用博弈论优化向量搜索的压缩，提升语义精度与效率。
Optimization of Latent-Space Compression using Game-Theoretic Techniques for Transformer-Based Vector Search
- 将压缩策略建模为检索精度与存储效率的零和博弈。
- 在FAISS上实现0.9981的平均相似度，显著优于对比组。
- 适合需要高语义保真的大模型检索系统集成。
向量相似性搜索在现代信息检索系统中至关重要，尤其在基于Transformer的嵌入表示下。然而，高维潜在表征常导致系统可扩展性和效率受限。本文提出一种新颖的博弈论框架，用于优化潜在空间压缩，以提升向量搜索的效率与语义效用。通过将压缩策略建模为检索准确率与存储效率之间的零和博弈，我们推导出一种能保留语义相似性并减少冗余的潜在变换。我们在FAISS这一广泛使用的向量搜索库上进行基准测试，结果表明，本方法在平均相似度（0.9981 vs. 0.5517）和效用（0.8873 vs. 0.5194）上均显著优于基线，尽管查询时间略有增加。该权衡凸显了博弈论驱动的潜在压缩在高效、高语义保真场景中的实用价值。所提系统可无缝集成至现有LLM流水线，实现更精准且高效的检索。
原文摘要 · Abstract (English)
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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