LLMs can perform multi-step reasoning through Chain-of-Thought (CoT), but they cannot reliably verify their own logic. Even when they reach correct answers, the underlying reasoning may be flawed, undermining trust in high-stakes scenarios. To mitigate this issue, we introduce VeriCoT, a neuro-symbolic method that extracts and verifies formal logical arguments from CoT reasoning. VeriCoT formalizes each CoT reasoning step into first-order logic and identifies premises that ground the argument in source context, commonsense knowledge, or prior reasoning steps. The symbolic representation enables automated solvers to verify logical validity while the NL premises allow humans and systems to identify ungrounded or fallacious reasoning steps. Experiments on the ProofWriter, LegalBench, and BioASQ datasets show VeriCoT effectively identifies flawed reasoning, and serves as a strong predictor of final answer correctness. We also leverage VeriCoT's verification signal for (1) inference-time self-reflection, (2) supervised fine-tuning (SFT) on VeriCoT-distilled datasets and (3) preference fine-tuning (PFT) with direct preference optimization (DPO) using verification-based pairwise rewards, further improving reasoning validity and accuracy.

翻译：大型语言模型（LLMs）能够通过思维链（CoT）进行多步推理，但无法可靠地验证其自身逻辑。即使得出正确答案，其底层推理过程仍可能存在缺陷，从而削弱高风险场景下的可信度。为缓解此问题，我们提出VeriCoT——一种从CoT推理中提取并验证形式化逻辑论证的神经符号方法。VeriCoT将每个CoT推理步骤形式化为一阶逻辑，并识别将论证锚定于源文本语境、常识知识或先前推理步骤的前提条件。符号化表征支持自动化求解器验证逻辑有效性，而自然语言前提则允许人类与系统识别未锚定或谬误的推理步骤。在ProofWriter、LegalBench和BioASQ数据集上的实验表明，VeriCoT能有效识别缺陷推理，并作为最终答案正确性的强预测指标。我们还利用VeriCoT的验证信号实现：（1）推理时自反思，（2）基于VeriCoT蒸馏数据集的监督微调（SFT），以及（3）采用验证驱动的成对奖励进行直接偏好优化（DPO）的偏好微调（PFT），从而进一步提升推理有效性与准确性。