REVIEW 3 major objections 3 minor
Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images
T0 review · 3 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash
Pith's one-line read Current vision-language models, including GPT-4o, Llama3.2, Pixtral, and JanusPro, fail to reliably identify relative positions of anatomical structures in medical images; visual markers help only moderately.
desk verdict A useful medical-VLM benchmark with a plausible but unverified negative result; the referee's job is to test whether the benchmark measures what it claims. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the MIRP benchmark: a set of medical images paired with questions about the relative positions of anatomical structures, each labeled with ground-truth spatial relationships. The evaluation protocol also includes a visual-prompting machinery that places alphanumeric or colored markers on structures, and the comparison between prompted and unprompted accuracy is what reveals the models' reliance on priors versus image content.
What would settle it
Run the MIRP evaluation on a set of images whose orientation is randomly flipped left-right and rotated 180 degrees; if a model's accuracy on relative-position questions stays high and invariant under these transformations, the claim that the model relies on anatomical priors rather than image content would be weakened, whereas a sharp drop in accuracy would support that claim. Alternatively, fine-tune a VLM on MIRP and then evaluate it on a separate clinical dataset; if it reaches expert-level accuracy on relative-position judgments, the original failure would be a training deficiency rather than a fundamental incapacity.
Extended reading notes
Core claim
This paper reports that four state-of-the-art vision-language models—GPT-4o, Llama3.2, Pixtral, and JanusPro—all fail when asked to judge relative positions of anatomical structures in medical images. Adding visual markers such as numbers or colored dots to the structures improves accuracy only moderately, and the improvement is much smaller than what has been seen for similar prompts on natural images. The authors interpret this gap as evidence that, in the medical setting, the models answer such questions by retrieving anatomical priors rather than by extracting spatial relationships from the image itself. To support this line of work, they present MIRP, a dedicated benchmark for evaluating relative-position understanding in medical imaging.
Load-bearing premise
The load-bearing premise is that the MIRP benchmark and its evaluation protocol—image selection, question phrasing, ground-truth labeling, and scoring—faithfully represent the real clinical task of judging relative positions, so that the observed failures reflect a genuine model limitation rather than an artifact of the test.
Editorial extensions
If this is right
- Clinicians cannot yet trust current VLMs for diagnostic workflows that require judging whether one structure lies above, below, left, or right of another.
- Simply adding visual markers to structures is not enough; the modest gains on medical images mean marker-based prompting alone will not close the gap.
- The observed reliance on anatomical priors implies that these models may answer correctly on typical anatomy but fail on unusual or atypical presentations, which are often the cases that matter clinically.
- The MIRP benchmark gives the research community a standard testbed for tracking progress in spatial reasoning for medical vision-language models.
- New training objectives or architectures that explicitly force models to ground spatial judgments in image content will be needed before clinical deployment is viable.
Reading between the lines
- A strong test of the paper's prior-reliance claim would be to run the MIRP evaluation on images that are mirrored or rotated; if accuracy drops substantially even though the spatial relation is unchanged, that would confirm the models are matching priors rather than reading pixels.
- Because the markers help only moderately, it is plausible that the models are using markers as anchors to invoke prior anatomical layouts rather than as cues for genuine spatial reasoning; this could be tested by placing markers on synthetic anatomy that does not match any known body layout.
- The same experimental design could be extended to non-visual settings, such as text-only anatomical descriptions, to isolate how much of the failure is a visual problem versus a general limitation in relational reasoning about anatomy.
- If the MIRP benchmark were used as a training set rather than just an evaluation set, it might serve as a diagnostic for whether current VLMs can learn relative positions at all when the anatomy is made explicit.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper evaluates four state-of-the-art vision-language models (GPT-4o, Llama3.2, Pixtral, JanusPro) on a new benchmark, MIRP, for identifying relative positions of anatomical structures in medical images. The abstract claims that all models fail at this task, that visual prompts such as alphanumeric or colored markers provide only moderate improvements, and that the models rely more on prior anatomical knowledge than on actual image content. The paper introduces the MIRP benchmark to support further research in this area.
