Multimodal documents contain diverse elements, such as tables, figures, and layouts, which can complicate retrieval tasks. While current approaches typically combine dense visual embedding models with supervised rerankers to achieve high-precision retrieval, they face inherent limitations. First, the coarse-grained nature of dense embeddings tends to obfuscate explicit semantics, failing to leverage structurally salient information. Second, supervised reranking models suffer from generalization bottlenecks, as their performance heavily relies on domain-specific training data. Furthermore, existing benchmarks often lack diverse assessment dimensions and comprehensive relevance annotations, limiting reliable evaluation. To address these challenges, we propose DocRetriever, a plug-and-play framework. It enhances visual retrieval via a layout-aware sparse embedding technique, enabling effective hybrid encoding without the overhead of optical character recognition (OCR). We also introduce a generalizable reranker that leverages reasoning-augmented demonstrations and optimized sampling to improve accuracy in few-shot settings. Finally, we construct a new benchmark, MultiDocR, to enable more rigorous evaluation. Experiments across diverse benchmarks validate DocRetriever's superiority over state-of-the-art methods.

翻译：多模态文档包含表格、图形和布局等多种元素，这使检索任务复杂化。当前方法通常将密集视觉嵌入模型与有监督重排序器相结合以实现高精度检索，但其存在固有局限。首先，密集嵌入的粗粒度特性易模糊显式语义，无法有效利用结构性显著信息。其次，有监督重排序模型面临泛化瓶颈，其性能严重依赖领域特定训练数据。此外，现有基准常缺乏多样化评估维度与全面相关性标注，限制了可靠评估。为应对这些挑战，我们提出即插即用框架DocRetriever。该框架通过布局感知稀疏嵌入技术增强视觉检索，在不增加光学字符识别（OCR）开销的情况下实现高效混合编码。我们还引入一种可泛化重排序器，利用推理增强演示与优化采样提升少样本场景中的精度。最终，我们构建新基准MultiDocR以实现更严格的评估。在多个基准上的实验验证了DocRetriever相较于现有最优方法的优越性。