{"id":"6c7a8e17-50e6-45dc-bf6a-4de5a109c62e","arxiv_id":"2508.05083","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":2,"one_line_summary":"MedMKEB is a medical multimodal knowledge-editing benchmark with four task types, on which existing editing methods underperform according to the authors.","lead":"This paper introduces MedMKEB, a test suite for checking whether medical AI models can update outdated or incorrect knowledge when new facts arrive. It matters because it offers developers a standard measure of knowledge editing in medical vision-language models, where current editing methods appear to fall short.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Construction and validation of MedMKEB edit targets cannot be verified from the available text; low editing-method scores may reflect benchmark artifacts rather than genuine editing failures.","rationale":"The reader's verdict is UNVERDICTED because the full text is corrupted, and my stress-test identifies the same load-bearing premise: the benchmark's edit targets and validation must genuinely measure knowledge-editing failures, not ambiguity or expert-opinion artifacts. The abstract alone cannot establish this premise, and the corrupted full text supplies no construction protocol, no validator agreement statistics, and no evidence that targets are uniquely answerable from the image/question. My proposed audit directly targets that premise: if target ambiguity is high, the paper's central empirical finding about editing-method limitations would be weakened. I do not see an internal inconsistency in the abstract-level claims; the concern is about unverifiable support, not known error. Therefore the appropriate outcome remains UNVERDICTED, consistent with the reader's verdict. I agree rather than partially agree because the reader's weakest-assumption statement and my attack are essentially the same concern, phrased with the same emphasis on benchmark validity rather than on the editing methods themselves. This is an honest non-finding in the sense that I cannot confirm the concern from the corrupted text either; the concrete test is the decisive next step.","tokens_in":17153,"tokens_out":2458,"duration_ms":28985,"concrete_test":"Obtain a clean, readable copy of the paper and extract the benchmark-construction section. Then run an answerability audit on a random sample of MedMKEB edit cases: strip the post-edit target, present the original image+question to several independent clinicians who are blind to the desired edit, and ask for the answer. Measure inter-annotator agreement (e.g., Fleiss' kappa) and the proportion of cases where the pre-edit model/humans already give the corrected answer. If a substantial fraction of targets are not uniquely determined by the image+question or experts disagree, re-run the headline editing experiments on the unambiguous subset and check whether the qualitative conclusion that editing methods underperform still holds. Also verify that reported human-validation statistics (e.g., agreement, sample size) match the benchmark release.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central empirical claim—that current knowledge-based editing methods transfer poorly to medical MLLMs—depends on MedMKEB's edit tasks being valid instruments for knowledge editing. The abstract asserts that tasks are 'carefully constructed' and that 'human expert validation' is incorporated, but the only available manuscript text is encoding-corrupted, so no construction protocol, validator inclusion criteria, inter-annotator agreement, or target-selection method is visible. If counterfactual/correction and adversarial targets are not uniquely answerable from the image+question, or if some targets encode expert opinion rather than unambiguous factual corrections, then low post-edit success rates reflect task ambiguity or benchmark artifact, not editing weakness. This would undercut the general conclusion that existing knowledge-based editing approaches 'demonstrate limitations' in medicine. The 'first comprehensive' novelty claim is likewise uncheckable against prior medical/multimodal editing benchmarks from the available text. This is not an internal inconsistency, but it is a load-bearing unverified premise; settling it requires access to the benchmark construction details and, ideally, the benchmark itself.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"MedMKEB proposes a benchmark for knowledge editing in medical multimodal large language models. The abstract claims it is the first comprehensive benchmark covering reliability, generality, locality, portability, and robustness, and that extensive single and sequential editing experiments on state-of-the-art general and medical MLLMs demonstrate the limitations of existing knowledge-based editing methods in medicine. The benchmark is said to be built on a medical VQA dataset, enriched with four editing-task types (counterfactual correction, semantic generalization, knowledge transfer, adversarial robustness), and to incorporate human expert validation. The provided full text is largely encoding-corrupted, so most construction details, task definitions, experimental tables, and related-work comparisons cannot be read from the available material.","tokens_in":17306,"tokens_out":3187,"duration_ms":42727,"significance":"If the benchmark is valid and released with its construction protocol, it would address a genuine gap: there is no standard instrument for evaluating multimodal knowledge editing in medicine. The proposed evaluation axes are appropriate for deployment-critical medical settings, and the aim of human expert validation is a positive step toward trustworthy targets. The empirical finding that current textual/multimodal editing methods transfer poorly would be useful if it is shown to be attributable to editing failure rather than benchmark artifacts. However, the current manuscript does not make this case verifiably: the task-construction and validation premises are asserted rather than documented, and the abstract's central empirical claim is qualitative. The significance therefore rests on load-bearing details that are not yet visible.","major_comments":[{"comment":"The central premise of the paper is that low post-edit success rates indicate genuine limitations of editing methods. This requires that the constructed edit targets are valid instruments. The abstract asserts 'human expert validation' and 'carefully constructed editing tasks,' but the available text