{"id":"0ca3dd43-1377-43d3-af03-13a0ba6f60fa","arxiv_id":"2412.00056","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":2.0,"correctness_risk":"high","formal_verification":"none","parameter_count":6,"one_line_summary":"A convex hull area over BERT embeddings of chest X-ray reports is proposed as an uncertainty metric, and the paper shows it grows with sampling temperature in LLM-CXR.","lead":"This paper measures how much a medical vision-language model's radiology reports vary when the sampling randomness setting is turned up, using the spread of word-embedding clusters as a convex hull uncertainty score. It finds reports grow more varied at higher temperature, but the score is not checked against whether the reports are actually correct.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The metric's validity is asserted, not demonstrated: convex hull area of BERT embeddings is never tied to diagnostic correctness or calibration, and the paper's own 'least uncertain' T=1.00 appendix shows clinically contradictory responses.","rationale":"The reader's weakest assumption identifies exactly the load-bearing gap: the paper assumes that geometric spread of BERT/PCA embeddings is a valid proxy for predictive uncertainty in a clinical setting, and it never validates this against any ground truth. My analysis of the full text supports that concern and adds two concrete weaknesses. First, the 'mathematical justification' in Section IV.B.1 is not a proof; it assumes that larger pairwise embedding distances imply larger convex hull area and then identifies larger area with greater uncertainty, which is the claim being tested. Second, the paper's own appendix provides a qualitative counterexample: the 'least uncertain' instance at T=1.00 includes reports that disagree on the presence of pneumothorax, volume overload, and normal findings, so by clinical judgment this should be highly uncertain, not minimally uncertain. This is not a disagreement with consensus; it is an internal inconsistency between the paper's labels and the clinical meaning of the responses. The diversity-temperature relationship itself is well established and may hold, but that does not support the healthcare trustworthiness conclusion. A straightforward validation against labeled data would settle whether A(p,t) has any predictive value for diagnostic reliability. Since the reader's verdict is REJECT and my concern lands on the same central claim, I see no reason to change the verdict.","tokens_in":17559,"tokens_out":5032,"duration_ms":50680,"concrete_test":"Select a labeled subset of MIMIC-CXR VQA items with ground-truth findings (or CheXpert labels). For each image and temperature setting, generate 30 responses and apply the paper's pipeline: BERT embedding, PCA to two dimensions, DBSCAN with eps=0.25 and min_samples=3, and sum of convex hull areas to obtain A(p,t). Then compute the rank correlation between A(p,t) and response accuracy against the ground-truth findings, and test whether low-A responses are systematically more often correct than high-A responses. If A does not significantly separate correct from incorrect responses, the central claim that A is an uncertainty metric fails.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing condition is that A(p,t) as defined in Section IV.A — the sum of DBSCAN cluster convex hull areas over BERT/PCA embeddings — tracks predictive uncertainty of the VLM in a clinical setting. The paper never checks this against accuracy, calibration, or expert judgment; it simply labels embedding diversity as uncertainty. The 'mathematical justification' in Section IV.B.1 is an assumption: it equates larger pairwise embedding distances with 'greater uncertainty' and then concludes ∂A(p,t)/∂t > 0. Temperature-induced text diversity is well-known, so the empirical trend only shows that higher temperature produces more diverse text. Moreover, the paper's own Appendix I ('Least Uncertain Instance at T=1.00') lists clinically contradictory reports — normal chest, right apical pneumothorax, volume overload — for the same image, which should be high uncertainty, not low. Without evidence that hull area separates correct from incorrect or reliable from unreliable answers, the abstract's healthcare trustworthiness claim is unsupported.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a convex-hull-based uncertainty metric for vision-language model (VLM) responses in a medical visual question answering setting. For a fixed prompt and chest X-ray image, the authors generate 30 radiology-report responses from the LLM-CXR model at temperatures 0.001, 0.25, 0.50, 0.75, and 1.00. Responses are encoded with BERT, projected to two dimensions with PCA, clustered with DBSCAN, and the sum of the convex hull areas of all non-noise clusters is defined as the uncertainty A(p,t). The paper reports histograms and descriptive statistics suggesting that A(p,t) increases with temperature and concludes that higher temperature settings lead to higher uncertainty, with implications for healthcare trustworthiness.","tokens_in":17782,"tokens_out":3298,"duration_ms":31114,"significance":"If the proposed metric were shown to track predictive uncertainty in a clinically meaningful sense, it could offer a simple, model-agnostic tool for evaluating VLM reliability. The paper makes its code available and applies the method to a concrete medical VQA task, which is a strength. However, the significance is currently limited because the metric is