Unlocking Multimodal Document Intelligence: From Current Triumphs To Future Frontiers Of Visual Document Retrieval

Abstract

With the rapid proliferation of multimodal information, Visual Document Retrieval (VDR) has emerged as a critical frontier in bridging the gap between unstructured visually rich data and precise information acquisition. Unlike traditional natural image retrieval, visual documents exhibit unique characteristics defined by dense textual content, intricate layouts, and fine-grained semantic dependencies. This paper presents the first comprehensive survey of the VDR landscape, specifically through the lens of the Multimodal Large Language Model (MLLM) era. We begin by examining the benchmark landscape, and subsequently dive into the methodological evolution, categorizing approaches into three primary aspects: multimodal embedding models, multimodal reranker models, and the integration of Retrieval-Augmented Generation (RAG) and Agentic systems for complex document intelligence. Finally, we identify persistent challenges and outline promising future directions, aiming to provide a clear roa

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyyan2026unlocking

Related papers

• M3DR: Towards Universal Multilingual Multimodal Document Retrieval (2025)0.00
• Modernvbert: Towards Smaller Visual Document Retrievers (2025)0.00
• MURE: Hierarchical Multi-resolution Encoding Via Vision-language Models For Visual Document Retrieval (2026)0.00
• Docmmir: A Framework For Document Multi-modal Information Retrieval (2025)3.46
• MIRACL-VISION: A Large, Multilingual, Visual Document Retrieval Benchmark (2025)0.00
• Globaldoc: A Cross-modal Vision-language Framework For Real-world Document Image Retrieval And Classification (2023)3.58
• Evo-retriever: Llm-guided Curriculum Evolution With Viewpoint-pathway Collaboration For Multimodal Document Retrieval (2026)0.00
• IDMR: Towards Instance-driven Precise Visual Correspondence In Multimodal Retrieval (2025)2.29