15014/) (2023)
- [SIEVE: Effective Filtered Vector Search with Collection of Indexes](https://mlsanity.com/paper/2507.11907/) (2025)
- [SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2411.12229/) (2024)
- [Constrained Approximate Similarity Search on Proximity Graph](https://mlsanity.com/paper/2210.14958/) (2022)
- [NaviX: A Native Vector Index Design for Graph DBMSs With Robust Predicate-Agnostic Search Performance](https://mlsanity.com/paper/2506.23397/) (2025)
- [iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search](https://mlsanity.com/paper/2409.02571/) (2024)
- [Dimensionality-Reduction Techniques for Approximate Nearest Neighbor Search: A Survey and Evaluation](https://mlsanity.com/paper/2403.13491/) (2024)
- [HAKES: Scalable Vector Database for Embedding Search Service](https://mlsanity.com/paper/2505.12524/) (2025)
- [Sieve: A Middleware Approach to Scalable Access Control for Database Management Systems](https://mlsanity.com/paper/2004.07498/) (2020)
- [Paired compressed cover trees guarantee a near linear parametrized complexity for all k-nearest neighbors search in an arbitrary metric space](https://mlsanity.com/paper/2201.06553/) (2022)

## Abstract (arXiv)

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× 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.