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New dataset RAP enables AI to generate conference posters from papers

Researchers have introduced RAP, a new dataset and benchmark designed for generating conference posters from academic papers. This dataset comprises over 25,000 verified paper-poster pairs, annotated with a content taxonomy, and includes a benchmark set of 150 test papers with their corresponding human-created posters. RAP facilitates paper-to-poster generation as a visual coding task, enabling both retrieval-based methods like PosterRAG and supervised fine-tuning with reinforcement learning. AI

IMPACT This dataset could accelerate research into AI systems capable of complex visual summarization and design tasks.

RANK_REASON The cluster describes a new dataset and benchmark for a specific AI task (paper-to-poster generation).

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New dataset RAP enables AI to generate conference posters from papers

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The cluster describes a new dataset and benchmark for a specific AI task (paper-to-poster generation).
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    RAP: A Dataset for Paper-to-Poster Generation via Declarative Markup from Human-Crafted Exemplars

    RAP introduces a large-scale dataset and benchmark for generating conference posters from papers as a visual coding task, providing verified paper-poster-markup triplets and both retrieval-based and trained generation methods.

  2. arXiv cs.CV TIER_1 English(EN) · Haonan Han, Yukang Lin, Minliang Lin, Haoxian Tan, Jun-Yan He, Rui Yang, Zhi-Qi Cheng, Jie Hu, Guanglu Wan, Xiu Li ·

    RAP: A Dataset for Paper-to-Poster Generation via Declarative Markup from Human-Crafted Exemplars

    arXiv:2610.23230v1 Announce Type: new Abstract: Making a conference poster is visual coding in miniature: an entire paper must be compressed onto a single page, which exercises summarization, visual design, and code implementation at once, using nothing beyond the paper itself. Y…