arXiv:2605.29507v1 Announce Type: new Abstract: 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 li
The proliferation of opaque high-dimensional embedding models in dense retrieval necessitates new methods to understand and explain their decisions, driving innovation in interpretability frameworks like Xetrieval.
Understanding the mechanistic basis of dense retrieval decisions, rather than just surface signals, is crucial for improving model reliability, debugging, and ultimately building more trustworthy AI systems.
The opaque 'black box' nature of dense retrieval models begins to yield to systematic, embedding-level explanation, moving beyond superficial explanations to deep mechanistic insights.
- · AI researchers
- · Developers of AI agents
- · Industries relying on AI search/retrieval
- · AI explainability platforms
- · Opaque AI systems
- · Debugging methods based purely on surface signals
Improved debugging and performance optimization for dense retrieval AI models.
Increased trust and adoption of advanced AI systems in critical applications through enhanced transparency.
The development of 'self-explaining' AI architectures that inherently provide mechanistic insights into their decisions.
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Read at arXiv cs.AI