arxiv: 2604.06165 · v2 · submitted 2026-04-07 · 💻 cs.CV · cs.LG

Recognition: 2 theorem links

· Lean Theorem

HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models

Reihaneh Zohrabi , Hosein Hasani , Akshita Gupta , Mahdieh Soleymani Baghshah , Anna Rohrbach , Marcus Rohrbach

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:52 UTC · model grok-4.3

classification 💻 cs.CV cs.LG

keywords object hallucinationsvision-language modelsBayesian detectionhallucination mitigationattention confoundersdecoding strategies

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The pith

Vision-language models hallucinate fewer objects when their decoding is guided by HaloProbe's Bayesian estimates of token probabilities.

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

The paper shows that attention weights used to detect object hallucinations in vision-language models are unreliable because of hidden confounders such as token position and how often objects repeat in a description. These create Simpson's paradox, reversing trends when data is pooled together. HaloProbe addresses this by using a Bayesian approach to separate external description statistics from internal model signals during decoding, training in a balanced way to isolate true evidence and adding a prior from external features for accurate posterior probabilities. This probability score then guides the generation process externally to reduce hallucinations. Experiments indicate this method cuts hallucinations more than methods that intervene inside the model and does so without reducing the model's overall usefulness or fluency.

Core claim

We reveal that coarse-grained attention-based analysis is unreliable due to hidden confounders, specifically token position and object repetition in a description. This leads to Simpson's paradox: the attention trends reverse or disappear when statistics are aggregated. Based on this observation, we introduce HaloProbe, a Bayesian framework that factorizes external description statistics and internal decoding signals to estimate token-level hallucination probabilities. HaloProbe uses balanced training to isolate internal evidence and combines it with a learned prior over external features to recover the true posterior. While intervention-based mitigation methods often degrade utility or flu

What carries the argument

HaloProbe, a Bayesian framework that factorizes external description statistics and internal decoding signals to estimate token-level hallucination probabilities using balanced training to isolate internal evidence and a learned prior over external features.

If this is right

HaloProbe-guided decoding reduces object hallucinations more effectively than existing intervention methods.
Utility and fluency are preserved because mitigation happens externally without altering the model.
Token-level hallucination probabilities allow precise, non-invasive intervention during text generation.
The Bayesian factorization avoids the pitfalls of aggregated attention statistics.

Where Pith is reading between the lines

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

HaloProbe could be adapted to mitigate other hallucination types like incorrect attributes or relations in descriptions.
The approach might improve reliability in downstream applications such as visual question answering or image-based storytelling.
Similar confounder issues could be investigated in attention mechanisms of other multimodal AI systems.
Testable extension: apply HaloProbe to newer vision-language models to verify if the performance gains hold.

Load-bearing premise

Coarse-grained attention-based analysis is unreliable due to hidden confounders like token position and object repetition that produce Simpson's paradox when statistics are aggregated.

What would settle it

An experiment that measures hallucination rates using HaloProbe-guided decoding versus standard decoding on a held-out set of images with known ground-truth objects, checking if hallucination frequency drops significantly without utility loss.

Figures

Figures reproduced from arXiv: 2604.06165 by Akshita Gupta, Anna Rohrbach, Hosein Hasani, Mahdieh Soleymani Baghshah, Marcus Rohrbach, Reihaneh Zohrabi.

**Figure 1.** Figure 1: Overview of HaloProbe. Given an image and a prompt, an LVLM generates a caption. HaloProbe adopts a Bayesian formulation that combines internal features (e.g., attention and logit statistics) with external caption features (e.g., object repetition and its token position) through a balanced estimator and a prior estimator to produce tokenlevel hallucination scores. HaloProbe enables reliable hallucination … view at source ↗

**Figure 3.** Figure 3: Distribution of object repetition counts (r ∈ {1, 2, 3, 4}) conditioned on class. Hallucinated objects are typically mentioned only once, while correct objects are more frequently repeated within a caption. expected attention Ec[A | y, t], where the expectation is taken over object tokens ct in the dataset with fixed hallucination label y and token position t. Empirically, for both hallucinated and correct… view at source ↗

