{"id":"74dffdd6-19e9-4e4b-ad1d-4ec7effee4c2","arxiv_id":"2508.09173","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"The paper's stated abstract (Camel, energy-aware LLM inference) is unsupported by a full text that instead presents PatchECG, an ECG arrhythmia detection model, making the submission internally incoherent.","lead":"This submission's abstract and title describe Camel, a system for energy-aware LLM inference on resource-constrained devices, but the full text is an unrelated study of PatchECG, a masked-training model for arrhythmia detection from digitized ECG images. The body reports AUROC numbers for ECG layouts and never discusses GPU frequency, batch size, or LLM inference.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The abstract's headline result—Camel reduces EDP by 12.4–29.9%—has no supporting experiments anywhere in the full text, which instead describes an unrelated ECG study (PatchECG). The central claim is unsupported.","rationale":"In good faith, the full text appears to be a complete manuscript on PatchECG; however, the artifact as submitted is titled and abstracted as Camel. The central claim of the submission—the one a reader would evaluate against the title—is the quantified EDP reduction. For that claim to hold, the manuscript must contain the Camel framework and its Jetson experiments. The provided full text contains neither. I therefore cannot locate a debatable technical assumption that would make the EDP claim fail; the claim simply has no evidentiary basis in the artifact. This is the most load-bearing issue because no amount of success in the PatchECG experiments can establish the missing energy/latency result. The reader's weakest_assumption focuses on masking fidelity and digitization noise in the PatchECG half of the paper; those are legitimate concerns for that separate claim, but they are not the condition on which the abstract's headline result depends. The concrete check of searching for EDP/Jetson terms is decisive and cheap. If such terms are absent, rejection is the only appropriate outcome; if zero occurrences are found, the body's PatchECG content does not rescue the submission. Thus the reader's REJECT verdict is unchanged.","tokens_in":21026,"tokens_out":8078,"duration_ms":87512,"concrete_test":"Perform an independent full-text term search over title, abstract, body, tables, figure captions, and appendices for 'Camel', 'EDP', 'energy-delay', 'Jetson', 'AGX Orin', 'GPU frequency', 'batch size', and 'latency'. Also inspect every table and figure for energy or EDP columns. If zero occurrences are found (as in the provided manuscript), the abstract claim is empirically unsupported and the submission cannot be accepted in any form.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The submission's stated central claim is that the Camel framework reduces energy-delay product by 12.4%–29.9% on Jetson AGX Orin by optimizing GPU frequency and batch size. The full text contains no Camel method section, no definition of EDP, no GPU frequency/batch-size search, no latency/energy measurements, and no comparison against the default configuration. Instead, the body is a complete, separate paper on PatchECG for arrhythmia detection from digitized ECG images. For the abstract claim to be true, a set of Jetson experiments would have to exist; as received, not even a single number, table, or figure in the manuscript pertains to EDP or energy-aware LLM inference. This is not a matter of a debatable assumption or a different consensus—the evidence required to evaluate the claim is absent. The body's PatchECG results, whatever their merits, cannot substitute for the missing Camel experiments, because they address a different model, task, and hardware platform.","agreement_with_reader":"disagree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The submission arXiv:2508.09173 presents an abstract claiming an LLM inference energy-management framework, 'Camel', which optimizes GPU frequency and batch size on the NVIDIA Jetson AGX Orin and reduces energy-delay product (EDP) by 12.4%–29.9% relative to the default configuration. The full text, however, is a different paper: 'Masked Training for Robust Arrhythmia Detection from Digitalized Multiple Layout ECG Images', proposing PatchECG. The body contains no Camel method section, no definition of EDP, no GPU frequency/batch-size search, no latency or energy measurements, and no comparison against a default configuration. Instead, it reports a patch-based ECG classifier trained on PTB-XL, evaluated on simulated layouts and on 400 external Chaoyang Hospital ECG images, with an overall AUROC of 0.778 and 0.893 on a 12×1 subset. The stated central claim of the submitted paper is therefore unsupported by the manuscript's content, while the body's PatchECG results constitute a separate contribution that cannot be evaluated as evidence for the abstract claim.","tokens_in":21211,"tokens_out":6677,"duration_ms":81854,"significance":"If the abstract's claim were supported, a 12.4%–29.9% EDP reduction on a Jetson-class device would be a practically valuable result for edge LLM deployment. But as submitted, no experiment, table, or figure pertains to EDP, GPU frequency, batch size, energy, or latency; the central claim is entirely unsubstantiated. The PatchECG content, considered independently, addresses a relevant clinical problem—directly modeling asynchronous and partially missing multi-lead ECG signals without interpolation—and has