@inproceedings{tao-etal-2026-momoka,
    title = "Momoka-{RAG}: {MCTS}-Organized Mapping of Knowledge Associations for Long-Document Retrieval Augmented Generation",
    author = "Tao, Wenyu  and
      Xing, Xiaofen  and
      Li, Zeliang  and
      Xu, Xiangmin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.183/",
    pages = "3749--3773",
    ISBN = "979-8-89176-395-1",
    abstract = "Existing frameworks remain trapped in a passive and mechanical approach in constructing knowledge structure, which only allows them to uncover superficial associations between chunks while lacking proactive exploration of deeper semantic relationships among them. To address the aforementioned issues, we propose **Momoka-RAG** (MCTS-Organized Mapping of Knowledge Associations for Long-Document Retrieval Augmented Generation). It employs the **Momoka-Map** to utilize Monte Carlo Tree Search (MCTS) to proactively uncover connections among chunks and construct optimal semantic information paths with the objective of completing semantic relationships. On this basis, the **Momoka-Trail Retriever** further expands and filters the chunk candidate pool to retrieve the chunks most relevant to the query. Experiments on datasets including Dragonball, SQUAD, NFCORPUS, SCI-DOCS, HotpotQA, and TriviaQA demonstrate that for long-document retrieval tasks, our framework achieves higher precision while maintaining competitive recall compared to other RAG frameworks."
}