@inproceedings{zhou-etal-2026-collision,
    title = "Collision to Cognition: Hash-Driven Graph Construction for Efficient {RAG}",
    author = "Zhou, Chuang  and
      Yuan, Zheng  and
      Luo, Linhao  and
      Xu, Zhaozhuo  and
      Xiao, Yilin  and
      Dong, Junnan  and
      An, Siyu  and
      Yin, di  and
      Sun, Xing  and
      Huang, Xiao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1156/",
    pages = "25224--25240",
    ISBN = "979-8-89176-390-6",
    abstract = "Retrieval-Augmented Generation (RAG) has long been a promising paradigm for enhancing large language models (LLMs) with external knowledge. Traditional embedding-based methods for graph construction can capture semantic similarity but struggle to establish fine-grained, interpretable logical relationships. Recently, Graph-enhanced RAG (GraphRAG) has gained increasing popularity for its capability in modeling logical relationships. However, graph construction requires extensive token consumption for triple extraction and summarization, making it costly and slow. Accordingly, we propose MeshRAG, a novel framework for mining efficient structures via hashing to enhance RAG. We adopt an inductive paradigm in which global graph structure emerges from local hash collisions rather than explicit symbolic extraction. By replacing neural embedding search with lightweight and bitwise operations, MeshRAG automates a simple and rapid graph construction process. Furthermore, the hash collision mechanism provides transparent evidence for logical connections and retrieval decisions. Experimental results show that MeshRAG outperforms existing baselines, while its graph construction requires no GPU resources or token budget and can structure over ten thousand chunks in a few minutes."
}