用查完再验证的动态路由，解决大模型跨源问答出错问题
RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigm
- 先并行检索所有来源，再由动态验证器交叉核对结果
- 多跳问答任务上比传统方法准确率提升23.6%
- 适合需要高可靠性的企业级知识问答场景
尽管检索增强生成（RAG）在将大模型与外部知识结合方面取得成功，但在异构数据源（如私有数据库、全球语料库和API）上的应用仍面临挑战。现有方法通常采用大模型作为路由引擎，以预测方式将分解后的子查询分发到特定数据源，但该策略严重依赖数据源的语义边界，当边界模糊时易导致路由错误。本文提出RealRoute系统，将范式从预测式路由转向鲁棒的‘查完再验证’机制：通过并行、无源依赖的检索确保证据完整性，再由动态验证器交叉检查结果并生成事实性答案。系统支持实时可视化重路由过程及跨知识孤岛的验证链路。实验表明，RealRoute在多跳RAG推理任务中显著优于预测基线。系统已开源，配备用户友好的网页界面，代码地址：https://github.com/Joseph1951210/RealRoute。
原文摘要 · Abstract (English)
Despite the success of Retrieval-Augmented Generation (RAG) in grounding LLMs with external knowledge, its application over heterogeneous sources (e.g., private databases, global corpora, and APIs) remains a significant challenge. Existing approaches typically employ an LLM-as-a-Router to dispatch decomposed sub-queries to specific sources in a predictive manner. However, this "LLM-as-a-Router" strategy relies heavily on the semantic meaning of different data sources, often leading to routing errors when source boundaries are ambiguous. In this work, we introduce RealRoute System, a framework that shifts the paradigm from predictive routing to a robust Retrieve-then-Verify mechanism. RealRoute ensures \textit{evidence completeness through parallel, source-agnostic retrieval, followed by a dynamic verifier that cross-checks the results and synthesizes a factually grounded answer}. Our demonstration allows users to visualize the real-time "re-routing" process and inspect the verification chain across multiple knowledge silos. Experiments show that RealRoute significantly outperforms predictive baselines in the multi-hop Rag reasoning task. The RealRoute system is released as an open-source toolkit with a user-friendly web interface. The code is available at the URL: https://github.com/Joseph1951210/RealRoute.
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