title: Temporal Consistency for LLM Reasoning Process Error Identification
summary: Verification is crucial for effective mathematical reasoning. We present a
  new temporal consistency method where verifiers iteratively refine their
  judgments based on the previous assessment. Unlike one-round verification or
  multi-model debate approaches, our method leverages consistency in a sequence
  of self-reflection actions to improve verification accuracy. Empirical
  evaluations across diverse mathematical process error identification benchmarks
  (Mathcheck, ProcessBench, and PRM800K) show consistent performance improvements
  over baseline methods. When applied to the recent DeepSeek R1 distilled models,
  our method demonstrates strong performance, enabling 7B/8B distilled models to
  outperform all 70B/72B models and GPT-4o on ProcessBench. Notably, the
  distilled 14B model with our method achieves performance comparable to
  Deepseek-R1. Our codes are available at
  https://github.com/jcguo123/Temporal-Consistency
paper_authors._id: 67da454fd5132b0eebd066ff
paper_authors.name: Jiacheng Guo
paper_authors.status: admin_assigned
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paper_authors.fullname: Jiacheng Guo
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paper_authors.user: gjcjcg
paper_authors._id: 67da454fd5132b0eebd06700
paper_authors.name: Yue Wu
paper_authors._id: 67da454fd5132b0eebd06701
paper_authors.name: Jiahao Qiu
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paper_authors.fullname: Jiahao Qiu
paper_authors.type: user
paper_authors.user: jiahaoq
paper_authors._id: 67da454fd5132b0eebd06702
paper_authors.name: Kaixuan Huang
paper_authors.status: admin_assigned
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paper_authors.fullname: huangkaixuan
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paper_authors.user: huangkaixuan
paper_authors._id: 67da454fd5132b0eebd06703
paper_authors.name: Xinzhe Juan
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paper_authors.fullname: Xinzhe Juan
paper_authors.type: user
paper_authors.user: ChrisJuan
paper_authors._id: 67da454fd5132b0eebd06704
paper_authors.name: Ling Yang
paper_authors._id: 67da454fd5132b0eebd06705
paper_authors.name: Mengdi Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:43:31.791Z
paper_authors._id: 6599415e8c8ac79295e0b5e3
paper_authors.avatarUrl: /avatars/85500bc8d2cd51444adcc19b1f8db313.svg
paper_authors.fullname: Mengdi Wang
paper_authors.type: user
paper_authors.user: Edify-Kd2024
paper_title: Temporal Consistency for LLM Reasoning Process Error Identification
paper_summary: Verification is crucial for effective mathematical reasoning. We present a
  new temporal consistency method where verifiers iteratively refine their
  judgments based on the previous assessment. Unlike one-round verification or
  multi-model debate approaches, our method leverages consistency in a sequence
  of self-reflection actions to improve verification accuracy. Empirical
  evaluations across diverse mathematical process error identification benchmarks
  (Mathcheck, ProcessBench, and PRM800K) show consistent performance improvements
  over baseline methods. When applied to the recent DeepSeek R1 distilled models,
  our method demonstrates strong performance, enabling 7B/8B distilled models to
  outperform all 70B/72B models and GPT-4o on ProcessBench. Notably, the
  distilled 14B model with our method achieves performance comparable to
  Deepseek-R1. Our codes are available at
  https://github.com/jcguo123/Temporal-Consistency
paper_ai_summary: A new iterative self-reflection method for verification improves mathematical reasoning accuracy over baselines, especially when applied to distilled models.
paper_ai_keywords: temporal consistency iterative refinement self-reflection verification accuracy Mathcheck ProcessBench PRM800K DeepSeek R1 distilled models GPT-4o