strengths: public data and code, comparison against several baselines, an external real-hospital cohort, and a quantitative interpretability evaluation against cardiologists. However, this is a different model, task, and hardware platform from the abstract's Camel contribution, and several key tables contain placeholder values. The manuscript in its current form cannot be accepted or meaningfully revised as a paper about energy-aware LLM inference.","major_comments":[{"comment":"The abstract's central claim—Camel reduces EDP by 12.4%–29.9% by optimizing GPU frequency and batch size on Jetson AGX Orin—has no supporting content in the full text. The body is a complete ECG manuscript (PatchECG) with no mention of Camel, EDP, Jetson, GPU frequency, batch-size search, latency, energy, or default configuration. The footer even carries a different arXiv identifier (2508.09165v3). This is not a local gap; the paper's stated central result cannot be checked at all.","section":"Abstract vs. full text"},{"comment":"The external-validation comparison tables render all baseline cells as placeholder symbols (e.g., 'ECGFounder ����� � ����'), and Table 5 leaves SimMTM-KNN and SimMTM-SAITS as '–'. The claims that PatchECG surpasses ECGFounder by 0.111 (overall) and 0.190 (12×1) therefore cannot be verified from the submitted text. These numbers are load-bearing for the body's headline result, so the missing values prevent reproducibility of the main performance comparison.","section":"Tables 4 and 5"},{"comment":"The training-time masking model in Eq. (1) samples one contiguous block with uniform start and length independently per lead. Appendix A, by contrast, describes layout-induced asynchrony as fixed offsets: 3×4 leads start at 0/2.5/5/7.5 s and 6×2 leads start at 0/5 s, with whole leads shifted. The relationship between the random-mask training distribution and the actual layout shifts is not established. Without evidence that the simulated missing patterns match the test-time digitization process, the claim of 'consistent' AUROC across layouts is a correctness risk.","section":"Eq. (1) and Appendix A"},{"comment":"Digitization quality for the Chaoyang cohort is reported as 'Avg SNR of � ���� dB and Avg PSNR of ����� dB', with the numeric values rendered as placeholders. The manuscript itself later describes the digitization quality as poor (case study, cases b and d), yet the external-cohort AUROC differences are the strongest evidence for the method. A load-bearing premise—that the digitized signals preserve enough diagnostic information—is therefore left unquantified.","section":"Eqs. (14)–(15) and following paragraph"},{"comment":"The 0.893 AUROC is reported on a '12×1 subset' selected from the Chaoyang Hospital cohort, but no sample size, inclusion criteria, or multiple-comparison adjustment is given for this subset; the whole-cohort result is 0.778. Highlighting a post-hoc subset as a headline result without these details is misleading and precludes assessing whether the gain is stable or a selection artifact.","section":"PatchECG Achieve Better Results on Real Hospital ECG Images"}],"minor_comments":[{"comment":"Typographical errors: 'imolementation', 'repositorv', and 'from left to tight' in the Figure 7 caption should be corrected.","section":"Code availability"},{"comment":"The notation in Eq. (1) is corrupted in the rendered text (e.g., '��� ∼ U��� ��� � � ∼ U����� � − ����'), making the precise sampling distribution hard to parse.","section":"Eqs. (1)–(2)"},{"comment":"Several URLs appear as placeholder strings ('��������') in both Data availability and Code availability; these need to be resolved.","section":"Data availability / Code availability"},{"comment":"The interpretability comparison table also contains placeholder values ('���� ± ����'), and the model-vs-clinician agreement is based on only 20 samples; the reported percentages should be read with appropriate uncertainty.","section":"Table 6"},{"comment":"The abstract says 'seven simulated layout conditions'; Table 3 has seven columns, but the text names only six layouts plus 'Random'. This should be clarified.","section":"Abstract"}],"recommendation":"reject","confidential_remarks":"The submitted manuscript is not a reviewable paper for the stated contribution: the abstract and the full text are disjoint, and the body's own arXiv identifier differs from the submission. This is not a methodological disagreement or a scope issue; the core claim is absent. If the authors intended to submit the PatchECG work, they would need to resubmit it as a separate paper with its own abstract and claims. As received, the manuscript cannot support any revision path toward the claimed Camel contribution."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"You should know two things about this submission. First, the abstract and title describe Camel, an energy-aware LLM inference framework claiming 12.4–29.9% EDP reduction on Jetson AGX Orin. The full text is entirely about PatchECG, an ECG arrhythmia detection model for digitized multi-layout images. There is no overlap: no Camel method, no EDP definition, no Jetson experiments, no energy or latency numbers anywhere in the body. The submission is internally incoherent on this point, and the central claim of the abstract is unsupported by any evidence in the manuscript.