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home / papers / 2026 / Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation

Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation

Paper · 2026 · Qianchi Zhang, Hainan Zhang, Liang Pang et al.

Summary

Retrieval-Augmented Generation is meant to reduce hallucinations, but this paper shows a weakness that has received little attention: reordering the same Top-5 retrieved passages can change the LLM's answer. This happens even when the gold document is present and placed first, and the context is under 1,000 tokens. The authors argue that this is not caused by noisy retrieval or by long-context positional bias. They link it to internal reasoning that diverges in middle and upper layers. Clustering last-token hidden states across all 120 orderings reveals this branching.

Stable-RAG works in three steps:

  1. It runs the generator over many document orders and applies spectral clustering to the final-layer hidden states.
  2. It decodes a representative state from each cluster to obtain candidate answers. It then sorts examples into fully correct, partially correct, fully incorrect but answerable, and fully incorrect and unanswerable, and builds preference pairs from these groups.
  3. It aligns the model with DPO.

On NQ, TriviaQA and HotpotQA, the method improves accuracy and cross-order consistency over robust-RAG and positional-bias baselines. This makes RAG answers more dependable whatever order the retrieved passages arrive in.

Key points

  • A Perturbation Success Rate (PSR) is computed on 1,000 NQ test instances. It shows that LLaMA models from 3.2-1B to 3.3-70B produce hallucinated answers under document reordering, even with the gold document in position 1.
  • Hidden-state clusters are mixed in shallow layers and separate by answer in deeper layers. Highly sensitive samples (10+ clusters) diverge mainly in the higher layers. Clustering-quality F1 at the last layer reaches 87.6 for Qwen3-8B and 83.9 for LLaMA3-8B-Instruct.
  • Baselines include SFT, RetRobust, ATM, RAAT, Pos2Distill and Ms-PoE. Stable-RAG obtains the best SubEM under both original and shuffled orders, with LLaMA3-8B-Instruct and the Contriever retriever. Examples include 72.05 → 71.76 and 38.91 → 37.50.
  • The accuracy drop under shuffling stays between 0.29 and 1.41 points for Stable-RAG. For the vanilla model it is 2.33–5.67 points. PSR is lower than the baselines for every gold-document position.
  • Cluster-based preference construction cuts annotation cost by roughly a factor of three compared with decoding every permutation. The method does not constrain intermediate-layer trajectories. Code is available at github.com/zqc1023/Stable-RAG.

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 · China194 works · 23,906 citations🎓Beihang UniversityBeijing · China65 works · 8,452 citations🔬Institute of Computing TechnologyBeijing · China51 works · 3,411 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 473 works

Cite

Cite as Qianchi Zhang, Hainan Zhang, Liang Pang, Hong-Wei Zheng and Zhiming Zheng. 2026. Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation. 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.1188

@inproceedings{zhang2026stable,
  title = {{Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation}},
  author = {Qianchi Zhang, Hainan Zhang, Liang Pang, Hong-Wei Zheng and Zhiming Zheng},
  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.1188},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1188}
}

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