[2602.03092] Generative Artificial Intelligence creates delicious, sustainable, and nutritious burgers

Skip to main content

arXiv is now an independent nonprofit!<br>Learn more<br>&times;

Search arXiv

Press Enter to search &middot; Advanced search

-->

Computer Science > Computational Engineering, Finance, and Science

arXiv:2602.03092 (cs)

[Submitted on 3 Feb 2026]

Title:Generative Artificial Intelligence creates delicious, sustainable, and nutritious burgers

Authors:Vahidullah Tac, Christopher Gardner, Ellen Kuhl<br>View a PDF of the paper titled Generative Artificial Intelligence creates delicious, sustainable, and nutritious burgers, by Vahidullah Tac and 2 other authors

View PDF<br>HTML (experimental)

Abstract:Food choices shape both human and planetary health; yet, designing foods that are delicious, nutritious, and sustainable remains challenging. Here we show that generative artificial intelligence can learn the structure of the human palate directly from large-scale, human-generated recipe data to create novel foods within a structured design space. Using burgers as a model system, the generative AI rediscovers the classic Big Mac without explicit supervision and generates novel burgers optimized for deliciousness, sustainability, or nutrition. Compared to the Big Mac, its delicious burgers score the same or better in overall liking, flavor, and texture in a blinded sensory evaluation conducted in a restaurant setting with 101 participants; its mushroom burger achieves an environmental impact score more than an order of magnitude lower; and its bean burger attains nearly twice the nutritional score. Together, these results establish generative AI as a quantitative framework for learning human taste and navigating complex trade-offs in principled food design.

Comments:<br>13 pages, 4 figures

Subjects:

Computational Engineering, Finance, and Science (cs.CE)

Cite as:<br>arXiv:2602.03092 [cs.CE]

(or<br>arXiv:2602.03092v1 [cs.CE] for this version)

https://doi.org/10.48550/arXiv.2602.03092

Focus to learn more

arXiv-issued DOI via DataCite

Submission history<br>From: Ellen Kuhl [view email]<br>[v1]<br>Tue, 3 Feb 2026 04:34:44 UTC (38,834 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled Generative Artificial Intelligence creates delicious, sustainable, and nutritious burgers, by Vahidullah Tac and 2 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.CE

next >

new<br>recent<br>| 2026-02

Change to browse by:

cs

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

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? |<br>Disable MathJax (What is MathJax?)

Major funding support from