{"id":"fb5cefbd-8940-44e5-a3fd-2dcf6732c6d9","arxiv_id":"2509.01264","paper_version":5,"verdict":"REJECT","confidence":"LOW","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":1,"one_line_summary":"The abstract's concealment-ray theorem and 71.08% optimal revelation threshold are not present in the submitted full text, which is an unrelated paper.","lead":"The abstract describes a paper about when a platform should reveal or conceal a first expert's forecast before a second expert revises, and it claims a 71.08% optimal revelation threshold. The supplied full text is a different paper on social learning from experts with unknown precision, so the abstract's central claim cannot be checked in this manuscript.","discovery_kind":"unclear","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The submitted full text contains no model, statement, or proof of the abstract's concealment-ray theorem or 71.08% threshold, so the central claim is unsupported by its own manuscript.","rationale":"The reader's overall verdict is REJECT, and my stress-test supports that verdict, but through a slightly different diagnosis. The reader's stated weakest assumption is the maintained regular-monotone equilibrium class; that would matter if the concealment-ray theorem and 71.08% optimum were actually derived in the text and only the equilibrium selection were questionable. The more load-bearing problem is that the full text is a different paper: no section, equation, or appendix states the model of silent pools, the concealment-ray theorem, or the numerical threshold. Therefore the central claim is unsupported at the level of the submitted object itself, not merely conditional on an unexamined selection. Under the rule that all manuscript content is in-scope evidence, this mismatch must be flagged explicitly. The proposed test is straightforward: search the body for the defining terms of the abstract's contribution; if they are absent, the abstract's result cannot be verified, reproduced, or even located in the submitted manuscript. This is not an ad hominem critique; it is a structural assessment of the arXiv submission. If a corrected version were uploaded with matching abstract and body, the underlying theory could be evaluated on its merits, and a different verdict might be appropriate.","tokens_in":21776,"tokens_out":1791,"duration_ms":23903,"concrete_test":"Run a full-text search over the submitted manuscript source for the exact strings 'concealment', 'ray', 'silent pool', 'locked-report', 'regular-monotone', and '71.08'. If none of these occur in the body, appendices, or supplementary material, the central claim is unsupported by the submitted document. Additionally, compare the arXiv metadata title/abstract with the first page title/abstract of the PDF; a mismatch confirms that the submitted object is internally inconsistent.","verdict_should_be":"REJECT","load_bearing_attack":"The central claim of the paper as submitted is the concealment-ray theorem and the computer-assisted 71.08% optimal agreement-revelation threshold. For that claim to hold, the full text must define the reveal-or-silence lottery, the two-dimensional design problem, the silent pool composition, the locked-report incentive slack, and the regular-monotone equilibrium class, and then provide the derivation and certificate. The full text does none of this: its title, abstract, model, propositions, and appendix describe a different paper on social learning from experts with unknown precision, with one-sided mixing around the prior, evaluation windows, and Gaussian mimicry. There is no occurrence in the body of 'concealment ray', 'silent pool', 'locked-report', 'regular-monotone', or the 71.08% figure. Consequently, the strongest asserted result is not merely under-proved; it is absent from the submitted object. This is a submission-level failure: the abstract promises a specific theorem and an exact numerical optimum, but the manuscript provides no matching definitions, derivations, or certificate, so the central claim cannot be checked or falsified from the submitted text. The reader's named weakest assumption about the maintained equilibrium class is a legitimate secondary concern, but the primary load-bearing problem is more basic: the body of the paper does not contain the claim at all.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The submission, as presented by its arXiv metadata and abstract, claims to study a platform that observes two independent binary forecasts and commits to a reveal-or-silence lottery conditioned on agreement. Its advertised central results are a \"concealment-ray theorem\" — along policy rays with fixed silent-pool composition, values are affine, collapsing the two-dimensional design problem to full disclosure and two one-dimensional boundaries — and a computer-assisted certificate of a unique state-classification optimum in which disagreement is never revealed and about 71.08% of agreements are revealed. The full text, however, is a different paper, titled \"Social Learning from Experts with Uncertain Precision.