Large language models (LLMs) have shown impressive reasoning capabilities through Chain-of-Thought (CoT). However, the reasoning processes remain inexplicable and uncontrollable. In this paper, we tackle the task hosted by (CITATION) by introducing a Step-Wise Reasoning and Verification (SWRV) framework, a two-stage Parser–Verifier one, that decomposes generated reasoning process into discrete inference steps and rigorously validates each one. First, our Parser extracts problem constraints and the sequence of reasoning steps from the LLM’s reasoning process. Then, our Verifier prompts itself or leverages a deterministic symbolic solver to formally check the logical correctness of every step. To ensure robust parsing, we also fine‐tune a compact LM on a small, high‐quality annotation set produced by a more powerful LLM. Experiments on the dataset (CITATION) demonstrate significant gains over baseline approaches, illustrating the effectiveness of our method for step‐wise analysis of LLM chain-of-thought reasoning. The code is publicly available at https://github.com/Teganone/XLLM_LLMSRhttps://github.com/Teganone/XLLM_LLMSR.