Problem
Research questions and friction points this paper is trying to address.
Large Language Models
Logical Reasoning
Hallucinations
Inconsistency
Chain-of-Thought
SymbolLKG: Towards Verifiable Logical Reasoning via Logical Knowledge Graph and Symbolic Solvers
🤖 AI Summary
为解决大语言模型在多步逻辑推理中的幻觉和不一致问题，提出结合逻辑知识图谱和动态求解器路由的神经-符号架构，提高推理准确性和可验证性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neuro-Symbolic Architecture
Logical Knowledge Graph (LKG)
Dynamic Solver Routing
Topology-Aware Hybrid Retrieval
Verifiable Reasoning
🔎 Similar Papers
