{"id":"8bfaa1f1-6d7a-450f-91d4-7b845d2a9def","arxiv_id":"2508.02122","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"The paper claims to be the first review of signal-processing algorithms for radar-based cardiac monitoring, with a new taxonomy and public dataset listings.","lead":"This preprint is supposed to review radar-based cardiac feature extraction, but the supplied full text is a different paper about sparse attention in language models. Only the abstract can be used to assess the claimed review, and the mismatch itself is flagged.","discovery_kind":"review","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Submission mismatch: the supplied full text is a sparse-attention LLM paper, not the claimed radar cardiac-feature review, so the first-review claim cannot be assessed.","rationale":"The reader's weakest-assumption analysis identified that the supplied full text is a different paper and that the radar review itself is unavailable. My read agrees: the central claim cannot be evaluated because the evidence required for evaluation is missing. The concern is not a scientific flaw in the sparse-attention manuscript, and it is not an ad hominem observation; it is a completeness condition for review. If the submission is a metadata or upload error, correction would make the claim assessable; as submitted, UNVERDICTED is the appropriate state. No adjustment to the reader's verdict is needed because the verdict already reflects the unavailability of the manuscript. I would add only that the mismatch is directly evidenced by the in-text arXiv identifier (2508.02124v6) differing from the submission identifier (2508.02122), which strengthens the case that the body was independently submitted or ingested. The concrete next step is the straightforward retrieval-and-compare check, followed by a literature check only if the correct review text is obtained.","tokens_in":23372,"tokens_out":2030,"duration_ms":27194,"concrete_test":"Download the source package and compiled PDF for arXiv:2508.02122 directly from arXiv and verify that the title, abstract, and full text are mutually consistent. If the body still begins with 'Trainable Dynamic Mask Sparse Attention' and contains no radar-related sections, the claimed radar review is unavailable and the first-review claim remains unverifiable. If a corrected manuscript is supplied, then perform a literature search (e.g., Scopus or Google Scholar) for prior algorithm-focused reviews of radar-based cardiac feature extraction to test the 'first' claim on its merits.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract's central claim is that this is the first review paper elaborating algorithms for extracting cardiac features from radar signals. The only supplied evidence for that claim is the full text, but the full text is an unrelated manuscript: 'Trainable Dynamic Mask Sparse Attention' (arXiv:2508.02124v6). It contains no radar signal processing, no cardiac feature extraction, no taxonomy of such algorithms, and none of the promised public radar datasets. The mismatch is visible in the manuscript header itself, which carries a different arXiv identifier. This is a structural completeness failure: the artifact under review does not contain the manuscript it claims to be. The load-bearing assumption is therefore not merely that the literature search was comprehensive; it is that the reviewed text corresponds to the claimed review at all. Without that correspondence, the first-review claim, the proposed taxonomy, and the dataset survey are all unverifiable. This is not a verdict on the merits of either paper; it is a statement that the claimed review is unavailable in the current submission.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The submission is registered as arXiv:2508.02122 (eess.SP), with an abstract announcing a first review paper on algorithms for contactless cardiac feature extraction from radar signals, including a new algorithm taxonomy, a survey of public radar datasets for cardiac feature extraction, and a discussion of open challenges. The full text supplied for review is, however, an unrelated manuscript titled 'Trainable Dynamic Mask Sparse Attention' (arXiv:2508.02124v6, cs.AI), which addresses sparse attention mechanisms for large language models. The supplied full text contains no radar signal processing, no cardiac feature extraction algorithms, no taxonomy of such algorithms, and no list of radar cardiac datasets.","tokens_in":23545,"tokens_out":1332,"duration_ms":18027,"significance":"If the claimed radar cardiac-feature review existed as described, it could be a useful entry point for researchers in contactless cardiac monitoring: the proposed taxonomy would organize algorithm-level choices, the dataset survey would support reproducible benchmarking, and the challenge list would map open problems. None of these contributions can be assessed from the submitted artifact, because the text under review is a completely different paper. The open-source kernel code and experimental results in the supplied full text are strengths of that other paper, but they provide no evidence bearing on the radar-review claims and cannot substitute for the missing survey content.","major_comments":[{"comment":"The submitted full text is not the manuscript advertised by the title and abstract. The header carries arXiv identifier 2508.02124v6 and the title 'Trainable Dynamic Mask Sparse Attention', whereas the claimed submission is arXiv:2508.02122, 'An Overview of Algorithms for Contactless Cardiac Feature Extraction from Radar Signals'. This is a load-bearing mismatch: the text under review