{"id":"1621265b-6df6-4320-98e0-d8abae0c6eeb","arxiv_id":"2509.07711","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A two-track Olympiad benchmark with deterministic integer answers and step-by-step proof grading shows frontier LLMs drop sharply versus older math benchmarks.","lead":"RIMO is a new benchmark that rewrites 335 IMO problems into a single-integer-answer format and decomposes 456 proof problems into graded sub-problems. Ten LLMs score much lower on it than on GSM8K and MATH, exposing a gap in Olympiad-level reasoning.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"RIMO-N's integer rewriting is asserted to preserve IMO difficulty but never validated; without equivalence/human-check evidence, the 'profound reasoning gap' conclusion may reflect answer-format and exact-match grading effects rather than Olympiad-level reasoning.","rationale":"The paper contributes a useful, deterministically gradable integer-answer benchmark and documents a large performance drop on it. However, the strongest empirical claim depends on the unvalidated assumption that converting IMO proof problems into single-integer questions preserves their logical difficulty. The paper asserts this in Section 3.1 but provides no human solve-rate comparison, no formal equivalence analysis, and no original-vs-remade model evaluation. The reported binary-subset results show that answer-space size alone can shift accuracy by up to 30 points, so format effects are real and potentially large. The RIMO-P track's reliance on deepseek-r1 as an LLM judge and the absence of a human baseline are additional concerns, but the difficulty-preservation issue is the most load-bearing for interpreting RIMO-N as evidence of an IMO-level reasoning gap. My read keeps the reader's CONDITIONAL verdict: the benchmark is promising and the deterministic grading is a genuine improvement, but the central interpretive claim needs direct validation before it should be accepted at face value.","tokens_in":10551,"tokens_out":7257,"duration_ms":78012,"concrete_test":"Take a stratified random sample of 40–50 RIMO-N problems spanning all four topic areas and answer magnitudes. For each, administer both the original IMO statement and the remade integer version to a panel of IMO-level human solvers under randomized order, identical time limits, and independent rubric-based grading. If the remade versions are solved correctly at a substantially higher rate (e.g., >20% relative) than the originals, the Section 3.1 difficulty-preservation assumption fails and RIMO-N scores cannot be interpreted as measuring IMO-level reasoning. As a complementary automated check, run the same ten models on both versions and compare pass@1 with identical exact-match grading; a large divergence would quantify the reformatting effect.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central empirical claim—that RIMO-N scores expose a 'profound reasoning gap' at IMO level—rests on Section 3.1's assertion that rewriting preserves 'the logical core and difficulty of the source problem.' No evidence is given for this. The transformations are not merely cosmetic: a concurrency proof becomes 'how many common points do the circumcircles have,' and a classification of integer triples becomes 'compute a+b+c.' Such reformulations can turn proof-construction into a finite decision/computation and impose all-or-nothing exact-match grading. The paper itself shows in §A.2 that exact-match grading can mark mathematically identical answers incorrect, and it reports that 96/335 RIMO-N answers are 0 or 1 and that accuracy jumps 8–30 points on that subset—confirming that output-space size materially changes measured performance. Without a difficulty-preservation check, the observed drop from MATH/GSM8K could be partly an artifact of the integer-answer format and grading protocol, not a clean measure of IMO-level reasoning. This is load-bearing because the paper uses the drop to claim a 'profound reasoning gap' and to market RIMO as a high-resolution yardstick.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces RIMO, a two-track benchmark built from IMO problems (1959–2023). RIMO-N contains 335 problems rewritten to have a single unique integer answer, graded by exact string match; RIMO-P contains 456 proof problems decomposed into guided sub-problems, graded by a sequential protocol in which a deepseek-r1 LLM judge decides correctness of each sub-solution. The authors evaluate ten LLMs on RIMO-N and three on RIMO-P, reporting large accuracy drops relative to GSM8K and MATH, and derive claims about scale, recency, reasoning optimization, and a persistent 'proof gap.' The central contribution is a deterministic, reproducible integer-answer track that appears to be substantially harder than existing benchmarks.","tokens_in":71,"tokens_out":3192,"duration_ms":112264,"significance":"If the difficulty-preservation claim for RIMO-N is substantiated, this benchmark would be a valuable, low-noise yardstick for Olympiad-level mathematical reasoning. The exact-match integer scoring is robust, the multi-source verification of ground-truth answers is a strength, and the analysis of the binary-answer subset is an insightful