epicure-core

Quick start

from epicure import Epicure

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

m.neighbors("chicken", k=5)
# -> [('pork', 0.58), ('beef', 0.57), ('chicken_broth', 0.55),
#     ('peanut', 0.52), ('cream_of_chicken_soup', 0.52)]

m.slerp("rice", "cuisine:South_Asian", theta_deg=30, k=5)
# -> [('turmeric', 0.76), ('mustard_seed', 0.76), ('fenugreek_seed', 0.75),
#     ('coriander', 0.74), ('cumin', 0.74)]

m.closest_mode("chocolate", kind="factor", k=3)

What is in this repo

Identical structure to the Cooc sibling. The Core-specific differences:

• modes.json: 193 modes across 44 properties (vs 150/41 for Cooc, 200/43 for Chem).
• factor_poles.npy shape: (87, 300).
• supervised_poles.json: 113 entries.

See the Cooc model card for the per-file inventory.

Reported numbers (this sibling)

From the paper:

• Isotropy: participation ratio PR = 94.2, average pairwise cosine 0.35. Concentrated geometry by design (10x I-I injection).
• Direction quality (5-fold CV Spearman rho): baked-in CF 0.40; held-out basic-taste CF 0.42; USDA macros 0.45. Cuisine Cohen's d mean 2.70.
• Emergent modes: 193 modes / 44 properties. Mean within-mode coherence 0.833 against random-pair baseline 0.348 (margin 0.485, tightest of the three siblings).

Core's concentrated geometry pulls both pole tightness and the all-pairs floor upward; the tightness margin (mode coherence minus baseline) is comparable to Cooc and Chem at ~0.5, so the concentration is a design lever, not a defect.

When to pick Core: you want chemistry-aware structure but cannot afford to lose recipe-context companionship entirely. Core's nearest-neighbour for chicken is pork (chemistry peer) but its full top-5 includes chicken_broth and cream_of_chicken_soup (recipe context).

Operator semantics

Same as Cooc. See epicure-cooc for the full operator reference. The three operator families (top-K neighbours, closest-mode lookup, SLERP direction arithmetic) are identical across siblings; only the geometry they act on differs.

Honesty about cuisine pole reconstruction

See the epicure-cooc model card for the full discussion. Short version: the eight cuisine-macro-region pole vectors used in the paper's Section 4.2 hero examples are reconstructed here as the unit-mean of every mode whose Claude label contains a cuisine keyword. Core happens to reproduce paper-genre results with high fidelity because the chemistry-mediated walks cluster cuisines by aroma-compound profile.

Limitations and citation

Same as Cooc. See the paper Section 5.3 for corpus imbalance, hub coverage, and LLM-dependence notes.

@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.