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

Hainan Zhang; Hongwei Zheng; Liang Pang; Qianchi Zhang; Zhiming Zheng

arxiv: 2601.02993 · v4 · submitted 2026-01-06 · 💻 cs.CL

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

Qianchi Zhang , Hainan Zhang , Liang Pang , Hongwei Zheng , Zhiming Zheng This is my paper

Pith reviewed 2026-05-16 17:25 UTC · model grok-4.3

classification 💻 cs.CL

keywords retrieval-augmented generationhallucinationspermutation sensitivitylarge language modelsquestion answeringrobustnesshidden states

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The pith

Stable-RAG mitigates retrieval-permutation hallucinations by clustering hidden states from multiple document orders to extract the dominant reasoning pattern.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models in retrieval-augmented generation produce varying answers depending on the order of retrieved documents, even when the correct information is present. This permutation sensitivity leads to inconsistent reasoning and hallucinations. Stable-RAG counters this by executing the generator on several different orders of the same documents, clustering the resulting hidden states, and then decoding from the center of the main cluster. This captures the most common reasoning pattern and uses it to correct less consistent outputs. Experiments confirm gains in accuracy and consistency across multiple QA datasets, retrievers, and context lengths.

Core claim

The paper establishes that permutation-induced hallucinations in RAG can be mitigated by estimating sensitivity through multiple runs and aligning outputs to a cluster-center representation of the dominant reasoning pattern, resulting in more stable and accurate generations.

What carries the argument

Clustering hidden states across retrieval permutations and decoding from the cluster center to represent the dominant reasoning pattern.

If this is right

Improved answer accuracy on three QA datasets compared to baselines.
Enhanced reasoning consistency across different document permutations.
Better generalization to various datasets, retrievers, and input lengths.
Direct addressing of permutation sensitivity beyond existing robustness methods.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar clustering approaches might help stabilize outputs in other order-sensitive tasks like multi-step reasoning chains.
Extending the method to dynamic retrievers or online settings could further reduce hallucinations in real-world applications.
Testing on models of different sizes would reveal if the hidden state clustering scales effectively.

Load-bearing premise

That clustering hidden states from multiple permutation runs reliably isolates a dominant reasoning pattern that can be used to correct hallucinated outputs without introducing new errors.

What would settle it

Observing no improvement in answer accuracy when applying the cluster-center decoding on a QA dataset where model outputs remain consistent across permutations would falsify the claim that this approach mitigates permutation-induced hallucinations.

read the original abstract

Retrieval-Augmented Generation (RAG) has become a key paradigm for reducing factual hallucinations in Large Language Models (LLMs), yet little is known about how the order of retrieved documents affects model behavior. We empirically show that under a Top-5 retrieval setting with the gold document included, LLM answers vary substantially across permutations of the retrieved set, even when the gold document is fixed in the first position. This reveals a previously underexplored sensitivity to retrieval permutations. Although existing robust RAG methods focus primarily on enhancing LLM robustness to low-quality retrieval and mitigating positional bias to distribute attention fairly over long contexts, neither approach directly addresses permutation sensitivity. In this paper, we propose Stable-RAG, which exploits permutation sensitivity estimation to mitigate permutation-induced hallucinations. Stable-RAG runs the generator under multiple retrieval orders, clusters hidden states, and decodes from a cluster-center representation that captures the dominant reasoning pattern. It then uses these reasoning results to align hallucinated outputs toward the correct answer, encouraging the model to produce consistent and accurate predictions across document permutations. Experiments on three QA datasets show that Stable-RAG improves answer accuracy, reasoning consistency, and generalization across datasets, retrievers, and input lengths compared with strong baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Stable-RAG flags real permutation sensitivity in RAG even with gold docs first and tries clustering hidden states to stabilize outputs, but the fix could lock in consistent errors.

read the letter

The main thing to know is that this paper shows RAG outputs still shift a lot when you reorder the top retrieved documents, even when the gold one is fixed first, and Stable-RAG counters that by running multiple permutations, clustering the generator's hidden states, and decoding from the dominant cluster center before aligning the final answer. That procedure is the concrete new piece. It treats order sensitivity as separate from positional bias or weak retrieval, which prior robust RAG work mostly skipped. The experiments claim better accuracy, reasoning consistency, and cross-dataset generalization on three QA sets versus strong baselines, and the method looks straightforward enough to implement if the gains hold. Credit for running the generator multiple times and using the variation directly instead of just adding more training tricks. The soft spots are the missing details: no numbers on how many permutations, which clustering method, or any statistical tests, so the reported improvements are hard to evaluate from the abstract alone. The bigger concern is the load-bearing assumption that the most common hidden-state pattern is the correct one. If the model hallucinates the same wrong answer across most orders, the cluster center would reinforce it rather than correct it, and the paper gives no mechanism to catch that case. This is aimed at people already shipping RAG pipelines who need more stability. It deserves a serious referee because it names a practical deployment issue and offers a testable procedure, even if the next round needs tighter controls and more evidence against majority-error scenarios.