not validated against correctness, calibration, or expert judgment, and the paper's own appendix contains examples that contradict the interpretation of low hull area as low uncertainty.","major_comments":[{"comment":"The proof of the lemma is invalid. The claim that larger pairwise distances between embedding points imply a larger convex hull area is false in general: a set of collinear points has zero hull area regardless of how far apart the points are, and adding a single outlier can increase hull area in a way that is not monotonic in pairwise distances. Furthermore, the proof does not account for the PCA projection to two dimensions, which does not preserve distances and can change the ordering of hull areas. Thus the 'mathematical justification' does not establish that A(p,t) measures uncertainty.","section":"Section IV.B.1, Lemma"},{"comment":"The central claim that A(p,t) is 'a measure of the uncertainty of the model's responses' is asserted rather than demonstrated. The metric is never compared against diagnostic accuracy, calibration, or expert judgment. This is not merely a philosophical issue: Appendix I, labeled 'Least Uncertain Instance at the Temperature Setting of 1.00', lists clinically contradictory responses for the same image — 'Lungs are fully expanded and clear', '1. Small right apical pneumothorax', 'Findings concerning for volume overload' — which a clinically useful uncertainty metric should identify as highly uncertain, not as the least uncertain instance. Without evidence that hull area separates reliable from unreliable answers, the abstract's claim about healthcare trustworthiness is unsupported.","section":"Section IV.A, definition of A(p,t)"},{"comment":"The numerical results in Table I are inconsistent with the histograms. For T=0.50, Figure 6 shows a normal distribution centered around a convex hull area of 25; for T=0.75, Figure 8 shows a main peak around 30; for T=1.00, Figure 10 shows a distribution spanning roughly 10-60. Yet Table I reports means of 0.1698, 0.2473, and 0.3117 for those temperatures. The paper does not explain whether the histograms are normalized, per-cluster, or in different units. Additionally, no error bars, confidence intervals, or statistical tests are reported across the images, so the claimed monotonic increase in uncertainty with temperature is not supported by the presented evidence.","section":"Table I and Figures 2-10"},{"comment":"The experimental setup omits several details that are needed to evaluate the method: the number of chest X-ray images used is not stated, the number of DBSCAN clusters and the proportion of noise points excluded from A(p,t) are not reported, and only two illustrative 'most uncertain' instances per temperature are shown. Because the total area is a sum over clusters, the number of clusters and the noise exclusion rule can dominate the result; without aggregate statistics over all images, the central empirical claim cannot be assessed.","section":"Section IV.A, experimental setup"}],"minor_comments":[{"comment":"The manuscript contains several typos and misspellings, including 'Evalaution' in the Section IV.A heading, 'resonances' in Section V, 'antipicated' in Section IV.C.5, and 'diffrence' in the discussion of Table I. A careful proofreading pass is needed.","section":"Throughout"},{"comment":"The column labeled '0.001-1.00' contains ratios (e.g., 3115, 114, 31) but the header does not state what is being divided by what; the intended computation should be explicitly defined.","section":"Table I"},{"comment":"The contour plots are difficult to interpret because the axes are unlabeled and no colorbar is provided, so the reader cannot determine what the background colors represent in terms of uncertainty values.","section":"Figures 3, 5, 7, 9, 11"},{"comment":"The GitHub URL 'https://github.com/ocatak/VLM Uncertainty' contains a space and is likely broken; please provide a valid link.","section":"Section IV.A"},{"comment":"The paper would benefit from citing prior work on semantic entropy or embedding-diversity-based uncertainty estimation in language models, to clarify the novelty of the proposed convex-hull metric relative to existing approaches.","section":"References"}],"recommendation":"reject","confidential_remarks":"The manuscript heavily relies on the authors' previous arXiv preprint [40] without clearly delineating the incremental contribution. More importantly, the central metric is not validated against any external ground truth, the theoretical justification is unsound, and the reported statistics are internally inconsistent. These issues are load-bearing and would require substantial additional experiments and analysis to address, which is beyond a typical revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The honest summary: this is a small paper with one empirical observation — LLM-CXR produces more textually diverse reports as temperature rises — wrapped in a metric called 'uncertainty' that is never validated. The convex hull pipeline is reused from the authors' own earlier LLM work; the new part is applying it to a chest X-ray VQA model. That application is routine.\n\nTo the paper's credit, the pipeline is transparent: 30 responses per image at five temperatures, BERT embeddings, PCA to 2D, DBSCAN clustering, hull areas. The histograms show a clear monotonic trend in spread versus temperature, and that claim is believable, matching known properties of sampling temperature. Including raw responses in the appendices is also helpful for checking.