{"id":"8af06bb5-a266-4351-af4f-487510c134cf","arxiv_id":"2607.18673","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"Ten leading VLMs mostly fail to report removed essential object parts as missing, and simulated detector evidence, image tools, longer reasoning, and an easier fine-tune barely improve accuracy.","lead":"This paper introduces a 118-image benchmark of objects with an essential part removed and tests ten vision-language models on whether they can report the part is missing. Across models and mitigation attempts, the models usually insist the part is still present.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Simulated 'perfect detector' injects only weak, low-confidence absence evidence; the claim that tool evidence cannot correct VLMs is not established.","rationale":"The reader's CONDITIONAL verdict is appropriate. The direct-query finding—that ten leading VLMs frequently report removed essential parts as present—is supported by the design and by the original-image baselines. However, the abstract's stronger claim that failures persist 'even when external tool evidence explicitly contradicts the model's visual perception' depends on the tool-conflict experiment. That experiment uses a simulated detector whose output is low-confidence 'undetected'—which is not equivalent to a perfect detector or to explicit evidence of absence. A model weighing this weak evidence against a clear visual impression is not necessarily displaying deeply embedded bias. The paper's own Figure 1 shows the model explicitly reasoning that detection tools can fail, which is a sensible interpretation of 'confidence 0.03'. Thus the tool-conflict conclusion is not established, but this does not overturn the core existence claim. The same concern was correctly identified by the reader as the weakest assumption; my check would settle whether it actually lands.","tokens_in":10352,"tokens_out":4575,"duration_ms":46861,"concrete_test":"On the same 118 images and models, replace the injected JSON with an explicit absence statement from a simulated perfect detector, e.g., `{\"object\": \"<target>\", \"present\": false, \"confidence\": 0.99}`, keeping the pre-answer protocol identical. If NV accuracy moves above 75% for most models, the reported 'tool evidence cannot correct' result is an artifact of low-confidence 'undetected' phrasing; if NV rates remain within a few points of Table 3's pre-answer column, the concern is resolved.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The most load-bearing link in the central claim is the inference from Tables 3–4 that external tool evidence 'explicitly contradicts' the model's perception yet fails to correct it. In §2.2 the simulated detector is described as 'representing a perfect detector,' but the injected message is `{\"detection confidence\": 0.03, \"result\": \"(<0.1) undetected\"}`. A real detection result of 'not detected above 0.1' does not assert that the object is absent; it asserts only that no high-confidence detection was produced. Because the confidence is explicitly low, a model that says 'detection tools might not always perfectly recognize objects' (Figure 1) is responding rationally to weak evidence, not overriding strong contradictory evidence. The pre-answer injection is therefore not an upper bound for tool-augmented systems: a tool that returned 'present: false, confidence 0.99' is a categorically different intervention. This leaves the direct-query finding intact but strips the tool-conflict and 'cannot be corrected' conclusions of support.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"MissingBench-Verified evaluates whether state-of-the-art VLMs recognize when an essential object part is absent. The authors construct 118 images (real photos and internet images) from which a human-specified element is removed via an image editor and human-verified. They query ten VLMs (proprietary and open-source) under several settings: direct query, pre-answer injection of a simulated 'perfect detector' result, post-answer injection, and 'thinking with images' tool use; they also vary reasoning effort for GPT-5.2 and compare against OWLv2/YOLOE. The main reported findings are that all ten models answer 'visible' at high rates on edited images; simulated detector injections improve some models but leave most below 75% accuracy; image tools give negligible gains; and object detectors exhibit poor sensitivity. The paper claims this demonstrates a fundamental limitation that cannot be corrected by current prompting or post-hoc correction techniques.","tokens_in":10573,"tokens_out":4704,"duration_ms":45169,"significance":"If the core existence result holds, the benchmark is a valuable stress test: it targets a realistic inspection scenario, uses real images rather than synthetic degradation, includes human verification, compares ten frontier models, and anchors difficulty against