diagnostic. However, the strong conclusion of a 'profound reasoning gap' rests on an unvalidated assumption that rewriting preserves problem difficulty, and the RIMO-P track reintroduces an LLM judge, contradicting the paper's stated goal of eliminating evaluation noise. With additional validation, the benchmark could become a useful community resource; in its current form, the claims outrun the evidence.","major_comments":[{"comment":"The central claim that rewriting IMO problems into single-integer form preserves 'the logical core and difficulty' is asserted without evidence. The examples are not merely cosmetic: a concurrency proof becomes a counting question, and a classification of triples becomes a single numerical computation. These transformations can change the nature of the task (from proof construction to finite search/computation) and impose all-or-nothing exact-match grading. The paper's own §4.1.1 (Figure 9) shows that restricting to binary answers improves accuracy by 8–30 points across models, demonstrating that output-space size materially affects measured performance. Without a difficulty-equivalence check—e.g., human solvers on original vs. rewritten problems, or an independent expert rating of the rewritten items—the observed drop from MATH/GSM8K cannot be cleanly attributed to 'Olympiad-level reaso","section":"§3.1, Figure 3"},{"comment":"RIMO-P grading is performed by deepseek-r1, which is itself one of the ten benchmarked models and the strongest on RIMO-N. This reintroduces exactly the type of model-based evaluation noise the paper promises to eliminate. No validation of the judge is provided (e.g., agreement with human expert graders on a sample), and the sequential rubric is prompt-based, so the reported 'step-by-step reasoning' scores are not noise-free. The claim that current models have a 'profound deficit in deductive ability' is therefore only as strong as the unvalidated judge. The authors should either validate the judge, use a judge that is not among the evaluated models, or present RIMO-P as a preliminary model-judged track rather than as part of a noise-free benchmark.","section":"§4.1.2, Eq. (1), Figure 11"},{"comment":"Only three models (Qwen3-8B, InternLM3-8B-instruct, Mathstral-7B) are evaluated on RIMO-P. The conclusions about domain-specific fine-tuning, recency versus training type, and the 'proof gap' relative to 'advanced human students' are drawn from this very small sample, and no human data are actually reported. The statement in §5 that 'advanced human students, all of whom used the same rubric' exhibit better performance is unsupported because no human-baseline results are given. The authors should either include human baseline scores or soften the corresponding claims to reflect the lack of direct comparison.","section":"§4.1.2, §5"}],"minor_comments":[{"comment":"The abstract says the benchmark 'eliminates evaluation noise' but RIMO-P uses an LLM judge; the wording should be qualified to refer only to RIMO-N, or the RIMO-P grading should be described as 'reduced-noise' rather than noise-free.","section":"Abstract"},{"comment":"For RIMO-P, the '# Unif.' column is marked with a dash; it would be helpful to clarify that the sub-problem outputs are not uniform in format, and that the 'Noise' value 'LLM' indicates the intended contrast with RIMO-N.","section":"Table 2"},{"comment":"The prompt header says 'eavluate' (typo). Also, the Omni-MATH judge prompt is excessively long for an appendix example; consider trimming.","section":"A.1"},{"comment":"The phrase 'Scores jump by 8 to 30 percentage points across in every baseline' has a grammatical issue ('across in'). Also, the Spearman correlation of 0.21 is reported without a p-value or confidence interval; given only ten data points, its interpretation is limited.","section":"§4.1.1"},{"comment":"The sentence 'Each shortlist problem retains the jury’s official integer' is unclear: if a shortlist problem is rewritten, it is not the original official integer but a new derived integer. Clarify the relationship between the rewriting and the official answer.","section":"§3.1"},{"comment":"The phrase 'expert-verified proofs' is not operationalized; specify the expertise of the verifiers and whether inter-rater agreement was measured.","section":"§3.2"}],"recommendation":"major_revision","confidential_remarks":"The manuscript's main strength is the RIMO-N integer-answer design, which genuinely enables deterministic grading. The difficulty-preservation assumption is the most important risk; a careful human or expert study would significantly strengthen the claims. The RIMO-P track is currently a weak point because the judge is one of the evaluated models; the authors may need to reposition RIMO-P as a preliminary effort or provide judge validation. The small number of RIMO-P models also limits the generality of the proof-gap conclusions. Overall, the paper is within scope for the journal and the core idea is sound, but the load-bearing evidence is missing."