ResearchStudio-Reel：实现从论文到海报、视频和博客的研究最后一公里自动化

中文摘要 AI 辅助

研究传播，即将论文转化为海报、演讲视频和博客文章，仍然是手动的最后一公里。以前的自动化方法孤立地处理每个工件，每个都从头重新提取论文，通常提供单向渲染，作者无法在PowerPoint或Word中重新打开，并且根据软VLM偏好分数来评估质量，而在承载部分仍为空时分数会趋于平稳。我们认为这最后一公里最好构建为技能组合：瘦代理可读契约，共享一个上游提取器，并在测量填充循环中包装确定性原语，其出口是硬通过/失败渲染门。我们将其实例化为ResearchStudio-Reel，五个Claude代码和Codex技能组织成一个共享提取器（Paper2Assets）、三个可编辑生成器（Paper2Poster、Paper2Video、Paper2Blog）和一个交互式收敛层（Paper2Reel）。Paper2Assets将每篇论文提取一次到一个共享包中，供每个下游技能重用；三个生成器生成一个可打印的海报、一个同步的演讲视频和一个双语博客，它们在事实层面上保持一致，并能通过PowerPoint或Word进行往返；Paper2Reel然后将这三个绑定到一个独立的HTML查看器中，其部分级点击会使视频、幻灯片、字幕和博客跳转到匹配的内容。在Paper2Poster基准测试中，我们的海报在美学和信息子标准方面领先于先前的自动化系统和单镜头前沿语言模型，在两名外部VLM评委的评估下，在美学方面超过了作者自己的海报，并在84%至93%的论文中总体获胜；能力审计进一步表明，通过将与叙述对齐的幻灯片亮点与由布局感知DOCX修复控制的双语博客独特配对，ResearchStudio-Reel是唯一能够提供所有三个可编辑工件的管道。项目可在此https URL上获取

英文摘要

Despite growing automation, turning a paper into a coherent poster, talk video, and blog piece often remains a labor-intensive last mile. Recent systems increasingly generate multiple dissemination formats, but a practical workflow must also keep the outputs editable in native tools and bound into one navigable deliverable for revision and reuse. We present ResearchStudio-Reel, a native-editable dissemination workspace that binds its three artifacts into one interactive deliverable at the experience level, implemented as five skills executable in Claude Code and Codex: one shared extractor, three editable artifact generators, and one interactive convergence layer. A shared asset bundle feeds a PowerPoint poster and video deck, plus a bilingual Word blog; rather than re-rendering the paper into a fourth format, Paper2Reel converges these already-produced artifacts at the experience level, binding poster regions, video segments, and blog passages into one interactive viewer. Artifact-specific release checks make this delivery contract testable, and Paper2Poster additionally uses a measured-fill loop. On the Paper2Poster benchmark, our Claude Code configuration achieves the best scores among automated systems on all three aesthetic sub-criteria and the best or tied-best scores on two of three information sub-criteria. Under two VLMjudges, it exceeds the authors' posters in average aesthetics (3.56 vs. 3.03) and wins on overall quality on 74 and 95 of the 100 papers under the two judges. The full pipeline additionally packages the native-editable source artifacts and their aligned viewer. Project is available at https://aka.ms/ResearchStudio

发表机构

• Microsoft Research（微软研究院）
• National University of Singapore（新加坡国立大学）
• Nanyang Technological University（南洋理工大学）
• Tsinghua University（清华大学）
• Peking University（北京大学）
• Shanghai Jiao Tong University（上海交通大学）
• Westlake University（西湖大学）
• CFAR, A*STAR（计算科学与工程研究所，新加坡科技研究局）

机构由 AI 辅助整理，请以论文原文为准。