@inproceedings{hu-etal-2026-doc,
title = "Doc-{REFRAG}: Rethinking Multimodal Document Retrieval-Augmented Generation",
author = "Hu, Ruofan and
Xu, Shengyang and
Hong, Minjie and
Yang, Xiaoda and
Zhou, Sashuai and
Lei, Ke and
Jin, Tao and
Zhao, Zhou",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.377/",
pages = "8624--8644",
ISBN = "979-8-89176-459-0",
abstract = "Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, existing multimodal RAG models are primarily designed for single-image or closed-document settings and exhibit limited accuracy in realistic multi-image scenarios. Moreover, processing numerous retrieved images incurs substantial computational overhead from irrelevant visual tokens. To address these challenges, we introduce DocLongRAG, a large-scale dataset of 343K question{--}answer pairs, each associated with an average of 37.4 retrieved images to reflect authentic RAG workflows. Building on this dataset, we propose Doc-REFRAG, a question-guided framework that compresses visual tokens into coarse chunks and selectively expands question-relevant ones via a lightweight RL-based selector. Experiments on six benchmarks show that Doc-REFRAG outperforms eleven strong baselines, achieving state-of-the-art accuracy with significantly lower inference latency. Our resources are available at https://github.com/Collab-Gen/Doc-REFRAG."
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<abstract>Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, existing multimodal RAG models are primarily designed for single-image or closed-document settings and exhibit limited accuracy in realistic multi-image scenarios. Moreover, processing numerous retrieved images incurs substantial computational overhead from irrelevant visual tokens. To address these challenges, we introduce DocLongRAG, a large-scale dataset of 343K question–answer pairs, each associated with an average of 37.4 retrieved images to reflect authentic RAG workflows. Building on this dataset, we propose Doc-REFRAG, a question-guided framework that compresses visual tokens into coarse chunks and selectively expands question-relevant ones via a lightweight RL-based selector. Experiments on six benchmarks show that Doc-REFRAG outperforms eleven strong baselines, achieving state-of-the-art accuracy with significantly lower inference latency. Our resources are available at https://github.com/Collab-Gen/Doc-REFRAG.</abstract>
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%0 Conference Proceedings
%T Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation
%A Hu, Ruofan
%A Xu, Shengyang
%A Hong, Minjie
%A Yang, Xiaoda
%A Zhou, Sashuai
%A Lei, Ke
%A Jin, Tao
%A Zhao, Zhou
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hu-etal-2026-doc
%X Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, existing multimodal RAG models are primarily designed for single-image or closed-document settings and exhibit limited accuracy in realistic multi-image scenarios. Moreover, processing numerous retrieved images incurs substantial computational overhead from irrelevant visual tokens. To address these challenges, we introduce DocLongRAG, a large-scale dataset of 343K question–answer pairs, each associated with an average of 37.4 retrieved images to reflect authentic RAG workflows. Building on this dataset, we propose Doc-REFRAG, a question-guided framework that compresses visual tokens into coarse chunks and selectively expands question-relevant ones via a lightweight RL-based selector. Experiments on six benchmarks show that Doc-REFRAG outperforms eleven strong baselines, achieving state-of-the-art accuracy with significantly lower inference latency. Our resources are available at https://github.com/Collab-Gen/Doc-REFRAG.
%U https://aclanthology.org/2026.emnlp-main.377/
%P 8624-8644
Markdown (Informal)
[Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation](https://aclanthology.org/2026.emnlp-main.377/) (Hu et al., EMNLP 2026)
ACL
- Ruofan Hu, Shengyang Xu, Minjie Hong, Xiaoda Yang, Sashuai Zhou, Ke Lei, Tao Jin, and Zhou Zhao. 2026. Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8624–8644, Budapest, Hungary. Association for Computational Linguistics.