**Figure 2.** Figure 2: Illustration of Simpson’s paradox induced by token position. (a) Token-position–conditioned image attention, averaged over heads, layers, and samples, for correct and hallucinated object tokens. Image attention is computed by averaging attention values from layers 5 to 18 of LLaVA-1.5-7B and over 5K samples from the MS COCO dataset. Across most positions, hallucinated tokens receive higher conditional atte… view at source ↗

**Figure 5.** Figure 5: Proportion of correct versus hallucinated objects across token positions in the 5K random samples of MS COCO dataset. The dataset is highly imbalanced, particularly at early token positions. positions, making them severely under-represented [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

**Figure 4.** Figure 4: Illustration of Simpson’s paradox induced by object repetition. (a) Token-position-conditioned image attention for correct and hallucinated object tokens, shown separately for first and non-first occurrences. First mentions consistently exhibit higher image attention, even when the object is hallucinated, while non-first mentions attend less to the image. Conditioning on object occurrence largely removes… view at source ↗

**Figure 6.** Figure 6: Qualitative comparison of image description results. Given the same user prompt, the baseline model LLaVA-1.5 produces a coherent description with a low repetition score, while the intervention induces severe repetitive generation, reflected by a high RE-4 score [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗

**Figure 7.** Figure 7: Qualitative comparison of image description results. Given the same user prompt, the baseline model LLaVA-1.5 produces a coherent description with a low repetition score, while the intervention induces severe repetitive generation, reflected by a high RE-4 score [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗

**Figure 8.** Figure 8: Averaged image attention for first-occurrence object tokens, averaged over early and late transformer layers. Early (first 10) layers exhibit a rapid decay in image attention as generation progresses, while late (last 10) layers maintain relatively stable attention across token positions. Attention in early layers is largely non-discriminative between correct and hallucinated tokens, whereas in late layers… view at source ↗

**Figure 9.** Figure 9: Consistent performance of HaloProbe across token positions. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗

**Figure 10.** Figure 10: ROC and Precision-Recall curves of HaloProbe for token-level hallucination detection, illustrating performance under class imbalance. Prompt: Please describe this image in detail. Input Image . Greedy Baseline The image depicts a busy city street with a crowd of people walking around. Among the crowd, a woman is standing on the sidewalk, holding a cell phone and looking at it. There are several other peop… view at source ↗

**Figure 11.** Figure 11: Qualitative comparison of image description results using LLaVA-1.5-7B. The Baseline model hallucinates objects that are not present in the image, while HaloProbe + Post-Process effectively removes such hallucinations while preserving most of the correctly mentioned objects and maintaining caption fluency. Green = correct object; red = hallucinated object. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗

**Figure 12.** Figure 12: Qualitative comparison of image description results using LLaVA-1.5-7B. The Baseline model hallucinates objects that are not present in the image, while HaloProbe + Post-Process effectively removes such hallucinations while preserving most of the correctly mentioned objects and maintaining caption fluency. Green = correct object; red = hallucinated object. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗

**Figure 13.** Figure 13: Qualitative comparison of image description results using LLaVA-1.5-7B. The Baseline model hallucinates objects that are not present in the image, while HaloProbe + Post-Process effectively removes such hallucinations while preserving most of the correctly mentioned objects and maintaining caption fluency. Green = correct object; red = hallucinated object. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗

**Figure 14.** Figure 14: Qualitative comparison of image descriptions using LLaVA-1.5-7B. The Beam Search baseline hallucinates objects not present in the image. In contrast, HaloProbe + Beam Search selects the beam with the fewest hallucinated words and the highest number of correctly mentioned objects. Green = correct object; red = hallucinated object. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗

