Retrieval-Augmented Generation (RAG) has been widely adopted to enhance large language models (LLMs) by incorporating external knowledge. However, the two main existing paradigms struggle with multi-hop reasoning: aggregate-first approaches suffer from high construction costs and limited adaptability to dynamic knowledge, while dynamic-first approaches rely heavily on LLM reasoning and are prone to error propagation across reasoning steps. To address these limitations, we propose SR-RAG, a symbolic reasoning framework for multi-hop question answering. SR-RAG integrates the advantages of both paradigms by dynamically generating sub-questions, performing information retrieval and symbolic encoding based on an on-the-fly graph, and using a symbolic verifier to formally validate intermediate reasoning steps to ensure the correctness of intermediate answers and the completeness of the reasoning chain . We evaluate SR-RAG on multiple multi-hop benchmarks and a medical dataset. Experimental results demonstrate that it significantly improves both accuracy and robustness.

Retrieval-Augmented Generation (RAG) grounds large language models (LLMs) in external evidence, but existing RAG systems remain poorly matched to visually rich documents. OCR-based methods discard layout and non-textual visual cues, and although vision-language models (VLMs) can better understand native visual information, existing visual retrieval approaches often compress entire pages into global embedding vectors, obscuring fine-grained spatial information such as table cells and local annotations. Meanwhile, discrete Top-K selection in cascaded architectures blocks gradient flow between retrieval and generation, preventing joint optimization.In this work, we propose E2E-VisRAG, an end-to-end visual RAG framework that operates directly on native visual patches. Building upon patch-level visual encoding, we introduce a differentiable Top-K selection mechanism to enable joint training of retrieval and generation, and employ a gated residual fusion module to inject retrieval-enhanced visual features while suppressing noise. This design abandons the traditional paradigms of relying on OCR, global embeddings, and retrieval–generation separation, maximizing the preservation of original document visual semantics. Experiments on Vidore and MMLongBench-Doc show that, under matched 3B generator capacity, E2E-VisRAG outperforms non-end-to-end visual retrieval baselines, improving both Recall@1 and Accuracy@1 over OCR-based, global-embedding, and patch-level retrieval methods.