epicure-cooc

Epicure-Cooc

A 300-dimensional skip-gram ingredient embedding over a 1,790-ingredient canonical vocabulary, trained on pure ingredient-ingredient random walks weighted by normalised pointwise mutual information (NPMI) over a 4.14M-recipe multilingual corpus.

Cooc is the recipe-context extreme of the three Epicure siblings. It sees no chemistry signal: walks traverse only the ingredient-ingredient NPMI graph (203,508 edges over 1,790 ingredients). Its neighbour structure answers "what else do I cook this with" rather than "what shares its flavour profile."

Companions in the family: epicure-core (blended) and epicure-chem (chemistry only).

Paper: Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings

Quick start

from epicure import Epicure

m = Epicure.from_pretrained("Kaikaku/epicure-cooc")

# nearest-neighbour pairings
m.neighbors("chicken", k=5)
# -> [('garlic', 0.39), ('onion', 0.37), ('black_pepper', 0.36),
#     ('turkey', 0.35), ('carrot', 0.34)]

# SLERP toward a supervised pole
m.slerp("corn", "cuisine:Latin_American", theta_deg=30, k=5)
# -> [('salsa_verde', 0.45), ('red_onion', 0.45), ('chorizo', 0.44), ...]

# closest emergent factor mode
m.closest_mode("miso", kind="factor", k=3)

epicure.py is shipped inside this repo. Install requirements with pip install huggingface_hub safetensors numpy.

What is in this repo

Reported numbers (this sibling)

From the paper:

• Isotropy: participation ratio PR = 173.6 of 300; average pairwise cosine in the 0.10-0.12 band. Cooc is one of the two isotropic siblings (Chem PR=183.1 is the other; Core is concentrated at PR=94.2).
• Direction quality (5-fold repeated CV Spearman rho, mean over probes): baked-in compound-feature 0.28; held-out basic-taste compound-feature 0.32; USDA macros 0.41. Cuisine macro-region Cohen's d mean 2.43 across the eight regions.
• Emergent modes: 150 modes across 41 properties. Mean within-mode pairwise cosine 0.611 against random-pair baseline 0.097 (a 6x tightness margin).
• Normalised mutual information against USDA food groups: 0.20-0.25 band. Soft NMI against cuisine macro-regions: 0.43-0.46 band.

The Cooc <= Core <= Chem ordering holds on every supervised direction-quality probe (basic taste, USDA macros, cuisine). For Cooc that means: it is the cleanest recipe-context model but the weakest supervised-direction extractor of the three; if you want a chemistry-aware navigation operator pick Core or Chem.

Operator semantics

Three operator families live on this embedding:

1. Nearest-neighbour pairings (m.neighbors(seed, k)). Top-K cosine neighbours over the L2-normalised matrix. Excludes the seed by default.
2. Closest-mode lookup (m.closest_mode(seed, kind=..., k)). Cosine of the seed to each mode pole; returns the top-K modes. kind filters to factor (emergent), continuous (sensory and USDA), or binary (food group, NOVA).
3. SLERP direction arithmetic (m.slerp(seed, direction, theta_deg, k)). Rotates the unit-normalised seed toward a unit direction by angle theta on the unit sphere. The direction is either a supervised pole key from supervised_poles.json or a raw 300-D numpy array. At theta=0 the rotated query equals the seed; at theta=60 deg cosine-to-seed = 0.5; by 90 deg the target neighbourhood dominates. The seed is excluded from results by default.

Honesty about cuisine pole reconstruction

The paper's SLERP-toward-cuisine results in Section 4.2 use cuisine-macro-region pole vectors built from the 8-region distinctive-marker tags introduced in Section 2.2. Those tag-to-ingredient mappings (the canonical_vocabulary.csv with cuisine_tags column) are not in the arXiv ancillary bundle, so this repo reconstructs cuisine pole vectors heuristically: the unit-mean of every mode-atlas pole whose Claude-generated label contains a cuisine macro-region keyword (e.g. "South Asian", "Mexican", "Italian"). Provenance is logged per cuisine in cuisine_pole_provenance.json.

Empirically the heuristic poles produce paper-genre results in two of three siblings:

The Cooc reconstruction matches the genre less tightly than Core or Chem because the Cooc sibling does not encode chemistry-mediated cuisine clustering, so the heuristic cuisine pole picks up generic Cooc co-occurrence companions. To exactly reproduce the paper's Cooc results, build the cuisine pole from the original per-ingredient cuisine tag set. We will add canonical_vocabulary.csv to the corpus-resources dataset in a follow-up; until then, paper_slerp_results.csv ships the full paper-reported SLERP table for offline verification.

Limitations

From Section 5.3 of the paper:

• Corpus imbalance. The 4.14M-recipe corpus is roughly half East Asian and a tenth Mediterranean; single-digit shares for South Asian, Eastern European, Latin American. Held-out-cuisine confidence intervals widen accordingly in smaller regions; the cross-region ranking of the three siblings is nevertheless stable.
• Hub coverage. Only 523 of 1,790 ingredients are chemistry hubs (those with active typed I-C edges after the min_compound_degree=2 filter). The remaining 1,267 non-hubs reach compound context only indirectly via the N-H-C[x]-H-N metapath. For pure-Cooc Cooc this is irrelevant; it matters for Core and Chem.
• LLM dependence. The canonical vocabulary, cuisine tags, sensory ground-truth scores, and mode/factor labels are LLM-generated under deterministic decoding. The embeddings themselves are LLM-free.

Intended use

Research and prototyping for chef-facing or recipe-assistant tools: ingredient pairing, cross-cuisine navigation, sensory- or nutrient-aware exploration. Not intended for nutritional or medical recommendation: USDA values are looked up against FoodData Central but the embedding only encodes their continuous projection, not the absolute numbers.

Citation

@article{radzikowski2026epicure,
  title   = {Epicure: Navigating the Emergent Geometry of Food Ingredient Embeddings},
  author  = {Radzikowski, Jakub and Chen, Josef},
  journal = {arXiv preprint arXiv:2605.22391},
  year    = {2026}
}

License

CC BY 4.0. Matches the arXiv submission. Attribution to Radzikowski and Chen, KAIKAKU.AI.