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arXiv · 2508.18877

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

Kushagra Agrawal·Nisharg Nargund·Oishani Banerjee
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Abstract

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.

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Kushagra Agrawal, Nisharg Nargund, Oishani Banerjee. 2025-08-26. Optimization of Latent-Space Compression using Game-Theoretic Techniques for Transformer-Based Vector Search. https://doi.org/10.1007/978-3-032-16632-6_24

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