{"id":"78290ac8-b811-41b9-9574-54e638feb6b1","arxiv_id":"2508.15842","paper_version":1,"verdict":"REJECT","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":2,"one_line_summary":"Hesitation words in reasoning chains are claimed to flag incorrect LLM answers, but the manuscript body is a different paper and contains no such study.","lead":"A study claims that words like 'guess' or 'stuck' in a model's chain of thought reliably flag wrong answers, and that reasoning length only helps on easier problems. The submitted manuscript, however, contains a different, unrelated paper on AI cybersecurity risk, so the claimed analysis is absent.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Central claim is unsupported: the submitted body is an unrelated CIA+TA risk-assessment paper (footer arXiv:2508.15839v1), with no CoT experiments, marker definitions, HLE/Omni-MATH results, or code.","rationale":"The reader's REJECT verdict is justified by the structural mismatch between abstract and body. My concern overlaps with the reader's weakest_assumption: the reader identifies label leakage in marker selection as the load-bearing premise; I agree that this premise is unverifiable, but I would place the more fundamental problem one level earlier—the submitted manuscript contains no CoT study whatsoever, so even a label-blind marker-selection protocol cannot be evaluated. This is not a manufactured objection; the footer of the provided full text (arXiv:2508.15839v1) and the complete absence of HLE, Omni-MATH, DeepSeek-R1, or Claude 3.7 terms in the body are objective facts. The proposed check isolates the decisive question: does an authoritative version of 2508.15842 exist that actually reports the claimed experiments, and if so, is the marker selection provably label-blind? Passing that check would change my verdict; failing it confirms REJECT. Status: UNCHANGED because my concern does not move the reader's conclusion.","tokens_in":10439,"tokens_out":4168,"duration_ms":46208,"concrete_test":"Obtain the authoritative full text of arXiv:2508.15842 (e.g., the PDF/source on arXiv or the authors' repository) and verify whether it contains a CoT-lexical-analysis section. If it does, locate the marker and sentiment-feature construction and confirm, from a preregistration, training/validation split, or commit history, that the uncertainty lexicon was fixed before HLE/Omni-MATH correctness labels were inspected. If the CoT section is absent, or the markers were selected/tested on the same labels, the central claim is a fit rather than a prediction; if both conditions hold, the rejection should be withdrawn.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract's central finding—that lexical uncertainty markers (e.g., 'guess', 'stuck', 'hard') in CoT are the strongest predictors of incorrect responses and provide a calibration signal—depends on an empirical protocol that is entirely absent from the submitted manuscript. The full text is 'CIA+TA Risk Assessment for AI Reasoning Vulnerabilities' by Yuksel Aydin, whose running footer identifies it as arXiv:2508.15839v1. None of the claimed analysis appears: no chain-of-thought traces, no uncertainty-lexicon construction, no sentiment feature definitions, no HLE/Omni-MATH sample sizes, no AUC/calibration tables, no comparison to self-reported probabilities. In particular, the reader's weakest-assumption concern—that markers such as 'guess'/'stuck'/'hard' may have been chosen after inspecting correctness labels—is unanswerable from this text because there is no feature-engineering section at all. The body's limitation section (§5.3) and self-referential notes concern the cybersecurity experiments and cannot support the abstract's CoT claims. This is a structural 'claim without derivation', not an internal inconsistency; but as submitted, the finding is unfalsifiable. If the authoritative version of 2508.15842 exists and contains the missing analysis, the verdict should be re-assessed against that content.