arXiv:2510.26615v4 Announce Type: replace 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. SlideAg
Advances in multimodal large language models are enabling more sophisticated approaches to complex document understanding, pushing the boundaries of what AI agents can achieve.
This development improves AI's ability to extract and reason over information in real-world, multi-page visual documents, a common format for critical business and technical communication.
AI systems can now process and interpret complex visual documents with finer-grained reasoning, moving beyond simple text extraction to understand layout, hierarchy, and cross-page references.
- · AI software developers
- · Consulting firms
- · Businesses with large archives of visual documents
- · Knowledge workers
- · Manual document analysis services
- · Legacy document parsing software
- · Routine data entry jobs
SlideAgent enables more accurate and automated analysis of pitch decks, manuals, and reports for businesses.
This improved document understanding could accelerate market research, due diligence processes, and knowledge retention within organizations.
The ability to rapidly digest and cross-reference complex visual information might lead to new forms of automated research and strategic analysis, impacting decision-making cycles.
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