Knowledge graph question answering (KGQA) is a promising approach for mitigating LLM hallucination by grounding reasoning in structured and verifiable knowledge graphs. Existing approaches fall into two paradigms: retrieval-based methods utilize small specialized models, which are efficient but often produce unreachable paths and miss implicit constraints, while agent-based methods utilize large general models, which achieve stronger structural grounding at substantially higher cost. We propose RouterKGQA, a framework for specialized--general model collaboration, in which a specialized model generates reasoning paths and a general model performs KG-guided repair only when needed, improving performance at minimal cost. We further equip the specialized with constraint-aware answer filtering, which reduces redundant answers. In addition, we design a more efficient general agent workflow, further lowering inference cost. Experimental results show that RouterKGQA outperforms the previous best by 3.57 points in F1 and 0.49 points in Hits@1 on average across benchmarks, while requiring only 1.15 average LLM calls per question. Codes and models are available at https://github.com/Oldcircle/RouterKGQA.

翻译：知识图谱问答（KGQA）通过在结构化且可验证的知识图谱中进行推理，是缓解大语言模型幻觉的一种有前景的方法。现有方法可分为两类：基于检索的方法利用小型专精模型，虽效率高但常产生不可达路径并遗漏隐式约束；而基于智能体的方法利用大型通用模型，能以显著更高的成本实现更强的结构化推理。我们提出RouterKGQA框架，这是一种专精—通用模型协作框架，其中专精模型生成推理路径，通用模型仅在必要时执行知识图谱引导的修复，从而以最小成本提升性能。我们进一步为专精模型配备约束感知答案过滤机制，可减少冗余答案。此外，我们设计了更高效的通用智能体工作流，进一步降低推理成本。实验结果表明，RouterKGQA在各基准测试上的F1值平均提升3.57分，Hits@1平均提升0.49个点，同时每问题平均仅需1.15次LLM调用。代码与模型已开源至https://github.com/Oldcircle/RouterKGQA。