@inproceedings{guo-etal-2026-deepsieve,
    title = "{D}eep{S}ieve: Information Sieving via {LLM}-as-a-Knowledge-Router",
    author = "Guo, Minghao  and
      Zeng, Qingcheng  and
      Zhao, Xujiang  and
      Liu, Yanchi  and
      Yu, Wenchao  and
      Du, Mengnan  and
      Chen, Haifeng  and
      Cheng, Wei",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.160/",
    pages = "3054--3077",
    ISBN = "979-8-89176-386-9",
    abstract = "Large Language Models (LLMs) excel at many reasoning tasks but struggle with knowledge-intensive queries due to their inability to dynamically access up-to-date or domain-specific information. Retrieval-Augmented Generation (RAG) has emerged as a promising solution, enabling LLMs to ground their responses in external sources. However, existing RAG methods lack fine-grained control over both the query and source sides, often resulting in noisy retrieval and shallow reasoning. In this work, we introduce \textit{\textbf{DeepSieve}}, an agentic RAG framework that incorporates information sieving via LLM-as-a-knowledge-router. DeepSieve decomposes complex queries into structured sub-questions and recursively routes each to the most suitable knowledge source, filtering irrelevant information through a multi-stage distillation process. Our design emphasizes modularity, transparency, and adaptability, leveraging recent advances in agentic system design. Experiments on multi-hop QA tasks across heterogeneous sources demonstrate improved reasoning depth, retrieval precision, and interpretability over conventional RAG approaches."
}