用知识图谱和符号求解器实现可验证的逻辑推理，解决大模型幻觉问题。
SymbolLKG: Towards Verifiable Logical Reasoning via Logical Knowledge Graph and Symbolic Solvers
- 构建逻辑知识图谱，将规则作为节点显式建模依赖关系。
- 动态调度任务至最优符号引擎，准确率显著超越现有方法。
- 适合需要高可靠推理的场景，如法律、医疗决策支持。
大型语言模型在自然语言理解上表现卓越，但在严格多步推理中常出现幻觉与不一致。现有方法如思维链（CoT）缺乏严谨验证机制，标准检索增强生成（RAG）又难以捕捉逻辑任务中的复杂结构依赖。为此，我们提出一种神经符号架构，融合逻辑知识图谱（LKG）与动态求解器路由。具体而言，设计基于本体的LKG，将逻辑规则与约束作为拓扑节点，显式建模从文本中提取的依赖关系；并引入逻辑路由器，通过拓扑感知的混合检索机制，动态分配任务至最优符号引擎。在逻辑推理基准上的实验表明，该框架显著优于先进提示与RAG基线，实现更高准确率与可验证的推理路径。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable proficiency in natural language understanding, yet they struggle with strict multi-step reasoning, frequently suffering from hallucinations and inconsistency. Existing solutions like Chain-of-Thought (CoT) lack rigorous verification mechanisms, while standard Retrieval-Augmented Generation (RAG) often misses the complex, structural dependencies inherent in logical tasks. To bridge this gap, we propose a Neuro-Symbolic architecture that integrates a Logical Knowledge Graph (LKG) with dynamic solver routing. Specifically, we introduce an ontology-based LKG that treats logical rules and constraints as first-class topological nodes, enabling explicit modeling of dependencies extracted from text. We further design a Logic Router to dynamically dispatch tasks to the optimal symbolic engine, which is supported by a topology-aware hybrid retrieval mechanism. Experimental results on logical reasoning benchmarks demonstrate that our framework significantly outperforms state-of-the-art prompting and RAG baselines, delivering higher accuracy and verifiable reasoning paths.
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