RH-Bench. The finding that leading VLMs frequently report a missing essential part as present, even after cropping or enhancement, is important for deployment in monitoring and inspection tasks. However, the broader conclusions—that external tool evidence 'explicitly contradicts' model perception and that the failure mode is uncorrectable—depend on an unvalidated weak-evidence simulation and are not supported as currently stated. The work is creditworthy for its dataset construction and multi-model evaluation, but the claims need to be narrowed and statistical support added.","major_comments":[{"comment":"The simulated detector is described as 'representing a perfect detector,' but the injected message is {'detection confidence': 0.03, 'result': '(<0.1) undetected'}. A 'not detected above 0.1' result does not assert absence; it asserts only that no confident detection was produced. A model that answers 'clearly visible' while acknowledging the low-confidence tool result (Figure 1) is not overriding contradictory evidence; it is discounting weak evidence. Consequently the phrases 'external tool evidence explicitly contradicts the model's visual perception' and the conclusion that tool-assisted verification cannot correct this failure are not supported. At minimum, add a condition with a high-confidence absence message (e.g., confidence 0.99, result: absent) or real detector output; the current design is not an upper bound for tool-augmented systems.","section":"§2.2, Tables 3–4"},{"comment":"The dataset has 118 edited images and all headline rates are reported as percentages without confidence intervals, error bars, or significance tests, while the abstract and conclusion call the failure rates 'significant.' With 118 trials, a difference of about 4 percentage points (e.g., ±4.3 in Table 3) is within sampling noise; the text acknowledges some decreases 'likely reflect noise' but still draws strong aggregate claims. Report Wilson intervals or exact tests, and state the human-verification baseline for the edited images (did a human correctly identify the part as absent in every case? at what agreement?). Without this, the 'consistent and significant' claim is not quantitatively supported.","section":"§2.1, §3.1"},{"comment":"The conclusion that 'fine-tuning on an easier dataset' provides negligible improvement and that the failure 'cannot be addressed through current prompting or post-hoc correction techniques' is too strong. The only fine-tuned model evaluated is NegAwareQwen from VSF, which was trained for a different distribution of missing objects; no model in the paper was fine-tuned on MissingBench-Verified or on a comparable hard-missing-part dataset. A null result on one off-the-shelf fine-tuned model does not license a claim about fine-tuning as a family. Please either remove the fine-tuning claim or test a model actually trained for this task.","section":"§3.1, §5"},{"comment":"The framing that a non-significant p-value 'strengthens our point' is statistically invalid: failing to reject the null is not evidence for the null, especially when the point estimate shows a 7.7-point drop between Low and xHigh for the 'not visible' rate. The reasoning-effort result should be reported as inconclusive, or accompanied by an equivalence test or power analysis, rather than as confirmation that the hallucination is 'embedded in the system.'","section":"§3.1, Table 7"}],"minor_comments":[{"comment":"There is a placeholder 'Section??' for the detailed failure analysis of why image processing tools did not help; this section is missing and should be provided or the reference removed.","section":"§3.1"},{"comment":"Typographical issues: 'Owlv2' should be 'OWLv2,' 'thershold' should be 'threshold,' 'Furture Work' should be 'Future Work.'","section":"Throughout"},{"comment":"The notation t0/t1 and 'Original Acc'/'Edited Acc' is confusing. The caption should define each threshold and clarify that the default threshold differs between OWLv2 (0.3) and YOLOE (0.25).","section":"Table 6"},{"comment":"The dash markers in the 'Think with Images' column could be clearer; consider a separate column or footnote explaining that only three models were tested in that setting.","section":"Table 1"}],"recommendation":"major_revision","confidential_remarks":"The direct-query phenomenon is plausible and worth publishing, but the 'cannot be corrected' claim is not established by the current weak-evidence simulation. I suggest requiring the authors to either strengthen the tool-evidence manipulation or explicitly reframe the contribution as a benchmark/existence study rather than a correction-impossibility result. The manuscript also leans heavily on the authors' own VSF/NegAwareQwen work; it is used as a baseline, which is acceptable, but the framing in Section 1 borders on promotional."