@inproceedings{zhao-etal-2026-r-3ag,
title = "{R}{\textasciicircum}3{AG}: Retriever Routing for Retrieval-Augmented Generation",
author = "Zhao, Tong and
Zhu, Yutao and
Tian, Yucheng and
Dou, Zhicheng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.939/",
pages = "20506--20522",
ISBN = "979-8-89176-390-6",
abstract = "Retrieval-augmented generation (RAG) has become a cornerstone for knowledge-intensive tasks. However, the efficacy of RAG is often bottlenecked by the ``one-size-fits-all'' retrieval paradigm, as different queries exhibit distinct preferences for different retrievers. While recent routing techniques attempt to select the optimal retriever dynamically, they typically operate under a `single and static capability' assumption, selecting retrievers solely based on semantic relevance. This overlooks a critical distinction in RAG: a retrieved document must not only be relevant but also effectively support the generator in producing correct answers. To address this limitation, we propose R{\textthreesuperior}AG, a novel routing framework that explicitly models the dynamic alignment between queries and retriever capabilities. Unlike previous approaches, R{\textthreesuperior}AG decomposes retriever capability into two learnable dimensions: retrieval quality and generation utility. We employ a contrastive learning objective that leverages complementary supervision signals, i.e., document assessments and downstream answer correctness, to capture query-specific preference shifts. Extensive experiments on diverse knowledge-intensive tasks demonstrate that R{\textthreesuperior}AG consistently outperforms both the best individual retrievers and state-of-the-art static routing methods."
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<abstract>Retrieval-augmented generation (RAG) has become a cornerstone for knowledge-intensive tasks. However, the efficacy of RAG is often bottlenecked by the “one-size-fits-all” retrieval paradigm, as different queries exhibit distinct preferences for different retrievers. While recent routing techniques attempt to select the optimal retriever dynamically, they typically operate under a ‘single and static capability’ assumption, selecting retrievers solely based on semantic relevance. This overlooks a critical distinction in RAG: a retrieved document must not only be relevant but also effectively support the generator in producing correct answers. To address this limitation, we propose R³AG, a novel routing framework that explicitly models the dynamic alignment between queries and retriever capabilities. Unlike previous approaches, R³AG decomposes retriever capability into two learnable dimensions: retrieval quality and generation utility. We employ a contrastive learning objective that leverages complementary supervision signals, i.e., document assessments and downstream answer correctness, to capture query-specific preference shifts. Extensive experiments on diverse knowledge-intensive tasks demonstrate that R³AG consistently outperforms both the best individual retrievers and state-of-the-art static routing methods.</abstract>
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%0 Conference Proceedings
%T R⌃3AG: Retriever Routing for Retrieval-Augmented Generation
%A Zhao, Tong
%A Zhu, Yutao
%A Tian, Yucheng
%A Dou, Zhicheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhao-etal-2026-r-3ag
%X Retrieval-augmented generation (RAG) has become a cornerstone for knowledge-intensive tasks. However, the efficacy of RAG is often bottlenecked by the “one-size-fits-all” retrieval paradigm, as different queries exhibit distinct preferences for different retrievers. While recent routing techniques attempt to select the optimal retriever dynamically, they typically operate under a ‘single and static capability’ assumption, selecting retrievers solely based on semantic relevance. This overlooks a critical distinction in RAG: a retrieved document must not only be relevant but also effectively support the generator in producing correct answers. To address this limitation, we propose R³AG, a novel routing framework that explicitly models the dynamic alignment between queries and retriever capabilities. Unlike previous approaches, R³AG decomposes retriever capability into two learnable dimensions: retrieval quality and generation utility. We employ a contrastive learning objective that leverages complementary supervision signals, i.e., document assessments and downstream answer correctness, to capture query-specific preference shifts. Extensive experiments on diverse knowledge-intensive tasks demonstrate that R³AG consistently outperforms both the best individual retrievers and state-of-the-art static routing methods.
%U https://aclanthology.org/2026.acl-long.939/
%P 20506-20522
Markdown (Informal)
[R^3AG: Retriever Routing for Retrieval-Augmented Generation](https://aclanthology.org/2026.acl-long.939/) (Zhao et al., ACL 2026)
ACL
- Tong Zhao, Yutao Zhu, Yucheng Tian, and Zhicheng Dou. 2026. R^3AG: Retriever Routing for Retrieval-Augmented Generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20506–20522, San Diego, California, United States. Association for Computational Linguistics.