Significance. If the results hold, the paper addresses an important and underexplored capability for clinical applications of VLMs, where spatial understanding is critical. The cross-model evaluation and the introduction of a dedicated benchmark are potentially valuable contributions to the community. However, the abstract provides no quantitative evidence, so the significance and reliability of the findings cannot be assessed from the presented text. The claims are strong and would have direct implications for clinical deployment, making a careful evaluation of the benchmark design and the experimental protocol essential.
major comments (3)
- [Abstract] The central claim that 'all models fail' is not supported by any numerical results, sample sizes, confidence intervals, or statistical tests. Since the failure rate is the paper's main finding, the abstract must at least report the key performance metrics and how 'failure' is defined (e.g., accuracy below chance, or below a clinical threshold).
- [Abstract] The mechanistic interpretation that 'VLMs rely more on prior anatomical knowledge than on actual image content' requires supporting ablations, such as comparisons on images with corrupted or absent anatomical cues, or analyses of response patterns. The abstract reports no such evidence, so this conclusion is currently unsupported.
- [Abstract] The validity of the MIRP benchmark is load-bearing. The abstract does not describe how ground-truth labels are defined (e.g., viewer orientation, radiology conventions for left/right), how question phrasing avoids ambiguity, or whether 'cannot determine' responses are distinguished from confident errors. Without these details, the reported failures could be artifacts of the test design rather than true model limitations.
minor comments (3)
- [Abstract] The phrase 'results remain significantly lower on medical images compared to observations made on natural images' uses 'significantly' without reporting the statistical test or the natural-image benchmark used for comparison.
- [Abstract] The benchmark name is not formatted consistently: 'MIRP , Medical Imaging Relative Positioning' contains an extra space before the comma.
- [Abstract] The abstract does not cite prior work on spatial reasoning in VLMs, which would help position the claimed novelty and define the baseline for 'state-of-the-art'.
Circularity Check
No circularity identifiable from abstract; benchmark evaluation is self-contained and no fitted input is relabeled as prediction.
full rationale
This is an abstract-only review. The paper's central claim is an empirical evaluation of VLMs on a new benchmark (MIRP), plus an intervention study with visual markers. No equation, fitted parameter, or self-citation chain is presented in the abstract. The evaluation does not reduce by construction: the MIRP benchmark is an input dataset, and the model outputs are measured against ground-truth relative-position labels; the conclusion that models fail is a measurement, not a consequence of how the benchmark was defined. The secondary claim that models rely more on prior anatomical knowledge than on image content is an interpretive statement, but the abstract does not exhibit a definitional reduction or a fitted parameter renamed as a prediction. Benchmark realism, prompt ambiguity, and labeling conventions are legitimate correctness risks, not circularity. Since no specific circular step can be quoted from the available text, the circularity score is 0.
Assumptions & free parameters
assumptions (2)
- domain assumption Relative position identification is a fundamental prerequisite for clinical decision-making.
- domain assumption Question-answering accuracy on visual prompts is a valid measure of a VLM's spatial reasoning ability.
invented entities (1)
-
MIRP (Medical Imaging Relative Positioning) benchmark
Cite this review
Pith. "Pith review of Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images." pith.science (2026). https://pith.science/paper/QNYMSVQL
@misc{pith2026250800549,
author = {Pith},
title = {Pith review of: Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images},
year = {2026},
howpublished = {\url{https://pith.science/paper/QNYMSVQL}},
note = {Machine review of arXiv:2508.00549}
}
read the original abstract
Clinical decision-making relies heavily on understanding relative positions of anatomical structures and anomalies. Therefore, for Vision-Language Models (VLMs) to be applicable in clinical practice, the ability to accurately determine relative positions on medical images is a fundamental prerequisite. Despite its importance, this capability remains highly underexplored. To address this gap, we evaluate the ability of state-of-the-art VLMs, GPT-4o, Llama3.2, Pixtral, and JanusPro, and find that all models fail at this fundamental task. Inspired by successful approaches in computer vision, we investigate whether visual prompts, such as alphanumeric or colored markers placed on anatomical structures, can enhance performance. While these markers provide moderate improvements, results remain significantly lower on medical images compared to observations made on natural images. Our evaluations suggest that, in medical imaging, VLMs rely more on prior anatomical knowledge than on actual image content for answering relative position questions, often leading to incorrect conclusions. To facilitate further research in this area, we introduce the MIRP , Medical Imaging Relative Positioning, benchmark dataset, designed to systematically evaluate the capability to identify relative positions in medical images.
Reviewed August 6, 2026 · model on record in the stance chip above.
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