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague, quick read of MissingBench-Verified. The core finding is real and worth knowing: ten strong VLMs, on human-verified edits where an essential part is removed, answer 'visible' at high rates. Direct-query accuracy topped out at 75% and Claude at 44%. That is a solid existence proof for a practically relevant failure mode, and the RH-Bench comparison gives an external anchor showing this scenario is harder. Credit where due: the image curation is careful, they flag their own limitations, and the failure case in Figure 4 (model attributes missing part to cropping) is a nice illustration.\n\nThe soft spots are real but not fatal. The dataset is 118 images, and headline tables have no confidence intervals or significance tests. There's no human baseline, so we don't know how obvious these absences are to people — that matters for judging whether 75% is 'fails' or 'reasonable under ambiguity.' No code or data shipped, which limits verification. These are fixable.\n\nThe bigger conceptual issue is the 'perfect detector' injection. The message is `{\"detection confidence\": 0.03, \"result\": \"(<0.1) undetected\"}`. That is not strong evidence of absence; it's weak evidence that nothing was found above a low threshold. A model that says 'the detector might miss things' is not overriding a contradiction — it's sensibly discounting a low-confidence signal. So the pre/post-answer results show that this particular textual nudge doesn't fix the models, but they don't establish that external tool evidence can't. The conclusion 'cannot be addressed through current prompting or post-hoc correction techniques' is broader than the evidence. If they had injected a strong, unambiguous 'object absent, confidence 0.99' signal, or an actual detector output with visual grounding, then the claim would be supported. As is, the direct-query finding stands; the tool-conflict interpretation should be softened.\n\nThe reasoning-effort experiment on GPT-5.2 is fine as a null result, but it's one model, and the p-value they report (0.174) doesn't support 'significant' anything — they correctly call it not significant. Fine.\n\nWho is this for? People working on object hallucination, VLM evaluation, and inspection/monitoring applications. It deserves a serious referee — the benchmark is a useful stress test and the existence claim is important. With data release, error bars, a human baseline, and a stronger tool-evidence condition, it could be a solid contribution.","headline":"Useful existence proof that SOTA VLMs miss removed essential parts; the tool-evidence 'cannot be corrected' claim overreaches because the simulated detector message is weak evidence, not a strong contradiction.","tokens_in":11080,"tokens_out":1670,"would_cite":false,"duration_ms":16814,"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":"Frontier vision-language models refuse to believe a perfect detector when an essential object part is missing.","keywords":["missing object parts","visual hallucination","vision-language models","MissingBench-Verified","existence bias","tool-assisted verification","parametric knowledge","multimodal robustness"],"falsifier":"Run MissingBench-Verified through a VLM-agent loop in which an actual open-vocabulary detector returns a marked-up image (no bounding box, low confidence) as a tool result before the model answers, and check whether 'not visible' accuracy rises above 75%. If it does, the paper's strongest claim is falsified; if it stays below, the claim is supported.","tokens_in":10230,"feed_emoji":"👁️","tokens_out":6805,"duration_ms":62381,"temperature":0.7,"pith_summary":"This paper argues that leading vision-language models systematically fail to notice when an essential part of an object has been removed—even when an external tool explicitly tells them the part is absent. It introduces a small human-verified benchmark, MissingBench-Verified, of 118 edited real images where an essential component is gone. Across ten frontier models, none identified the absence more than 75% of the time, and several said the removed part was 'clearly visible' more often than they said it was missing. Injecting a simulated perfect detection result, providing image-editing tools, increasing reasoning effort, or fine-tuning on easier hallucination data all produced negligible improvement. The paper concludes that the failure is rooted in internal prior knowledge and visual processing, requiring architectural or training-level changes rather than prompting or post-hoc correction.","feed_headline":"Ten leading AI vision models fail to detect missing object parts","feed_subtitle":"Even a perfect detector's 'not found' output cannot override their false belief—prompting, tools, and longer reasoning all fail.","key_machinery":"MissingBench-Verified—a human-verified set of 118 real photos in which an essential component has been digitally or manually removed—is the test bed. The evaluation