# CRouting: Reducing Expensive Distance Calls in Graph-Based Approximate Nearest Neighbor Search

arXiv: https://arxiv.org/abs/2509.00365 · HTML: https://mlsanity.com/paper/2509.00365/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [HNSW with Accuracy Guarantees Using Graph Spanners](https://mlsanity.com/paper/2607.02338/) (2026)

## Abstract (arXiv)

Approximate nearest neighbor search (ANNS) is a crucial problem in information retrieval and AI applications. Recently, there has been a surge of interest in graph-based ANNS algorithms due to their superior efficiency and accuracy. However, the repeated computation of distances in high-dimensional spaces constitutes the primary time cost of graph-based methods. To accelerate the search, we propose a novel routing strategy named CRouting, which bypasses unnecessary distance computations by exploiting the angle distributions of high-dimensional vectors. CRouting is designed as a plugin to optimize existing graph-based search with minimal code modifications. Our experiments show that CRouting reduces the number of distance computations by up to 41.5% and boosts queries per second by up to 1.48× on two predominant graph indexes, HNSW and NSG. Code is publicly available at https://github.com/ISCS-ZJU/CRouting.