{"id":"369526a4-0186-46db-8a16-0176d99199bb","arxiv_id":"2508.00549","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"State-of-the-art vision-language models fail to reliably identify relative positions in medical images, and visual markers improve results only moderately.","lead":"This paper tests whether vision-language models can correctly judge relative positions of anatomical structures in medical images, and finds that current models fail at this task. It also introduces MIRP, a new benchmark for measuring this capability.","discovery_kind":"unclear","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Benchmark realism and prompt ambiguity are the load-bearing risk; an abstract-only review cannot rule them out, but the claim as stated is not internally contradicted.","rationale":"The reader's verdict is UNVERDICTED with LOW confidence, based solely on the abstract. My stress-test agrees: there is no internally inconsistent step visible from the abstract, and the central claim is coherent. The load-bearing risk is benchmark validity—whether MIRP and the prompting/scoring protocol measure 'relative-position understanding' rather than convention knowledge, prior-anatomy recall, or prompt artifacts. This is precisely the reader's weakest_assumption, so my agreement is full. I do not see grounds to move the verdict: the abstract provides no code, data, protocol details, or ablations, so the appropriate disposition remains UNVERDICTED. The concrete tests I propose would settle the concern: expert label agreement, an explicit spatial-convention control in the prompts, and a masked-image ablation to test the 'prior knowledge vs. image content' claim. If those tests pass, the benchmark would be credible; if they fail, the headline claim about model failure would need to be weakened or re-scoped. Because the full manuscript might already contain these checks, the recommendation is UNCHANGED pending access to the full text and benchmark.","tokens_in":703,"tokens_out":1785,"duration_ms":14813,"concrete_test":"Obtain the MIRP benchmark and reproduce the central evaluation on a held-out subset with three protocol checks: (a) have two expert radiologists independently re-label the ground-truth relative positions from the images alone, with the question prompts hidden, and measure inter-annotator agreement; (b) re-run the GPT-4o evaluation with prompts that explicitly state the spatial convention (e.g., 'in radiological view, patient left is image right') and compare accuracy; and (c) run a control condition with the anatomical structures removed or masked while keeping the markers, to test whether accuracy is driven by image content rather than prior knowledge. If expert labels agree at or near ceiling and the accuracy shift from the convention clarifier is large, the benchmark is not measuring what the abstract claims.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that the MIRP benchmark and its evaluation protocol faithfully capture the clinical task of judging relative positions in medical images, and that the observed 'failure' is a true model limitation rather than an artifact of the test. Because the full text is unavailable, the decisive premises cannot be verified: (1) that each image has a unique, unambiguous ground-truth relative-position label (e.g., anterior/posterior, left/right, superior/inferior) that does not depend on viewer orientation, imaging modality conventions, or window/level settings; (2) that the question phrasing maps cleanly onto that label and is not leading, elliptical, or answerable by anatomical priors in a way that penalizes models for appropriate prior use; (3) that the scoring treats 'cannot determine' or refusals distinctly from confident wrong answers, since clinical safety hinges on knowing when a model should abstain; and (4) that the model families and prompting schemes are representative of current state-of-the-art VLMs. If, for example, the benchmark uses axial slices where 'left' on the image is patient-right (radiology convention), and the prompts do not state the convention, then 'left/right' failures could reflect an unstated labeling convention rather than a deficit in relative-position understanding. The abstract's secondary claim that VLMs 'rely more on prior anatomical knowledge than on actual image content' is even harder to evaluate without ablation details (e.g., blanked/perturbed-image controls, cross-split analyses). None of this is an internal inconsistency; it is an unverifiability concern that the abstract alone cannot resolve.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":975,"tokens_out":2407,"duration_ms":24462,"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":[{"comment":"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).","section":"Abstract"},{"comment":"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.","section":"Abstract"},{"comment":"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.","section":"Abstract"}],"minor_comments":[{"comment":"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.","section":"Abstract"},{"comment":"The benchmark name is not formatted consistently: 'MIRP , Medical Imaging Relative Positioning' contains an extra space before the comma.","section":"Abstract"},{"comment":"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'.