Temporal Consistency for LLM Reasoning Process Error Identification
Authors: name: Jiacheng Guo, name: Yue Wu, name: Jiahao Qiu, name: Kaixuan Huang, name: Xinzhe Juan, name: Ling Yang, name: Mengdi Wang

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