README.md

metadata

title: Epicure Explorer
emoji: 🌶
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 5.7.1
app_file: app.py
pinned: false
license: cc-by-4.0
short_description: Operators behind the FlavourBench culinary benchmark
models:
  - Kaikaku/epicure-cooc
  - Kaikaku/epicure-core
  - Kaikaku/epicure-chem
datasets:
  - Kaikaku/epicure-corpus-resources

Epicure Explorer

Epicure powers FlavourBench, the executable culinary reasoning benchmark for frontier language models. The benchmark turns versioned Epicure operations into answer keys, evaluates 20 current endpoints on 32 tasks, and measures the same model-task pairs with and without Epicure. Read the paper or reproduce the leaderboard.

Interactive chef-facing demo of the three Epicure sibling ingredient embeddings (Cooc, Core, Chem). Three operator tabs:

1. Pairings: top-K cosine neighbours plus the closest emergent mode for any of 1,790 ingredients.
2. Supervised SLERP: rotate a seed toward a supervised direction (cuisine macro-region, food group, NOVA level, sensory category, USDA macro) by a continuous angle.
3. Emergent SLERP: rotate a seed toward an unsupervised factor-mode pole discovered via multi-seed-stable FastICA + GMM.

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

Try

• Pairings, chicken, Cooc -> garlic, onion, black_pepper, turkey, carrot (recipe companions).
• Pairings, chicken, Chem -> beef, pork, cream_of_chicken_soup, buffalo_wing_sauce, peanut (chemistry peers).
• Supervised SLERP, rice + cuisine:South_Asian, 30 deg, Core -> turmeric, mustard_seed, fenugreek_seed, coriander, cumin.
• Supervised SLERP, corn + cuisine:Latin_American, 30 deg, Chem -> poblano_pepper, corn_tortilla, salsa, queso_fresco, chipotle_pepper.

Citation: Radzikowski and Chen, 2026.