FlavourBench: Ranking Frontier Language Models with Executable Culinary Ground Truth
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Computer Science > Artificial Intelligence
arXiv:2608.20574 (cs)
[Submitted on 20 Aug 2026]
Title:FlavourBench: Ranking Frontier Language Models with Executable Culinary Ground Truth
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Abstract:Open-ended language-model benchmarks usually inherit a judge: a human preference panel, another model, or a brittle exact-match key. We introduce FlavourBench, an automated benchmark in which a versioned culinary system supplies dense, executable ground truth. Each task presents eight ingredients and asks for a three-ingredient portfolio; before model execution, Epicure scores all 56 possible portfolios. We evaluate 27 frontier endpoints on an identical 534-task core spanning substitution, pairing, and constrained composition. Every ranked model has exactly 89 valid responses per panel and family (14,418 model-task cells total), eliminating differential missingness from the leaderboard. The FlavourBench Score is the equal-family mean of the frozen task scores. We use 50,000 anchor-cluster bootstrap replicates for simultaneous 95% score bands and 100,000 sign-flip draws for all 351 paired model contrasts, with Holm control. The two independently compiled panels correlate at r = 0.89 (rank rho = 0.80). Grok 4.6 has the largest point estimate at 65.1 (simultaneous 95% CI 61.0-69.2); 101 of 351 model pairs are resolved. The release includes the prompts, all portfolio score maps, raw responses, exact routes, content hashes, and an offline verifier that reconstructs every result.
| Comments: | 10 pages, 5 figures. Evaluation of 27 frontier language-model endpoints on 534 identical tasks per model, comprising 14,418 scored model-task cells. Code: this https URL Dataset: this https URL Interactive leaderboard: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Software Engineering (cs.SE) |
| Cite as: | arXiv:2608.20574 [cs.AI] |
| (or arXiv:2608.20574v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.20574
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Josef Liyanjun Chen [view email][v1] Thu, 20 Aug 2026 21:14:52 UTC (107 KB)
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