{"id":"25a56b71-d31f-42cf-ab0f-bccf11a5d4c0","arxiv_id":"2611.17052","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"Given the same prompt, three frontier LLMs differ by up to about 50 percentage points in detecting injected paper errors, and enforced citation retrieval only helps where the model does not already know the literature.","lead":"This paper builds a benchmark that plants objective errors of four types into real astrophysics and machine-learning papers and then measures how often twelve AI reviewer systems catch them, along with how often those systems raise false alarms on clean papers. It also introduces Skepthical, a multi-agent reviewer that checks citation-backed claims against the retrieved cited papers, mathematics with computer algebra, and numbers with executed code.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek/deepseek-v4-flash","headline":"LLM adjudication is the load-bearing instrument; the one-paper human check cannot rule out a review-style bias that would shift SKEPTHICAL and false-positive comparisons, though the ~50 pp model-gap result survives a uniform bias.","rationale":"The reader's weakest assumption points at the same instrument: LLM adjudication as ground truth. I agree that this is the most load-bearing measurement choice, but I would sharpen the verdict in two ways. First, the central model-choice result—a ~50 pp gap between frontier LLMs under the same prompt—is not seriously endangered by the observed bias: the one-paper human check found a 3.7 pp over-credit, and even doubling that would not move a 48.6–52.8 pp gap. Second, the fragile comparisons are the SKEPTHICAL-specific ones and the false-positive rates. The citation margin of 20 pp has a paired-bootstrap p of 0.033, and the false-positive general-issues panel has r as low as 0.32, with no human validation at all. The paper is genuinely careful: cross-family panels, family-pairing tests, paper-level bootstraps, released logs, and an honest limitations section all support the benchmark. But the one-sided direction of the single human check, combined with SKEPTHICAL's uniquely structured output, leaves a plausible path by which its margins over single-LLM and Claude Code baselines are partly an artifact of adjudication style preference. That does not overturn the paper; it justifies the conditional verdict the reader reached. If the proposed human re-adjudication sample lands with uniform bias, the condition would be discharged and the paper could move toward acceptance on the detection side; until then, the SKEPTHICAL and FP claims need the stated qualification.","tokens_in":39764,"tokens_out":13832,"duration_ms":93178,"concrete_test":"Run a human re-adjudication study on a stratified sample: take three papers (one astro, one ML, one from the concise-prompt runs of Appendix H), have two independent annotators blind to system identity re-judge every detection verdict for those papers (about 144 per paper) and every clean-copy flag with the full PDFs, then recompute Table 2 rows and the FP rates of Figure 2 for the affected systems using only human labels. If per-system over-crediting is approximately uniform, the SKEPTHICAL citation and total margins survive and the conditional concern is resolved; if the panel disproportionately credits audit-style or verbose outputs, the SKEPTHICAL margins and FP orderings shift, and the paper's claims about enforced retrieval and low FP rates would need to be weakened.","verdict_should_be":"UNCHANGED","load_bearing_attack":"All rates in Table 2 and Figure 2 are produced by LLM adjudication, not objective labels. Detection is matched by a three-judge panel with PDF-attached escalation (§2.4), and false positives are classified by a separate LLM pipeline whose general-issues panel has pairwise agreement of only r=0.32–0.58 (Appendix D), while the detection panel agrees at r=0.90–0.93. The human check (Appendix F) covers one astro paper, 132 adjudications, and detection only: the five disagreements all go in one direction, with the panel crediting detections the human rejected, raising that paper's rate from 31.1% to 34.8%. The authors acknowledge these limits (Appendix F, §4), but the concern is unresolved rather than answered: there is no human check of the false-positive pipeline, and no per-system or per-error-type breakdown of the one-sided over-crediting. Appendix E explicitly leaves open a preference shared by all three judges for a particular review style; SKEPTHICAL's reviews are stylistically unique because they append separate citation, mathematics, and numerics audits, so the one mechanism that could differentially inflate its detection rate is exactly the mechanism the paper cannot test. A uniform 3.7 pp inflation would not threaten the headline ~50 pp model gap, so that central result is robust. What is threatened is the secondary but load-bearing comparison set: SKEPTHICAL's 20 pp citation margin over structured Claude Code (p=0.033, Appendix G), its total-rate margin over the best single LLM, and every false-positive ranking, all of which rest on an instrument tested on one paper and never on the FP side.