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Agentic Publication Protocol: An Attempt to Modernize Scientific Publication

arXiv AI Archived Jun 29, 2026 ✓ Full text saved

arXiv:2606.27386v1 Announce Type: cross Abstract: Scientific publication is still organized primarily around static manuscripts, even though much of scientific progress depends on tacit know-how: how to run code, reproduce figures, interpret edge cases, choose useful follow-up directions, and avoid failed paths. Large language model agents create an opportunity to publish not only knowledge, but also operational know-how in a form that future readers and researchers can directly use. This paper

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    Computer Science > Digital Libraries [Submitted on 15 Jun 2026] Agentic Publication Protocol: An Attempt to Modernize Scientific Publication Sirui Lu, Xiao-Liang Qi Scientific publication is still organized primarily around static manuscripts, even though much of scientific progress depends on tacit know-how: how to run code, reproduce figures, interpret edge cases, choose useful follow-up directions, and avoid failed paths. Large language model agents create an opportunity to publish not only knowledge, but also operational know-how in a form that future readers and researchers can directly use. This paper outlines the Agentic Publication Protocol (APP), a lightweight repository format for packaging a paper together with code, data, environment information, reproducibility instructions, and an agent-facing instruction file. APP treats a version-controlled repository as the publication object and uses \texttt{this http URL} and optional skills to define a paper agent that can explain the work, reproduce key results when possible, and support follow-up research. We describe the design principles and details of the protocol, as well as the agent skills useful for publishing papers under the protocol. We also describe development tools for evaluating and improving the protocol and associated agent skills. Finally, we provide a broader discussion of the future of scientific research in the agent era. Comments: 16 pages, 5 figures and 1 table Subjects: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI) Cite as: arXiv:2606.27386 [cs.DL]   (or arXiv:2606.27386v1 [cs.DL] for this version)   https://doi.org/10.48550/arXiv.2606.27386 Focus to learn more Submission history From: Xiao-Liang Qi [view email] [v1] Mon, 15 Jun 2026 08:52:43 UTC (309 KB) Access Paper: HTML (experimental) view license Current browse context: cs.DL < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv AI
    Category
    ◬ AI & Machine Learning
    Published
    Jun 29, 2026
    Archived
    Jun 29, 2026
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