把论文变成海报，现在有了2.5万份标准答案
做学术海报本质上是一次「视觉编程」:把整篇论文压进一页，同时考验总结、设计和写代码。但过去这个任务几乎没有像样的训练数据——已有的资源又小又只标了布局框，评价只能靠 AI 打分或人工检查，没法客观对比。研究者搞了个叫 RAP 的数据集，从 2022 到 2026 年的机器学习会议里扒出 2.5 万+ 对「论文-海报」，再用全自动流程把每张海报拆成带样式的 HTML/CSS 模板，每个槽位都能重新填内容，而且渲染出来能和原海报逐像素对比验证。基于这套数据，他们还做了两种生成方式：一种是不用训练的检索式管线，直接找一张相似海报、把内容换掉、布局和样式原样保留；另一种是用强化学习专门训练模型。这不是你明天能拿来用的工具，但它第一次让「论文转海报」有了可量化、可复现的基准，以后做学术海报的 AI 会靠谱得多。
📄 原文摘要(英文)
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. Yet benchmarks for this task in the context of visual coding remain scarce: existing resources are small and annotated only with layout boxes, so evaluation must fall back on VLM-as-judge or human checklists rather than objective render-and-compare, and the generation methods they support design layouts from scratch, leaving human-crafted posters unusable as either a retrievable design library or as verified supervision. We introduce RAP (Reconstruct A Poster), a dataset and benchmark for paper-to-poster generation as a visual coding task. RAP contains 25K+ verified paper-poster pairs from various machine learning conferences held between 2022 and 2026, annotated with a three-level content taxonomy, plus a held-out benchmark of 150 test papers each paired with its human poster. A fully automated derendering pipeline turns each poster into styled HTML/CSS of typed, refillable slots, so every sample is a paper-poster-markup triplet verifiable by rendering against the source poster. The corpus serves both inference and training: PosterRAG, a training-free multi-agent pipeline, retrieves an exemplar and refills its slots without touching layout or styling, while an SFT+RL recipe trains a dedicated model from the same triplets. An online demo is available at https://reconstruct-a-poster.github.io/.