arxiv: 2605.05045 · v2 · submitted 2026-05-06 · 💻 cs.CV · cs.CL

Recognition: 2 theorem links

· Lean Theorem

When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise

Philip Wootaek Shin , Ajay Narayanan Sridhar , Sivani Devarapalli , Rui Zhang , Jack Sampson , Vijaykrishnan Narayanan

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:48 UTC · model grok-4.3

classification 💻 cs.CV cs.CL

keywords vision-language modelsrelation hallucinationvisual perturbationsrotationnoiserelational reasoningobject relationsmultimodal robustness

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The pith

Vision-language models generate false object relations under even mild image rotations and added noise.

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

The paper tests how small visual changes affect vision-language models' ability to describe how objects relate to each other in a scene. It finds that modest rotations or noise levels cause clear drops in correct relational answers across multiple models and image collections. This matters for any use of these models in real settings where photos are rarely perfectly aligned or clean. The authors also try prompt changes and image cleanup steps but show these only reduce the errors without removing them. Their results separate basic visual perception from the harder task of consistent relational logic.

Core claim

Even mild distortions significantly degrade relational reasoning across models and datasets. Prompt-based augmentation and preprocessing strategies such as orientation correction and denoising offer partial improvements but do not fully resolve hallucinations. The findings point to an underlying gap between perceptual robustness and relational understanding.

What carries the argument

Relation hallucination, measured as incorrect descriptions of inter-object spatial or interaction relationships when input images receive controlled rotation or noise.

If this is right

Relational accuracy falls consistently once images receive small rotations or noise.
Prompt engineering and basic image preprocessing reduce but do not eliminate the errors.
The shortfall appears across different vision-language models and different test collections.
Improved model designs must incorporate explicit geometry awareness to close the gap.

Where Pith is reading between the lines

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

Training data that already contains rotated and noisy versions of scenes might reduce the observed failures.
The same perturbation sensitivity could appear in other tasks that require spatial or interaction reasoning.
New evaluation suites for multimodal models should include systematic rotation and noise tests as standard.

Load-bearing premise

The selected rotation angles, noise intensities, datasets, and metrics for counting hallucinations truly capture real relational failures and are not skewed by prompt wording or image choice.

What would settle it

Running the same models on the same datasets but finding no measurable rise in relation errors after applying the tested rotations and noise levels would falsify the degradation claim.

Figures

Figures reproduced from arXiv: 2605.05045 by Ajay Narayanan Sridhar, Jack Sampson, Philip Wootaek Shin, Rui Zhang, Sivani Devarapalli, Vijaykrishnan Narayanan.

**Figure 1.** Figure 1: VLM response under visual perturbations. view at source ↗

**Figure 2.** Figure 2: Effect of rotation metadata on VLM accuracy. view at source ↗

**Figure 3.** Figure 3: Effect of corruption severity on relation hallu view at source ↗

read the original abstract

Vision-language models (VLMs) achieve strong multimodal performance but remain prone to relation hallucination, which requires accurate reasoning over inter-object interactions. We study the impact of visual perturbations, specifically rotation and noise, and show that even mild distortions significantly degrade relational reasoning across models and datasets. We further evaluate prompt-based augmentation and preprocessing strategies (orientation correction and denoising), finding that while they offer partial improvements, they do not fully resolve hallucinations. Our results reveal a gap between perceptual robustness and relational understanding, highlighting the need for more robust, geometry-aware VLMs.

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

The paper shows relation hallucinations rise under rotation and noise but does not isolate that from general VLM degradation.

read the letter

This paper finds that even mild rotations and noise increase relation hallucinations in VLMs, but the evidence does not clearly separate that from the models just getting worse at everything. The authors run perturbation tests across models and datasets, then check whether orientation correction, denoising, or prompt tweaks reduce the problem. They report partial gains from the fixes but no full resolution. That gives a practical snapshot of where current VLMs fall short on relational tasks when images are not pristine. The work is straightforward empirical testing that builds on existing hallucination studies by focusing on these specific visual changes. It supplies numbers that matter for anyone deploying VLMs in robotics or scene analysis where inputs can be rotated or noisy. The main limitation is the missing controls for whether relational reasoning itself is the weak point. If object detection or overall caption quality drops under the same distortions, the hallucination counts will rise without proving a geometry-specific failure. The abstract claims a gap between perceptual robustness and relational understanding, yet nothing in the reported setup shows object-level metrics staying flat while relations degrade. That leaves the central interpretation open to the stress-test concern. Readers working on VLM robustness and evaluation will get usable data points from the experiments. The paper is coherent enough on its own terms to deserve peer review, though referees should ask for the additional baselines that would make the relational claim stick.

