RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigm

arXiv cs.AI / 4/25/2026

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Key Points

The paper introduces RealRoute, a framework for dynamic query routing in RAG that addresses difficulties when information is spread across heterogeneous sources such as private databases, public corpora, and APIs.
Instead of using an LLM-as-a-router to predict which source to query, RealRoute performs source-agnostic parallel retrieval to ensure evidence completeness, followed by a dynamic verifier that cross-checks retrieved results.
RealRoute also provides a way to visualize real-time re-routing and inspect a verification chain across multiple knowledge silos, improving transparency during reasoning.
Experiments indicate that RealRoute substantially outperforms predictive routing baselines on multi-hop RAG reasoning tasks.
The authors release RealRoute as an open-source toolkit with a user-friendly web interface, along with publicly available code on GitHub.

Abstract

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.