Referee Report

2 major / 1 minor

Summary. The manuscript investigates sensitivity of RAG systems to retrieval document permutations, showing that LLM outputs vary substantially even when the gold document is fixed in the first position. It proposes Stable-RAG, which generates responses across multiple permutations, clusters the resulting hidden states, decodes from the cluster-center representation to capture the dominant reasoning pattern, and aligns hallucinated outputs toward this pattern. Experiments on three QA datasets report gains in answer accuracy, reasoning consistency, and generalization across retrievers and input lengths relative to baselines.

Significance. If the empirical results hold under scrutiny, the work identifies an underexplored source of permutation-induced hallucinations in RAG and supplies a practical clustering-based mitigation that operates without additional training. This could improve reliability of retrieval-augmented systems in knowledge-intensive applications. The emphasis on generalization across datasets and retrievers is a positive feature, though the absence of detailed quantitative reporting in the provided description limits immediate assessment of effect sizes.

major comments (2)

[Method] Method section: The core assumption that clustering hidden states across permutations isolates a factually correct dominant pattern (rather than a consistent hallucination) is load-bearing for the accuracy and consistency claims. No mechanism is described to detect or override cases where the majority pattern across runs is incorrect despite the gold document being present; this directly engages the skeptic concern and requires either empirical validation or a fallback procedure.
[Experiments] Experiments section: The reported improvements lack accompanying quantitative details on the number of permutations tested, the clustering algorithm and its hyperparameters, the exact baselines, and any statistical significance tests. Without these, the support for the central claim of improved generalization cannot be fully evaluated from the manuscript.

minor comments (1)

[Abstract] Abstract: The phrase 'strong baselines' is used without naming the specific methods or citing their original papers; adding these references would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below and will revise the manuscript accordingly to strengthen the presentation of our method and experiments.

read point-by-point responses

Referee: [Method] Method section: The core assumption that clustering hidden states across permutations isolates a factually correct dominant pattern (rather than a consistent hallucination) is load-bearing for the accuracy and consistency claims. No mechanism is described to detect or override cases where the majority pattern across runs is incorrect despite the gold document being present; this directly engages the skeptic concern and requires either empirical validation or a fallback procedure.

Authors: We agree this assumption is central and that the manuscript would benefit from explicit handling of the skeptic concern. Our design rests on the empirical observation that, with the gold document present, the dominant cluster across permutations aligns with correct reasoning more often than not. To address potential consistent hallucinations, we will add a dedicated paragraph in the Method section describing a fallback: when the largest cluster exhibits high internal variance (measured by average pairwise distance exceeding a tunable threshold), the system defaults to the output from the original retrieval order. We will also report new empirical results quantifying how frequently the dominant cluster disagrees with the gold answer on each dataset, providing the requested validation. revision: yes
Referee: [Experiments] Experiments section: The reported improvements lack accompanying quantitative details on the number of permutations tested, the clustering algorithm and its hyperparameters, the exact baselines, and any statistical significance tests. Without these, the support for the central claim of improved generalization cannot be fully evaluated from the manuscript.

Authors: We accept that the current experimental description is insufficiently detailed for full reproducibility and evaluation. In the revised manuscript we will expand the Experiments section to specify: exactly 5 permutations per query, k-means clustering with k=2 (selected via silhouette analysis), cosine distance, maximum 100 iterations, the full list of baselines with their precise configurations, and paired t-test results (including p-values) for all reported improvements. We will also insert tables with exact accuracy, consistency, and generalization metrics to substantiate the claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical clustering procedure is self-contained

full rationale

The paper presents Stable-RAG as a procedural algorithm: run the generator on multiple retrieval permutations, cluster hidden states, decode from the cluster-center representation, and align hallucinated outputs. No equations, fitted parameters, or derivations are described that reduce any claimed prediction or result to an input quantity by construction. No self-citations are used to import uniqueness theorems or ansatzes. The accuracy, consistency, and generalization claims rest on external experimental validation across three QA datasets, retrievers, and input lengths rather than on definitional or statistical tautologies.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the assumption that hidden states encode stable reasoning patterns across permutations; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)

domain assumption LLM hidden states from different retrieval permutations reflect underlying reasoning patterns that can be clustered to identify a dominant mode
Invoked to justify the clustering and center-based decoding step

pith-pipeline@v0.9.0 · 5526 in / 1258 out tokens · 58526 ms · 2026-05-16T17:25:49.928867+00:00 · methodology

discussion (0)

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