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arXiv · 2510.21727

Your Dense Retriever is Secretly an Expeditious Reasoner

Yichi Zhang·Jun Bai·Zhixin Cai·Shuhan Qin·Zhuofan Chen·Jinghua Guan·Wenge Rong
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Abstract

Dense retrievers enhance retrieval by encoding queries and documents into continuous vectors, but they often struggle with reasoning-intensive queries. Although Large Language Models (LLMs) can reformulate queries to capture complex reasoning, applying them universally incurs significant computational cost. In this work, we propose Adaptive Query Reasoning (AdaQR), a hybrid query rewriting framework. Within this framework, a Reasoner Router dynamically directs each query to either fast dense reasoning or deep LLM reasoning. The dense reasoning is achieved by the Dense Reasoner, which performs LLM-style reasoning directly in the embedding space, enabling a controllable trade-off between efficiency and accuracy. Experiments on large-scale retrieval benchmarks BRIGHT show that AdaQR reduces reasoning cost by 28% while preserving-or even improving-retrieval performance by 7%.

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Yichi Zhang, Jun Bai, Zhixin Cai, Shuhan Qin, Zhuofan Chen, Jinghua Guan, Wenge Rong. 2025-09-27. Your Dense Retriever is Secretly an Expeditious Reasoner. https://arxiv.org/abs/2510.21727

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