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arXiv — NLP / Computation & Language · · 3 min read

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

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Computer Science > Computation and Language

arXiv:2510.26615 (cs)
[Submitted on 30 Oct 2025 (v1), last revised 5 Jun 2026 (this version, v4)]

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

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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%).
Comments: ACL 2026 Main Conference. this https URL
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2510.26615 [cs.CL]
  (or arXiv:2510.26615v4 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2510.26615
arXiv-issued DOI via DataCite

Submission history

From: Yiqiao Jin [view email]
[v1] Thu, 30 Oct 2025 15:41:15 UTC (2,446 KB)
[v2] Sat, 1 Nov 2025 21:48:18 UTC (2,430 KB)
[v3] Thu, 23 Apr 2026 14:14:19 UTC (2,484 KB)
[v4] Fri, 5 Jun 2026 02:31:19 UTC (2,484 KB)
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