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The arXiv metadata and abstract describe an empirical study of chain-of-thought (CoT) features as calibration signals: lexical markers of uncertainty (e.g., 'guess', 'stuck', 'hard') are claimed to be the strongest indicators of incorrect responses, with sentiment volatility a weaker complementary signal and CoT length informative only on Omni-MATH. The analysis is said to use DeepSeek-R1 and Claude 3.7 Sonnet on HLE and Omni-MATH. However, the submitted full text is a different paper, titled 'CIA+TA Risk Assessment for AI Reasoning Vulnerabilities' by Yuksel Aydin, whose running footer identifies it as arXiv:2508.15839v1. This body develops a cognitive-cybersecurity framework (CCS-7, CIA+TA), a quantitative risk methodology, and empirical results from 12,180 AI trials and 151 human participants. It contains no CoT traces, no HLE or Omni-MATH experiments, no uncertainty-lexicon definitions, no sentiment analysis, and no calibration results. The abstract's central claim is therefore entirely unsupported by the submitted manuscript.","tokens_in":10619,"tokens_out":2217,"duration_ms":25046,"significance":"If the abstract's finding were established, it would be practically valuable: a lightweight, post-hoc calibration signal derived from CoT text, complementary to self-reported probabilities, would require no retraining and could improve reliability assessment for low-accuracy benchmarks. The abstract also states a falsifiable benchmark-dependent length effect and an asymmetry between uncertainty and confidence markers. These are interesting and testable claims. However, the submitted manuscript provides no evidence for any of them. The empirical protocol, the marker lists, the datasets, and the quantitative results are all absent. The significance of the claimed result does not compensate for the absence of the claimed analysis.","major_comments":[{"comment":"The full text is not the paper described in the abstract. The body is titled 'CIA+TA Risk Assessment for AI Reasoning Vulnerabilities' and deals with cognitive cybersecurity, OWASP/ATLAS mappings, CCS-7 vulnerabilities, and a risk-assessment framework. There is no section describing chain-of-thought experiments, no mention of DeepSeek-R1 or Claude 3.7 Sonnet, no HLE or Omni-MATH results, and no analysis of lexical markers, sentiment volatility, or CoT length. This is a load-bearing mismatch: the central claim of the submission is completely absent from the manuscript.","section":"Abstract vs. full text"},{"comment":"The abstract's central finding depends on how the uncertainty lexicon (e.g., 'guess', 'stuck', 'hard') and the sentiment-volatility features were constructed. The manuscript contains no feature-engineering section and no definition of these features. Consequently, the reader cannot determine whether the marker list was chosen or pruned after inspecting correctness labels on HLE and Omni-MATH. This is not a minor omission; it makes the claimed predictive strength unfalsifiable from the submitted text.","section":"Feature definitions"},{"comment":"No quantitative results support the abstract's four claims: no sample sizes for HLE or Omni-MATH, no AUC/accuracy/calibration tables, no effect sizes for lexical markers, no comparison with self-reported probabilities, and no analysis of the reported length-by-benchmark interaction. The only empirical content in the body concerns the cybersecurity experiments, e.g., Table 2, Eq. (2)-(5), and §5.3's limitations, all of which are unrelated to CoT calibration.","section":"Empirical results"}],"minor_comments":[{"comment":"The manuscript's title, abstract, and body must be brought into agreement. If the submitted text is a clerical error, the correct version should be resubmitted; as it stands, the abstract cannot be evaluated against the full text.","section":"General"},{"comment":"Table 1's caption contains 'OW ASP' (missing space) and the text has several formatting inconsistencies (e.g., collapsed words, missing spaces). These are presentation issues secondary to the structural mismatch.","section":"Table 1"}],"recommendation":"reject","confidential_remarks":"This is not a case where the central claim is merely weakly supported; the abstract and the full text are two different papers. The body's footer identifies it as arXiv:2508.15839v1, and the content matches that identifier, not the claimed CoT-calibration study. If this is a submission-assembly error, the editor may wish to contact the author; however, based on the submitted manuscript, rejection is the only appropriate recommendation. The absence of any feature-engineering section also precludes even a partial re-analysis of the abstract's claim."