Delta1与LLM：符号与神经整合用于可信且可解释的推理
Delta1 with LLM: symbolic and neural integration for credible and explainable reasoning
AI总结：
本文提出Delta1与LLM结合的框架，通过符号推理与神经网络整合，实现可信且可解释的推理，适用于医疗、合规和监管领域。
摘要：
神经符号推理日益需要将逻辑的正式严谨性与大语言模型（LLMs）的可解释性相结合的框架。我们介绍了一个端到端的可解释性构建管道，将基于完整三角标准矛盾（FTSC）的自动定理生成器Delta1与LLMs整合。Delta1在多项式时间内确定性地构建最小不可满足子句集和完整定理，通过构建确保正确性和最小性。LLM层将每个定理和证明轨迹转化为连贯的自然语言解释和可操作的见解。在医疗、合规和监管领域中的实证研究显示，Delta1和LLM能够实现可解释、可审计且领域对齐的推理。这项工作推动了逻辑、语言和学习的融合，将构造定理生成定位为神经符号可解释AI的原理性基础。
英文摘要：
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.
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