VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical
  Consistency Checks
Authors: name: Yu Feng; name: Nathaniel Weir; name: Kaj Bostrom; name: Sam Bayless; name: Darion Cassel; name: Sapana Chaudhary; name: Benjamin Kiesl-Reiter; name: Huzefa Rangwala
Published: 2025-11-06 18:50:08

Abstract: 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.