contains no radar signal processing, no cardiac feature extraction, no algorithm taxonomy, and no cardiac radar dataset survey, so none of the abstract's central assertions can be verified from the submitted material.","section":"Manuscript header and full text"},{"comment":"The abstract's claim that 'to the best of the author knowledge, this is the first review paper' cannot be evaluated because the review itself is absent. Even if the literature search were comprehensive, the claim of firstness requires a defined search strategy, inclusion criteria, and a comparison against prior surveys; the submitted text provides none of these, and for the same reason the proposed taxonomy and dataset tables promised in the abstract are not present.","section":"Abstract, first-review claim"},{"comment":"The paper promises 'pros and cons evaluated in detail' for cardiac feature extraction algorithms and 'public datasets containing the received radar signal and ground-truth cardiac feature signal' with 'detailed configurations'. No such evaluations or dataset tables appear anywhere in the supplied full text. The absence is structural rather than local: the supplied text's sections, equations, experiments, and references all concern sparse attention in transformers, making it impossible to fix the review content by minor revision.","section":"Entire manuscript (claimed review content)"}],"minor_comments":[{"comment":"The title and abstract describe a radar cardiac feature extraction review, while the body is a sparse-attention methods paper; the inconsistency is visible already in the arXiv identifier on the first page, which does not match the claimed submission number.","section":"Title/abstract vs. body"},{"comment":"The abstract contains phrasing such as 'to the best of the author knowledge' and 'can be served as a guide'; these are presentation issues that would need correction in any resubmission of the actual review.","section":"Abstract wording"},{"comment":"The abstract does not mention the search strategy, inclusion criteria, or period covered by the literature review; a survey paper should state these explicitly, but this point is secondary to the identity mismatch documented above.","section":"Survey methodology"}],"recommendation":"reject","confidential_remarks":"This appears to be a submission error: the uploaded full text is an unrelated cs.AI manuscript rather than the claimed eess.SP radar review. The editor may wish to verify the file attachment before further processing, since the present artifact cannot serve as the basis for any assessment of the claimed radar cardiac feature extraction review."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The submission is not the paper it claims to be. The full text is \"Trainable Dynamic Mask Sparse Attention\" (arXiv:2508.02124v6), a completely unrelated LLM methods paper. The header carries a different arXiv ID than the 2508.02122 submission. So the abstract's central claims—first algorithm-focused review, new taxonomy, public radar dataset tables—cannot be checked at all. This should be bounced back to the authors for an administrative correction, not sent to peer review in its current form.\n\nWhat the abstract promises is plausible and potentially useful. A survey of cardiac feature extraction algorithms from radar, organized by a taxonomy with dataset configurations and future directions, would be a handy reference for the radar sensing and biomedical signal processing communities. If the actual manuscript delivers what the abstract describes, it could deserve a serious referee.\n\nThere are also smaller soft spots in the abstract itself. The \"first review\" claim needs a documented search strategy and evidence that existing surveys don't already cover the same algorithms. The phrase \"high accuracy and robustness at the same time\" is loose and undefined. The abstract does not state inclusion criteria for either algorithms or datasets. These are fixable in review, but they would need attention.\n\nThe supplied full text, taken on its own, looks like a serious systems paper. It proposes a trainable dynamic-mask sparse attention mechanism, ships a CUDA kernel with open-source code, and reports scaling-law, associative recall, and needle-in-a-haystack experiments. But it is a different paper with a different author list and a different arXiv identifier. It should be submitted separately under its own ID.\n\nBottom line: the current artifact is un-reviewable. Return it for correction, then evaluate the actual review. If the corrected text matches the abstract in substance, it deserves a serious referee.","headline":"Submission mismatch: the supplied PDF is a sparse-attention LLM paper, not the claimed radar cardiac-feature review, so the review's central claims are unverifiable.","tokens_in":24052,"tokens_out":2672,"would_cite":false,"duration_ms":32800,"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":"This review claims to be the first dedicated to signal-processing algorithms for extracting cardiac features from radar, supported by a new taxonomy and a public-dataset guide.","keywords":["radar-based cardiac monitoring","contactless vital-sign monitoring","cardiac feature extraction","radar signal processing","algorithm taxonomy","public radar datasets","smart home health monitoring"],"falsifier":"Search scholarly databases for a review article published before this paper's submission whose stated focus is algorithms for extracting cardiac features from radar signals; finding one would falsify the first-review claim even if the rest of the review remains