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

• URL: http://arxiv.org/abs/2508.07021v1
• Date: Sat, 09 Aug 2025 15:32:52 GMT
• Title: DocRefine: An Intelligent Framework for Scientific Document Understanding and Content Optimization based on Multimodal Large Model Agents
• Authors: Kun Qian, Wenjie Li, Tianyu Sun, Wenhong Wang, Wenhan Luo,
• Abstract summary: DocRefine is an innovative framework designed for intelligent understanding, content refinement, and automated summarization of scientific PDF documents.<n>It orchestrates a sophisticated multi-agent system comprising six specialized and collaborative agents.<n>It consistently outperforms state-of-the-art baselines across various tasks.
• Score: 25.190790899297788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases [7.75137961900221]
  We present bftextCodeWiki, a unified framework for automated repository-level documentation across seven programming languages.<n>CodeWiki introduces three key innovations: (i) hierarchical decomposition that preserves architectural context across multiple levels of granularity, (ii) recursive multi-agent processing with dynamic task delegation for scalable generation, and (iii) multi-modal synthesis that integrates textual descriptions with visual artifacts such as architecture diagrams and data-flow representations.<n>CodeWiki achieves a 68.79% quality score with proprietary models, outperforming the closed-source DeepWiki baseline (64.06%) by 4.73%
  arXiv  Detail & Related papers  (2025-10-28T13:52:46Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• Zero-Shot Document Understanding using Pseudo Table of Contents-Guided Retrieval-Augmented Generation [4.875345207589195]
  DocsRay is a training-free document understanding system.<n>It integrates pseudo Table of Contents (TOC) generation with hierarchical Retrieval-Augmented Generation (RAG)
  arXiv  Detail & Related papers  (2025-07-31T03:14:45Z)
• Benchmarking Multimodal Understanding and Complex Reasoning for ESG Tasks [56.350173737493215]
  Environmental, Social, and Governance (ESG) reports are essential for evaluating sustainability practices, ensuring regulatory compliance, and promoting financial transparency.<n>MMESGBench is a first-of-its-kind benchmark dataset to evaluate multimodal understanding and complex reasoning across structurally diverse and multi-source ESG documents.<n>MMESGBench comprises 933 validated QA pairs derived from 45 ESG documents, spanning across seven distinct document types and three major ESG source categories.
  arXiv  Detail & Related papers  (2025-07-25T03:58:07Z)
• Docopilot: Improving Multimodal Models for Document-Level Understanding [87.60020625241178]
  We present a high-quality document-level dataset, Doc-750K, designed to support in-depth understanding of multimodal documents.<n>This dataset includes diverse document structures, extensive cross-page dependencies, and real question-answer pairs derived from the original documents.<n>Building on the dataset, we develop a native multimodal model, Docopilot, which can accurately handle document-level dependencies without relying on RAG.
  arXiv  Detail & Related papers  (2025-07-19T16:03:34Z)
• M-DocSum: Do LVLMs Genuinely Comprehend Interleaved Image-Text in Document Summarization? [49.53982792497275]
  We investigate whether Large Vision-Language Models (LVLMs) genuinely comprehend interleaved image-text in the document.<n>Existing document understanding benchmarks often assess LVLMs using question-answer formats.<n>We introduce a novel and challenging Multimodal Document Summarization Benchmark (M-DocSum-Bench)<n>M-DocSum-Bench comprises 500 high-quality arXiv papers, along with interleaved multimodal summaries aligned with human preferences.
  arXiv  Detail & Related papers  (2025-03-27T07:28:32Z)
• VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation [100.06122876025063]
  This paper introduces VisDoMBench, the first comprehensive benchmark designed to evaluate QA systems in multi-document settings.<n>We propose VisDoMRAG, a novel multimodal Retrieval Augmented Generation (RAG) approach that simultaneously utilizes visual and textual RAG.
  arXiv  Detail & Related papers  (2024-12-14T06:24:55Z)
• DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding [40.38251904765156]
  Text-rich document understanding (TDU) requires comprehensive analysis of documents containing substantial textual content and complex layouts.<n>We introduce DocLayLLM, an efficient multi-modal extension of Multimodal Large Language Models (MLLMs) specifically designed for TDU.
  arXiv  Detail & Related papers  (2024-08-27T13:13:38Z)
• mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding [55.4806974284156]
  Document understanding refers to automatically extract, analyze and comprehend information from digital documents, such as a web page. Existing Multi-model Large Language Models (MLLMs) have demonstrated promising zero-shot capabilities in shallow OCR-free text recognition.
  arXiv  Detail & Related papers  (2023-07-04T11:28:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.