Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval

2026-02-23

AI Summary

Visual document retrieval faces significant challenges due to dense text, complex layouts, and fine-grained semantic dependencies, which hinder precise information access. This work presents the first systematic survey of the field in the era of multimodal large language models, establishing a comprehensive research framework that encompasses benchmark evaluation, methodological evolution, and future challenges. The study proposes a novel paradigm integrating multimodal embeddings, re-ranking models, retrieval-augmented generation (RAG), and agent-based systems to clarify the technological trajectory, identify critical bottlenecks, and offer a clear roadmap for advancing multimodal document intelligence.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Image and Video Retrieval
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

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 roadmap for future multimodal document intelligence.

Problem

• Visual Document Retrieval
• Multimodal Document Intelligence
• Multimodal Large Language Model
• Document Layout
• Semantic Dependencies

Innovation

• Visual Document Retrieval
• Multimodal Large Language Model
• Retrieval-Augmented Generation
• Multimodal Embedding
• Agentic Systems