Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

[2608.26519] Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science Skip to main content Search Submit Donate Log in Computer Science > Computational Engineering, Finance, and Science arXiv:2608.26519 (cs) [Submitted on 27 Aug 2026 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science Authors: Lois Curfman McInnes , Dorian Arnold , Prasanna Balaprakash , Mike Bernhardt , Franck Cappello , Beth Cerny , Deborah DiazGranados , Anshu Dubey , Nichole Etienne , Roscoe Giles , Diego Gomez-Zara , Denice Ward Hood , Mary Ann Leung , Vanessa Lopez-Marrero , Olivia B. Newton , Irene Qualters , Keita Teranishi , Stefan M. Wild , Gabrielle Allen , Richard Arthur , Alexandra Ballow , Tony Baylis , David E. Bernholdt , Daniel Bielich , Johanna Cohoon , Jeremy Crampton , Charles Ferenbaugh , Stephen M. Fiore , Thomas Herault , Tanzima Islam , Stephen Jacobsohn , Meifeng Lin , Charles Lively , Satoshi Matsuoka , Stasa Milojevic , Daniel Nichols , Chris Oehmen , Santiago Ospina Tabares , Michael E. Papka , Katherine Riley , Damian Rouson , Sudip K. Seal , Brittany Segundo , John Shalf , Andrew Siegel , Valerie Taylor , Jim Willenbring , Lou Woodley View PDF HTML (experimental) Abstract: Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only computational tools but also the institutions, workforce models, and collaborative practices that support scientific discovery. This report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing, the second in a three-year series focused on strengthening scientific computing ecosystems through socio-technical co-design. Workshop discussions identified four interdependent strategic themes: software ecosystems for AI-enabled scientific discovery; trust, validation, and traceability; human-AI teaming and paradigm shifts; and workforce, pedagogy, and governance. The report translates these themes into eight priorities for community action spanning shared research infrastructure, trust and traceability, user experience, human-AI teaming, workforce development, cross-sector coordination, stewardship and sustainability, and evaluation of scientific value. Together, these priorities outline directions for building scientific computing ecosystems that remain trustworthy, sustainable, innovative, and resilient as AI assumes a growing role in scientific work. Comments: 27 pages, 2 figures Subjects: Computational Engineering, Finance, and Science (cs.CE) ; Software Engineering (cs.SE) MSC classes: 68T01, 68U01, 97M10 ACM classes: I.6.0; I.2.0; G.4; D.0 Report number: ANL-26/32 Cite as: arXiv:2608.26519 [cs.CE] (or arXiv:2608.26519v2 [cs.CE] for this version) https://doi.org/10.48550/arXiv.2608.26519 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Lois Curfman McInnes [ view email ] [v1] Thu, 27 Aug 2026 01:44:54 UTC (3,640 KB) [v2] Fri, 28 Aug 2026 02:44:26 UTC (3,640 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CE < prev | next > new | recent | 2026-08 Change to browse by: cs cs.SE References & Citations SciX 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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