Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for Academic Presentations

• URL: http://arxiv.org/abs/2510.05571v2
• Date: Tue, 21 Oct 2025 21:04:18 GMT
• Title: Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for Academic Presentations
• Abstract summary: textbfEvoPresent is a self-improvement agent framework for academic papers.<n>It unifies coherent narratives, aesthetic-aware designs, and realistic presentation delivery via virtual characters.<n>Central to EvoPresent is textbfPresAesth, a multi-task reinforcement learning (RL) aesthetic model.
• Score: 45.976132936852316
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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