# Optimization of Latent-Space Compression using Game-Theoretic Techniques for Transformer-Based Vector Search

arXiv: https://arxiv.org/abs/2508.18877 · HTML: https://mlsanity.com/paper/2508.18877/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Score-based Generative Modeling in Latent Space](https://mlsanity.com/paper/2106.05931/) (2021)
- [ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding](https://mlsanity.com/paper/2203.10886/) (2022)
- [Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization](https://mlsanity.com/paper/1807.04252/) (2018)

## Abstract (arXiv)

Vector similarity search plays a pivotal role in modern information retrieval systems, especially when powered by transformer-based embeddings. However, the scalability and efficiency of such systems are often hindered by the high dimensionality of latent representations. In this paper, we propose a novel game-theoretic framework for optimizing latent-space compression to enhance both the efficiency and semantic utility of vector search. By modeling the compression strategy as a zero-sum game between retrieval accuracy and storage efficiency, we derive a latent transformation that preserves semantic similarity while reducing redundancy. We benchmark our method against FAISS, a widely-used vector search library, and demonstrate that our approach achieves a significantly higher average similarity (0.9981 vs. 0.5517) and utility (0.8873 vs. 0.5194), albeit with a modest increase in query time. This trade-off highlights the practical value of game-theoretic latent compression in high-utility, transformer-based search applications. The proposed system can be seamlessly integrated into existing LLM pipelines to yield more semantically accurate and computationally efficient retrieval.