{"id":"a2697a84-3058-4dba-b8d2-271dbc688060","arxiv_id":"2508.11252","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Per the abstract, large reasoning models systematically fail to ask for missing information on under-specified math problems, a skill standard benchmarks never test.","lead":"This work, according to its abstract, adds a benchmark of underspecified math problems and finds that large reasoning models usually guess instead of asking for missing information. The result matters because question-asking is a core real-world skill that current benchmarks never measure, though the submitted body text is an unrelated paper and the study could not be verified.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Central claim is unverifiable: supplied full text is an unrelated nuclear physics paper, so the dataset and evaluation protocol supporting 'LRMs fail to ask' are absent.","rationale":"The reader's verdict is UNVERDICTED, and the stress-test pass identifies the same fundamental issue: the central claim cannot be assessed because the methods and data are missing. The reader's weakest assumption (benchmark ground truth) is correct, but it is impossible to test that assumption without the actual evaluation text; the supplied full text is an unrelated paper. The concern is not about the internal logic of a described experiment but about the total absence of that experiment. This is the single most load-bearing weakness because everything in the abstract — the dataset, the evaluation, the findings about overthinking and hallucination, and the SFT results — depends on an empirical protocol that does not appear in the submitted manuscript. The concrete test (retrieving the true full text from arXiv) would settle whether this absence is a submission/pipeline artifact or the actual state of the paper. Since the reader already declared the paper UNVERDICTED, this stress test does not change the verdict; it reinforces it. No adjustment is needed.","tokens_in":8960,"tokens_out":4391,"duration_ms":48682,"concrete_test":"Query the arXiv API (or the arXiv listing) for the paper whose abstract is 'Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information', and retrieve its full text. If the returned full text is the 162Lu nuclear physics manuscript (i.e., the supplied body belongs to arXiv:2508.11251v2), then the evaluation protocol for the central claim is absent and the claim is unsupported. If instead an AI-related full text is recovered, then inspect that true body to verify whether the dataset includes a human baseline and whether the scoring rubric requires identifying the specific missing information (rather than accepting any question).","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract's central claim is an empirical generalization: large reasoning models systematically fail to ask for missing information. This claim can only be supported by the dataset of incomplete problems and the evaluation rubric that decides when asking is correct. The full text submitted for review, however, is a nuclear physics paper titled 'Low-lying level structures in 162Lu' (arXiv:2508.11251v2 [nucl-th]), which contains no LRMs, no dataset, no rubric, and no evaluation results. Consequently, the reported inability could be an artifact of a flawed measurement instrument: the incomplete problems might be solvable under a reasonable reading, the rubric might reward any interrogative output rather than a specific clarifying request, or the models might have been penalized for asking appropriate questions. None of these alternatives can be checked against the supplied manuscript. This is a missing-support concern, not an ad hominem: the load-bearing premise of the argument is that the evaluation measured what it claims, yet that premise is entirely absent from the submitted text. The reader's weakest assumption about benchmark ground truth is precisely the part that cannot be validated, because the benchmark itself is not described anywhere in the full text.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The submission consists of an abstract claiming a systematic evaluation of Large Reasoning Models (LRMs) on incomplete mathematics problems, followed by a full text that is actually a nuclear-structure paper, arXiv:2508.11251v2 [nucl-th], 'Low-lying level structures in 162Lu'. The abstract's central claim is that LRMs fail to proactively ask for information when problems lack sufficient information, and that this inability is revealed by a new dataset of two types of incomplete problems. The submitted body text contains no dataset description, no model list, no evaluation protocol, no metrics, no baseline, and no results related to LRMs. Consequently, the central empirical claim and the benchmark on which it rests are entirely unsupported in the submitted manuscript.","tokens_in":9039,"tokens_out":2001,"duration_ms":26322,"significance":"The high-level evaluation goal is timely and potentially important: if LRMs perform well on well-posed math problems but systematically fail to ask clarifying questions on underspecified problems, this would be a meaningful limitation for deployment and a useful benchmark contribution. However, the submitted text provides no methods or results that could support such a claim. No dataset, rubric, model, or evaluation artifact is present to assess. The significance of the claimed finding therefore cannot be evaluated beyond the abstract's assertion.","major_comments":[{"comment":"The body of the manuscript is arXiv:2508.11251v2 [nucl-th], 'Low-lying level structures in 162Lu', which is a nuclear-structure study with no content on LRMs, incomplete math problems, dataset construction, or evaluation results. The abstract and the full