ArrowSpace: introducing Spectral Indexing for vector search

arrowspace (Moriondo, 2025) is a Rust (Rust Team, 2015) library (and relative data structure ArrowSpace) for vector similarity search that goes beyond traditional distance metrics by incorporating spectral graph properties to find structural patterns in high-dimensional data.ArrowSpace adds a spectral dimension that captures structural patterns, enabling more nuanced similarity matching for scientific and structured data applications.arrowspace combines traditional semantic similarity with graph-based spectral properties (Mahadevan, 2006;Spielman, 2007).The library introduces taumode (in mathematical expressions , lambda-tau) indexing, which blends Rayleigh quotient smoothness energy from graph Laplacians (Bai, 2007;Bai & Hancock, 2010) with edge-wise dispersion statistics to create bounded, comparable spectral scores.This enables similarity search that considers both semantic content and spectral characteristics of high-dimensional vector datasets.