{"id":"492106a5-ae7b-4975-9ce1-72dc4297bb49","arxiv_id":"2508.03685","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"A benchmark problem that is IMO-adjacent and whose solution is public was not solved by any tested commercial or open-source LLM.","lead":"The authors report that no off-the-shelf large language model could solve a specific olympiad-style geometry problem, Yu Tsumura's 554th problem, even though a solution is publicly available and likely appeared in training data. The result is a concrete counterexample to claims that LLMs have reached IMO-level problem-solving competence.","discovery_kind":"incremental","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The universal negative claim that no existing off-the-shelf LLM can solve Tsumura's 554th is not testable from the submitted materials: the supplied full text is a different paper, and the abstract omits the model set, prompts, sampling budget, and judging protocol.","rationale":"Taking the reader's strongest claim at face value, the paper wants to demonstrate an existence result with a universal negative corollary. The most load-bearing assumption is not about the mathematics of problem 554th, which may be exactly as described, but about the exhaustiveness of the empirical survey. Because the supplied full text is an unrelated submission, the review cannot confirm whether the evaluation actually spanned a diverse model zoo, varied prompts, sufficient sampling, or independent verification. This is the same weakness the reader flagged, so I set agreement to agree. My recommendation is UNCHANGED: the already-UNVERDICTED verdict is appropriate, and no evidence in the current packet would make ACCEPT, CONDITIONAL, or REJECT more justified. If the real paper turns out to contain a robust protocol and reproducible code, the verdict could shift upward; if the protocol is shallow, the universal claim should be weakened. I am not alleging author misconduct; the concern is purely about what the submitted material demonstrates.","tokens_in":22213,"tokens_out":3787,"duration_ms":46126,"concrete_test":"Locate the actual arXiv:2508.03685 PDF and inspect the evaluation section for the model list, prompt templates, number of samples, and proof verification protocol. Then rerun the strongest setup used in the paper with 100 independent generations and human-verified correctness; if any off-the-shelf LLM produces a correct proof under reasonable prompting, criterion (e) is refuted as stated, while a null result with detailed protocol would support the claim. If the full text supplied here remains the FairLangProc paper, the claim stays unverifiable.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim (abstract, criterion e) is a universal negative: Yu Tsumura's 554th problem cannot be readily solved by any existing off-the-shelf LLM. Such a claim is only as strong as the evaluation protocol behind it. The submitted full text, however, is arXiv:2508.03677 (FairLangProc), an unrelated NLP fairness package, so no method section for the actual paper is reviewable. From the abstract alone, the unstated premise is that the tested models, prompts, sampling budget, and answer verification are representative enough to rule out all off-the-shelf LLMs. This premise is also the weakest: LLM performance on hard mathematical problems is highly sensitive to prompt phrasing, few-shot exemplars, sampling temperature, self-consistency, and verification criteria, so failure on a narrow protocol would not establish 'cannot be readily solved.' The abstract also leaves 'readily solved,' 'proof sophistication,' and 'fewer proof techniques' undefined, making criteria (a), (c), and (e) hard to falsify. The concern is evidentiary, not internal inconsistency: if the full paper reports broad, robust evaluations the claim may hold, but nothing in the submitted material supports the universal quantifier.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The abstract announces a negative empirical claim about large language models: Yu Tsumura's 554th problem allegedly satisfies five criteria (a) through (e), most importantly that it cannot be readily solved by any existing off-the-shelf LLM, and the paper suggests this contradicts optimism fueled by recent IMO gold medals. The submitted full text, however, is a completely different manuscript, \"FairLangProc: A Python package for fairness in NLP\" (arXiv:2508.03677v1), which contains no experimental protocol, results, or analysis concerning Tsumura's problem or LLM mathematical reasoning. Consequently, the submission as provided contains no evidence for its central claim.","tokens_in":22420,"tokens_out":3381,"duration_ms":36455,"significance":"If established through a broad, carefully controlled evaluation, the claimed existence of an IMO-scope problem with a public solution that no off-the-shelf LLM can readily solve would be a substantive negative result for the LLM reasoning literature, and criteria (a) through (d) could serve as a useful benchmark-selection checklist. However, because the submission contains none of the experimental apparatus, including model identities, prompts, sampling budget, verification protocol, or even the problem statement, the claim is not assessable in its current form. The universal quantifier over \"any existing off-the-shelf LLM\" in particular demands a sampling and protocol justification that is entirely absent. The paper's potential significance is real, but the submitted manuscript does not realize it.","major_comments":[{"comment":"The manuscript body (pp. 1-40) is arXiv:2508.03677v1, \"FairLangProc: A Python package for fairness in NLP,\" which is unrelated to the abstract. No definition of Yu Tsumura's 554th problem, no evaluation protocol, no model list, and no results appear in the submission. The central