text are from different papers. The load-bearing premise that a systematic evaluation was performed is therefore entirely unsupported in the submitted text. This is not a local typographical or formatting issue; the evidence for the central claim is absent.","section":"Full Text / overall manuscript"},{"comment":"Even if the abstract is considered alone, it does not specify dataset size, model list, prompting protocol, evaluation metrics, or a human/expert baseline. The phrase 'our systematical evaluation of LRMs reveals their inability in proactively asking for information' cannot be checked without the underlying dataset and rubric. In particular, the ground truth for when asking is the correct behavior, and the criteria for what counts as a useful clarifying question, are undefined. Without these details, the reported inability could be an artifact of rubric design; the manuscript provides no way to rule this out.","section":"Abstract"}],"minor_comments":[{"comment":"Grammar and wording: 'we proposes' should be 'we propose'; 'systematical' is non-standard (use 'systematic'); 'responding users' requests' is unclear and should be rephrased, e.g., 'in responding to users' requests'.","section":"Abstract"},{"comment":"The arXiv identifier in the abstract context (2508.11252) does not match the header of the supplied full text (arXiv:2508.11251v2 [nucl-th]). This mismatch is consistent with the wrong full text being attached.","section":"Header/Abstract"}],"recommendation":"reject","confidential_remarks":"The submitted full text appears to be a different paper entirely. This is a missing-support concern at the most fundamental level: none of the methodology or results needed to evaluate the abstract's claims is present. I recommend rejection; if a correct manuscript is submitted, it would need fresh review from scratch. I am not commenting on the merits of the nuclear-physics text, which appears unrelated to the claimed contribution."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"You should know the punchline immediately: the abstract describes an AI benchmark on whether large reasoning models ask clarifying questions, and the supplied full text is a nuclear physics paper about level structures in 162Lu. Same document, two different papers. This is not a case where the body is weak or missing details; it is a case where the load-bearing evidence for the claimed finding is entirely absent.\n\nThat said, there is a genuinely good idea in the abstract. Existing math benchmarks only test whether models solve well-defined problems. Asking whether models recognize underspecification and proactively request missing information is an important, under-tested evaluation dimension. If the claimed result holds—that LRMs systematically fail to ask—that would be a useful correction to the overly optimistic picture from math-answer benchmarks. The authors also flag overthinking and hallucination as related behaviors, which is a sensible direction.\n\nBut the central claim cannot be checked. The empirical finding depends on the dataset construction, the rubric that decides what counts as an appropriate question, and a human baseline. None of that appears in the submitted text. The abstract alone gives no dataset size, no model list, no metrics, no error bars. The phrase \"systematical evaluation\" is doing a lot of work. The reported inability could easily be an artifact of the measurement instrument: the problems might be solvable under a reasonable reading, or the rubric might reward any interrogative output, or the models might actually ask good questions that get penalized. We have no way to tell.\n\nThis is not a subtle objection. The paper cannot be verified or falsified on its merits because the paper we are asked to review is not the paper described in the abstract. The mismatch should be flagged as a desk-reject-level integrity issue, not a minor packaging problem.\n\nIf the actual evaluation paper exists, I would be happy to see it resubmitted with the correct full text. The idea deserves serious refereeing. But this submission, as it stands, is not a citable work and does not merit referee time.\n\nRecommendation: desk reject. If the authors resubmit the actual paper, then send it to peer review.","headline":"The abstract and the full text are different papers; the evaluation claim is unverifiable from what is submitted.","tokens_in":9673,"tokens_out":1744,"would_cite":false,"duration_ms":21302,"reading_group":"no","serious_thinker":"no","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Large reasoning models that handle well-defined math problems ably nevertheless fail to ask for missing information when problems are underspecified, according to a new evaluation dataset, and the ability to ask is only partially learnable","keywords":["large reasoning models","clarifying questions","underspecified problems","proactive information seeking","benchmark dataset","supervised fine-tuning","math reasoning","hallucination"],"falsifier":"Manually re-annotate the dataset to see whether a large share of problems can actually be solved under a reasonable assumption by human solvers; if so, the reported inability to ask would be a dataset artifact rather than a model deficiency. Alternatively, show one large reasoning model that, when explicitly instructed to ask for missing information, asks informative questions at a human-comparable rate on the same problems; that would confine the paper's inability claim to a particular prompting or training regime.","tokens_in":8702,"feed_emoji":"❓","tokens_out":4201,"duration_ms":50258,"temperature":0.7,"pith_summary":"The paper tries to show that large reasoning models, despite their strength on fully specified math problems, do not