{"ID":2871223,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.11062","arxiv_id":"2509.11062","title":"Auto-Slides: An Interactive Multi-Agent System for Creating and Customizing Research Presentations","abstract":"The rapid progress of large language models (LLMs) has opened new opportunities for education. While learners can interact with academic papers through LLM-powered dialogue, limitations still exist: the lack of structured organization and the heavy reliance on text can impede systematic understanding and engagement with complex concepts. To address these challenges, we propose Auto-Slides, an LLM-driven system that converts research papers into pedagogically structured, multimodal slides (e.g., diagrams and tables). Drawing on cognitive science, it creates a presentation-oriented narrative and allows iterative refinement via an interactive editor to better match learners' knowledge level and goals. Auto-Slides further incorporates verification and knowledge retrieval mechanisms to ensure accuracy and contextual completeness. Through extensive user studies, Auto-Slides demonstrates strong learner acceptance, improved structural support for understanding, and expert-validated gains in narrative quality compared with conventional LLM-based reading. Our contributions lie in designing a multi-agent framework for transforming academic papers into pedagogically optimized slides and introducing interactive customization for personalized learning.","short_abstract":"The rapid progress of large language models (LLMs) has opened new opportunities for education. While learners can interact with academic papers through LLM-powered dialogue, limitations still exist: the lack of structured organization and the heavy reliance on text can impede systematic understanding and engagement wit...","url_abs":"https://arxiv.org/abs/2509.11062","url_pdf":"https://arxiv.org/pdf/2509.11062v3","authors":"[\"Yuheng Yang\",\"Wenjia Jiang\",\"Yang Wang\",\"Yi Song\",\"Yiwei Wang\",\"Chi Zhang\"]","published":"2025-09-14T03:05:54Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.MA\"]","methods":"[\"Large Language Model\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}