\n\nSecond, the body itself is a real paper with a plausible contribution. The task—classifying arrhythmias from digitized ECG images with asynchronous leads and partial blackout—is genuinely under-served, and PatchECG’s interpolation-free patch approach is a reasonable response. Training on PTB-XL and validating on 400 real Chaoyang Hospital images across 3×4, 6×2, and 12×1 layouts is a meaningful step, and the attention-versus-cardiologist comparison is a nice touch. The parts are assembled with competence, and the writing is clear.\n\nBut the body has real soft spots. The headline 0.893 AUROC comes from a post-hoc 12×1 subset of the external cohort, not the overall result (0.778). The baseline comparison tables render many values as placeholder symbols, so I cannot verify the claimed margins. The digitization quality metrics are low (SNR/PSNR), and the authors’ defense—visual inspection and reference-signal caveats—is weak. The training masking simulation gives each lead at most one contiguous missing block of random length, which does not resemble the structured 2.5s/5s offset asynchrony of real digitized layouts; that gap between simulated and real missingness is load-bearing for the method’s motivation. There are also no significance tests, and the interpretability analysis uses only 20 samples.\n\nAs a submission, this should be desk-rejected. The mismatch between abstract and body is fatal and cannot be fixed by revision; it needs to be split into two papers, and the Camel half needs actual experiments. The PatchECG half, resubmitted standalone and strengthened—especially on the external validation, baseline readability, and masking realism—would deserve a serious referee. But for this manuscript as received, I would not send it out.","headline":"The submission is two unrelated papers stapled together: the abstract promises an LLM energy-management result the body never mentions, while the body is a competent ECG-arrhythmia paper that deserves a clean resubmission, not review under this wrapper.","tokens_in":21815,"tokens_out":2395,"would_cite":false,"duration_ms":28429,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The full-text paper claims PatchECG, a masked-training patch classifier, detects arrhythmias from digitized multi-layout ECG images without interpolation, reaching 0.893 on real 12-lead images; the abstract's Camel energy-management claim a","keywords":["Camel","energy-delay product","ECG image digitization","arrhythmia detection","masked training","partial blackout","atrial fibrillation"],"falsifier":"Re-train PatchECG using masks sampled from the actual layout schedules—3×4 leads starting at 0, 2.5, 5.0, and 7.5 s; 6×2 leads at 0 and 5 s—allowing multiple missing blocks per lead, and evaluate on the same external cohort. If the 0.893 (12×1) and 0.778 (mixed-layout) values drop materially, the single-block uniform mask in Eq. (1) did not represent real digitization structure.","tokens_in":20811,"feed_emoji":"🫀","tokens_out":15336,"duration_ms":164849,"temperature":0.7,"pith_summary":"This manuscript has two incompatible layers. The abstract and title claim Camel, an energy-management framework that reduces energy-delay product for edge LLM inference by 12.4%-29.9%, but the full text contains no Camel design, experiment, or result. The body is a complete paper about PatchECG, an interpolation-free arrhythmia classifier for digitized multi-layout ECG images, trained on PTB-XL and validated on simulated layouts and 400 real hospital images; that is what a sympathetic reader can take as the paper's real contribution. If PatchECG's numbers hold, it would make decades of paper ECG archives usable for automated diagnosis in settings where only images exist. The abstract's Camel claim must be treated separately, since nothing in the provided text substantiates it.","feed_headline":"Scanned-ECG model beats a 10M-ECG pretrained model by 0.190","feed_subtitle":"The masked-training model inside also reaches 0.893 on 12-lead images; its abstract's LLM energy claim has no body support.","key_machinery":"The machinery is the adaptive variable-block masking plus patch-drop attention: each lead receives at most one contiguous missing block of uniformly random start and length, the signal is cut into fixed-length patches, fully missing patches are discarded rather than imputed, and a learned Segment-Shuffle-Stitch reordering plus lead/time embeddings lets a transformer attend across leads and time regardless of layout. This lets the model process arbitrary lead counts and lengths without interpolation.","core_discovery":"PatchECG's central claim is that arrhythmia detection can be made robust to the messy output of ECG image digitization—where leads start at different times (2.5 s or 5 s offsets) and contiguous chunks of signal are lost ('partial blackout')—without filling in or interpolating missing data. The model divides each lead into fixed-length patches, drops patches that are entirely missing, marks the rest with a binary observed/missing indicator, and passes them through a patch encoder followed by a learned, layout-agnostic attention module. Trained on PTB-XL and tested under seven simulated layouts, it reports an average AUROC of about 0.835; on 400 real hospital ECG images it reports 0.778 for at","pith_inferences":["A reader should check the abstract's 'approximately 0.835 average' against Table 3: the seven listed layouts for the Net1D encoder average about 0.831, and 0.835 appears as the Random-layout value.","A fair attribution test would fine-tune ECGFounder under the same masking and calibration before comparing; the reported margins may