\" The body models repeated public opinions by experts of unknown precision, characterizes one-sided low-type mixing, analyzes light-touch design via evaluation windows and convex deviation costs, and gives a Gaussian extension. It contains no definition or statement of the concealment-ray theorem, no reveal-or-silence lottery, no silent pool, no locked-report incentive slack, no regular-monotone equilibrium class, and no 71.08% certificate. The abstract's final qualifications (Blackwell incomparability, uninformative equilibria in every policy, maintained class) are likewise not developed in the body.","tokens_in":22017,"tokens_out":4104,"duration_ms":49854,"significance":"If the advertised result were actually established, it would be a notable contribution: it would turn non-revelation into a designed instrument, reduce a two-dimensional design problem to one-dimensional boundaries, and provide an exact numerical optimum with a computer-assisted certificate. The body does contain credible formal work of its own — stationary Markov PBE with closed-form one-sided mixing (Proposition 2.1, Eq. (7)), light-touch design restoring strict informativeness (Propositions 5.2–5.3), and a Gaussian mimicry-coefficient analogue (Lemma 5.1) — and it mentions a replication package. Those strengths belong to the full-text paper, not to the submission's advertised contribution. Because the concealment-ray theorem and the 71.08% certificate are absent from the submitted object, the significance of the manuscript as submitted cannot be assessed.","major_comments":[{"comment":"The submitted abstract claims a concealment-ray theorem and a 71.08% optimal agreement-revelation threshold. The full text does not define a reveal-or-silence lottery, a silent pool, a locked-report incentive slack, or a regular-monotone equilibrium class; these phrases do not occur in the body. The body instead derives a different model: a persistent binary state, one-sided low-type mixing (Eq. (7), Proposition 2.1), evaluation windows and convex costs (Section 5.4), and a Gaussian extension (Section 5.1). No theorem, derivation, or certificate matching the abstract's claims appears anywhere. The central result is therefore absent from the manuscript and cannot be checked or falsified from the submitted text.","section":"Abstract vs. full text (global)"},{"comment":"The abstract states that at an exact rational profile a computer-assisted certificate identifies the unique state-classification optimum within the maintained class, with approximately 71.08% of agreements revealed. The body provides no definition of the maintained class, no proof of uniqueness within it, and no certificate. The abstract itself notes that every policy admits an uninformative equilibrium, so the claimed uniqueness is conditional on an equilibrium selection that is not formulated in the body. At minimum, the authors would need to state the model, the maintained class, and the certificate; this is load-bearing for the advertised numerical optimum.","section":"Abstract, uniqueness claim"},{"comment":"The full text's design instruments are announced evaluation windows scored by strictly proper rules and small convex deviation costs (Section 5.4, Propositions 5.2–5.3). These are not the reveal-or-silence lottery of the abstract. No connection is made between the body's 'silence' or non-revelation and the abstract's claim that silence is a designed instrument rather than the absence of disclosure. The reader cannot use the body to evaluate the abstract's central mechanism. This is a submission-level mismatch, not a local presentation issue.","section":"Scope of the body's design results"}],"minor_comments":[{"comment":"The submission title ('Designing Silence: Peer Feedback under Reputational Concerns') and the full-text title ('Social Learning from Experts with Uncertain Precision') differ. The journal metadata should match the manuscript body, and the arXiv abstract should be the abstract of the body.","section":"Title/version"},{"comment":"The proof of Proposition 5.2 in Appendix A is a sketch: it asserts local equivalence of Bregman and KL divergences without stating the required smoothness or quantifying c_S. For the body's own results this is a completeness issue, but it is secondary to the abstract–body mismatch.","section":"Section 5.4 / Appendix A"},{"comment":"Table 2's expected-time calculation is a constant-drift approximation, not an exact expectation. This should be labeled as illustrative, since the text already says so.","section":"Table 2"}],"recommendation":"reject","confidential_remarks":"The submitted object contains two incompatible papers: an abstract advertising a concealment-ray theorem and a 71.08% optimum, and a full text on a different social-learning model. This is not a local fix; adding the missing model, definitions, theorem, and certificate would constitute a new manuscript. If the intended submission is the full-text paper, the abstract and title must be replaced to match it; if the intended submission is the silence-design paper, the body must be written. Either way, the current manuscript cannot be accepted or sensibly revised."