Explaining why dense retrievers assign high relevance scores remains challenging because retrieval decisions are made through opaque high-dimensional embeddings. Existing explanations often focus on surface signals, such as lexical matches, token alignments, or post-hoc textual rationales, and thus provide limited insight into the latent factors that shape dense retrieval behavior at the embedding level. We propose \textit{Xetrieval}, an embedding-level mechanistic framework for explaining dense retrieval. \textit{Xetrieval} first introduces a lightweight reasoning internalizer that approximates Chain-of-Thought reasoning directly in the embedding space with a single forward pass, enriching sentence embeddings with reasoning-oriented information while avoiding expensive autoregressive generation. It then decomposes these reasoning-enhanced embeddings into sparse, human-interpretable features, each associated with a coherent natural language description. By aggregating sparse feature overlaps across multiple document-side views, \textit{Xetrieval} provides feature-level explanations of individual retrieval decisions. Experiments on diverse retrievers and benchmarks show that \textit{Xetrieval} uncovers coherent interpretable features, yields stronger pair-level intervention effects, and supports task-level feature steering. The project page and source code are available at https://hihiczx.github.io/Xetrieval .

翻译：解释稠密检索器为何赋予高相关性分数仍然具有挑战性，因为检索决策是通过不透明的高维嵌入做出的。现有解释通常聚焦于表层信号，如词汇匹配、token对齐或事后文本理由，因此对塑造稠密检索行为在嵌入层面的潜在因素提供的洞察有限。我们提出\textit{Xetrieval}，一种面向嵌入层面的机制框架，用于解释稠密检索。\textit{Xetrieval}首先引入轻量级推理内化器，通过单次前向传播在嵌入空间中直接近似链式推理，在避免昂贵自回归生成的同时，用面向推理的信息丰富句子嵌入。随后，它将这些增强推理的嵌入分解为稀疏、可人类解释的特征，每个特征关联一条连贯的自然语言描述。通过聚合多个文档侧视图上的稀疏特征重叠，\textit{Xetrieval}提供单个检索决策的特征级解释。在多种检索器和基准上的实验表明，\textit{Xetrieval}能揭示连贯的可解释特征，产生更强的成对干预效果，并支持任务级特征引导。项目页面和源代码见https://hihiczx.github.io/Xetrieval。