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arxiv: 2604.02784 · v2 · pith:M6T2WLTQnew · submitted 2026-04-03 · 💻 cs.CV · cs.CL

EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors

Ryuhei Miyazato , Shunsuke Kitada , Kei Harada This is my paper

Pith reviewed 2026-05-21 10:22 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords hallucination detectionvision-language modelsensemble learninginternal representationsvisual question answeringmultimodal robustnessattention outputshidden states
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The pith

Ensembling detectors trained on separate internal representations of vision-language models raises hallucination detection accuracy.

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

The paper introduces EnsemHalDet to detect when vision-language models produce factually incorrect or ungrounded responses to image-based questions. It trains separate detectors on different internal signals such as attention outputs and hidden states, then merges their results through ensemble learning. A reader would care because current single-representation methods leave some hallucination signals undetected, and a more complete approach could make model outputs more reliable for visual tasks without extra external verification steps. If the claim holds, detection would become less dependent on any one internal view and more consistent across datasets and model types.

Core claim

EnsemHalDet trains independent detectors on multiple internal representations of VLMs, including attention outputs and hidden states, then combines their predictions through ensemble learning. Experiments across several VQA datasets and different VLMs show this yields higher AUC scores than prior detection methods and than models that use only one internal representation.

What carries the argument

The ensemble of independent detectors each trained on a distinct internal representation such as attention outputs or hidden states, with their outputs combined to produce the final hallucination score.

If this is right

  • Hallucination detection gains robustness when it draws on multiple distinct internal signals instead of relying on one.
  • The performance advantage appears consistently across different VQA datasets and across several VLMs.
  • Ensembling internal detectors outperforms both prior methods and single-detector baselines in AUC.
  • Detection can proceed from internal states alone, without needing to examine only the final generated output.

Where Pith is reading between the lines

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

  • The same ensemble structure could be tested on other multimodal models that generate text from images or other inputs.
  • If certain representations prove redundant in the ensemble, future versions might drop them to lower computation cost.
  • Deployed systems could run the ensemble in parallel with generation to flag questionable answers in real time.
  • The approach might combine with output-sampling techniques to catch hallucinations that internal signals miss.

Load-bearing premise

Different internal representations supply sufficiently independent signals about hallucinations so that training separate detectors on them and combining the results improves detection over any single representation.

What would settle it

Measure AUC for the full ensemble and for its strongest single-detector component on a fresh VQA dataset not used in the original experiments; if the ensemble AUC is not higher, the benefit of the ensemble approach is refuted.

Figures

Figures reproduced from arXiv: 2604.02784 by Kei Harada, Ryuhei Miyazato, Shunsuke Kitada.

Figure 1
Figure 1. Figure 1: VLMs can produce hallucinated responses that are inconsistent with factual knowledge or image content. However, such hallucinations leave detectable signals in the model’s internal representations. We lever￾age multiple internal states of VLMs to achieve robust and accurate hallucination detection Kalai et al. (2025) argue that hallucinations are an inevitable consequence of large language mod￾els (LLMs) b… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of EnsemHalDet: This method extracts attention heads and hidden states across multiple layers. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the detector-level ensemble process. For attention-head-based features (AH), we train [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Prompt used for hallucination evaluation. We [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
read the original abstract

Vision-Language Models (VLMs) excel at multimodal tasks, but they remain vulnerable to hallucinations that are factually incorrect or ungrounded in the input image. Recent work suggests that hallucination detection using internal representations is more efficient and accurate than approaches that rely solely on model outputs. However, existing internal-representation-based methods typically rely on a single representation or detector, limiting their ability to capture diverse hallucination signals. In this paper, we propose EnsemHalDet, an ensemble-based hallucination detection framework that leverages multiple internal representations of VLMs, including attention outputs and hidden states. EnsemHalDet trains independent detectors for each representation and combines them through ensemble learning. Experimental results across multiple VQA datasets and VLMs show that EnsemHalDet consistently outperforms prior methods and single-detector models in terms of AUC. These results demonstrate that ensembling diverse internal signals significantly improves robustness in multimodal hallucination detection.

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.

Referee Report

1 major / 2 minor

Summary. The paper proposes EnsemHalDet, an ensemble-based hallucination detection framework for Vision-Language Models that trains independent detectors on multiple internal representations (attention outputs and hidden states) and combines them via ensemble learning. Experiments across multiple VQA datasets and VLMs report consistent AUC improvements over prior methods and single-detector baselines.

Significance. If the results hold after appropriate controls, the work would demonstrate that ensembling complementary internal signals can improve robustness in hallucination detection, addressing a practical limitation in VLM reliability for multimodal tasks. The multi-dataset, multi-model evaluation provides a reasonable empirical basis for the approach.

major comments (1)
  1. [§4 (Experiments)] §4 (Experiments): The AUC gains for the full ensemble over single-representation detectors are presented as evidence for the value of diverse internal signals, but the manuscript lacks a control ablation in which multiple detectors are trained on the identical representation (e.g., repeated hidden-state detectors) and then ensembled. Without this comparison, the reported outperformance cannot be attributed specifically to cross-representation diversity rather than generic benefits of ensembling.
minor comments (2)
  1. [Abstract] Abstract and §4: The claim of 'consistent AUC gains' should be accompanied by exact improvement magnitudes, dataset sizes, number of VLMs tested, and any statistical significance tests (e.g., p-values or confidence intervals) to allow readers to assess the practical and statistical strength of the results.
  2. [§3 (Method)] §3 (Method): Specify the precise ensemble aggregation rule (e.g., mean of probabilities, majority vote) and the training procedure for each detector, including hyperparameter selection and whether any representation-specific tuning was performed.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment below and will revise the paper accordingly.

read point-by-point responses

  1. Referee: [§4 (Experiments)] §4 (Experiments): The AUC gains for the full ensemble over single-representation detectors are presented as evidence for the value of diverse internal signals, but the manuscript lacks a control ablation in which multiple detectors are trained on the identical representation (e.g., repeated hidden-state detectors) and then ensembled. Without this comparison, the reported outperformance cannot be attributed specifically to cross-representation diversity rather than generic benefits of ensembling.

    Authors: We agree that a same-representation control ablation is necessary to isolate the contribution of cross-representation diversity from generic ensemble effects. In the revised manuscript we will add experiments that train multiple independent detectors on identical internal representations (e.g., several hidden-state detectors with different random seeds or slight architectural variations) and ensemble their outputs. We will report AUC for this control ensemble alongside the single-detector baselines and the proposed diverse-representation ensemble, thereby clarifying whether the observed gains stem specifically from signal diversity. revision: yes

Circularity Check

0 steps flagged

Empirical ensemble framework with no derivation chain

full rationale

The paper proposes EnsemHalDet as a practical ensemble of detectors trained on distinct internal representations (attention outputs and hidden states) of VLMs, with performance measured via AUC on external VQA datasets and multiple models. No equations, fitted parameters, or load-bearing steps are described that reduce the reported results to quantities defined by the authors' own choices or prior self-citations. The central claims rest on experimental comparisons against baselines rather than any self-referential construction, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the approach assumes internal states contain usable hallucination signals and that ensemble combination improves detection without further justification visible here.

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Reference graph

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11 extracted references · 11 canonical work pages · 1 internal anchor

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