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Worth knowing before you open it: the abstract and the full text are different papers. The abstract announces a concealment-ray theorem and a numerically certified 71.08% optimal revelation threshold for a platform that decides whether a second expert sees the first's report. The body is a self-contained paper on social learning from experts with unknown precision—one-sided mixing around the prior, log-likelihood aggregation, evaluation windows, Gaussian mimicry. Neither the theorem nor the threshold appears anywhere in the text. No 'concealment ray', no 'silent pool', no 'locked-report', no 71.08%. So the submitted object fails the basic test: the central claim is unsupported because it is absent.\n\nIf the body is meant to be a separate paper, it has some substance. The binary baseline gives a clean closed form for low-type mixing, the aggregation result is a standard but useful log-additive decomposition, and the evaluation-window design restores informativeness with straightforward proofs. The Gaussian mimicry coefficient is a nice knife-edge. But none of that is the advertised 'Designing Silence'.\n\nThe stress-test is right. The mismatch is not a minor presentational slip; it makes the manuscript unassessable. The abstract's own caveat about the maintained regular-monotone class to handle the uninformative equilibrium is a legitimate secondary concern—if the selection is doing the work, the uniqueness claim is conditional. But you can't even get to that question because the model is not in the text. I can't verify whether the claimed theorem is true, novel, or robust. The full text might deserve a separate review under its own title, but that's not what's in front of us.\n\nWho is this for? Not for readers of 'Designing Silence'. If the authors meant to submit the social learning paper, they should fix the metadata. As submitted, a serious editor should desk-reject: there is nothing to referee in the claimed paper. If a corrected version appears with matching abstract and body, it could be worth a look—the research question is good and the full text shows competence. But this version doesn't clear the bar.","headline":"The abstract and the full text are two different papers; the advertised concealment-ray theorem and 71.08% threshold never appear in the body, so the submission is not a coherent object.","tokens_in":22515,"tokens_out":2540,"would_cite":false,"duration_ms":30328,"reading_group":"no","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":["91A28","91B44"],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper establishes that silence is a design instrument: a concealment-ray theorem collapses the design space, and the unique state-classification optimum reveals about 71.08 percent of agreements and never reveals disagreement.","keywords":["concealment-ray theorem","reveal-or-silence lottery","reputational incentives","information design","state classification","equilibrium selection","peer feedback","forecast revision"],"falsifier":"The claim would be settled by finding (1) a policy outside the maintained class that strictly beats the certified optimum on state classification, (2) a finite downstream decision problem and a policy ray on which its value is not affine, or (3) an alternative equilibrium selection within the maintained class that yields a different optimal reveal rate; any of these checks could in principle be run on the paper's primitives.","tokens_in":21607,"feed_emoji":"🤐","tokens_out":17605,"duration_ms":158420,"temperature":0.7,"pith_summary":"How an organization should control what its experts learn from one another between a first opinion and a final one is the question this paper addresses. The paper studies a platform that collects two independent binary forecasts and commits to a reveal-or-silence lottery: whether the second expert may see the first expert's lodged report before revising is decided by chance, with the probability depending on whether the reports agree. Because withholding a report is itself informative, silence becomes an instrument of design rather than the absence of one. The central result is a concealment-ray theorem: along policy rays that hold fixed the composition of the silent pool, the value of every finite downstream decision problem and every locked-report incentive slack is affine, which collapses a two-dimensional design problem to full disclosure plus two one-dimensional boundaries. At an exact rational profile, a computer-assisted certificate identifies the unique state-classification optimum within the maintained equilibrium class - disagreements are never revealed and about 71.08 percent of agreements are revealed, set just high enough that silence becomes unfavorable news and eliminates a low-ability expert's tendency to stand by a stale forecast; the policy strictly outperforms both sealing and full disclosure, though the gain over full disclosure is modest, so if correct the paper shows that silence is an optimizable design choice.","feed_headline":"Reveal 71% of agreements, never reveal a disagreement","feed_subtitle":"For reputation-driven experts, silence is a design lever, not a lack of disclosure.","key_machinery":"The load-bearing object is the reveal-or-silence lottery: the platform's commitment to reveal the first expert's lodged report to the second with a probability that depends on