DocRefine: An Intelligent Framework for Scientific Document Understanding and Content Optimization based on Multimodal Large Model Agents

2025-08-09

AI Summary

Existing approaches for intelligent understanding and summarization of scientific PDFs—characterized by complex layouts and multimodal content—suffer from low accuracy, while monolithic large models lack controllability and visual fidelity. Method: We propose a six-agent collaborative, closed-loop multimodal large language model system, driven by natural language instructions and integrating layout parsing, cross-modal semantic understanding, instruction decomposition, content refinement, summary generation, and consistency verification. Contribution/Results: Our key innovation is a dual-objective optimization architecture balancing semantic accuracy and layout fidelity, significantly enhancing editorial controllability and output consistency. On the DocEditBench benchmark, our method achieves 86.7% semantic consistency, 93.9% layout fidelity, and 85.0% instruction adherence—outperforming all state-of-the-art methods across all metrics.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Summarization
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The exponential growth of scientific literature in PDF format necessitates advanced tools for efficient and accurate document understanding, summarization, and content optimization. Traditional methods fall short in handling complex layouts and multimodal content, while direct application of Large Language Models (LLMs) and Vision-Language Large Models (LVLMs) lacks precision and control for intricate editing tasks. This paper introduces DocRefine, an innovative framework designed for intelligent understanding, content refinement, and automated summarization of scientific PDF documents, driven by natural language instructions. DocRefine leverages the power of advanced LVLMs (e.g., GPT-4o) by orchestrating a sophisticated multi-agent system comprising six specialized and collaborative agents: Layout & Structure Analysis, Multimodal Content Understanding, Instruction Decomposition, Content Refinement, Summarization & Generation, and Fidelity & Consistency Verification. This closed-loop feedback architecture ensures high semantic accuracy and visual fidelity. Evaluated on the comprehensive DocEditBench dataset, DocRefine consistently outperforms state-of-the-art baselines across various tasks, achieving overall scores of 86.7% for Semantic Consistency Score (SCS), 93.9% for Layout Fidelity Index (LFI), and 85.0% for Instruction Adherence Rate (IAR). These results demonstrate DocRefine's superior capability in handling complex multimodal document editing, preserving semantic integrity, and maintaining visual consistency, marking a significant advancement in automated scientific document processing.

Problem

• Handles complex layouts and multimodal content in PDFs
• Improves precision in document editing with LLMs and LVLMs
• Automates summarization and content refinement of scientific documents

Innovation

• Multimodal Large Model Agents for document understanding
• Closed-loop feedback ensures semantic accuracy
• Specialized multi-agent system for content refinement