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"RIMO is a solid benchmark paper with one unvalidated load-bearing assumption. The RIMO-N integer rewrite track is a real step forward for reproducible evaluation, and the empirical results are worth having. But the paper's strong claims—'profound reasoning gap,' 'peak Olympiad difficulty preserved'—are not supported by the evidence presented, mainly because difficulty preservation is asserted, not demonstrated.\n\nWhat's genuinely new: 335 IMO problems rewritten so each has a single integer answer. That's a concrete improvement over Omni-MATH's LLM-judge pipeline and OlymMATH's numeric-but-heterogeneous answers. The sequential sub-problem grading for RIMO-P is also a useful idea, though its execution has problems. The binary-answer analysis in §4.1.1 is a good self-check, and the table of ten models under greedy decoding is clean and reproducible.\n\nThe main soft spot is the central one. Section 3.1 says the logical core and difficulty remain intact, but the paper gives no evidence. Rewriting a concurrency proof into 'how many common points' changes the cognitive task; you even show that the output-space size matters—accuracy jumps 8–30 points on the 96 problems whose answers are 0 or 1. That doesn't make the benchmark useless, but it means the observed drop from MATH/GSM8K is partly a format effect, so the 'profound gap' language is overreach. Also, RIMO-P's judge is deepseek-r1, one of the benchmarked models, so the proof track does not deliver the promised noise-free evaluation; only three models are graded on it; and the Discussion's claim about a 'large gap' versus advanced human students is not backed by any human data in the paper. These are real issues, but they are fixable.\n\nThe paper is honest, the writing is clear, and it cites related work fairly. It deserves peer review, though a serious reviewer should push for a difficulty-preservation check and a more measured framing. I'd cite it, with a caveat about the format effect.","headline":"A useful integer-answer IMO benchmark whose 'profound reasoning gap' claim outruns the evidence, because difficulty preservation is asserted rather than shown.","tokens_in":11296,"tokens_out":3179,"would_cite":true,"duration_ms":37029,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper introduces RIMO, a benchmark of IMO problems rewritten to admit one unique integer answer, and reports that the top of ten frontier LLMs scores 62.96% while smaller models fall below 25%, a large drop from their scores on GSM8K an","keywords":["mathematical reasoning benchmark","International Mathematical Olympiad","LLM evaluation","exact-match grading","proof decomposition","answer sparsity","RIMO-N","RIMO-P"],"falsifier":"For a random sample of RIMO-N items, have expert solvers attempt both the original IMO statement and the rewritten integer version and compare solve rates and rated difficulty; if the rewritten forms are solved substantially more often, the difficulty-preservation claim fails. A model-level version: run the same ten models on both forms and check whether original-form accuracy is materially lower than rewritten-form accuracy.","tokens_in":10486,"feed_emoji":"🏅","tokens_out":6518,"duration_ms":66470,"temperature":0.7,"pith_summary":"RIMO is a two-track benchmark built from International Mathematical Olympiad problems published between 1959 and 2023. The paper's central claim is that once IMO problems are rewritten so each admits a single unique integer answer—enabling exact string-match grading—frontier LLMs show a large performance drop relative to their near-saturated scores on GSM8K and MATH. The best model evaluated, DeepSeek-R1-671B, reaches 62.96% on the numeric track, while several 7–8B models score below 25%. The proof track, RIMO-P, decomposes 456 problems into guided subproblems and reports even lower performance, which the paper interprets as evidence that answer-finding and rigorous proof-writing are distinct capabilities. If the difficulty-preserving rewrite is sound, RIMO supplies a reproducible, high-resolution yardstick for tracking progress toward Olympiad-level reasoning.","feed_headline":"Best AI scores just 63% on remade Olympiad problems","feed_subtitle":"New RIMO benchmark gives every IMO problem one integer answer; models that ace MATH and GSM8K still fall short.","key_machinery":"The load-bearing mechanisms are (1) the integer-remake: IMO statements are rewritten—tightening hypotheses, renaming variables, reframing objectives—so the answer is a unique integer, making grading a constant-time string comparison with no LLM judge; and (2) the proof decomposition: each RIMO-P problem is split into one to four guided subproblems derived from an expert-verified solution, and a model is credited only for consecutive correct steps according to a strict judge prompt. The integer-remake is what gives RIMO-N its zero-noise claim, and the binary-answer subset is used as a probe to show how answer-space size contributes to measured hardness.","core_discovery":"The discovery is stated as an empirical gap: on RIMO-N, all ten evaluated models drop sharply compared with GSM8K and MATH, with scores ranging from 15.82% to 62.96%. The gap is not uniform: when the answer is restricted to 0 or 1, every model's score rises by 8 to 30 percentage points, indicating that part of the difficulty is locating an exact integer on a wide numerical