**Figure 15.** Figure 15: Qualitative comparison of image descriptions using LLaVA-1.5-7B. The Beam Search baseline hallucinates objects not present in the image. In contrast, HaloProbe + Beam Search selects the beam with the fewest hallucinated words and the highest number of correctly mentioned objects Green = correct object; red = hallucinated object. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_15.png] view at source ↗

**Figure 16.** Figure 16: Qualitative comparison of image descriptions using LLaVA-1.5-7B. The Beam Search baseline hallucinates objects not present in the image. In contrast, HaloProbe + Beam Search selects the beam with the fewest hallucinated words and the highest number of correctly mentioned objects. Green = correct object; red = hallucinated object. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_16.png] view at source ↗

read the original abstract

Large vision-language models can produce object hallucinations in image descriptions, highlighting the need for effective detection and mitigation strategies. Prior work commonly relies on the model's attention weights on visual tokens as a detection signal. We reveal that coarse-grained attention-based analysis is unreliable due to hidden confounders, specifically token position and object repetition in a description. This leads to Simpson's paradox: the attention trends reverse or disappear when statistics are aggregated. Based on this observation, we introduce HaloProbe, a Bayesian framework that factorizes external description statistics and internal decoding signals to estimate token-level hallucination probabilities. HaloProbe uses balanced training to isolate internal evidence and combines it with a learned prior over external features to recover the true posterior. While intervention-based mitigation methods often degrade utility or fluency by modifying models' internals, we use HaloProbe as an external scoring signal for non-invasive mitigation. Our experiments show that HaloProbe-guided decoding reduces hallucinations more effectively than state-of-the-art intervention-based methods while preserving utility.

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

HaloProbe flags a real Simpson's paradox in attention-based hallucination detection and offers a Bayesian factorization to fix it, but the balancing step that carries the experimental claims is unverified.

read the letter

The paper's useful observation is that coarse attention weights on visual tokens reverse or vanish when you account for token position and object repetition, producing Simpson's paradox in the aggregates. That diagnosis is direct and points to a flaw in prior detection methods that relied on raw attention scores. HaloProbe then factorizes external description statistics from internal decoding signals, uses balanced training to isolate the internal evidence, and folds in a learned prior to estimate per-token hallucination probabilities. The mitigation step applies this score for guided decoding without altering model weights, which avoids the utility drops common in intervention approaches. This framing is new and practical for keeping the base model intact. The central weakness is that the superiority claim over state-of-the-art methods rests on the balanced training actually removing the confounders. No specifics appear on how the datasets are matched for position and repetition counts, and no ablations isolate that step's contribution. Without those, the posterior estimates could still carry the same biases, and the reported gains remain hard to trust. The learned prior adds free parameters that could also influence results depending on training choices. This work is aimed at researchers improving reliability in vision-language models for captioning, VQA, or robotics. A reader focused on hallucination mitigation would get value from the problem setup and the non-invasive scoring idea. The paper engages the literature honestly by critiquing attention methods on their own terms and proposing a structured alternative. It deserves peer review so the experiments and balancing procedure can be checked in detail.

Referee Report

2 major / 2 minor

Summary. The paper claims that coarse-grained attention weights are unreliable for detecting object hallucinations in VLMs because token position and object repetition act as hidden confounders, producing Simpson's paradox when statistics are aggregated. It introduces HaloProbe, a Bayesian framework that factorizes external description statistics from internal decoding signals via balanced training plus a learned prior over external features, yielding token-level hallucination posteriors. These posteriors are then used as an external scoring signal for non-invasive guided decoding that reduces hallucinations more effectively than intervention-based methods while preserving utility.