whether the two reports agree, and to withhold it otherwise. The concealment-ray theorem is the identity that carries the argument: holding fixed the composition of the silent pool, the value of every finite downstream decision problem and every locked-report incentive slack - the slack in the incentive constraints attached to already-lodged reports - is affine along policy rays. This affine structure is what reduces the two-dimensional policy space to full disclosure together with two one-dimensional boundaries, makin","core_discovery":"The core claim is a structural theorem plus a computed optimum. On the structural side, the concealment-ray theorem says that if the composition of the silent pool - the set of report-outcome combinations whose content is withheld - is held fixed, then as a policy scales along a ray, the value of every finite downstream decision problem and every locked-report incentive slack is affine in the scale. The design problem therefore collapses from two dimensions to full disclosure plus two one-dimensional boundaries. On the computational side, at an exact rational profile of the primitives, a computer-assisted certificate verifies that within the maintained regular-monotone equilibrium class ther","pith_inferences":["Beyond the paper: if the affine collapse of the concealment-ray theorem survives larger panels and richer report spaces, optimal silence policies could be computed in settings where the naive policy space would otherwise be intractable.","Testable extension: the 71.08 percent threshold predicts a sharp behavioral boundary; a platform experiment that varies the reveal rate around this threshold should detect a discontinuous change in revision behavior at the point where silence switches from favorable to unfavorable news.","Implication the paper leaves implicit: because sealing and full disclosure are Blackwell incomparable, the state-classification optimum is one point on a policy frontier, and platforms whose objectives emphasize ranking experts rather than classifying the state should expect different optimal reveal rates.","The uniqueness result is conditional on the maintained regular-monotone equilibrium class; whether standard refinement criteria select that class is an open question, so the exact 71.08 percent figure should be read as conditional on that selection."],"forward_implications":["Silence is an optimizable instrument: a reveal-or-silence lottery strictly outperforms both total sealing and full disclosure for reputation-motivated experts, though the gain over full disclosure is modest.","The design problem collapses from two dimensions to full disclosure plus two one-dimensional boundaries, so the entire policy space reduces to a small set of candidate rules.","The unique state-classification optimum has a sharp qualitative shape: never reveal disagreements, reveal about 71.08 percent of agreements - just enough to make silence unfavorable news and deter a low-ability expert from defending a stale forecast.","The optimum is locally stable: recalibrating the threshold on an open set of nearby primitives leaves the policy uniquely optimal.","No protocol dominates: sealing and full disclosure are Blackwell incomparable, so no single reveal-or-silence rule is best for every downstream objective."],"supporting_citations":[],"fun_headline_variants":["Hide all disagreements, reveal 71% of agreements","Silence as design: optimal feedback for reputation-driven experts","Never reveal disagreement, reveal some agreement","Concealment-ray theorem: silence is a design lever","For reputation, optimal policy hides disagreements"],"cache_read_input_tokens":2688,"weakest_assumption_plain":"The comparison and the uniqueness of the 71.08 percent optimum are conducted within a maintained regular-monotone equilibrium class, and because every policy also admits an uninformative equilibrium, the central optimum stands or falls with that equilibrium selection.","fun_headline_variants_meta":{"raw":{"variants":["Hide all disagreements, reveal 71% of agreements","Silence as design: optimal feedback for reputation-driven experts","Never reveal disagreement, reveal some agreement","Concealment-ray theorem: silence is a design lever","For reputation, optimal policy hides disagreements"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000895,"raw_usage":{"total_tokens":3715,"prompt_tokens":788,"completion_tokens":2927,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":532,"completion_tokens_details":{"reasoning_tokens":2854}},"tokens_in":532,"tokens_out":2927,"duration_ms":23877,"temperature":1.0,"reasoning_tokens":2854,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T12:41:38.634557+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"The claim would be settled by finding (1) a policy outside the maintained class that strictly beats the certified optimum on state classification, (2) a finite downstream decision problem and a policy ray on which its value is not affine, or (3) an alternative equilibrium selection within the maintained class that yields a different optimal reveal rate; any of these checks could in principle be run on the paper's primitives.","supporting_citations":[],"review_version":1}