provides no construction protocol, no validator inclusion criteria, no sample size, and no inter-annotator agreement statistics. If counterfactual or adversarial targets are ambiguous, not uniquely answerable from the image, or encode expert opinion rather than unambiguous factual corrections, then the reported failures could be benchmark artifacts. Please provide the full task-generation protocol and validation statistics, including examples and exclusion rules.","section":"Abstract / §2 (Benchmark Construction)"},{"comment":"The abstract's main empirical claim—'extensive single editing and sequential editing experiments ... demonstrate the limitations of existing knowledge-based editing approaches'—is stated without a single number, model name, or metric. The portions of the full text that appear to contain tables are corrupted (rows show repeated 'C' placeholders and unreadable headers), so the experiments cannot be checked. Please report the model list, number of edits, per-task success rates, and the evaluation rule (e.g., exact match vs. semantic equivalence) for each of the five assessed properties, with standard errors or significance tests where appropriate.","section":"Abstract / §4 (Experiments)"},{"comment":"The four editing task types (counterfactual correction, semantic generalization, knowledge transfer, adversarial robustness) are named but not formally defined in the abstract or in any readable portion of the text. These definitions are load-bearing because they determine what counts as a successful edit and therefore whether a low score reflects an editing-method failure or an ill-posed task. Please provide formal definitions, generation rules, and at least one worked example per task type, together with the criteria used to decide that the edited target is the correct answer.","section":"§2, Task Types"},{"comment":"The abstract claims MedMKEB is 'the first comprehensive benchmark' for this setting. This claim cannot be verified from the available text because the related-work section is unreadable. Even if this is a novelty claim rather than a scientific result, it should be substantiated by an explicit comparison with existing medical knowledge-editing benchmarks and multimodal editing benchmarks, including any recently released alternatives. If any prior benchmark covers a subset of the proposed dimensions, the claim should be qualified accordingly.","section":"Related Work / Novelty Claim"}],"minor_comments":[{"comment":"The submitted text is encoding-corrupted (mojibake); most paragraphs and all tables are unreadable in the version provided. A properly encoded, machine-readable PDF is required for review. This is not a scientific criticism of the content, but it blocks verification.","section":"Whole document"},{"comment":"The visible tables appear with 'C' symbols in place of numeric entries and with unlabeled rows/columns. Please ensure all tables have clear column headers, numeric values, and a caption describing the metric and the evaluation protocol.","section":"Tables"},{"comment":"The abstract would be materially improved by including a concrete headline result, such as the average edit-success rate of the best baseline under single editing and the degradation under sequential editing. This would make the 'limitations' claim falsifiable from the abstract alone.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":"The submission in its current form is unreadable due to encoding corruption, so I could not verify the experimental tables or any formal definitions. I recommend asking the authors to resubmit a clean, machine-readable PDF with a construction-protocol appendix and validation statistics before further review. The bench-mark artifact concern raised in the stress-test is real and not addressed by the abstract; it must be resolved with concrete benchmark documentation."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the gap is real and the task taxonomy is sensible, but I can't vet the numbers because the supplied full text is mojibake. The abstract describes MedMKEB as the first comprehensive benchmark for editing medical MLLMs, with counterfactual correction, semantic generalization, knowledge transfer, and adversarial robustness tasks, plus human expert validation. That is a genuine niche: text and general multimodal editing benchmarks exist, but a medical VQA-focused one with these axes would be useful. The design choices they list are the right ones for this space.\n\nWhat's good: the paper acknowledges existing textual knowledge editing work and positions itself against it; the four task types match standard editing desiderata (reliability, generality, locality, portability, robustness); and the decision to incorporate human validation is the right instinct for medical content. If the artifact ships with the data and code, it could become a standard evaluation tool.\n\nWhere I get stuck: I can't check any of it. The full text is encoding-corrupted (the header even shows a different arXiv ID), so there are no tables, no model names, no metrics, no construction protocol, no inter-annotator agreement. The abstract's claim that 'extensive experiments demonstrate limitations' is qualitative. The stress-test worry is legitimate but not confirmed: if the counterfactual or adversarial targets aren't uniquely grounded in the images, low post-edit accuracy could be an artifact. That is a premise to test, not a demonstrated flaw. Also, 'first comprehensive' is an assertion I can't verify against prior benchmarks from the visible text.