Significance. If the central experimental claim holds, HaloProbe would provide a practical, non-invasive alternative to internal intervention methods that often trade off fluency or utility. The identification of Simpson's paradox in attention-based analysis is a useful methodological caution for the field. The Bayesian factorization approach is conceptually clean and could generalize if the balancing step is shown to be robust.

major comments (2)

[Methods / Balanced Training] The section describing the balanced training procedure (and any associated ablations) does not specify how training examples are matched or stratified on token position and object repetition. Without these details or an ablation isolating the balancing step's contribution, it is unclear whether the claimed isolation of internal signals from confounders is achieved; this is load-bearing for the assertion that HaloProbe recovers a true posterior superior to attention baselines and for the experimental superiority claim.
[Experiments] The experimental results section reports that HaloProbe-guided decoding outperforms SOTA intervention methods, but the provided abstract and summary contain no quantitative metrics, confidence intervals, or statistical tests. A load-bearing comparison requires explicit numbers (e.g., hallucination rate reduction and utility preservation scores) with controls for the same confounders identified in the attention analysis.

minor comments (2)

[Methods] Notation for the learned prior and the factorization of external vs. internal factors should be introduced with an explicit equation early in the methods to improve readability.
[Abstract] The abstract would be strengthened by including one or two key quantitative results (e.g., percentage reduction in hallucinations) rather than a purely qualitative claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and insightful comments on our manuscript. We address each major comment point by point below, providing clarifications on our methodology and committing to specific revisions that strengthen the presentation of both the balanced training procedure and the experimental results.

read point-by-point responses

Referee: [Methods / Balanced Training] The section describing the balanced training procedure (and any associated ablations) does not specify how training examples are matched or stratified on token position and object repetition. Without these details or an ablation isolating the balancing step's contribution, it is unclear whether the claimed isolation of internal signals from confounders is achieved; this is load-bearing for the assertion that HaloProbe recovers a true posterior superior to attention baselines and for the experimental superiority claim.

Authors: We agree that the manuscript's description of the balanced training procedure lacks sufficient detail on the matching and stratification process with respect to token position and object repetition. This information is necessary to fully validate the isolation of internal decoding signals from the confounders identified in our attention analysis. In the revised version, we will expand the Methods section with a precise account of the stratification criteria, the matching algorithm employed, and the resulting dataset composition. We will also add a dedicated ablation that directly compares HaloProbe performance with and without the balancing step, thereby isolating its contribution and supporting the claim that the Bayesian factorization yields a superior posterior. revision: yes
Referee: [Experiments] The experimental results section reports that HaloProbe-guided decoding outperforms SOTA intervention methods, but the provided abstract and summary contain no quantitative metrics, confidence intervals, or statistical tests. A load-bearing comparison requires explicit numbers (e.g., hallucination rate reduction and utility preservation scores) with controls for the same confounders identified in the attention analysis.

Authors: We acknowledge that the abstract and summary in the submitted version are concise and omit explicit quantitative metrics, confidence intervals, and statistical tests. Although the experimental results section of the full manuscript presents comparative outcomes, we agree that greater transparency is warranted given the load-bearing nature of the superiority claim. In the revision, we will update the abstract to include key quantitative results (hallucination rate reductions and utility preservation scores) together with confidence intervals. We will further augment the experiments section with statistical significance tests and explicitly report results stratified by token position and object repetition, thereby applying the same controls identified in the attention analysis. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation or claims

full rationale

The paper first empirically demonstrates that attention-based hallucination detection suffers from Simpson's paradox due to confounders (token position, object repetition). It then proposes HaloProbe as an external Bayesian scoring method that applies balanced training to separate internal decoding signals from external statistics, combines this with a learned prior, and uses the resulting token-level probabilities for non-invasive guided decoding. This chain does not reduce any claimed prediction or posterior to its inputs by construction: the balancing step is a data-preprocessing choice whose contribution is assessed via ablation-style experiments, the prior is fitted on held-out external features, and the superiority claim rests on direct comparison against intervention baselines on utility and hallucination metrics. No self-citation chains, uniqueness theorems, or ansatzes imported from prior author work are load-bearing; the framework remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on the ability to isolate internal evidence via balanced training and to combine it with a learned external prior; these steps introduce fitted components and domain assumptions about signal factorization.

free parameters (1)

parameters of the learned prior over external features
The prior is learned from data to recover the true posterior and is therefore fitted rather than derived from first principles.

