All of human cooking compressed into 2 megabytes
Researchers have developed Epicure, a new AI model that compresses a vast amount of culinary knowledge into just 2 megabytes. This model utilizes a multilingual recipe corpus containing over 4 million recipes to create ingredient embeddings. The study explores the relationships between ingredients and compounds through various graph-based approaches.
- ▪Epicure is a family of three sibling skip-gram ingredient embeddings retrained from scratch on a multilingual recipe corpus.
- ▪The model aggregates 4.14 million recipes from 11 sources across seven languages.
- ▪It normalizes raw ingredient strings to 1,790 canonical entries using an LLM-augmented pipeline.
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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2605.22391 |
| Publication time | Wed, 27 May 2026 08:14:40 +0000 |
| Retrieval time | 2026-05-27T08:37:56.893Z |
| Last seen | 2026-05-27T08:37:56.893Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | XddCkhEFuf5x |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.22391 (cs) [Submitted on 21 May 2026] Title:Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings Authors:Jakub Radzikowski, Josef Chen View a PDF of the paper titled Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings, by Jakub Radzikowski and Josef Chen View PDF HTML (experimental) Abstract:We present Epicure, a family of three sibling skip-gram ingredient embeddings retrained from scratch on a multilingual recipe corpus. We aggregate 4.14M recipes from 11 sources spanning seven languages, English, Chinese, Russian, Vietnamese, Spanish, Turkish, Indonesian, German, and Indian-English, and normalise the raw ingredient strings to 1,790 canonical entries via an LLM-augmented pipeline.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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