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"This review is based solely on the abstract because the full text was not available. The strong negative claims and the mechanistic interpretation require the full experimental protocol and dataset description to be verifiable. I recommend the editor ensure that the full manuscript is provided to reviewers, and that the authors be asked to report quantitative results, benchmark construction details, and ablation studies."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Here's my take. The real contribution is the MIRP benchmark and the systematic evaluation of four VLMs on relative-position questions in medical images. That's a needed resource, and the authors are honest that all four models fail. Adding visual markers helps only moderately—a useful empirical negative result for the field.\n\nThe soft spots are exactly where the stress test lands. The claim 'VLMs fail' is only meaningful if MIRP actually measures what clinical users mean by relative position. The abstract alone can't tell us whether the ground-truth labels handle imaging conventions (patient left vs. image left in axial slices), whether the prompts are unambiguous, or whether scoring separates refusals from confident errors. The secondary claim about relying on prior anatomical knowledge needs ablation controls (scrambled or blanked images) to be credible. None of this makes the paper wrong; it makes it incomplete relative to the abstract's claims.\n\nStatistical reporting is also thin in the abstract: no sample sizes, error bars, or tests. That's normal for an abstract, but the referee should ask for the full protocol. The citation pattern can't be judged from the abstract, though the 'underexplored' claim needs checking against existing spatial-reasoning benchmarks for natural images.\n\nBottom line: this deserves peer review. The benchmark could become a standard evaluation set, and the negative result is worth publishing if the evaluation survives scrutiny. I'd send it out, with referees asked to focus on MIRP's construct validity and the prior-knowledge interpretation.","headline":"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.","tokens_in":1518,"tokens_out":1651,"would_cite":true,"duration_ms":17637,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"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.","keywords":["vision-language models","medical imaging","relative positioning","spatial reasoning","visual prompting","benchmark","anatomical priors","VLM evaluation"],"falsifier":"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.","tokens_in":546,"feed_emoji":"🩻","tokens_out":3612,"duration_ms":34937,"temperature":0.7,"pith_summary":"This paper tries to establish that state-of-the-art vision-language models cannot reliably identify the relative positions of anatomical structures in medical images, a basic spatial skill that clinical decisions often depend on. The authors evaluate four widely used VLMs and find that all of them perform poorly at this task. They then test whether visual prompts—such as alphanumeric or colored markers placed on the structures—help, and report only moderate improvements, far weaker than the gains previously observed on natural images. The paper argues that on medical images these models lean on prior anatomical knowledge rather than on the actual image content, and it introduces the MIRP benchmark to make this failure measurable and comparable across models.","feed_headline":"VLMs fail to judge relative positions in medical scans","feed_subtitle":"Even with markers on anatomy, four top models improve only slightly—and lean on prior knowledge, not the pixels.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[],"fun_headline_variants":["Top VLMs fail at relative positions in medical scans","Four VLMs can't locate anatomy positions, even with markers","Medical VLMs rely on prior knowledge, not pixels, for positions","New benchmark exposes VLM failure on left-right in scans","Markers only mildly aid VLMs on medical position tasks"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["Top VLMs fail at relative positions in medical scans","Four VLMs can't locate anatomy positions, even with markers","Medical VLMs rely on prior knowledge, not pixels, for positions","New benchmark exposes VLM failure on left-right in scans","Markers only mildly aid VLMs on medical position tasks"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000275,"raw_usage":{"total_tokens":1615,"prompt_tokens":890,"completion_tokens":725,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":506,"completion_tokens_details":{"reasoning_tokens":642}},"tokens_in":506,"tokens_out":725,"duration_ms":7428,"temperature":1.0,"reasoning_tokens":642,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T10:04:07.676881+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[],"review_version":1}