\n\nBottom line: this is a paper the community would likely want to see, but only in readable form. My verdict is not about the substance; it's about the evidence available. A serious referee should be asked to evaluate a proper PDF, not this corrupted artifact. I would not cite it yet, but I'd bring it to the reading group once a clean version exists.","headline":"A sensible medical multimodal knowledge-editing benchmark, but the full text we received is unreadable, so the empirical claims are unverifiable from this submission.","tokens_in":17854,"tokens_out":1967,"would_cite":false,"duration_ms":22654,"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":"This paper introduces MedMKEB, a benchmark for testing whether medical multimodal large language models can have their knowledge edited, and reports that current editing methods fail to transfer to medicine.","keywords":["knowledge editing","multimodal large language models","medical visual question answering","benchmark","counterfactual editing","sequential editing","reliability","robustness"],"falsifier":"Recruit independent medical experts to answer a random sample of MedMKEB counterfactual and adversarial questions from image and question alone, without applying or knowing the edit. If a substantial fraction of experts give the pre-edit answer rather than the target answer, then the benchmark's targets are ambiguous and its reported failure scores would need re-interpretation.","tokens_in":16980,"feed_emoji":"🩺","tokens_out":4319,"duration_ms":49477,"temperature":0.7,"pith_summary":"This paper introduces MedMKEB, which it claims is the first comprehensive benchmark for evaluating knowledge editing in medical multimodal large language models. The paper argues that updating outdated or incorrect medical knowledge without retraining requires five properties—reliability, generality, locality, portability, and robustness—and that no existing benchmark tests them jointly for vision-plus-text medical models. MedMKEB is built on a high-quality medical visual question-answering dataset and adds four kinds of constructed editing tasks: counterfactual correction, semantic generalization, knowledge transfer, and adversarial robustness, with human expert validation. Through single-editing and sequential-editing experiments on state-of-the-art general and medical MLLMs, the paper reports that existing knowledge-based editing approaches underperform, showing that they do not transfer well to medicine and that specialized editing strategies are needed.","feed_headline":"Current editing methods fail medical multimodal LLMs","feed_subtitle":"MedMKEB tests five editing properties across four task types; results point to specialized strategies for medicine.","key_machinery":"The load-bearing object is the benchmark itself. A MedMKEB instance couples an image, a question, an edit target, and a validation answer, so evaluation can ask not only whether the edit took, but also whether the model preserves unrelated knowledge (locality), applies the edit to new phrasings (generality), carries it to related medical cases (portability), and resists adversarial inputs (robustness), both after a single edit and after a sequence of edits. Scoring along these five axes is the mechanism that turns abstract editing desiderata into measurable failure.","core_discovery":"The paper's central claim is that MedMKEB is the first benchmark comprehensive enough to assess knowledge editing in medical multimodal large language models, and that under this benchmark current editing methods show clear limitations. MedMKEB starts from a high-quality medical visual question-answering dataset and adds editing tasks of four kinds: counterfactual correction, which replaces an outdated fact; semantic generalization, which applies the edit to paraphrases; knowledge transfer, which carries the edit to related medical cases; and adversarial robustness, which checks that edits survive tricky inputs. Every edit target is human-validated. The paper then runs single-editing and seq","pith_inferences":["The four editing task types could be reused as a training curriculum: a method trained to pass counterfactual correction and adversarial robustness might learn to separate image-derived facts from stored encyclopedic facts.","If MedMKEB becomes standard, comparing medical MLLMs against text-only medical LLMs on the same edit targets would isolate how much of the failure is caused by the image modality.","A testable extension is to measure calibration and confidence alongside accuracy: in clinical settings, an edit that changes the answer but leaves the model overconfident may be more dangerous than one that fails outright.","The benchmark may also expose a distinction between correcting genuinely outdated facts and overriding facts still visible in the image; resolving that distinction is a design question for future editions."],"forward_implications":["Knowledge-based editing methods that work in general text or multimodal settings will need rethinking before medical deployment: MedMKEB reportedly shows low reliability after single edits.","Sequential editing—updating a model many times in a row—is a realistic clinical need, and the paper reports that current methods struggle to preserve earlier edits while making new ones.","With human-validated edit targets, MedMKEB can serve as a standard evaluation instrument for future medical knowledge-editing algorithms and as a safety check before deployment.","The paper draws a direct implication that specialized editing strategies for medicine are needed rather than straightforward reuse of general-domain methods."],"supporting_citations":[],"fun_headline_variants":["Medical knowledge editing benchmark exposes model limits","New benchmark shows medical MLLMs struggle with knowledge edits","MedMKEB: first benchmark for medical multimodal editing","Edits fail in medical multimodal models, benchmark finds","Medical MLLMs can't keep knowledge fresh"],"cache_read_input_tokens":2816,"weakest_assumption_plain":"The edit targets and human-validated answers used in MedMKEB are correct and unambiguous, so a method's low score is attributable to the method rather than to flawed or unanswerable benchmark questions.","fun_headline_variants_meta":{"raw":{"variants":["Medical knowledge editing benchmark exposes model limits","New benchmark shows medical MLLMs struggle with knowledge edits","MedMKEB: first benchmark for medical multimodal editing","Edits fail in medical multimodal models, benchmark finds","Medical MLLMs can't keep knowledge fresh"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000768,"raw_usage":{"total_tokens":3236,"prompt_tokens":733,"completion_tokens":2503,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":477,"completion_tokens_details":{"reasoning_tokens":2429}},"tokens_in":477,"tokens_out":2503,"duration_ms":17465,"temperature":1.0,"reasoning_tokens":2429,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T23:33:45.277271+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Recruit independent medical experts to answer a random sample of MedMKEB counterfactual and adversarial questions from image and question alone, without applying or knowing the edit. If a substantial fraction of experts give the pre-edit answer rather than the target answer, then the benchmark's targets are ambiguous and its reported failure scores would need re-interpretation.","supporting_citations":[],"review_version":1}