{"id":"391cf6d5-528e-4ef4-89dc-57d7192ad9e9","arxiv_id":"2508.08237","paper_version":5,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"VGGSounder is a corrected, multi-label audio-visual test set with modality annotations that exposes modality interference in foundation models.","lead":"This paper introduces VGGSounder, a re-annotated multi-label audio-visual test set designed to fix flaws in the VGGSound benchmark. It also proposes a modality confusion metric that reveals when adding a second input modality hurts a model's performance.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The submitted full text is a different paper (ODYSSEY, cs.RO), so VGGSounder's benchmark, annotation methodology, and modality confusion metric cannot be examined; the central claim is unverifiable from this submission.","rationale":"The reader's verdict is UNVERDICTED, which matches what the evidence supports. The submitted full text is visibly a different paper: its title is 'ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks', its arXiv ID is 2508.08240, and its content concerns quadruped robot manipulation, not audio-visual evaluation. The VGGSounder abstract promises a re-annotated multi-label test set, detailed modality annotations, and a modality confusion metric, but none of these are defined or analyzed in the received text. No annotation statistics, no label-construction procedure, no metric formula, and no experiments are available for inspection. Therefore, the weakest assumption is not merely that the annotations may be biased; it is that there is no annotation methodology to scrutinize at all. I partially agree with the reader's identification of annotation correctness as a load-bearing assumption, but I would locate the primary concern earlier: the supporting artifact is missing, making every downstream assumption untestable. This does not move the verdict because the reader already assigned UNVERDICTED; it confirms that assignment. A change to REJECT would overstate the case, since the abstract alone does not disprove the claims—it simply cannot establish them. If the correct VGGSounder full text is later supplied, the annotation methodology and metric definitions should then be reviewed in detail.","tokens_in":4350,"tokens_out":2180,"duration_ms":23818,"concrete_test":"Re-fetch the arXiv record for 2508.08237 from the arXiv API and compare the title, abstract, and first page against the VGGSounder title and abstract. If the full text is the ODYSSEY robotics paper, the submission cannot be evaluated as VGGSounder; the verdict should remain UNVERDICTED until the correct PDF or dataset artifact is supplied.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim—that VGGSounder is a comprehensively re-annotated multi-label test set with detailed modality annotations and a modality confusion metric—rests entirely on the abstract. The full text of this submission is 'ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks' (arXiv:2508.08240), which does not contain VGGSounder's methods, annotation protocol, label-construction details, metric definitions, or experiments. Because the artifact that would support the claim is absent, any verdict about correctness would be speculative. This is a submission-level integrity issue, not an argument-level flaw: if the PDF is genuinely the wrong paper, the abstract alone cannot support acceptance; if the correct PDF was omitted, it must be provided before review. The reader's weakest assumption about annotation correctness is real but secondary; the first-order blocker is the absence of the content needed to test it.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The submission, listed as arXiv:2508.08237 (cs.MM), presents an abstract claiming the introduction of VGGSounder, a comprehensively re-annotated, multi-label test set extending VGGSound for the evaluation of audio-visual foundation models, including detailed modality annotations and a new 'modality confusion metric.' The abstract further asserts that an analysis identifies limitations in VGGSound—incomplete labelling, partially overlapping classes, and misaligned modalities—that distort evaluations. However, the full text supplied with the submission is a completely different paper: 'ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks' (arXiv:2508.08240, cs.RO), which concerns legged mobile manipulation and contains no mention of VGGSound, audio-visual classification, or the proposed metric. No methodology, annotation protocol, experimental results, or metric definitions for VGGSounder appear anywhere in the submission. The central claims are therefore unverifiable from the provided material.","tokens_in":4666,"tokens_out":4363,"duration_ms":44103,"significance":"If substantiated, the proposed contribution would be of real value to the audio-visual learning community: VGGSound is a widely used evaluation resource, and documented label problems together with a metric for modality interference would meaningfully strengthen benchmarking practice. Credit is due for stating a concrete problem and for recognizing that VGGSound's label noise and modality alignment issues can distort audio-visual evaluations. However, because the submission contains only the abstract and an unrelated robotics paper, no methodological contribution, reproducible artifact, or quantitative evidence can be assessed; the significance of the work cannot be established beyond the claims made in the abstract.","major_comments":[{"comment":"The body of the submission is arXiv:2508.08240 ('ODYSSEY'), a quadruped-robot manipulation paper with no connection to VGGSound, audio-visual evaluation, or the proposed VGGSounder benchmark. This is the single load-bearing problem: the abstract's central claims—a limitations analysis of VGGSound, a re-annotation protocol, modality annotations, and a modality confusion metric—have no supporting content in the submission, so the