Referee Report

2 major / 2 minor

Summary. The paper claims that vision-language models (VLMs) exhibit relation hallucination that is exacerbated by visual perturbations such as rotation and noise; even mild distortions degrade relational reasoning across models and datasets, while prompt-based augmentation and preprocessing (orientation correction, denoising) yield only partial mitigation, exposing a gap between perceptual robustness and relational understanding.

Significance. If the central empirical findings are confirmed with proper controls, the work would usefully document a specific failure mode in current VLMs, motivating geometry-aware architectures. The breadth across models and datasets is a strength, but the absence of reported statistical details and independent baselines reduces the immediate impact.

major comments (2)

[Methods / Evaluation] The evaluation lacks reported controls that isolate relational reasoning from general performance degradation (e.g., object detection accuracy, attribute binding, or non-relational caption quality under the same perturbations). Without these, the increase in hallucinated subject-predicate-object triples cannot be unambiguously attributed to impaired inter-object reasoning rather than uniform drops in generation quality.
[Results] The abstract and results assert a clear degradation under rotation/noise, yet the manuscript provides no details on data splits, statistical significance testing, or normalization of hallucination rates against overall caption coherence. This leaves open the possibility that post-hoc dataset or prompt choices drive the observed gap.

minor comments (2)

[Introduction] Define 'relation hallucination' more explicitly in the introduction, distinguishing it from other caption errors.
[Experimental Setup] Specify the exact rotation angles, noise levels, and prompt templates used; include example outputs to illustrate the metric.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify how to strengthen the attribution of our findings to relational reasoning specifically. We address each major comment below and will incorporate the suggested controls and details in the revised manuscript.

read point-by-point responses

Referee: [Methods / Evaluation] The evaluation lacks reported controls that isolate relational reasoning from general performance degradation (e.g., object detection accuracy, attribute binding, or non-relational caption quality under the same perturbations). Without these, the increase in hallucinated subject-predicate-object triples cannot be unambiguously attributed to impaired inter-object reasoning rather than uniform drops in generation quality.

Authors: We agree that isolating the effect on relational reasoning requires additional controls. In the revision we will add evaluations of object detection accuracy (using standard detectors on perturbed images) and non-relational caption quality metrics (attribute binding accuracy and overall caption coherence scores) under identical rotation and noise conditions. These will be presented alongside the relation hallucination rates to show that the increase in erroneous subject-predicate-object triples exceeds the general degradation observed in non-relational components. revision: yes
Referee: [Results] The abstract and results assert a clear degradation under rotation/noise, yet the manuscript provides no details on data splits, statistical significance testing, or normalization of hallucination rates against overall caption coherence. This leaves open the possibility that post-hoc dataset or prompt choices drive the observed gap.

Authors: We will revise the results section and appendix to report the exact data splits used for each dataset and model. We will also include statistical significance testing (paired t-tests across multiple random seeds) on the hallucination rate differences and normalize relation hallucination rates by overall caption coherence metrics (e.g., BLEU-4 and CIDEr computed on the same perturbed captions). These additions will demonstrate that the observed relational degradation is not an artifact of dataset or prompt selection. revision: yes

Circularity Check

0 steps flagged

Purely empirical evaluation with no derivations or self-referential predictions

full rationale

The paper performs an empirical study measuring how rotation and noise affect relation hallucination rates in VLMs across existing models and datasets. No equations, fitted parameters, ansatzes, or uniqueness theorems appear in the provided abstract or description. Central claims rest on direct observation of model outputs rather than any derivation chain that reduces to its own inputs by construction. Self-citations, if present, are not load-bearing for the reported degradation patterns, which are externally falsifiable via the same benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Empirical diagnostic study; no new mathematical derivations, free parameters, or postulated entities are introduced.

axioms (1)

domain assumption Standard VLM evaluation protocols and common benchmarks capture relational reasoning failures
Relies on existing models, datasets, and hallucination metrics without new validation.

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discussion (0)

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Relation between the paper passage and the cited Recognition theorem.

We study the impact of visual perturbations, specifically rotation and noise, and show that even mild distortions significantly degrade relational reasoning across models and datasets.

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Reference graph

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