将逻辑生成与大模型结合，实现可验证的可解释推理
Delta1 with LLM: symbolic and neural integration for credible and explainable reasoning
- 用Delta1生成最小不满足子句集和完整定理，保证结论可靠
- 大模型将证明过程转为自然语言解释，提升可读性
- 在医疗、合规等领域验证了推理的可审计性和领域适配性
神经符号推理亟需融合逻辑严谨性与大语言模型可解释性的框架。本文提出一个端到端可解释性构建管道，将基于全三角标准矛盾（FTSC）的自动定理生成器Delta1与大语言模型（LLMs）结合。Delta1能在多项式时间内确定性地构造最小不满足子句集和完整定理，通过构造保证结论的可靠性和最小性。大模型层将每个定理及其证明轨迹转化为连贯的自然语言解释和可操作洞察。在医疗、合规与监管领域的实证研究显示，该方法实现了可解释、可审计且符合领域要求的推理。本工作推动了逻辑、语言与学习的融合，确立了构造性定理生成作为神经符号可解释AI的坚实基础。
原文摘要 · Abstract (English)
Neuro-symbolic reasoning increasingly demands frameworks that unite the formal rigor of logic with the interpretability of large language models (LLMs). We introduce an end to end explainability by construction pipeline integrating the Automated Theorem Generator Delta1 based on the full triangular standard contradiction (FTSC) with LLMs. Delta1 deterministically constructs minimal unsatisfiable clause sets and complete theorems in polynomial time, ensuring both soundness and minimality by construction. The LLM layer verbalizes each theorem and proof trace into coherent natural language explanations and actionable insights. Empirical studies across health care, compliance, and regulatory domains show that Delta1 and LLM enables interpretable, auditable, and domain aligned reasoning. This work advances the convergence of logic, language, and learning, positioning constructive theorem generation as a principled foundation for neuro-symbolic explainable AI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。