axioms (1)

domain assumption Hallucination probability can be factorized into external description statistics and internal decoding signals
This factorization is the core modeling choice that enables the Bayesian posterior estimation.

invented entities (1)

HaloProbe no independent evidence
purpose: Bayesian token-level hallucination probability estimator
Newly proposed framework whose effectiveness is asserted via experiments.

pith-pipeline@v0.9.0 · 5496 in / 1222 out tokens · 42409 ms · 2026-05-10T18:52:10.629822+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

HaloProbe uses balanced training to isolate internal evidence and combines it with a learned prior over external features to recover the true posterior.
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We reveal that coarse-grained attention-based analysis is unreliable due to hidden confounders... This leads to Simpson’s paradox

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

8 extracted references · 3 canonical work pages · 1 internal anchor

[1]

Leng, S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., and Bing, L

URLhttps://arxiv.org/abs/2405.05256. Leng, S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., and Bing, L. Mitigating object hallucinations in large vision-language mod- els through visual contrastive decoding. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, pp. 13872–13882, 2024. 11 Bayesian Detection and Mitigation...

work page arXiv 2024
[2]

Evaluating Object Hallucination in Large Vision-Language Models

URLhttps://arxiv.org/abs/2305.10355. Lin, T.-Y ., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll´ar, P., and Zitnick, C. L. Microsoft coco: Common objects in context. InEuropean conference on computer vision, pp. 740–755. Springer, 2014. Liu, H., Li, C., Li, Y ., and Lee, Y . J. Improved baselines with visual instruction tuning. InProcee...

work page internal anchor Pith review arXiv 2014
[3]

""You are a text-editing assistant that improves image captions by removing hallucinated objects marked with`$`while keeping the caption fluent and faithful

doi: 10.18653/v1/2025.emnlp-main.273. URL https:// aclanthology.org/2025.emnlp-main.273/. Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M. Minigpt-4: Enhancing vision-language understanding with advanced large language models.arXiv preprint arXiv:2304.10592, 2023. 13 Bayesian Detection and Mitigation of Object Hallucinations A. Detailed Experimental...

work page doi:10.18653/v1/2025.emnlp-main.273 2025
[4]

The image shows a spacious studio apartment kitchen with wooden cabinets and $refrigerator

**Remove Only Hallucinated Objects:** - The objects marked with`$`are hallucinated, and you need to **remove only those hallucinated objects** from the caption. For example: - "The image shows a spacious studio apartment kitchen with wooden cabinets and $refrigerator."→"The image shows a spacious studio apartment kitchen with wooden cabinets." - Do **not*...
[5]

For example: - **Do not delete** entire sentence structures unless absolutely necessary to maintain clarity

**Minimal Changes:** - If removing a hallucinated object causes awkward phrasing, make minimal edits to improve the fluency of the sentence. For example: - **Do not delete** entire sentence structures unless absolutely necessary to maintain clarity
[6]

Do not introduce new details, objects, or replace hallucinated objects with new ones (e.g., don't replace`$refrigerator`with another new object `microwave`)

**Faithfulness to the Original Caption:** - Ensure that the edited caption remains **faithful** to the original context. Do not introduce new details, objects, or replace hallucinated objects with new ones (e.g., don't replace`$refrigerator`with another new object `microwave`). - The resulting text should **not lose any original meaning** or introduce new...
[7]

Do not over-edit the original content

**Clarity and Brevity:** - The edited caption should be clear and concise without being overly terse. Do not over-edit the original content. Make sure that the edited text does not contain objects that are marked with $ in the input text
[8]

"`), without any additional text or explanations. The input caption is:

**Output Format:** - Provide only the final, edited caption inside **double quotes** (`""`), without any additional text or explanations. The input caption is: """ E. Effect of Attention Intervention on Decoding Stability While attention intervention has been proposed as a mechanism to improve grounding and reduce hallucination, directly manipu- lating at...

2023