{"ID":2859146,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05571","arxiv_id":"2510.05571","title":"Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for Academic Presentations","abstract":"The promotion of academic papers has become an important means of enhancing research visibility. However, existing automated methods struggle limited storytelling, insufficient aesthetic quality, and constrained self-adjustment, making it difficult to achieve efficient and engaging dissemination. At the heart of those challenges is a simple principle: \\emph{there is no way to improve it when you cannot evaluate it right}. To address this, we introduce \\textbf{EvoPresent}, a self-improvement agent framework that unifies coherent narratives, aesthetic-aware designs, and realistic presentation delivery via virtual characters. Central to EvoPresent is \\textbf{PresAesth}, a multi-task reinforcement learning (RL) aesthetic model that provides reliable aesthetic scoring, defect adjustment, and comparative feedback, enabling iterative self-improvement even under limited aesthetic training data. To systematically evaluate the methods, we introduce \\textbf{EvoPresent Benchmark}, a comprehensive benchmark comprising: \\textit{Presentation Generation Quality}, built on 650 top-tier AI conference papers with multimodal resources (slides, videos and scripts) to assess both content and design; and \\textit{Aesthetic Awareness}, consisting of 2,000 slide pairs with varying aesthetic levels, supporting joint training and evaluation on scoring, defect adjustment, and comparison. Our findings highlight that (i) High-quality feedback is essential for agent self-improvement, while initial capability alone does not guarantee effective self-correction. (ii) Automated generation pipelines exhibit a trade-off between visual design and content construction. (iii) Multi-task RL training shows stronger generalization in aesthetic awareness tasks.","short_abstract":"The promotion of academic papers has become an important means of enhancing research visibility. However, existing automated methods struggle limited storytelling, insufficient aesthetic quality, and constrained self-adjustment, making it difficult to achieve efficient and engaging dissemination. At the heart of those...","url_abs":"https://arxiv.org/abs/2510.05571","url_pdf":"https://arxiv.org/pdf/2510.05571v2","authors":"[\"Chengzhi Liu\",\"Yuzhe Yang\",\"Kaiwen Zhou\",\"Zhen Zhang\",\"Yue Fan\",\"Yanan Xie\",\"Peng Qi\",\"Xin Eric Wang\"]","published":"2025-10-07T04:24:26Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}