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arXiv · 2510.26615

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

Yiqiao Jin·Rachneet Kaur·Zhen Zeng·Sumitra Ganesh·Srijan Kumar
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Abstract

Multi-page visual documents such as manuals, brochures, presentations, and posters convey key information through layout, colors, icons, and cross-slide references. While multimodal large language models (MLLMs) 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 significantly improves accuracy over both proprietary (+7.9%) and open-source models (+9.8%).

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Yiqiao Jin, Rachneet Kaur, Zhen Zeng, Sumitra Ganesh, Srijan Kumar. 2025-10-30. SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding. https://arxiv.org/abs/2510.26615

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