# ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data

arXiv: https://arxiv.org/abs/2403.04871 · HTML: https://mlsanity.com/paper/2403.04871/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2024.

## Built on by (most cited first, 30 of 32)

- [Semantic Operators: A Declarative Model for Rich, AI-based Data Processing](https://mlsanity.com/paper/2407.11418/) (2024)
- [RetroInfer: A Vector Storage Engine for Scalable Long-Context LLM Inference](https://mlsanity.com/paper/2505.02922/) (2025)
- [MicroNN: An On-device Disk-resident Updatable Vector Database](https://mlsanity.com/paper/2504.05573/) (2025)
- [TransitGPT: A Generative AI-based framework for interacting with GTFS data using Large Language Models](https://mlsanity.com/paper/2412.06831/) (2024)
- [SQUASH: Serverless and Distributed Quantization-based Attributed Vector Similarity Search](https://mlsanity.com/paper/2502.01528/) (2025)
- [AlayaDB: The Data Foundation for Efficient and Effective Long-context LLM Inference](https://mlsanity.com/paper/2504.10326/) (2025)
- [HONEYBEE: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning[Technical Report]](https://mlsanity.com/paper/2505.01538/) (2025)
- [Benchmarking Filtered Approximate Nearest Neighbor Search Algorithms on Transformer-based Embedding Vectors](https://mlsanity.com/paper/2507.21989/) (2025)
- [A Super Fast K-means for Indexing Vector Embeddings](https://mlsanity.com/paper/2603.20009/) (2026)
- [Bang for the Buck: Vector Search on Cloud CPUs](https://mlsanity.com/paper/2505.07621/) (2025)
- [ML-Asset Management: Curation, Discovery, and Utilization](https://mlsanity.com/paper/2509.23577/) (2025)
- [An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments and Analysis]](https://mlsanity.com/paper/2603.23710/) (2026)
- [Opal: Private Memory for Personal AI](https://mlsanity.com/paper/2604.02522/) (2026)
- [Mandol: An Agglomerative Agent Memory System for Long-Term Conversations](https://mlsanity.com/paper/2606.29778/) (2026)
- [Text2VectorSQL: Towards a Unified Interface for Vector Search and SQL Queries](https://mlsanity.com/paper/2506.23071/) (2025)
- [Bootstrapping Conditional Retrieval for User-to-Item Recommendations](https://mlsanity.com/paper/2508.16793/) (2025)
- [All-in-one Graph-based Indexing for Hybrid Search on GPUs](https://mlsanity.com/paper/2511.00855/) (2025)
- [Learning Filter-Aware Distance Metrics for Nearest Neighbor Search with Multiple Filters](https://mlsanity.com/paper/2511.04073/) (2025)
- [Exqutor: Extended Query Optimizer for Vector-augmented Analytical Queries](https://mlsanity.com/paper/2512.09695/) (2025)
- [Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval](https://mlsanity.com/paper/2512.12458/) (2025)
- [Filtered Approximate Nearest Neighbor Search in Vector Databases: System Design and Performance Analysis](https://mlsanity.com/paper/2602.11443/) (2026)
- [CubeGraph: Efficient Retrieval-Augmented Generation for Spatial and Temporal Data](https://mlsanity.com/paper/2604.06616/) (2026)
- [FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances](https://mlsanity.com/paper/2605.07770/) (2026)
- [EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updates](https://mlsanity.com/paper/2606.00734/) (2026)
- [Self-Conditioned Positional HNSW for Overlap-Aware Retrieval in Chunked-Document RAG Systems: Method and Industrial Evidence-Quality Audit](https://mlsanity.com/paper/2606.01542/) (2026)
- [Filtered ANN as a Phase Transition: When Selectivity-Estimation Error Causes Plan Regret](https://mlsanity.com/paper/2606.16341/) (2026)
- [Policy-aware Vector Search: A Vision for Fine Grained Access Control in Vector Databases](https://mlsanity.com/paper/2606.19803/) (2026)
- [HNSW with Accuracy Guarantees Using Graph Spanners](https://mlsanity.com/paper/2607.02338/) (2026)
- [ANNLib: A Development Framework for Efficient Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2607.17582/) (2026)
- [Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings](https://mlsanity.com/paper/2608.14648/) (2026)

## Abstract (arXiv)

Applications increasingly leverage mixed-modality data, and must jointly search over vector data, such as embedded images, text and video, as well as structured data, such as attributes and keywords. Proposed methods for this hybrid search setting either suffer from poor performance or support a severely restricted set of search predicates (e.g., only small sets of equality predicates), making them impractical for many applications. To address this, we present ACORN, an approach for performant and predicate-agnostic hybrid search. ACORN builds on Hierarchical Navigable Small Worlds (HNSW), a state-of-the-art graph-based approximate nearest neighbor index, and can be implemented efficiently by extending existing HNSW libraries. ACORN introduces the idea of predicate subgraph traversal to emulate a theoretically ideal, but impractical, hybrid search strategy. ACORN's predicate-agnostic construction algorithm is designed to enable this effective search strategy, while supporting a wide array of predicate sets and query semantics. We systematically evaluate ACORN on both prior benchmark datasets, with simple, low-cardinality predicate sets, and complex multi-modal datasets not supported by prior methods. We show that ACORN achieves state-of-the-art performance on all datasets, outperforming prior methods with 2-1,000x higher throughput at a fixed recall.