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nYou should know this one off the bat: the abstract and the submission body are different papers. The abstract is about lexical markers of uncertainty in chain-of-thought reasoning — guess, stuck, hard — as post-hoc signals for LLM response accuracy, tested on DeepSeek-R1 and Claude 3.7 Sonnet across HLE and Omni-MATH. The full text is \"CIA+TA Risk Assessment for AI Reasoning Vulnerabilities\" by Yuksel Aydin, and the footer identifies it as arXiv:2508.15839, not 15842.\n\nI'll give credit where it's due. The abstract, taken on its own, is clean and plausible. The regime-dependent finding — CoT length predicts correctness only at intermediate difficulty, not on the near-saturated or near-floor benchmarks — is the kind of concrete result someone might build on. The idea that uncertainty markers are more salient than confidence markers is also interesting, and a lightweight lexical calibration signal would be a genuinely useful complement to model-reported probabilities. If this study were actually backing those claims, I'd want to read the tables.\n\nBut it isn't. The body contains zero chain-of-thought experiments, zero marker definitions, zero sentiment feature descriptions, zero HLE/Omni-MATH results, zero code, and no baseline comparisons. The reader flagged the key problem: we can't tell whether the marker list was tuned on correctness labels, because there is no feature-engineering section at all. That's not a minor omission; it makes the main claim unfalsifiable from the submitted record. The limitations section and self-referential notes in the body all concern the cybersecurity experiments, so they don't rescue anything.\n\nThis looks like an arXiv file mix-up rather than a failed scientific argument. But as a reviewer, I can only judge what's in front of me, and what's in front of me is a record whose abstract cannot be checked against its own full text. The reader's REJECT verdict is right, and the stress-test note holds up.\n\nMy recommendation: desk reject this submission and ask the authors to upload the correct full text for 2508.15842. If the real body contains the CoT analysis, it deserves a proper referee — this is a useful empirical question and the abstract suggests the authors have specific, testable results. But this version should not go to review.\n\nBest,\n[You]","headline":"The abstract describes a CoT-calibration study, but the submitted full text is an unrelated cognitive-cybersecurity paper; as submitted, the empirical claims have no supporting analysis.","tokens_in":11215,"tokens_out":1516,"would_cite":false,"duration_ms":16848,"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":"Chain-of-thought words such as 'guess', 'stuck', and 'hard' are claimed to be the strongest lexical signals that an LLM's answer is incorrect, supporting a lightweight post-hoc calibration signal.","keywords":["chain-of-thought","calibration","lexical uncertainty markers","sentiment volatility","DeepSeek-R1","Claude 3.7 Sonnet","Humanity's Last Exam","Omni-MATH"],"falsifier":"Fix the lexeme list and sentiment features in advance, then apply the same pipeline to fresh, unseen items from the same benchmarks with both models; if chain-of-thought uncertainty markers do not predict incorrect answers above base rate out of sample, the central claim fails.","tokens_in":10229,"feed_emoji":"🎯","tokens_out":8431,"duration_ms":85388,"temperature":0.7,"pith_summary":"The paper is trying to establish that a language model's chain-of-thought text leaks information about whether the final answer is correct. Across DeepSeek-R1 and Claude 3.7 Sonnet, on a frontier benchmark (Humanity's Last Exam) and a saturated one (Omni-MATH), it reports that lexical markers of uncertainty—words like 'guess', 'stuck', and 'hard'—are the strongest indicators of an incorrect response. Sentiment volatility is a weaker but complementary signal, and chain-of-thought length is informative only on the moderate-difficulty benchmark, suggesting length works inside the model's demonstrated capability but not at the frontier. The payoff would be a lightweight post-hoc calibration signal that improves on the models' own unreliable confidence scores without retraining.","feed_headline":"Uncertainty words in LLM reasoning expose wrong answers","feed_subtitle":"Chain-of-thought signals like 'guess' and 'stuck' beat self-reported confidence as error flags.","key_machinery":"The central objects are three feature classes extracted from the chain-of-thought: (i) CoT length, (ii) intra-CoT sentiment volatility—how