claim in the abstract is therefore completely unsupported by the submitted materials.","section":"Full Text"},{"comment":"The universal negative claim that the problem \"cannot be readily solved by any existing off-the-shelf LLM (commercial or open-source)\" requires a specification of the tested models, prompts, sampling temperature and budget, few-shot exemplars, and answer-verification method. None of these are reported in the abstract or body, so the claim is unfalsifiable from the submission. Furthermore, \"readily solved\" is undefined; without a threshold for success or effort, the criterion cannot be tested.","section":"Abstract, criterion (e)"},{"comment":"The comparative assessments that the problem is \"within the scope of an IMO problem in terms of proof sophistication\" and \"requires fewer proof techniques than typical hard IMO problems\" are not operationalized. No rubric, baseline set of IMO problems, or definition of \"proof techniques\" is given, making criteria (a) and (c) impossible to verify or contest.","section":"Abstract, criteria (a) and (c)"}],"minor_comments":[{"comment":"The full text is labeled arXiv:2508.03677v1 [cs.CL], while the abstract is drawn from arXiv:2508.03685; the identifiers do not match, compounding the difficulty of verifying the submission's provenance.","section":"Full Text, header"},{"comment":"Even the set of \"off-the-shelf\" LLMs is not defined; it is unclear whether this includes models available via API only, open-weight models, or both, and at what cutoff date.","section":"Abstract, criterion (e)"},{"comment":"If the unrelated full text is included by accident, its numerous typos and formatting issues (e.g., \"developement,\" \"progessively,\" \"refered\") should not be counted against the actual paper; however, the mismatch itself must be corrected before review can proceed.","section":"FairLangProc full text"}],"recommendation":"reject","confidential_remarks":"The submitted full text is a different paper (FairLangProc). I suspect a submission or pipeline mix-up; the editor may wish to return the manuscript to the authors to upload the correct PDF. Even with the correct PDF, the universal negative claim will require careful scrutiny of the evaluation protocol; the abstract as written gives no grounds for accepting it."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this submission is an abstract for a potentially interesting LLM evaluation claim, but the full text attached is a different paper (FairLangProc, arXiv:2508.03677). So there is no way to review the actual work. The abstract says Yu Tsumura's 554th problem is IMO-scope, non-combinatorial, proof-light, with a public solution, and that no off-the-shelf LLM can solve it. If the full paper documents a broad, careful evaluation, that would be a useful counterexample to IMO-medal hype and a sharper probe for math benchmarks. Credit for picking a concrete problem with a public solution and stating criteria explicitly.\n\nThe soft spots are severe. The universal negative \"cannot be readily solved by any existing off-the-shelf LLM\" is only as strong as the model set, prompts, sampling budget, and answer verification. The abstract gives none of these. LLM math performance is brittle to prompt phrasing and sampling; a narrow failure set would not justify the universal quantifier. Terms like \"readily solved,\" \"proof sophistication,\" and \"fewer proof techniques\" are undefined and hard to falsify. And the mismatch between abstract and full text means the submitted manuscript is not the paper. That alone is a desk-reject condition unless the authors uploaded the wrong file.\n\nWho is this for? LLM evaluation researchers tracking IMO-level reasoning; they would read a properly supported version of this paper. But as submitted, this is an abstract, not a reviewable article. The citation pattern is not assessable, and the data are absent. I would not spend referee time on it until the correct full text is provided with the evaluation protocol.\n\nRecommendation: return the submission to the authors to correct the file and to report models, prompts, compute budget, and verification. If those are present and the evaluation is robust, the paper deserves peer review.","headline":"A possibly useful LLM-evaluation counterexample, but the submitted full text is a different paper, so there is nothing reviewable here yet.","tokens_in":22924,"tokens_out":1852,"would_cite":false,"duration_ms":23033,"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":"Yu Tsumura's 554th problem is an olympiad-scope, non-combinatorial problem with a public solution that no off-the-shelf LLM can readily solve.","keywords":["large language models","mathematical reasoning","olympiad problems","International Mathematical Olympiad","Yu Tsumura's 554th problem","LLM problem solving","negative result","benchmark counterexample"],"falsifier":"Run Yu Tsumura's 554th problem against a broad panel of current off-the-shelf LLMs, using standard prompting and an explicit rule for what counts as a correct solution; if any model produces a correct accepted solution, criterion (e) is false. A second check is to verify that the solution is genuinely public and likely in training data, since criterion (d) is also load-bearing.","tokens_in":22000,"feed_emoji":"🧮","tokens_out":11697,"duration_ms":120031,"temperature":0.7,"pith_summary":"The paper sets out to demonstrate a single negative result: Yu Tsumura's 554th problem is an olympiad-scope problem that no off-the-shelf large language model, commercial or open-source, can readily solve, even though it is comparatively light on proof machinery and has a publicly available solution that is likely already in the models' training data. The authors argue that the problem is within the proof-sophistication scope of an