\n\nThe problems are in the interpretation. The paper defines uncertainty as hull area but never shows the metric separates correct from incorrect answers, correlates with calibration, or tracks clinical risk. The mathematical justification is circular: hull area is a measure of spread, and the conclusion is that spread increases with temperature — that is built into the definition. The first lemma is also wrong as stated: larger pairwise distances do not guarantee a larger convex hull area, especially after PCA projection. The internal evidence is worse: the 'least uncertain' instance at T=1.00 (Appendix I) contains mutually contradictory reports — normal chest, right apical pneumothorax, volume overload — for the same image. If that is the most confident response set, the metric is not capturing diagnostic uncertainty. There are also numerical inconsistencies between Table I and the histograms, and the GitHub link appears malformed.\n\nWho is this for? Someone interested in output diversity measurement might find a simple recipe, but it adds little over existing diversity metrics. The paper does not deserve a serious referee in its current form. The right move is to desk reject or send back for a major rethink, asking the authors either to validate the metric against accuracy/calibration or to reframe the paper as a study of temperature-induced output diversity and drop the 'uncertainty' and healthcare trustworthiness claims.","headline":"A transparent but unvalidated hull-area diversity metric applied to a medical VLM: the temperature trend is real but trivial, and the 'uncertainty' claim is unsupported, with the appendix showing contradictory responses in the supposedly least-uncertain case.","tokens_in":18282,"tokens_out":2350,"would_cite":false,"duration_ms":23217,"reading_group":"no","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper proposes that the total convex hull area of a vision-language model's response embeddings is a measure of its uncertainty, and reports that this measure increases with sampling temperature in medical chest X-ray question…","keywords":["uncertainty quantification","vision-language models","convex hull","visual question answering","temperature sampling","LLM-CXR","chest X-ray","DBSCAN"],"falsifier":"Generate a labeled set of chest X-ray questions with known correct answers, run the same 30-response pipeline at a fixed temperature, and compute both the total convex hull area and the proportion of correct answers. If images whose responses are tightly clustered (small hull area) are not more likely to be answered correctly than those with large hull area, then the claimed uncertainty interpretation is falsified.","tokens_in":17348,"feed_emoji":"🩻","tokens_out":4335,"duration_ms":34690,"temperature":0.7,"pith_summary":"The paper aims to give medical AI users a simple, model-agnostic way to see when a vision-language model is uncertain. It proposes measuring the geometric spread of generated radiology reports: the model answers the same prompt 30 times, encodes the answers with BERT, projects them into two dimensions, clusters them, and sums the convex hull areas of the clusters. That sum, $A(p,t)$, is claimed to quantify the uncertainty of the model's responses to prompt $p$ at temperature $t$. Applying this to the LLM-CXR model on chest X-rays, the paper reports that uncertainty is nearly zero at very low temperature and grows steadily as temperature rises to 1.00. If valid, the measure would offer a cheap way to flag unreliable answers before a model is used in a clinical setting.","feed_headline":"Convex hull area reveals when medical AI is uncertain","feed_subtitle":"A new metric measures the spread of a vision-language model's radiology reports, and it climbs with sampling temperature.","key_machinery":"The central object is the total convex hull area $A(p,t)$, defined as the sum of the areas of the convex hulls of all DBSCAN clusters (excluding noise points) found among the two-dimensional PCA projections of BERT embeddings of the model's 30 responses to prompt $p$ at temperature $t$. The machinery works in three stages: BERT converts each generated radiology report into a high-dimensional semantic vector; PCA projects these vectors to two dimensions for visualization and clustering; DBSCAN groups nearby points; and the convex hull area of each cluster is summed to a single scalar uncertainty score. The claim is that the scalar tracks the diversity, and therefore the uncertainty, of the model's outputs.","core_discovery":"The paper's central claim is that the total convex hull area over DBSCAN clusters of BERT-encoded, PCA-projected response embeddings is a measure of the uncertainty of a VLM's responses to a given prompt. Formally, $A(p,t) = \\sum_{c \\in L, c \\neq -1} \\text{Area}(\\text{ConvexHull}(c))$, where larger area means higher uncertainty. The authors argue this follows because diversity of responses increases with uncertainty, and the convex hull area reflects the spatial spread of embeddings. They demonstrate the measure on LLM-CXR generating 30 radiology reports per chest X-ray at temperatures 0.001, 0.25, 0.50, 0.75, and 1.00, observing that mean uncertainty rises monotonically with temperature, from 0.0001 to 0.3117. They also note that the model produces confident-sounding reports even for noisy or irrelevant images, which they attribute to poor data quality affecting reliability.","pith_inferences":["A