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.

Recent studies have increasingly explored the combination of existing LoRA modules for effective adaptation to unseen tasks in data-scarce scenarios. However, current LoRA selection methods typically rely on a few task samples, making it difficult to capture the full scope of task-relevant information. Furthermore, even after selection, a knowledge gap remains between the selected LoRA modules and the target task, which existing coarse-grained LoRA aggregation strategies struggle to bridge. To address these challenges, we propose Selection and Convolution for LoRA aggregation (SC-LoRA), a two-stage framework that first selects appropriate LoRA modules based on parameter clustering and then aggregates them using a convolutional LoRA aggregator. Our LoRA selection strategy ensures comprehensive coverage of task-relevant LoRA modules by leveraging their distance in the parameter space. Building on this, the convolutional LoRA aggregator extracts useful knowledge in a fine-grained manner, seamlessly bridging the gap to the target task. Our experiments demonstrate that SC-LoRA excels in aggregating multiple LoRA modules for effective adaptation to unseen tasks.