Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for
  Academic Presentations
Authors: name: Chengzhi Liu, name: Yuzhe Yang, name: Kaiwen Zhou, name: Zhen Zhang, name: Yue Fan, name: Yannan Xie, name: Peng Qi, name: Xin Eric Wang
Organization: UCSB AI Group

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: there is no way to
improve it when you cannot evaluate it right. To address this, we introduce
EvoPresent, a self-improvement agent framework that unifies coherent
narratives, aesthetic-aware designs, and realistic presentation delivery via
virtual characters. Central to EvoPresent is 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 EvoPresent Benchmark, a
comprehensive benchmark comprising: 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 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.