natural next test the paper does not run: calibrate $A(p,t)$ against answer accuracy on a labeled benchmark. If a small hull area sometimes accompanies wrong but confident answers, the metric would capture diversity but not clinical reliability.","The same geometry could serve as an abstention rule: a system could refuse to answer when the total hull area exceeds a threshold, and the threshold could be tuned per deployment.","The temperature-diversity relationship likely holds for any stochastic text generator, but the mapping from hull area to diagnostic risk may be dataset- and task-specific, so the measured values should not be compared across different prompt sets without normalization.","The paper's reliance on PCA projection means the hull area depends on the projection; testing robustness with other dimensionality reductions (e.g., UMAP) or with the BERT embedding dimension itself would clarify whether the uncertainty signal is an artifact of 2D projection."],"forward_implications":["If $A(p,t)$ is a valid uncertainty measure, low-temperature generation (near 0.001) should be preferred when deploying a medical VLM, because responses are nearly deterministic and the metric reports near-zero uncertainty.","The metric provides a way to compare the reliability of different VLMs or different prompts on the same clinical task without needing ground-truth labels.","Because the measure only requires repeatedly sampling the model and embedding its outputs, it can be applied to any black-box VLM that generates text.","The observation that noisy or irrelevant X-ray images still yield confident-looking reports implies that input data quality directly affects the usefulness of any uncertainty score on this pipeline."],"supporting_citations":[{"why":"Provides the LLM-CXR medical VLM under test, the model whose response uncertainty is measured.","marker":"[35]"},{"why":"Supplies the chest X-ray dataset and metadata used to generate radiology reports for the experiments.","marker":"[39]"},{"why":"Introduces the convex hull-based uncertainty setup for LLMs from which this paper's experimental design is derived.","marker":"[40]"},{"why":"Supplies the theoretical grounding that consistency of answers, rather than accuracy, can quantify predictive uncertainty in black-box VQA models.","marker":"[21]"},{"why":"Gives the self-consistency rationale and temperature-based decoding background that motivates measuring diversity of sampled responses.","marker":"[25]"},{"why":"Is the MIMIC CXR JPG dataset on which LLM-CXR was trained, grounding the model's medical domain relevance.","marker":"[38]"}],"fun_headline_variants":["Convex hull area exposes medical AI uncertainty","Medical AI's uncertainty: measure it with convex hulls","How unsure is medical AI? Convex hull has the answer","Convex hull metric reveals AI doubt in radiology reports","New uncertainty gauge for medical AI: convex hull area"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the geometric spread of response embeddings, after BERT encoding, PCA projection, and DBSCAN clustering, faithfully tracks how uncertain the model really is in a clinical setting; the paper never validates this against accuracy, calibration, or expert judgment.","fun_headline_variants_meta":{"raw":{"variants":["Convex hull area exposes medical AI uncertainty","Medical AI's uncertainty: measure it with convex hulls","How unsure is medical AI? Convex hull has the answer","Convex hull metric reveals AI doubt in radiology reports","New uncertainty gauge for medical AI: convex hull area"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000447,"raw_usage":{"total_tokens":2242,"prompt_tokens":913,"completion_tokens":1329,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":529,"completion_tokens_details":{"reasoning_tokens":1251}},"tokens_in":529,"tokens_out":1329,"duration_ms":12861,"temperature":1.0,"reasoning_tokens":1251,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T13:42:55.431731+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Generate a labeled set of chest X-ray questions with known correct answers, run the same 30-response pipeline at a fixed temperature, and compute both the total convex hull area and the proportion of correct answers. If images whose responses are tightly clustered (small hull area) are not more likely to be answered correctly than those with large hull area, then the claimed uncertainty interpretation is falsified.","supporting_citations":[{"cited_title":"Neural networks for convex hull computation,","cited_arxiv_id":null,"evidence_quote":"Provides the LLM-CXR medical VLM under test, the model whose response uncertainty is measured."},{"cited_title":"Mimic-cxr-jpg-chest radiographs with structured labels,","cited_arxiv_id":null,"evidence_quote":"Supplies the chest X-ray dataset and metadata used to generate radiology reports for the experiments."},{"cited_title":"Consistency and uncertainty: Identifying unreliable responses from black-box vision-language models for selective visual question answering,","cited_arxiv_id":null,"evidence_quote":"Supplies the theoretical grounding that consistency of answers, rather than accuracy, can quantify predictive uncertainty in black-box VQA models."},{"cited_title":"Free dolly: Introducing the world’s first truly open instruction-tuned llm,","cited_arxiv_id":null,"evidence_quote":"Is the MIMIC CXR JPG dataset on which LLM-CXR was trained, grounding the model's medical domain relevance."}],"review_version":1}