# A Brief Comparison of Training-Free Multi-Vector Sequence Compression Methods

arXiv: https://arxiv.org/abs/2603.22434 · HTML: https://mlsanity.com/paper/2603.22434/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2026.

## Built on by (most cited first, 3 of 3)

- [AnchorFold: A Focus-Then-Fold Framework via Recursive Attention Propagation for Efficient Multi-Vector Visual Document Retrieval](https://mlsanity.com/paper/2608.08732/) (2026)
- [EigenLI: Spectral Approximations to Late Interaction](https://mlsanity.com/paper/2609.07561/) (2026)
- [Simple Linear Corrections for Compression in Late Interaction](https://mlsanity.com/paper/2610.14196/) (2026)

## Abstract (arXiv)

While multi-vector retrieval models outperform single-vector models of comparable size in retrieval quality, their practicality is limited by substantially larger index sizes, driven by the additional sequence-length dimension in their document embeddings. Because document embedding size dictates both memory overhead and query latency, compression is essential for deployment. In this work, we present an evaluation of training-free methods targeting the token sequence length, a dimension unique to multi-vector retrieval. Our findings suggest that token merging is strictly superior to token pruning for reducing index size while maintaining retrieval effectiveness.