MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding

• URL: http://arxiv.org/abs/2503.13964v1
• Date: Tue, 18 Mar 2025 06:57:21 GMT
• Title: MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding
• Authors: Siwei Han, Peng Xia, Ruiyi Zhang, Tong Sun, Yun Li, Hongtu Zhu, Huaxiu Yao,
• Abstract summary: MDocAgent is a novel RAG and multi-agent framework that leverages both text and image.<n>Our system employs five specialized agents: a general agent, a critical agent, a text agent, an image agent and a summarizing agent.<n>Preliminary experiments on five benchmarks demonstrate the effectiveness of our MDocAgent, achieve an average improvement of 12.1%.
• Score: 40.52017994491893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document Question Answering (DocQA) is a very common task. Existing methods using Large Language Models (LLMs) or Large Vision Language Models (LVLMs) and Retrieval Augmented Generation (RAG) often prioritize information from a single modal, failing to effectively integrate textual and visual cues. These approaches struggle with complex multi-modal reasoning, limiting their performance on real-world documents. We present MDocAgent (A Multi-Modal Multi-Agent Framework for Document Understanding), a novel RAG and multi-agent framework that leverages both text and image. Our system employs five specialized agents: a general agent, a critical agent, a text agent, an image agent and a summarizing agent. These agents engage in multi-modal context retrieval, combining their individual insights to achieve a more comprehensive understanding of the document's content. This collaborative approach enables the system to synthesize information from both textual and visual components, leading to improved accuracy in question answering. Preliminary experiments on five benchmarks like MMLongBench, LongDocURL demonstrate the effectiveness of our MDocAgent, achieve an average improvement of 12.1% compared to current state-of-the-art method. This work contributes to the development of more robust and comprehensive DocQA systems capable of handling the complexities of real-world documents containing rich textual and visual information. Our data and code are available at https://github.com/aiming-lab/MDocAgent.

Related papers

• DocAgent: A Multi-Agent System for Automated Code Documentation Generation [7.653779364214401]
  We introduce DocAgent, a novel multi-agent collaborative system using topological code processing for incremental context building. Specialized agents (Reader, Searcher, Writer, Verifier, Orchestrator) then collaboratively generate documentation. We also propose a multi-faceted evaluation framework assessing Completeness, Helpfulness, and Truthfulness.
  arXiv  Detail & Related papers  (2025-04-11T17:50:08Z)
• 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. Existing document understanding benchmarks often assess LVLMs using question-answer formats. We introduce a novel and challenging Multimodal Document Summarization Benchmark (M-DocSum-Bench) 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)
• LVAgent: Long Video Understanding by Multi-Round Dynamical Collaboration of MLLM Agents [18.580893774836845]
  We introduce LVAgent, a framework enabling multi-round dynamic collaboration of MLLM agents in long video understanding.<n>Our methodology consists of four key steps: Selection, Perception, Action and Reflection.<n>Our LVAgent achieves an accuracy of 80% on four mainstream long video understanding tasks.
  arXiv  Detail & Related papers  (2025-03-13T09:35:09Z)
• A Multi-Agent Perspective on Modern Information Retrieval [12.228832858396368]
  The rise of large language models (LLMs) has introduced a new era in information retrieval (IR)<n>This shift challenges some long-standing IR paradigms and calls for a reassessment of both theoretical frameworks and practical methodologies.<n>We advocate for a multi-agent perspective to better capture the complex interactions between query agents, document agents, and ranker agents.
  arXiv  Detail & Related papers  (2025-02-20T18:17:26Z)
• 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)
• TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal Large Language Models [9.232693392690702]
  TextHawk is a document-oriented Multimodal Large Language Model (MLLM) It is designed to explore efficient fine-grained perception by designing four dedicated components. We conduct extensive experiments on both general and document-oriented MLLM benchmarks, and show that TextHawk outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-04-14T09:48:37Z)
• Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation [72.6168579583414]
  CompAgent is a training-free approach for compositional text-to-image generation with a large language model (LLM) agent as its core. Our approach achieves more than 10% improvement on T2I-CompBench, a comprehensive benchmark for open-world compositional T2I generation.
  arXiv  Detail & Related papers  (2024-01-28T16:18:39Z)
• VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks [93.85005277463802]
  VisualWebArena is a benchmark designed to assess the performance of multimodal web agents on realistic tasks. To perform on this benchmark, agents need to accurately process image-text inputs, interpret natural language instructions, and execute actions on websites to accomplish user-defined objectives.
  arXiv  Detail & Related papers  (2024-01-24T18:35:21Z)
• UniDoc: A Universal Large Multimodal Model for Simultaneous Text Detection, Recognition, Spotting and Understanding [93.92313947913831]
  We introduce UniDoc, a novel multimodal model equipped with text detection and recognition capabilities. To the best of our knowledge, this is the first large multimodal model capable of simultaneous text detection, recognition, spotting, and understanding.
  arXiv  Detail & Related papers  (2023-08-19T17:32:34Z)

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.