soundness, statistical validity, and reproducibility of the proposed benchmark cannot be reviewed.","section":"Full text (entire submission)"},{"comment":"The claimed analysis of VGGSound's limitations (incomplete labelling, partially overlapping classes, misaligned modalities) is asserted without any supporting data, examples, or described method; the manuscript must show how these limitations were identified and quantified before the motivation for VGGSounder can be accepted.","section":"Abstract"},{"comment":"The re-annotation methodology is not described anywhere, so the reader cannot rule out circularity: if the same foundation models under evaluation were used to generate the new labels or modality annotations, the benchmark would not fix the distortions it claims to address. The full manuscript must specify the annotation procedure, label schema, inter-annotator agreement, and independence from the models being evaluated.","section":"Abstract"}],"minor_comments":[{"comment":"The phrase 'for evaluation audio-visual classification' contains a grammatical error and should read 'for evaluating audio-visual classification.'","section":"Abstract"},{"comment":"The submission should cite the original VGGSound dataset paper and provide pointers to the released annotations, code, and evaluation scripts, as is standard for benchmark papers.","section":"Abstract"},{"comment":"The entire supplied PDF content (Figures 1, 9–11 and Table 8) pertains to the ODYSSEY robotics framework; this content should be replaced with the VGGSounder manuscript so that sections, equations, and tables can actually be located during review.","section":"Full text"}],"recommendation":"major_revision","confidential_remarks":"To the editor: this reads like an incorrect PDF was uploaded at submission time. The provided full text is an unrelated cs.RO paper, so the manuscript cannot be reviewed in its current form. Before further processing, I recommend requesting that the authors confirm the correct full text matching the VGGSounder abstract and explicitly describe how the re-annotations were produced, including whether any evaluated foundation models were involved. The novelty claim also depends on VGGSounder's label quality relative to prior fixes of VGGSound, so the authors should compare against existing re-annotation or cleaning efforts."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"You asked me to look at 2508.08237. The submission is not reviewable: the full text is ODYSSEY, a quadruped robotics paper (2508.08240), not the VGGSounder audio-visual evaluation paper described in the title and abstract. So the central claims are unverifiable from what's submitted.\n\nWhat the abstract proposes is, on its face, reasonable. VGGSound is known to be noisy; a re-annotated multi-label test set is genuinely useful for the audio-visual foundation model community. The modality confusion metric is a sensible idea — measuring whether adding a modality hurts performance could expose modality interference. If the re-annotation is done carefully, this could be a solid contribution. But \"comprehensively re-annotated\" is a strong phrase; the abstract gives no methodology, no annotation protocol, no agreement statistics, no description of how labels were constructed. The most important unknown is whether the same foundation models being evaluated were used to produce the new labels. If so, the benchmark could be circular.\n\nThe reader's scores reflect that: soundness 2/10, confidence low. I think that's fair. There is no argument-level flaw to analyze because there is no argument beyond the abstract. The first-order problem is submission integrity, not the paper's content. Maybe the authors pasted the wrong PDF; it happens. But as it stands, an editor cannot send this to referees — the referees would be reviewing ODYSSEY.\n\nMy recommendation: desk reject (or return) this submission and ask the authors to submit the correct PDF. If the actual VGGSounder paper is then provided, it deserves serious peer review, because the question it addresses is real and the proposed tool is potentially valuable. But we can't judge the math, the data, or the citation pattern from this artifact.\n\nIn short: not a reviewable paper, but not necessarily a bad paper. Resubmit with the right file.","headline":"The submitted full text is a different paper (ODYSSEY, a quadruped robotics paper), so VGGSounder's actual benchmark, annotation methodology, and metric cannot be evaluated from this artifact.","tokens_in":5031,"tokens_out":2003,"would_cite":false,"duration_ms":20454,"reading_group":"no","serious_thinker":"unclear","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper argues that VGGSound misleads audio-visual evaluation and offers VGGSounder, a re-annotated multi-label benchmark, plus a metric that measures modality interference.","keywords":["audio-visual foundation models","VGGSound","benchmark evaluation","multi-label annotation","modality confusion","modality annotations","multi-modal understanding","audio-visual classification"],"falsifier":"An independent annotation study that re-labels a random sample of VGGSounder clips without access to the original VGGSound labels, then measures agreement with VGGSounder's labels, would settle whether the ground truth is reliable. A second check: if the modality confusion metric is meaningful, models ought to show a similar ordering of audio-only, video-only, and audio-visual accuracy when evaluated on a small human-verified subset where the modality annotations are checked clip by clip.","tokens_in":4168,"feed_emoji":"🎥","tokens_out":3765,"duration_ms":36495,"temperature":0.7,"pith_summary":"This paper argues that the widely used VGGSound benchmark misrepresents audio-visual models because its labels are incomplete, its classes partly overlap, and its audio and visual tracks are often misaligned. To fix this, the authors introduce VGGSounder, a