@inproceedings{xu-etal-2026-query,
    title = "Query-Aware Graph Attention for Precise Subgraph Retrieval in Knowledge-Augmented Reasoning",
    author = "Xu, Yuanye  and
      Guo, Linyi  and
      Zhang, Yue  and
      Ning, Fu",
    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.398/",
    pages = "8127--8148",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) increasingly rely on external knowledge to mitigate hallucinations, yet retrieving precise multi-hop evidence for knowledge-augmented reasoning remains difficult. Existing Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) systems insufficiently model the interaction between query semantics and relation types, resulting in imprecise subgraph retrieval and unstable reasoning. We propose Query-aware Subgraph Retrieval Augmented Generation (QSRAG), a retrieval framework built upon a Query-Relational Graph Attention Network (QR-GAT) that integrates query semantics and relation embeddings directly into the attention mechanism, enabling fine-grained triple scoring and scalable subgraph construction. This query{--}relation conditioning improves relevance estimation and suppresses noisy edges, producing faithful reasoning subgraphs. Experiments on WebQSP and CWQ establish new state-of-the-art results in both Triple Recall and Answer Recall, and significantly enhance LLMs reasoning accuracy without fine-tuning. These findings underscore the effectiveness of modeling query{--}relation interactions for reliable knowledge-augmented reasoning."
}