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Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts

Paper · 2026 · Xiaowei Yuan, Ziyang Huang, Zhao Yang et al.

Summary

Retrieval-augmented generation (RAG) works well when the retrieved passages are clean. Accuracy drops sharply when the passages are noisy, outdated or contradict each other. The authors study whether a model's own knowledge can act as a reference point for judging such evidence.

Using Qwen2.5-7B-Instruct on the conflict-oriented RAG-Bench (NQ, TriviaQA, WebQ), they compare several setups. In "Prior-Guided CoT," the model first states what it already knows about the query and then reasons over the documents. This helps as distractors accumulate but hurts when only the gold document is present, because the model's own biases win.

They therefore propose BrPr, which uses reinforcement learning to teach the model to reconcile its priors with retrieved evidence. A Bernoulli-gated dropout randomly removes the explicit prior during training, so the skill is absorbed into the weights. The result is a fast single-pass variant with no prior generation. BrPr is reported as more robust to conflicts and noise than baselines such as GRPO-RAG, and it generalizes to Wikipedia-based retrieval. This matters for any RAG system that operates over unreliable sources.

Key points

  • Analysis setup: On RAG-Bench with 1, 2, 5 or 10 documents, the authors test five inference strategies: answering from the model's internal knowledge alone, answering directly from the documents, ordinary chain-of-thought, chain-of-thought that reasons jointly over the inputs, and Prior-Guided CoT. Beyond the gold passage, the extra documents are noise or conflicts.
  • Downside of priors: With only the gold passage, the explicit prior reduces NQ EM from 60.46 (Direct Inference) to 52.95 (Prior-Guided CoT).
  • Information-theoretic check: An entropy-based estimate of conditional mutual information I(Y;Z|X,D) shows the prior lowers uncertainty most in "wrong→correct" cases. In some other cases it adds noise.
  • Method and variants: BrPr combines RL with Bernoulli-gated prior dropout and an implicit KL constraint. It comes in two versions: a 2-turn version with an explicit prior and an efficient 1-turn latent version.
  • Limitation: The 1-turn model approaches but does not exceed the 2-turn model's reasoning quality. When all documents are wrong, BrPr is better at falling back on correct parametric knowledge.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏛️Chinese Academy of SciencesBeijing · China246 works · 24,952 citations🔬Institute of AutomationBeijing · China87 works · 14,883 citations🎓University of Chinese Academy of SciencesBeijing · China188 works · 8,744 citations🔬Beijing Academy of Artificial IntelligenceBeijing · China46 works · 8,682 citations🏢MeituanBeijing · China26 works · 3,953 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 worksInformation Systems · 1,730 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1,685 worksInformation retrieval & search · 552 works

Cite

Cite as Xiaowei Yuan, Ziyang Huang, Zhao Yang, Yequan Wang, Jun Zhao and Kang Liu. 2026. Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1013

@inproceedings{yuan2026efficient,
  title = {{Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts}},
  author = {Xiaowei Yuan, Ziyang Huang, Zhao Yang, Yequan Wang, Jun Zhao and Kang Liu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1013},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1013}
}

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