Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for
  Academic Presentations
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.
68e5cce3975ac4c405ef20fd
Chengzhi Liu
68e5cce3975ac4c405ef20fe
Yuzhe Yang
claimed_verified
2025-10-21T14:06:48.442Z
63f622c69cbd6730302783eb
https://cdn-avatars.huggingface.co/v1/production/uploads/63f622c69cbd6730302783eb/9cb96JVKiOm_JhF-shbFw.jpeg
Yuzhe Yang
user
TobyYang7
68e5cce3975ac4c405ef20ff
Kaiwen Zhou
68e5cce3975ac4c405ef2100
Zhen Zhang
68e5cce3975ac4c405ef2101
Yue Fan
68e5cce3975ac4c405ef2102
Yannan Xie
68e5cce3975ac4c405ef2103
Peng Qi
68e5cce3975ac4c405ef2104
Xin Eric Wang
Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for
  Academic Presentations
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.
EvoPresent, a self-improvement agent framework using multi-task reinforcement learning, enhances academic paper promotion by generating coherent narratives, aesthetically pleasing designs, and realistic presentations, supported by a comprehensive benchmark.
self-improvement agent framework PresAesth multi-task reinforcement learning aesthetic model aesthetic scoring defect adjustment comparative feedback EvoPresent Benchmark Presentation Generation Quality Aesthetic Awareness