protocol asks each model a three-choice visibility question and forces it to produce reasoning in JSON. The key mechanism that isolates the failure is the simulated perfect detector: a text line saying 'undetected, low confidence' is injected into the context either before or after the model's first answer. This gives the model a maximally strong external signal that the object is absent, so any persistent 'visible' answer demonstrates that internal priors override external evidence. A third setting lets the model call image-proc","core_discovery":"The central discovery is that current frontier VLMs exhibit a strong existence bias for essential object parts: after a part like an airplane's engine or a phone's home button is edited out, the models often answer that it is 'clearly visible,' and they keep that answer even when a simulated perfect detector reports 'undetected, low confidence' in the context. Across ten leading models, no model identified the absence more than 75% of the time, and the worst model was correct only 44% of the time. Pre-answer and post-answer tool injection, image cropping and contrast tools, longer reasoning, and fine-tuning on an easier hallucination dataset all failed to raise performance meaningfully. The","pith_inferences":["The tool-conflict result is an upper bound on real tool-augmented systems: the model saw the tool message as text, not as a spatially grounded detection visualization. A real detector returning a marked-up image may carry more perceptual weight, so the conclusion that 'tools cannot help' should be read as 'textual tool reports cannot help.'","The benchmark's design implies a testable gradient: the more an object part is implied by world knowledge (airplane engines, phone buttons), the stronger the existence bias. A natural extension is to vary the 'essentialness' of the removed part and measure whether the failure rate tracks it.","If this bias is as deep as reported, it suggests that multimodal pretraining rewards statistical co-occurrence of parts and objects so strongly that it outweighs the visual signal. That points to a training-level fix—such as augmenting data with missing-part examples or counterfactual supervision—rather than inference-time intervention."],"forward_implications":["Current VLMs are not reliable for automated visual inspection or monitoring when essential components may be absent; every tested model failed on a substantial share of cases.","Tool-augmented pipelines inherit the model's bias: even a simulated perfect detector leaves most models below 75% accuracy on detecting absence.","More reasoning time does not help; accuracy stayed flat or slightly decreased as reasoning effort increased from none to maximum.","Fine-tuning on related, easier hallucination data does not transfer to this harder setting.","Architectural or training-level changes are needed so models can override internal expectations when visual evidence contradicts them."],"fun_headline_variants":["AI vision models fail to spot missing parts even with tool proof","VLMs can't override existence bias for missing object parts","Even perfect detectors can't convince VLMs a part is missing","Tool-assisted inspection fails to fix VLM missing-part blindness","Vision-language models consistently miss missing parts, top 75% accuracy"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The conclusion that external tool evidence cannot correct the hallucination rests on treating a text-injected simulated perfect detector as equivalent to a real detector's visually grounded output; if models trust real tool outputs more than text messages, the conclusion may not hold.","fun_headline_variants_meta":{"raw":{"variants":["AI vision models fail to spot missing parts even with tool proof","VLMs can't override existence bias for missing object parts","Even perfect detectors can't convince VLMs a part is missing","Tool-assisted inspection fails to fix VLM missing-part blindness","Vision-language models consistently miss missing parts, top 75% accuracy"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000352,"raw_usage":{"total_tokens":1753,"prompt_tokens":739,"completion_tokens":1014,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":483,"completion_tokens_details":{"reasoning_tokens":929}},"tokens_in":483,"tokens_out":1014,"duration_ms":8982,"temperature":1.0,"reasoning_tokens":929,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-01T14:40:37.097870+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run MissingBench-Verified through a VLM-agent loop in which an actual open-vocabulary detector returns a marked-up image (no bounding box, low confidence) as a tool result before the model answers, and check whether 'not visible' accuracy rises above 75%. If it does, the paper's strongest claim is falsified; if it stays below, the claim is supported.","supporting_citations":[],"review_version":1}