mix the contributions of masking, architecture, and pre-training.","The training mask allows one contiguous missing block per lead, while digitized 3×4 and 6×2 images have lead groups starting at fixed 2.5s/5s offsets; testing with masks that follow those schedules would stress the robustness claim.","The manual digitization fidelity metrics are reported as placeholders, so the external-hospital accuracy numbers should be read as conditional on digitization quality until paired electronic-ground-truth images are evaluated."],"forward_implications":["If PatchECG's numbers hold, digitized ECG images from any layout can be classified directly as signals, so interpolation noise is never introduced.","Existing signal encoders—including a large pre-trained ECG foundation model—can be dropped into PatchECG as patch encoders, letting them work on arbitrary lead counts and lengths.","On real hospital data, the model's AUROC is higher on complete 12×1 layouts (0.893) than on mixed layouts (0.778), suggesting layout completeness is a performance driver.","Attention scores align with cardiologist-selected patches at a rate approaching inter-clinician agreement (up to 36.0% top-20 overlap vs 41.8% between doctors), supporting signal-grounded interpretability.","At 21.72M parameters and about 1.09 ms per sample, the model is light enough for real-time use alongside the proposed digitization workflow."],"supporting_citations":[{"why":"Supplies the 21,388 labeled 12-lead ECGs and the official 10-fold split used to train PatchECG.","marker":"[33]"},{"why":"Digitizes generated 3×4 ECG images back into 1-D signals for the simulated-layout evaluation.","marker":"[6]"},{"why":"Manually digitizes the external hospital ECG images used for real-world validation.","marker":"[7]"},{"why":"Provides ECGFounder, the large pre-trained baseline and frozen PatchEncoder variant that PatchECG is compared against.","marker":"[13]"},{"why":"Supplies the Segment, Shuffle, and Stitch layer used to reorder patch segments for cross-lead attention.","marker":"[32]"},{"why":"Provides the Net1D residual SE-convolution backbone used as the default PatchEncoder.","marker":"[31]"},{"why":"Supplies SimMTM, the masked time-series model combined with zero-padding, KNN, and SAITS baselines.","marker":"[20]"},{"why":"Supplies Medformer, the multi-granularity patching transformer baseline designed for medical time series.","marker":"[21]"},{"why":"Supplies TimeXer, the baseline that treats missingness as exogenous information.","marker":"[27]"}],"fun_headline_variants":["Camel framework cuts LLM energy delay product by up to 29.9%","Energy-aware LLM inference on edge devices with Camel","Camel balances LLM latency and energy on Jetson AGX Orin","GPU frequency and batch size tuning cuts LLM energy on edge","Camel achieves 12-30% energy delay product reduction for LLMs"],"cache_read_input_tokens":2816,"weakest_assumption_plain":"The results stand on the assumption that the single random missing-segment-per-lead used in training matches the timing shifts and blackouts that occur when real multi-layout ECG images are digitized, and that the manually digitized hospital signals preserve enough diagnostic information for the reported accuracy scores to reflect model ability rather than digitization noise.","fun_headline_variants_meta":{"raw":{"variants":["Camel framework cuts LLM energy delay product by up to 29.9%","Energy-aware LLM inference on edge devices with Camel","Camel balances LLM latency and energy on Jetson AGX Orin","GPU frequency and batch size tuning cuts LLM energy on edge","Camel achieves 12-30% energy delay product reduction for LLMs"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.0007,"raw_usage":{"total_tokens":3000,"prompt_tokens":746,"completion_tokens":2254,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":490,"completion_tokens_details":{"reasoning_tokens":2159}},"tokens_in":490,"tokens_out":2254,"duration_ms":16123,"temperature":1.0,"reasoning_tokens":2159,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T23:39:02.068563+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-train PatchECG using masks sampled from the actual layout schedules—3×4 leads starting at 0, 2.5, 5.0, and 7.5 s; 6×2 leads at 0 and 5 s—allowing multiple missing blocks per lead, and evaluate on the same external cohort. If the 0.893 (12×1) and 0.778 (mixed-layout) values drop materially, the single-block uniform mask in Eq. (1) did not represent real digitization structure.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the 21,388 labeled 12-lead ECGs and the official 10-fold split used to train PatchECG."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Manually digitizes the external hospital ECG images used for real-world validation."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides ECGFounder, the large pre-trained baseline and frozen PatchEncoder variant that PatchECG is compared against."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the Segment, Shuffle, and Stitch layer used to reorder patch segments for cross-lead attention."},{"cited_title":"Holmes: Health online model ensemble serving for deep learning models in intensive 767 care units","cited_arxiv_id":null,"evidence_quote":"Provides the Net1D residual SE-convolution backbone used as the default PatchEncoder."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies SimMTM, the masked time-series model combined with zero-padding, KNN, and SAITS baselines."}],"review_version":1}