We present a compression method for unit-norm embeddings that achieves 1.5$\times$ compression, 25% better than the best prior lossless method. The method exploits that spherical coordinates of high-dimensional unit vectors concentrate around $π/2$, causing IEEE 754 exponents to collapse to a single value and high-order mantissa bits to become predictable, enabling entropy coding of both. Reconstruction error is below 1e-7, under float32 machine epsilon. Evaluation across 26 configurations spanning text, image, and multi-vector embeddings confirms consistent improvement.

翻译：本文提出一种针对单位范数嵌入向量的压缩方法，可实现1.5倍的压缩率，较现有最佳无损方法提升25%。该方法利用高维单位向量的球坐标分量集中于$π/2$附近的特性，使得IEEE 754浮点数格式中的指数位坍缩为单一数值，同时高阶尾数位呈现可预测模式，从而实现对两者的熵编码。重构误差低于1e-7，在float32机器精度范围内。通过在涵盖文本、图像及多向量嵌入的26种配置上进行评估，验证了该方法具有稳定的性能提升。