useful.","tokens_in":23200,"feed_emoji":"📡","tokens_out":6737,"duration_ms":79167,"temperature":0.7,"pith_summary":"This review paper claims to be the first survey devoted to the signal-processing algorithms that turn radar returns into cardiac features such as heartbeat timing, rather than to the radar hardware itself. It proposes a taxonomy that sorts the algorithms by their core working principle, evaluates the pros and cons of each class, and lists public datasets that pair radar signals with ground-truth cardiac measurements. The intended payoff is practical: a researcher or practitioner can use it to select an algorithm, find a suitable dataset, and see which open challenges stand in the way of contactless radar cardiac monitoring in smart homes and vehicles. A caution for the reader: the full text supplied with this entry is a different manuscript on sparse attention for language models, so the review's own substance could not be checked here and the account above rests on the paper's abstract.","feed_headline":"First review maps radar cardiac extraction algorithms","feed_subtitle":"A taxonomy of algorithm types plus public radar datasets helps researchers pick methods for heartbeat monitoring.","key_machinery":"The working machinery is the review's classification scheme: a taxonomy of cardiac feature extraction algorithms organized by their core feature, a structured statement of the pros and cons of each algorithm class, and a table of public datasets with detailed configurations and ground-truth cardiac signals. The taxonomy does the argumentative work by making a scattered literature navigable, and the dataset inventory anchors algorithm comparisons in reproducible data. For a review, these organizing devices are what carry the claim that the field can be understood and advanced from the algorithm side.","core_discovery":"On its own terms, this paper's central claim is a claim about the state of the literature: no earlier review has concentrated on algorithms for extracting cardiac features from received radar signals. To make that point useful, it introduces a new taxonomy designed to reveal the core feature of each algorithm, evaluates each algorithm's advantages and disadvantages in detail, and catalogues public datasets that contain both the received radar signal and the ground-truth cardiac feature signal, with configurations and evaluations meant to help readers choose among them. It closes by stating unsolved challenges and suggesting future research directions. The intended conclusion is that radar can give unobtrusive, accurate, and reliable contactless cardiac monitoring once the algorithm side of the field is systematically understood.","pith_inferences":["A quantitative comparison of the catalogued algorithms run on the same public datasets would be a natural extension, since a taxonomy plus pros-and-cons discussion does not by itself rank methods by accuracy or reliability.","The same taxonomy could plausibly be adapted to other contactless sensing modalities, such as cameras or Wi-Fi-based sensing, where the cardiac feature extraction problem has a similar structure.","Because the supplied full text is a different paper, these extensions should be treated as inferences from the abstract; checking the review's actual taxonomy and dataset details against the published version is the first step before relying on them."],"forward_implications":["Researchers new to the area can use the taxonomy to compare algorithm families by their underlying principle instead of by the radar hardware used.","The public-dataset list gives the field a shared reference point for benchmarking new cardiac feature extraction algorithms against recorded radar signals with ground truth.","A clear statement of pros and cons for each algorithm class shows where current methods are mature and where they fall short, directing future effort to the limiting steps.","The challenges and future directions listed in the paper supply a ready agenda for work aimed at making contactless radar cardiac monitoring practical in smart homes and in-cabin settings."],"supporting_citations":[],"fun_headline_variants":["First radar cardiac algorithm review with taxonomy","New review charts radar heartbeat extraction methods","Radar cardiac feature extraction: first algorithm review","First survey of radar cardiac methods, datasets, challenges"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the authors' literature search was comprehensive, so no earlier review focused on radar cardiac feature extraction algorithms was missed; if such a review exists, the claim of being first fails.","fun_headline_variants_meta":{"raw":{"variants":["First radar cardiac algorithm review with taxonomy","New review charts radar heartbeat extraction methods","Radar cardiac feature extraction: first algorithm review","First survey of radar cardiac methods, datasets, challenges"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00017,"raw_usage":{"total_tokens":1260,"prompt_tokens":930,"completion_tokens":330,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":546,"completion_tokens_details":{"reasoning_tokens":274}},"tokens_in":546,"tokens_out":330,"duration_ms":4286,"temperature":1.0,"reasoning_tokens":274,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T05:08:55.076921+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Search scholarly databases for a review article published before this paper's submission whose stated focus is algorithms for extracting cardiac features from radar signals; finding one would falsify the first-review claim even if the rest of the review remains useful.","supporting_citations":[],"review_version":1}