proactively ask for clarification when a problem lacks necessary information. To test this, it introduces a new dataset of two types of incomplete problems across diverse contexts and evaluates current large reasoning models on them. The evaluation reveals a systematic inability to ask for information, along with observable behaviors of overthinking and hallucination. Supervised fine-tuning can induce some asking behavior but does not fully close the gap. If the paper is right, existing benchmarks overstate the readiness of these models for real-world requests, where information is often missing or ambiguous.","feed_headline":"AI math whizzes fail to ask when problems omit information","feed_subtitle":"New benchmark of underspecified problems shows guessing and hallucination where asking is required; fine-tuning helps only partly.","key_machinery":"The evaluation is carried by a new dataset of incomplete math problems, organized into two types of underspecification with diverse contexts. This dataset defines the target behavior: when a problem lacks sufficient information, the correct response is to ask for what is missing rather than to solve or guess. It turns the ability to ask for information into a measurable capability separate from problem-solving skill, and provides the basis for evaluating and fine-tuning large reasoning models.","core_discovery":"The central claim is that large reasoning models lack the ability to proactively ask for information when presented with problems that do not contain sufficient information to be solved. The paper grounds this claim in a new dataset of two types of incomplete problems with diverse contexts. On this evaluation, the models tend to answer, guess, or reason from unstated assumptions rather than request the missing details, and their responses exhibit overthinking and hallucination. The paper also reports that supervised fine-tuning on such incomplete problems can improve asking behavior to a degree, but the ability remains incomplete. This positions 'asking for information' as a distinct capabil","pith_inferences":["A natural next step would be to test whether asking behavior transfers across domains such as code generation, medical diagnosis, or customer support, or whether it remains confined to math word problems unless trained broadly.","If supervised fine-tuning only partially teaches asking, a plausible hypothesis is that reinforcement learning with a reward for informative clarifying questions, or an explicit ask-then-solve protocol, would yield larger gains than fine-tuning alone.","The reported hallucination behavior suggests a measurable extension: compare the frequency of fabricated constraints or invented assumptions before and after fine-tuning, to see whether asking training reduces hallucination or merely relocates it."],"forward_implications":["Current benchmarks that use only well-defined problems overstate how ready large reasoning models are for real user requests, which are frequently incomplete or ambiguous.","Proactive asking can be treated as a separate, trainable capability rather than an automatic byproduct of strong reasoning.","Supervised fine-tuning on underspecified problems can produce some asking behavior, suggesting the ability is at least partially learnable with the right data.","Overthinking and hallucination are concrete failure modes that surface when models face underspecified problems, giving future work specific targets to measure and reduce.","Evaluation of intelligent agents should include tasks where the correct action is to request information, not merely to produce an answer."],"supporting_citations":[],"fun_headline_variants":["Math AI can't ask for missing info, study finds","Reasoning models guess instead of asking when data's missing","Asking for info: the missing skill in large reasoning models","When math problems omit details, AI fails to ask","LRMs struggle to request missing information in math"],"cache_read_input_tokens":2816,"weakest_assumption_plain":"The evaluation rests on the assumption that the incomplete problems are genuinely underspecified in the way real requests are, and that the scoring rubric recognizes useful clarifying questions rather than rewarding generic filler; the abstract reports no dataset statistics, rubric details, or human baseline, so this premise cannot be checked from the paper's own description.","fun_headline_variants_meta":{"raw":{"variants":["Math AI can't ask for missing info, study finds","Reasoning models guess instead of asking when data's missing","Asking for info: the missing skill in large reasoning models","When math problems omit details, AI fails to ask","LRMs struggle to request missing information in math"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000854,"raw_usage":{"total_tokens":3504,"prompt_tokens":656,"completion_tokens":2848,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":400,"completion_tokens_details":{"reasoning_tokens":2769}},"tokens_in":400,"tokens_out":2848,"duration_ms":23711,"temperature":1.0,"reasoning_tokens":2769,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T20:03:01.010767+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Manually re-annotate the dataset to see whether a large share of problems can actually be solved under a reasonable assumption by human solvers; if so, the reported inability to ask would be a dataset artifact rather than a model deficiency. Alternatively, show one large reasoning model that, when explicitly instructed to ask for missing information, asks informative questions at a human-comparable rate on the same problems; that would confine the paper's inability claim to a particular prompting or training regime.","supporting_citations":[],"review_version":1}