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek/deepseek-v4-flash","summary":"The paper proposes a benchmark for auditing AI peer reviewers: it injects four error types (general, mathematics, numerics, citations) at three severities into 30 recent arXiv preprints (15 astrophysics, 15 ML), reviews clean and injected copies with twelve reviewer systems, and measures per-type/per-severity detection rates together with false-positive rates on clean copies. It also introduces SKEPTHICAL, a multi-agent reviewer with enforced citation retrieval, computer-algebra and code-based mathematics/numerics checks, and a cross-family general-issues ensemble. The main empirical claims are that, with the same prompt, three frontier LLMs differ by roughly 50 percentage points in total detection on both corpora; that the structured prompt narrows this spread by lifting weaker models; that enforced retrieval gives SKEPTHICAL a large citation-detection lead on astrophysics but not on ML; that tools help only when they supply information the model lacks; and that false-positive rates are below 10% for most systems, with mathematics flags the dominant source. The evaluation pipeline, injection catalogues, and adjudication logs are released as a benchmark. The authors explicitly qualify the results by small one-day corpora, within-type-only severity calibration, and an LLM-based adjudication pipeline whose human validation covers one paper and detection only.","tokens_in":40065,"tokens_out":6170,"duration_ms":48350,"significance":"If the results withstand scrutiny, this is a substantial contribution: it is, to my knowledge, the first controlled comparison of AI reviewer systems on a real non-ML corpus that simultaneously varies severity, controls false positives, includes mechanical verifiers, and uses cross-family adjudication. The statistical practice is strong and deserves explicit credit: paper-level bootstrap confidence intervals, permutation tests for the severity trend, a within-injection test for judge--reviewer family preference, and a panel-removal robustness analysis. The central result, that model choice dominates detection under a fixed prompt, is robust to a uniform adjudication bias, because such a bias would shift all systems in the same direction. The contributions that are not yet fully established are the secondary, more granular claims: the specific SKEPTHICAL citation and false-positive margins rest on an adjudication instrument whose documented one-sided disagreements and low false-positive-panel agreement have not been resolved by human validation.","major_comments":[{"comment":"The one-paper human check does not yet protect the head-to-head claims that the benchmark's secondary results depend on. In the 132 adjudications checked, all five disagreements ran in the same direction: the LLM panel credited detections that the human annotator rejected, inflating that paper's detection rate from 31.1% to 34.8%. Appendix E explicitly acknowledges that a preference shared by all three judges, for example for a certain review style, cannot be detected by its test, and SKEPTHICAL's reviews are stylistically distinct because they append separate citation, mathematics, and numerics audits. The paper therefore does not rule out a differential adjudication advantage for SKEPTHICAL in precisely the comparisons where it matters: the +20.0 pp citation margin over structured Claude Code (Table 9) and the +9.3 pp total margin over structured GPT-5.5. The authors acknowledge the limited human check, but the concern is unresolved rather than answered; the manuscript should either provide a human-validated sample stratified by system and error type, or a sensitivity analysis that shows how large a correlated over-crediting bias would have to be to overturn the marginal comparisons.","section":"Appendix F and §3.1","authors_acknowledge":"A preference that is shared by all three, for instance for a certain style of review prose, would affect every reviewer system alike and cannot be detected by this test.","authors_acknowledge_p":0.81},{"comment":"The false-positive findings are presented as results even though the entire false-positive pipeline is unvalidated against human adjudication. Appendix D reports pairwise agreement of only r=0.32-0.58 for the general-issues false-positive panel, and Appendix F states explicitly that the false-positive panel was not checked against a human. Yet Figure 2 and §3.2 use those panel verdicts to support ordering claims such as SKEPTHICAL's low false-positive rate (2.8% and 4.0%) versus the structured single-LLM rates (11.5-22.7% on ML). Given the low inter-judge agreement, a different plausible resolution of ambiguous flags could change these orderings. The authors note the reliability gap in Appendix D, but the main-text false-positive conclusions are stated without that caveat; a human annotation