SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding

• URL: http://arxiv.org/abs/2510.26615v2
• Date: Sat, 01 Nov 2025 21:48:18 GMT
• Title: SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding
• Authors: Yiqiao Jin, Rachneet Kaur, Zhen Zeng, Sumitra Ganesh, Srijan Kumar,
• Abstract summary: We introduce SlideAgent, a versatile agentic framework for understanding multi-modal, multi-page, and multi-slide documents.<n>During inference, SlideAgent selectively activates specialized agents for multi-level reasoning and integrates their outputs into coherent, context-aware answers.
• Score: 28.839192349010048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-page visual documents such as manuals, brochures, presentations, and posters convey key information through layout, colors, icons, and cross-slide references. While large language models (LLMs) offer opportunities in document understanding, current systems struggle with complex, multi-page visual documents, particularly in fine-grained reasoning over elements and pages. We introduce SlideAgent, a versatile agentic framework for understanding multi-modal, multi-page, and multi-layout documents, especially slide decks. SlideAgent employs specialized agents and decomposes reasoning into three specialized levels-global, page, and element-to construct a structured, query-agnostic representation that captures both overarching themes and detailed visual or textual cues. During inference, SlideAgent selectively activates specialized agents for multi-level reasoning and integrates their outputs into coherent, context-aware answers. Extensive experiments show that SlideAgent achieves significant improvement over both proprietary (+7.9 overall) and open-source models (+9.8 overall).

Related papers

• APD-Agents: A Large Language Model-Driven Multi-Agents Collaborative Framework for Automated Page Design [28.89702589792701]
  We propose APD-agents, a large language model driven multi-agent framework for app page design.<n>Our work fully leverages the automatic collaboration capabilities of large-model-driven multi-agent systems.<n> Experimental results on the RICO dataset show that APD-agents achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-11-18T03:39:26Z)
• DocLens : A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding [59.4112754806335]
  We propose DocLens, a tool-augmented multi-agent framework that effectively zooms in'' on evidence like a lens.<n>It first navigates from the full document to specific visual elements on relevant pages, then employs a sampling-adjudication mechanism to generate a single, reliable answer.<n>It achieves state-of-the-art performance on MMLongBench-Doc and FinRAG-V, surpassing even human experts.
  arXiv  Detail & Related papers  (2025-11-14T18:42:18Z)
• ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents [17.497004687630742]
  Vision-language models (VLMs) excel at interpreting text-rich images but struggle with long, visually complex documents.<n>We present Active Long-DocumEnt Navigation (ALDEN), a multi-turn reinforcement learning framework that fine-tunes VLMs as interactive agents.
  arXiv  Detail & Related papers  (2025-10-29T16:32:26Z)
• MMESGBench: Pioneering Multimodal Understanding and Complex Reasoning Benchmark 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)
• MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding [40.52017994491893]
  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%.
  arXiv  Detail & Related papers  (2025-03-18T06:57:21Z)
• AppAgent v2: Advanced Agent for Flexible Mobile Interactions [57.98933460388985]
  This work introduces a novel LLM-based multimodal agent framework for mobile devices.<n>Our agent constructs a flexible action space that enhances adaptability across various applications.<n>Our results demonstrate the framework's superior performance, confirming its effectiveness in real-world scenarios.
  arXiv  Detail & Related papers  (2024-08-05T06:31:39Z)
• PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering [13.625303311724757]
  Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD) We propose PDF-MVQA, which is tailored for research journal articles, encompassing multiple pages and multimodal information retrieval.
  arXiv  Detail & Related papers  (2024-04-19T09:00:05Z)
• 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)
• DOC2PPT: Automatic Presentation Slides Generation from Scientific Documents [76.19748112897177]
  We present a novel task and approach for document-to-slide generation. We propose a hierarchical sequence-to-sequence approach to tackle our task in an end-to-end manner. Our approach exploits the inherent structures within documents and slides and incorporates paraphrasing and layout prediction modules to generate slides.
  arXiv  Detail & Related papers  (2021-01-28T03:21:17Z)

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.