much the reasoning text's emotional tone swings—and (iii) lexicographic hints, the presence or absence of hedging and uncertainty words such as 'guess', 'stuck', and 'hard'. The load-bearing mechanism is the lexical marker set: the paper reports it as the strongest signal, and its definition determines whether the result is a genuine prediction or a post-hoc fit.","core_discovery":"The central claim is that chain-of-thought text contains readable traces of whether the model's final answer is correct, and that among three feature classes—CoT length, sentiment volatility, and lexicographic hints—the lexical markers of uncertainty ('guess', 'stuck', 'hard') are the strongest predictors of an incorrect response. The paper reports this pattern consistently on two frontier models (DeepSeek-R1 and Claude 3.7 Sonnet) and two benchmarks of very different difficulty (Humanity's Last Exam at roughly 9% accuracy, Omni-MATH at roughly 70%). It further claims that CoT length is informative only on Omni-MATH and carries no signal on HLE, and that uncertainty indicators are more salie","pith_inferences":["Editorial note: the supplied full-text body is a different manuscript (a cognitive-cybersecurity risk framework), not the lexical-hints experiments; the abstract's empirical claims have no supporting body text in this submission.","Because the paper reports errors easier to predict than successes, the practical deployment pattern is asymmetric: treat uncertainty markers as triggers for review, but do not treat clean reasoning as strong evidence of correctness.","Sentiment volatility should transfer across models and domains better than a fixed word list, since specific words are style-dependent while emotional tone is more general; that is a testable extension.","A pre-registered or leave-one-out selection of the marker list would separate genuine prediction from post-hoc fit; the abstract does not describe such a procedure."],"forward_implications":["A deployable post-hoc calibration flag: outputs whose chain-of-thought contains uncertainty lexemes can be routed to human review or down-weighted without retraining the model.","Because uncertainty indicators outweigh confidence markers, a model's reasoning words are safer evidence of error than its stated confidence.","CoT length should not be used as a confidence proxy on frontier-hard benchmarks; it is informative only in the difficulty band where the model already performs well.","The asymmetry makes flagging one-sided: absence of uncertainty words is weak evidence of correctness, so automated mitigation should focus on what the model says when it is unsure."],"supporting_citations":[],"fun_headline_variants":["Hedging words in LLM thoughts predict wrong answers","CoT 'guess' and 'stuck' flag LLM errors","Reasoning-chain wording reveals LLM confidence","Lexical cues in CoT beat reported confidence","Uncertainty words in reasoning signal mistakes"],"cache_read_input_tokens":2688,"weakest_assumption_plain":"The load-bearing premise is that the uncertainty-word list and sentiment features were fixed before the authors looked at which HLE and Omni-MATH answers were wrong; if the words were chosen or pruned using those labels, the reported predictive strength is a fit, not a forecast, and the supplied full text does not show the feature-selection procedure or the experiments.","fun_headline_variants_meta":{"raw":{"variants":["Hedging words in LLM thoughts predict wrong answers","CoT 'guess' and 'stuck' flag LLM errors","Reasoning-chain wording reveals LLM confidence","Lexical cues in CoT beat reported confidence","Uncertainty words in reasoning signal mistakes"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000663,"raw_usage":{"total_tokens":2928,"prompt_tokens":869,"completion_tokens":2059,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":613,"completion_tokens_details":{"reasoning_tokens":1983}},"tokens_in":613,"tokens_out":2059,"duration_ms":14069,"temperature":1.0,"reasoning_tokens":1983,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T18:44:31.390805+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Fix the lexeme list and sentiment features in advance, then apply the same pipeline to fresh, unseen items from the same benchmarks with both models; if chain-of-thought uncertainty markers do not predict incorrect answers above base rate out of sample, the central claim fails.","supporting_citations":[],"review_version":1}