sample of the false-positive flags, or an explicit robustness analysis against ambiguous-flag reassignment, is needed before the false-positive comparisons are licensed.","section":"§3.2 and §2.4","authors_acknowledge":"Third, the check covers detection only: the false-positive panel, which Appendix D shows to be the less reliable of the two, was not checked against a human.","authors_acknowledge_p":0.89},{"comment":"Conclusion (3), 'Detection rises with severity for every capable system', is stronger than the paper's own trend tests support. The pooled severity data in Appendix G, Table 10, show non-monotonic patterns on astrophysics: general issues go 53.5% -> 41.0% -> 69.3% across S1-S2-S3 and mathematics go 47.3% -> 40.4% -> 59.3%, and only citations passes the Benjamini-Hochberg threshold on that corpus. The main text of §3.1 hedges appropriately ('most individual error-type curves'), so the conclusion should be reworded to match those results; as written, it overstates a headline finding.","section":"§5 and Appendix G","authors_acknowledge":null,"authors_acknowledge_p":null}],"minor_comments":[{"comment":"The figure legend labels the external detector 'PaperAudit-R (Deep)', while §2.1 and Table 2 use 'PaperAudit-Bench Deep'; the labels should be harmonized.","section":"Figure 2","authors_acknowledge":null,"authors_acknowledge_p":null},{"comment":"The legend writes 'Skepthical' with mixed capitalization while the text consistently uses 'SKEPTHICAL'; the figure should follow the text for consistency.","section":"Figure 1","authors_acknowledge":null,"authors_acknowledge_p":null},{"comment":"The appendix reports the prompt as 'structured (5,946 words)' while §2.1 describes the same prompt as '978-line'; the relationship between the two characterizations is clear, but one consistent description would avoid confusion.","section":"Appendix H","authors_acknowledge":null,"authors_acknowledge_p":null}],"recommendation":"major_revision","confidential_remarks":"To the editor: the stress-test concern is largely valid but does not undermine the central model-choice result, which survives a uniform adjudication bias. The work is substantial and the benchmark release is a real asset. My main reservation is that the SKEPTHICAL-specific citation and false-positive claims are load-bearing and currently rest on an under-validated adjudication instrument; that is fixable within the manuscript's scope with additional targeted human adjudication or sensitivity analyses, so I recommend major revision rather than rejection."},"author_rebuttal":null,"desk_editor":{"model":"deepseek/deepseek-v4-flash","letter":"This is a careful, well-disclosed empirical contribution to the evaluation of LLM-based peer reviewers. The benchmark design is the strongest part: severity-graded injection across four error types, false-positive control, mechanical verifiers, a cross-family judge panel, and paper-level bootstrap statistics. The headline finding, that model choice dominates detection with a gap of roughly 50 percentage points under the same prompt, is robust on both corpora and is the paper's most important result.\n\nThe verdict is CONDITIONAL because the measurement instrument that produces all rates is LLM adjudication validated on one of thirty papers, with the false-positive pipeline never human-checked and showing low inter-judge agreement; and because some conclusions exceed the authors' own data, notably 'detection rises with severity for every capable system' versus the non-monotone S1-S2-S3 pattern in Appendix G. In addition, the SKEPTHICAL advantage over the best single LLM is not significant against the concise-prompt baseline of Appendix H (+1.7 and +0.6 pp) and shrinks to 2.6 pp under a worst-case panel variant, so the paper's emphasis on the multi-agent pipeline should be read alongside those numbers.\n\nI found no citation-context mismatches in the references as quoted. The released artifacts were not independently verified from this document since no commit hash appears in the text.\n\nWho this is for: anyone working on AI-assisted peer review, especially those building or deploying reviewer systems. The benchmark is reusable and the methodology is transferable. The paper deserves a serious referee—the central result is important and the design is thoughtful—but the referee should push for a larger human validation of the adjudication pipeline, especially on the false-positive side, and for a more measured presentation of the SKEPTHICAL comparisons.","headline":"The paper's central result—model choice dominates detection by ~50 pp—is robust; the SKEPTHICAL advantage and false-positive rankings rest on an LLM adjudication instrument validated on one of thirty papers.","tokens_in":40779,"tokens_out":470,"would_cite":false,"duration_ms":33703,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"pith_extraction":null,"created_at":"2026-11-13T10:43:38.202869+00:00","model_set":{"reader":"deepseek/deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}