高维嵌入空间中相似度失真，导致检索结果不稳定，影响生成质量。
High-Dimensional Concentration and Retrieval Instability in Embedding Spaces: Implications for Retrieval-Augmented Generation
- 通过模拟数据研究高维空间中的距离集中现象
- 维度越高，相似度对比越弱，检索结果越不稳定
- 适合关注检索系统可靠性的开发者和研究者
基于嵌入的检索系统依赖于高维表示空间中几何接近性反映语义相关性的假设。然而，高维几何会引发浓度现象，削弱相似度度量的区分能力，并使最近邻检索变得不稳定。本文通过多组受控数值实验，系统研究了距离集中、余弦集中、对比坍缩、枢纽性及检索不稳定性等问题。结果表明，随着维度增加，相似度信号逐渐丧失对比度，导致检索行为不稳定，并在最近邻选择中产生结构性偏差。一个简化的检索增强生成实验进一步表明，这些效应会损害生成前的上下文锚定可靠性。这些发现强调了对嵌入系统进行几何感知诊断与鲁棒性优化的重要性。实验设计为纯模拟，以隔离内在几何效应的影响。
原文摘要 · Abstract (English)
Embedding-based retrieval systems rely on the assumption that geometric proximity in highdimensional representation spaces reflects semantic relevance. However, high-dimensional geometry induces concentration phenomena that can reduce the discriminative power of similarity measures and can destabilize nearest-neighbor retrieval. This work studies distance concentration, cosine concentration, contrast collapse, hubness, and retrieval instability through controlled numerical experiments across multiple synthetic distributions. The results show that similarity signals progressively lose contrast as dimension increases, leading to unstable retrieval behavior and structural bias in nearest-neighbor selection. A simplified Retrieval-Augmented Generation experiment further suggests that these effects can degrade grounding reliability upstream of generation. These findings motivate geometry-aware diagnostics and robustness-oriented retrieval strategies for embedding-based AI systems. The experiments are intentionally synthetic in order to isolate intrinsic geometric effects. High-dimensional embedding space Distance and cosine concentration Score-gap collapse and hubness Retrieval instability under perturbations Weak or incomplete retrieved context Potential degradation of grounding 1.
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