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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.29507 |
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| _version_ | 1866913169837916160 |
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| author | Cai, Zhixin Bai, Jun Liu, Yang Li, Jiaqi Zhang, Yichi Li, Taichuan Chen, Zhuofan Jia, Zixia Zheng, Zilong Rong, Wenge |
| author_facet | Cai, Zhixin Bai, Jun Liu, Yang Li, Jiaqi Zhang, Yichi Li, Taichuan Chen, Zhuofan Jia, Zixia Zheng, Zilong Rong, Wenge |
| contents | 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 . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29507 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Xetrieval: Mechanistically Explaining Dense Retrieval Cai, Zhixin Bai, Jun Liu, Yang Li, Jiaqi Zhang, Yichi Li, Taichuan Chen, Zhuofan Jia, Zixia Zheng, Zilong Rong, Wenge Artificial Intelligence Information Retrieval 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 . |
| title | Xetrieval: Mechanistically Explaining Dense Retrieval |
| topic | Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2605.29507 |