mlsanityData to 14 Oct 2026

Does CoT-Pass@k Really Check the CoT? A Multilingual Mathematical Audit

arXiv 2609.32622 ↗First posted 26 Sep 2026cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Pass@k measures whether a model reaches a correct answer under repeated sampling, but never how: a lucky guess counts the same as sound reasoning. CoT-Pass@k was proposed to close that gap, adding an LLM-as-judge that must assess a solution's reasoning chain before it counts. Its value rests entirely on one assumption: that the judge catches flawed reasoning. That assumption has never been tested inside the metric that depends on it, and never outside English, though the metric's claims concern models used in many languages. We report the first audit of that verification step, run under the metric's own protocol on a multilingual suite of five mathematical benchmarks in English, Turkish and Portuguese, two of them natively written. We corrupt correct solutions with deterministic edits that damage the chain and the final answer separately. We observe that all three judges accept corrupted chains almost as often as clean ones. V4-Flash and Qwen3.6 reject a solution sharply only when its final answer is wrong and accept a wrong answer more readily when the chain agrees with it; the metric's own judge accepts most wrong answers as well. Our study shows that chain-answer agreement dominates the two larger judges' verdicts and that all three fail to reliably detect the tested reasoning errors. Consequently the difference Pass@k - CoT-Pass@k averages 19.7 points on an earlier solver generation but only 4.1 on the current one. What little remains depends on the token budgets on both sides and on the generation mode; raising the generation budget moves Pass@64 by more than fifty points while the difference stays at zero. We close with two checks any judged reasoning metric should pass before its numbers are read as evidence about reasoning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 46 references that are in the map. First posted on arXiv 26 Sep 2026.

Builds on

28 of 46 references.

References · 28

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2023Let's Verify Step by Stepcited by 3,789
  3. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  4. 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
  5. 2024Large Language Monkeys: Scaling Inference Compute with Repeated Samplingcited by 991
  6. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?cited by 982
  7. 2022Solving math word problems with process- and outcome-based feedbackcited by 901
  8. 2024ProcessBench: Identifying Process Errors in Mathematical Reasoningcited by 292
  9. 2025Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMscited by 289
  10. 2024Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluationscited by 148
  11. 2025Measuring what Matters: Construct Validity in Large Language Model Benchmarkscited by 119
  12. 2025PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Modelscited by 99
16 more
  1. 2024A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chainscited by 65
  2. 2024Are Your LLMs Capable of Stable Reasoning?cited by 65
  3. 2025Crosslingual Reasoning through Test-Time Scalingcited by 46
  4. 2025Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoningcited by 37
  5. 2025Don't Pass@k: A Bayesian Framework for Large Language Model Evaluationcited by 15
  6. 2024Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoningcited by 13
  7. 2024MaskLID: Code-Switching Language Identification through Iterative Maskingcited by 11
  8. 2025Beyond Pass@k: Breadth-Depth Metrics for Reasoning Boundariescited by 9
  9. 2025Cetvel: A Unified Benchmark for Evaluating Language Understanding, Generation and Cultural Capacity of LLMs for Turkishcited by 8
  10. 2025A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across Languagescited by 8
  11. 2026Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoningcited by 7
  12. 2025MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoningcited by 6
  13. 2026TurkBench: A Benchmark for Evaluating Turkish Large Language Modelscited by 5
  14. 2026How Long Reasoning Chains Influence LLMs' Judgment of Answer Factualitycited by 2
  15. 2026MATH-PT: A Math Reasoning Benchmark for European and Brazilian Portuguesecited by 2
  16. 2026An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Modelscited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026The Potential of CoT for Reasoning: A Closer Look at Trace Dynamics
  2. 2026The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics
  3. 2026Does Out-of-Sight Equal Out-of-Mind in CoT Monitorability?
  4. 2024To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
  5. 2023Measuring Faithfulness in Chain-of-Thought Reasoning
  6. 2025Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation
  7. 2025Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification
  8. 2023Faithful Chain-of-Thought Reasoning