[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83820-en":3,"doc-seo-83820-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83820,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog","ResearchStudio-Reel addresses the manual “last mile” of research dissemination, where turning an accepted paper into a conference poster, a narration-aligned talk video, and a blog post remains largely manual and artifact-specific. Prior automation treats each deliverable in isolation, producing one-way renders and using soft VLM preference gates that can overlook empty or missing load-bearing content. ResearchStudio-Reel composes thin agent-readable contracts over a shared Paper2Assets extractor, editable generators (Paper2Poster/Video/Blog), and a convergence layer (Paper2Reel) that cross-links all artifacts in a navigable HTML viewer.","arXiv :2607 .04438v 1 [ cs .CV] 5 Jul 2026  \nJuly 2026  \nResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog  \nLingao Xiao 1,2* Yalun Dai 1,3* Yangyu Huang 1*‡ Qihao Zhao3 Wenshan Wu 1 Hugo He♭ Ruishuo Chen4 Jin Jiang5 Qianli Ma6 Jiahuan Zhang7 Xin Zhang 1 Ying Xin 1 Yang Ou 1 Yan Xia 1 Scarlett Li 1 Longbo Huang4 Zhipeng Zhang6 Yang He2,8‡ Yap Kim Hui3‡ Yan Lu 1  \n1Microsoft Research 2National University of Singapore 3Nanyang Technological University 4Tsinghua University 5Peking University 6 Shanghai Jiao Tong University 7Westlake University 8CFAR, A*STAR  \nResearch dissemination, turning a paper into a poster, a talk video, and a blog post, is still a manual last mile. Prior automation treats each artifact in isolation that each re-extract the paper from scratch, usually ship one-way renders the author cannot reopen in PowerPoint or Word, and gate quality on soft VLM-preference scores that plateau while load-bearing sections still read as empty. We argue this last mile is best built as a composition of skills: thin agent-readable contracts that share one upstream extractor and wrap deterministic primitives in a measured-fill loop whose exits are hard pass/fail render gates. We instantiate this as ResearchStudio-Reel, five Claude Code and Codex skills organized into one shared extractor (Paper2Assets), three editable generators (Paper2Poster, Paper2Video, Paper2Blog), and one interactive convergence layer (Paper2Reel) . Paper2Assets extracts each paper once into a shared bundle that can be reused by every downstream skill; the three generators produce a print-ready poster, a synchronized talk video, and a bilingual blog that stay factually consistent and round-trip through PowerPoint or Word; Paper2Reel then binds all three into a self-contained HTML viewer whose section-level clicks jump the video, slides, captions, and blog to matching content. On the Paper2Poster benchmark, our posters lead every aesthetic and information sub-criterion against both prior automated systems and single-shot frontier LLMs, surpassing the authors’ own on aesthetics under two held-out VLM judges and winning overall on 84% to 93% of papers; capability audits further show that, by uniquely pairing narration-aligned on-slide highlights with a bilingual blog gated by layout-aware DOCX repair, ResearchStudio-Reel is the only pipeline to ship all three editable artifacts.  \nProject: [https://aka.ms/ResearchStudio](https://aka.ms/ResearchStudio)  \n(a) Poster (PAPER2POSTER) (b) Talk video (PAPER2VIDEO) (c) Bilingual blog (PAPER2BLOG) Figure 1: Three editor-ready artifacts from one paper. From a single accepted PDF, ResearchStudio-Reel produces a print-ready conference poster, a narration-aligned talk video, and a bilingual blog spread, shown here across example papers. Every deliverable stays editable in its native tool (PowerPoint for the poster and the video deck, Word for the blog), and the three are cross-linked into one navigable surface. A single shared paper2assets extraction (Figure 2)  \nkeeps the artifacts factually consistent rather than re-deriving the paper three times.  \n*  \nEqual contribution.  \n♭The author and maintainer of ppt-master.  \n‡Corresponding author: [yanghuan@microsoft.com](yanghuan@microsoft.com), [he_yang@a-star.edu.sg](he_yang@a-star.edu.sg), [EKHYap@ntu.edu.sg](EKHYap@ntu.edu.sg)  \n1 Introduction  \nThe dissemination layer of research is structurally separate from the paper itself and consumes time at exactly the moment authors have the least to spare. This layer covers three artifacts: the poster you stand next to at a conference session, the talk video that lives on the venue’s virtual track or on the lab’s YouTube channel, and the blog post that announces the paper to a non-expert audience. Each is its own production pipeline involving figure cropping, layout, narration, and audience-appropriate prose, and each is conventionally produced by hand in the days immediately after accep","cbCaitg8gUMpKwtp","https://ap.wps.com/l/cbCaitg8gUMpKwtp","pdf",32984473,1,24,"English","en",105,"# Introduction\n## Research dissemination last mile\n## Gaps in prior automation\n## Proposed composition of skills","[{\"question\":\"What problem does ResearchStudio-Reel target in research dissemination?\",\"answer\":\"It targets the manual last mile of converting an accepted paper into three public-facing artifacts—poster, talk video, and blog—where production is time-consuming and usually handled by hand right after acceptance.\"},{\"question\":\"What recurring gaps (G1–G3) does the work identify in prior automation?\",\"answer\":\"It identifies isolated extraction (G1), one-way renders that are hard to reopen and edit (G2), and soft quality gates based on VLM/aesthetic scores that may accept content even when key sections are empty (G3).\"},{\"question\":\"How does ResearchStudio-Reel ensure factual consistency and editability across deliverables?\",\"answer\":\"It uses a shared Paper2Assets extractor so downstream generators do not re-extract the paper independently, and it outputs three editable artifacts (poster/video decks in PowerPoint and blog in Word) cross-linked through Paper2Reel into a single navigable HTML 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