International Mathematical Olympiad (IMO) problem, is not a combinatorics problem, and requires fewer proof techniques than typical hard IMO problems. If the claim holds, it undercuts the optimism fueled by recent gold-medal results on olympiad-style problems: a public, in-scope problem exists that separates current off-the-shelf LLMs from human solvers. The paper's contribution is the identification of a specific counterexample to the idea that recent LLM successes on olympiad problems generalize broadly.","feed_headline":"No off-the-shelf LLM solves one public olympiad problem","feed_subtitle":"The problem fits IMO proof scope, is not combinatorics, has a public solution, and still no tested model solves it.","key_machinery":"The central object is Yu Tsumura's 554th problem itself, used as a probe of LLM mathematical reasoning. The load-bearing properties are the five criteria stated in the abstract: olympiad-level proof sophistication, non-combinatorial content, low proof-technique demand, a public solution that is likely in training data, and failure across tested off-the-shelf models. The mechanism of the argument is selection: by choosing a problem that is easy on the dimensions that should favor LLMs — public solution, low proof complexity, and no combinatorics — the authors make a reported failure carry more weight than a failure on a hard, obscure, or combinatorial problem would. The problem functions as a controlled test instance for separating current off-the-shelf LLM capability from human-level olympiad solving.","core_discovery":"In the paper's own terms, the discovery is that Yu Tsumura's 554th problem satisfies five criteria simultaneously. It is within the proof-sophistication scope of an IMO problem. It is not a combinatorics problem, so the failure cannot be blamed on the genre that has historically caused LLMs difficulty. It requires fewer proof techniques than typical hard IMO problems. Its solution is publicly available and likely in the training data of the models. And yet, the paper asserts, no existing off-the-shelf LLM — commercial or open-source — can readily solve it. The problem thus stands as a counterexample to the claim that LLMs with olympiad-level medal results can handle the full range of olympiad-scope mathematics.","pith_inferences":["The full text supplied with the abstract is a different manuscript, so the models, prompts, sampling budgets, and answer-checking behind the failure claim are not visible in the provided material; the sweeping claim about all off-the-shelf LLMs therefore cannot be checked from what is supplied.","A natural testable extension is to run neighboring problems from the same source as Yu Tsumura's 554th problem, if such a numbered list exists, to see whether the failure is specific to this problem or common across the collection.","Another extension is to give models more sampling or stronger prompting while keeping them off-the-shelf; success under those conditions would not refute the paper's claim but would show how close current models are to solving it.","If the result replicates, it suggests that obscure olympiad-scope problems with public solutions may be a more realistic measure of LLM mathematical reasoning than high-profile medal benchmarks."],"forward_implications":["If the claim holds, a public solution in the training data is not enough for an off-the-shelf LLM to reproduce or apply that solution on demand.","If the claim holds, olympiad medal results by LLMs cannot be read as evidence that the models can solve olympiad-scope problems generally, because a deliberately easy, non-combinatorial, public-solution problem remains unsolved.","If the claim holds, the problem becomes a concrete benchmark item: any off-the-shelf LLM that solves it under standard prompting would refute criterion (e).","If the claim holds, failure is not confined to combinatorics, so explanations of LLM olympiad failure that point only at combinatorial reasoning are incomplete."],"supporting_citations":[],"fun_headline_variants":["Public olympiad problem defeats every LLM tested","554th problem: known solution, yet no LLM solves it","IMO-scope problem with public answer stumps all LLMs","No LLM cracks Yu Tsumura's 554th problem despite public solution"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The claim that no existing off-the-shelf LLM can readily solve the problem depends on the assumption that the models, prompts, sampling budget, and answer-verification method used in the tests fairly represent all off-the-shelf LLMs; the abstract reports none of these details, and the accompanying full text is a different manuscript.","fun_headline_variants_meta":{"raw":{"variants":["Public olympiad problem defeats every LLM tested","554th problem: known solution, yet no LLM solves it","IMO-scope problem with public answer stumps all LLMs","No LLM cracks Yu Tsumura's 554th problem despite public solution"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00112,"raw_usage":{"total_tokens":4584,"prompt_tokens":795,"completion_tokens":3789,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":411,"completion_tokens_details":{"reasoning_tokens":3716}},"tokens_in":411,"tokens_out":3789,"duration_ms":29632,"temperature":1.0,"reasoning_tokens":3716,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T04:12:11.452054+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run Yu Tsumura's 554th problem against a broad panel of current off-the-shelf LLMs, using standard prompting and an explicit rule for what counts as a correct solution; if any model produces a correct accepted solution, criterion (e) is false. A second check is to verify that the solution is genuinely public and likely in training data, since criterion (d) is also load-bearing.","supporting_citations":[],"review_version":1}