SlideAgent: New AI Framework Improves Understanding of Multi-Page Visual Documents
Researchers have developed SlideAgent, an AI framework designed to better understand complex multi-page visual documents like presentations and manuals. The system uses specialized agents operating at three levels—global, page, and element—to analyze documents more effectively than existing systems. The advancement could improve how AI systems process and extract information from visually complex documents.
SlideAgent is a hierarchical agentic framework that addresses limitations in how current multimodal large language models (MLLMs) handle multi-page visual documents. The system decomposes reasoning into three specialized levels: global (overarching themes), page (individual slide analysis), and element (fine-grained visual and textual details). During inference, the framework selectively activates specialized agents and integrates their outputs into coherent answers. Experimental results demonstrate significant improvements, with SlideAgent achieving 7.9% higher accuracy than proprietary models and 9.8% higher accuracy than open-source models on relevant benchmarks. The research has been accepted to the ACL 2026 Main Conference, indicating peer review validation of the approach.
What's missing
The specific datasets and benchmarks used for evaluation are not detailed in the abstract. Additionally, computational requirements, inference speed comparisons, and limitations of the approach are not discussed in the provided abstract.
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- arXiv cs.CLCenter
SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding
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