AI is happening. The future is updating.epicure-core
A 300-dimensional skip-gram ingredient embedding over a 1,790-ingredient canonical vocabulary, trained on a blend of (i) typed FlavorDB ingredient-compound metapath walks and (ii) injected pure ingredient-ingredient walks at `ii_repeat=10`. Core is the middle sibling on the chemistry-vs-recipe-context spectrum. The 10x I-I injection is the design lever that concentrates Core's geometry: participation ratio drops to 94.2 of 300 (vs ~180 for the isotropic Cooc and Chem siblings), average pairwise cosine rises to 0.35, and the resulting concentration coincides with the tightest emergent modes of the three. 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.
A 300-dimensional skip-gram ingredient embedding over a 1,790-ingredient canonical vocabulary, trained on a blend of (i) typed FlavorDB ingredient-compound metapath walks and (ii) injected pure ingredient-ingredient walks at `ii_repeat=10`. Core is the middle sibling on the chemistry-vs-recipe-context spectrum. The 10x I-I injection is the design lever that concentrates Core's geometry: participation ratio drops to 94.2 of 300 (vs ~180 for the isotropic Cooc and Chem siblings), average pairwise cosine rises to 0.35, and the resulting concentration coincides with the tightest emergent modes of the three. 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.