@inproceedings{zhan-etal-2026-flowrag,
    title = "{F}low{RAG}: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow",
    author = "Zhan, Bihao  and
      Cao, Zongsheng  and
      Zhou, Jie  and
      Zhang, Bo  and
      He, Liang",
    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.1050/",
    pages = "20926--20936",
    ISBN = "979-8-89176-395-1",
    abstract = "Graph-based retrieval-augmented generation (GraphRAG) is effective for knowledge-intensive and multi-hop query tasks; however, many existing methods primarily seed entity-based graphs and rely on implicit semantic relevance propagation. This often (i) under-retrieves when user queries are abstract and semantically sparse at the entity level, and (ii) suffers from brittle multi-hop reasoning, where noisy activations can derail entity-to-entity transitions and corrupt the inferred relation chain, yielding unreliable conclusions. To this end, we propose $\texttt{FlowRAG}$, a semantic-aware retrieval framework that improves both semantic recall and explicit reasoning. Specifically, $\texttt{FlowRAG}$ constructs a quad-level heterogeneous graph over passages, summaries, sentences, and entities, where summary nodes serve as a coarse semantic hub. At retrieval time, a dual-granularity activation module combines summary{--}query alignment with sentence-level matching to activate relevant entities under paraphrase and abstraction robustly. We then introduce a frequency-aware weighted flow module that routes relevance through entity{--}passage links weighted by within-passage term frequency, pruning noisy connections and extracting high-confidence reasoning paths as an explicit logic skeleton for generation. Extensive experiments show that obtains state-of-the-art performance on complex reasoning benchmarks."
}