re-annotated multi-label test set built by extending VGGSound, with separate annotations for what is audible and what is visible in each clip. They also propose a modality confusion metric that measures how a model's performance changes when one input modality is added to another. The aim is a benchmark on which performance differences reflect genuine audio-visual understanding rather than label noise. If the benchmark is sound, it would give the field a more trustworthy way to compare foundation models and would expose a concrete failure mode: adding a modality can hurt, not help, a model's predictions.","feed_headline":"New benchmark exposes when audio-video fusion hurts models","feed_subtitle":"VGGSounder re-labels VGGSound to separate audio and visual truth, revealing modality interference in foundation models.","key_machinery":"The load-bearing objects are VGGSounder, a re-annotated multi-label extension of VGGSound, and the modality confusion metric. VGGSounder supplies per-clip ground truth that separates auditory labels from visual labels, so a model's audio-only, video-only, and audio-visual behavior can be compared against the same underlying content. The modality confusion metric measures the performance delta when another input modality is added; a negative delta signals that the extra modality actively misleads the model. Together they turn the benchmark from a single scalar accuracy number into a diagnostic for which modality carries the signal and which one interferes.","core_discovery":"The paper's central claim is that VGGSound evaluation scores are distorted by three fixable defects: incomplete labels, partially overlapping classes, and modalities that do not actually line up in the recordings. VGGSounder addresses each defect with a re-annotated, multi-label test set that extends VGGSound and records modality-specific ground truth, so a clip can be labeled separately for its sound content and its visual content. Using this test set, the paper reports that existing audio-visual models often perform worse when a second modality is supplied, a phenomenon it quantifies with the modality confusion metric. The discovery, stated on the paper's own terms, is that a cleaner benchmark changes what evaluation reveals: apparent audio-visual competence partly dissolves, and modality interference becomes visible.","pith_inferences":["A natural next step, not pursued in the abstract, is to release the annotation instructions and inter-annotator agreement; without those, the benchmark's objectivity cannot be audited.","The modality confusion metric could be extended beyond classification to retrieval or generation, where 'adding a modality' might mean conditioning a text-to-audio model on video frames; the same negative-delta diagnostic would apply.","If VGGSounder's labels are accepted, it would imply that part of the apparent progress in audio-visual classification over the last few years is an artifact of benchmark noise, and that future comparisons should control for modality alignment.","The benchmark could be used to test whether contrastive audio-visual pretraining actually learns modality-specific features, by measuring whether fusion improves or degrades per-modality performance."],"forward_implications":["VGGSound-based leaderboards that rank audio-visual models by top-1 accuracy may need revisiting, because a portion of the score differences reflects label and alignment artifacts rather than model competence.","Model developers can use the modality confusion metric to detect when fusing audio and video degrades performance, and can target fusion layers or training data to fix it.","The re-annotated multi-label format lets evaluators report separate audio and visual accuracies, so applications that care about one modality get a more precise comparison.","A model that looks strong on VGGSound may appear weaker on VGGSounder; conversely, models trained with explicit modality supervision may be rewarded more fairly."],"supporting_citations":[],"fun_headline_variants":["Better benchmark reveals audio-video fusion pitfalls","Re-annotated VGGSound exposes modality interference","When adding audio or video hurts model performance","New test set shows audio-video models conflict","VGGSounder: cleaner evaluation, clearer model limits"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that VGGSounder's re-annotated labels and modality annotations are correct ground truth; if the annotation process is biased, subjective, or depends on the very foundation models under evaluation, the new benchmark would inherit the distortions it claims to fix.","fun_headline_variants_meta":{"raw":{"variants":["Better benchmark reveals audio-video fusion pitfalls","Re-annotated VGGSound exposes modality interference","When adding audio or video hurts model performance","New test set shows audio-video models conflict","VGGSounder: cleaner evaluation, clearer model limits"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000148,"raw_usage":{"total_tokens":1130,"prompt_tokens":824,"completion_tokens":306,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":440,"completion_tokens_details":{"reasoning_tokens":234}},"tokens_in":440,"tokens_out":306,"duration_ms":3068,"temperature":1.0,"reasoning_tokens":234,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T17:34:55.731584+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"An independent annotation study that re-labels a random sample of VGGSounder clips without access to the original VGGSound labels, then measures agreement with VGGSounder's labels, would settle whether the ground truth is reliable. A second check: if the modality confusion metric is meaningful, models ought to show a similar ordering of audio-only, video-only, and audio-visual accuracy when evaluated on a small human-verified subset where the modality annotations are checked clip by clip.","supporting_citations":[],"review_version":1}