spectrum. Reasoning-optimized models consistently outperform size-matched vanilla models, by up to 19.4 points, so the paper argues that explicit reasoning training, rather than scale or recency, is what moves the needle at this level. On RIMO-P, models solve only a small proportion of consecutive proof subproblems, leavin","pith_inferences":["The authors implicitly assume that their rewrite preserves difficulty; a straightforward control—running the same models on original statements under an exact or normalized grader—would quantify any reformatting effect and is not reported.","The binary-answer boost suggests the measured gap is partly a search-space effect: models may know the truth of a claim yet fail to name the integer. That distinction could be tested by giving models narrowed ranges or scaffolding.","RIMO-P's step grading is performed by deepseek-r1, so despite RIMO-N's exact-match design, the proof track still carries LLM-judge noise; cross-checking a sample with independent judges or formal proof checking would establish how much.","If the difficulty-preservation assumption holds, the integer-remake technique could turn other hard problem sets, such as shortlist problems, into exact-match evaluation tasks."],"forward_implications":["A model that clears the current best RIMO-N score of 62.96% under the same greedy-decoding protocol would demonstrate a substantive advance over frontier models on IMO-level numeric reasoning, not just on saturated benchmarks.","RIMO-P extends the same material into a process-oriented measurement: because scores are low and answer-finding does not guarantee proof success, it can separate models that retrieve answers from models that can sustain deductive chains.","The finding that binary-answer items inflate accuracy by 8 to 30 points implies that answer-space restriction is itself a difficulty dial for future benchmark design.","The 0.21 Spearman correlation between release date and accuracy suggests that incremental model releases should not be expected to improve Olympiad reasoning; progress has to be checked specifically on this kind of benchmark.","The RIMO-P results point to step verification, external tools, and explicit proof planning as likely levers for closing the proof gap, since sheer scale or recency did not produce it."],"supporting_citations":[{"why":"Provides the GSM8K baseline whose near-saturation motivates a harder benchmark; the paper compares RIMO-N scores against it.","marker":"Cobbe et al., 2021"},{"why":"Provides the MATH dataset and its high baseline scores; RIMO-N results are framed as a sharp drop from MATH.","marker":"Hendrycks et al., 2021"},{"why":"Serves as the main prior Olympiad-scale benchmark whose LLM-judge evaluation noise RIMO-N's integer format is designed to remove.","marker":"Gao et al., 2024"},{"why":"Prior Olympiad benchmark with numeric answers that still need symbolic equivalence logic, establishing the grading-noise problem RIMO addresses.","marker":"Sun et al., 2025"},{"why":"Dynamic competition with hidden test sets that motivates RIMO's static, reproducible evaluation design.","marker":"Frieder et al., 2025"}],"fun_headline_variants":["Best AI scores 63% on hardest math benchmark yet","New benchmark RIMO stumps top models, scores drop sharply","Why AI ace GSM8K but fall short on Olympiad-style RIMO","Integer answers expose AI's math reasoning gap","RIMO: Easy to grade, brutal for AI, scores 15-63%"],"cache_read_input_tokens":2688,"weakest_assumption_plain":"The load-bearing premise is that rewriting an IMO problem to produce a unique integer answer does not reduce its logical difficulty; if the rewrites systematically simplify the problems, RIMO-N's sharp drop measures reformatting rather than Olympiad reasoning.","fun_headline_variants_meta":{"raw":{"variants":["Best AI scores 63% on hardest math benchmark yet","New benchmark RIMO stumps top models, scores drop sharply","Why AI ace GSM8K but fall short on Olympiad-style RIMO","Integer answers expose AI's math reasoning gap","RIMO: Easy to grade, brutal for AI, scores 15-63%"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000775,"raw_usage":{"total_tokens":3291,"prompt_tokens":795,"completion_tokens":2496,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":539,"completion_tokens_details":{"reasoning_tokens":2407}},"tokens_in":539,"tokens_out":2496,"duration_ms":18988,"temperature":1.0,"reasoning_tokens":2407,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-04T21:47:00.421264+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"For a random sample of RIMO-N items, have expert solvers attempt both the original IMO statement and the rewritten integer version and compare solve rates and rated difficulty; if the rewritten forms are solved substantially more often, the difficulty-preservation claim fails. A model-level version: run the same ten models on both forms and check whether original-form accuracy is materially lower than rewritten-form accuracy.","supporting_citations":[{"cited_title":"C., Buzzard, K., Gowers, T., Liu, P","cited_arxiv_id":null,"evidence_quote":"Dynamic competition with hidden test sets that motivates RIMO's static, reproducible evaluation design."}],"review_version":1}