Epicure: Multidimensional Flavor Structure in Food Ingredient Embeddings
A chef's intuition about flavor, texture, and cultural identity represents tacit knowledge that is difficult to articulate yet central to culinary practice. We show that this knowledge is already encoded in FlavorGraph's 300-dimensional ingredient embeddings, trained on recipe cooccurrence and food chemistry, and that it can be systematically recovered. An LLM-augmented curation pipeline consolidates 6,653 raw FlavorGraph ingredients into 1,032 canonical entries, substantially strengthening the recoverable structure. We identify at least fifteen independently classifiable dimensions spanning taste, texture, geography, food processing, and culture.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Computers and Society arXiv:2604.22776 (cs) [Submitted on 2 Apr 2026] Title:Epicure: Multidimensional Flavor Structure in Food Ingredient Embeddings Authors:Jakub Radzikowski, Josef Chen View a PDF of the paper titled Epicure: Multidimensional Flavor Structure in Food Ingredient Embeddings, by Jakub Radzikowski and Josef Chen View PDF HTML (experimental) Abstract:A chef's intuition about flavor, texture, and cultural identity represents tacit knowledge that is difficult to articulate yet central to culinary practice. We show that this knowledge is already encoded in FlavorGraph's 300-dimensional ingredient embeddings, trained on recipe cooccurrence and food chemistry, and that it can be systematically recovered.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI