SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification
Large language models (LLMs) increasingly serve as data-driven reasoners, yet their chains-of-thought (CoT) can be unfaithful even when final answers are correct. Most existing ``verification'' signals are not diagnostic: answer matching observes only the outcome, LLM-as-judge provides subjective and non-verifiable critiques, and scalar rewards (e.g., PRMs/RMs) offer little insight into where a multi-step derivation fails.We propose \textbf{SymDiag}, a neuro-symbolic framework that \textbf{reframes reasoning verification as structured failure diagnosis}. SymDiag translates natural-language CoT into symbolic constraints and performs step-level satisfiability/entailment checks to (i) locali...
| Authors | Wenyao Cui, Huaping Zhang, Yongyi Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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