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 SymDiag, a neuro-symbolic framework that reframes reasoning ver
Record details
Published: 8 August 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Healthcare · Transparency
Retrieved: 12 August 2026
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ethics.ai (8 August 2026), “SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification,” evidence record 18388, https://ethics.ai/record/18388 (originally published by HuggingFace Daily Papers).
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