@inproceedings{yuan-etal-2026-efficient,
title = "Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts",
author = "Yuan, Xiaowei and
Huang, Ziyang and
Yang, Zhao and
Wang, Yequan and
Zhao, Jun and
Liu, Kang",
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.1013/",
pages = "22149--22164",
ISBN = "979-8-89176-390-6",
abstract = "Retrieval-Augmented Generation (RAG) has become a standard paradigm for grounding Large Language Models (LLMs) with external knowledge. However, RAG performance often degrades substantially when faced with noisy, outdated, or conflicting retrieved information. In this work, we empirically demonstrate that Prior-Guided Reasoning{---}a strategy that explicitly elicits the model{'}s parametric knowledge as prior information to guide reasoning on retrieved documents{---}effectively mitigates the impact of external conflicts. Building on this, we propose BrPr (Bernoulli-gated reinforcement learning for Prior-Guided reasoning), a framework that achieves robust performance across varying degrees of external inconsistency. Furthermore, by employing a Bernoulli-gated dropout mechanism during training, BrPr distills the prior-driven reasoning capability into the model parameters, enabling efficient latent reasoning without explicit prior generation. The experimental results demonstrate that BrPr consistently exhibits superior robustness to external conflicts and noise."
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<abstract>Retrieval-Augmented Generation (RAG) has become a standard paradigm for grounding Large Language Models (LLMs) with external knowledge. However, RAG performance often degrades substantially when faced with noisy, outdated, or conflicting retrieved information. In this work, we empirically demonstrate that Prior-Guided Reasoning—a strategy that explicitly elicits the model’s parametric knowledge as prior information to guide reasoning on retrieved documents—effectively mitigates the impact of external conflicts. Building on this, we propose BrPr (Bernoulli-gated reinforcement learning for Prior-Guided reasoning), a framework that achieves robust performance across varying degrees of external inconsistency. Furthermore, by employing a Bernoulli-gated dropout mechanism during training, BrPr distills the prior-driven reasoning capability into the model parameters, enabling efficient latent reasoning without explicit prior generation. The experimental results demonstrate that BrPr consistently exhibits superior robustness to external conflicts and noise.</abstract>
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%0 Conference Proceedings
%T Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts
%A Yuan, Xiaowei
%A Huang, Ziyang
%A Yang, Zhao
%A Wang, Yequan
%A Zhao, Jun
%A Liu, Kang
%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 yuan-etal-2026-efficient
%X Retrieval-Augmented Generation (RAG) has become a standard paradigm for grounding Large Language Models (LLMs) with external knowledge. However, RAG performance often degrades substantially when faced with noisy, outdated, or conflicting retrieved information. In this work, we empirically demonstrate that Prior-Guided Reasoning—a strategy that explicitly elicits the model’s parametric knowledge as prior information to guide reasoning on retrieved documents—effectively mitigates the impact of external conflicts. Building on this, we propose BrPr (Bernoulli-gated reinforcement learning for Prior-Guided reasoning), a framework that achieves robust performance across varying degrees of external inconsistency. Furthermore, by employing a Bernoulli-gated dropout mechanism during training, BrPr distills the prior-driven reasoning capability into the model parameters, enabling efficient latent reasoning without explicit prior generation. The experimental results demonstrate that BrPr consistently exhibits superior robustness to external conflicts and noise.
%U https://aclanthology.org/2026.acl-long.1013/
%P 22149-22164
Markdown (Informal)
[Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts](https://aclanthology.org/2026.acl-long.1013/) (Yuan et al., ACL 2026)
ACL