Artificial Intelligence and the Generative Science of Food Formulation

[2607.09529] Artificial Intelligence and the Generative Science of Food Formulation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computational Engineering, Finance, and Science arXiv:2607.09529 (cs) [Submitted on 10 Jul 2026 ( v1 ), last revised 14 Aug 2026 (this version, v2)] Title: Artificial Intelligence and the Generative Science of Food Formulation Authors: Vahidullah Tac , Ellen Kuhl View a PDF of the paper titled Artificial Intelligence and the Generative Science of Food Formulation, by Vahidullah Tac and Ellen Kuhl View PDF HTML (experimental) Abstract: Food formulation requires balancing taste, nutrition, sustainability, and cost. Traditionally, new foods have emerged through empirical experimentation, expert intuition, and iterative refinement. Now, artificial intelligence offers the opportunity to accelerate this process. Yet despite rapid advances across food science, most AI applications remain isolated prediction and optimization tasks rather than parts of a broader scientific approach. Here we integrate these emerging technologies into a unified framework--the generative science of food formulation--in which digital food representations enable artificial intelligence to predict, discover, generate, organize, simulate, and optimize. We illustrate this approach through sustainability and nutrition, where generative artificial intelligence transforms environmental and nutritional metrics from post hoc evaluation criteria into explicit design objectives. Finally, we identify the data, models, benchmarks, and automation that will establish computational food design as a rigorous scientific discipline. Together, these advances have the potential to transform food formulation from an empirical discipline into a generative science. Comments: 20 pages, 5 figures, 1 table Subjects: Computational Engineering, Finance, and Science (cs.CE) MSC classes: 00, 92 Cite as: arXiv:2607.09529 [cs.CE] (or arXiv:2607.09529v2 [cs.CE] for this version) https://doi.org/10.48550/arXiv.2607.09529 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ellen Kuhl [ view email ] [v1] Fri, 10 Jul 2026 15:36:21 UTC (12,857 KB) [v2] Fri, 14 Aug 2026 22:29:17 UTC (12,887 KB) Full-text links: Access Paper: View a PDF of the paper titled Artificial Intelligence and the Generative Science of Food Formulation, by Vahidullah Tac and Ellen Kuhl View PDF HTML (experimental) TeX Source view license Current browse context: cs.CE < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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