@inproceedings{zheng-etal-2026-neurag,
    title = "{N}eu{RAG}: End-to-End Neural Knowledge Augmentation via Hyper-Neurons",
    author = "Zheng, Liwei  and
      Liu, Xuemin  and
      Liu, Jie",
    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-workshops/2026.findings-acl.1516/",
    pages = "30324--30343",
    ISBN = "979-8-89176-395-1",
    abstract = "Retrieval-Augmented Generation (RAG) systems have become a standard approach for grounding large language models in external knowledge. However, they are constrained by a decoupled architecture: retrieval and reasoning operate as separate stages, with retrieved text merely prepended as passive context. This prevents deep integration of knowledge into the model{'}s parametric reasoning, leading to fragmented responses for complex queries requiring multi-document synthesis or conflict resolution. To bridge this gap, we propose NeuRAG, an end-to-end Neuralized RAG framework that unifies knowledge retrieval and fusion through Hyper-Neurons{---}parameterized modules encoding entire documents directly into the model{'}s parameter space. In NeuRAG, each document is encoded as a lightweight LoRA module, conceptualized as a knowledge neuron. These neurons collectively form a document-adaptive Hyper-Layer, which dynamically activates and fuses knowledge neurons via an attention mechanism conditioned on the input hidden-state query. This enables the model to jointly retrieve and reason within a single forward pass, seamlessly integrating external knowledge into its inference pathway. Extensive experiments across multiple datasets and LLMs demonstrate NeuRAG{'}s strong and consistent performance as a promising novel RAG paradigm."
}