Abstract

Flavor development in the food industry is increasingly challenged by the need for rapid innovation and precise flavor profile creation. Traditional flavor research methods typically rely on iterative, subjective testing, which lacks the efficiency and scalability required for modern demands. This paper presents three contributions to address these challenges. Firstly, we define a new problem domain for scientific agents in flavor science, conceptualized as the generation of hypotheses for flavor profile sourcing and understanding. By leveraging their capacity to identify relevant evidence and reason within large context spaces, language model-backed agents can perform the labor-intensive tasks of flavor sourcing and understanding with enhanced efficiency and precision. To facilitate research in this area, we introduce the FoodPuzzle dataset, a challenging benchmark consisting of 978 food items and 1,766 flavor …

Date

August 3, 2025

Authors

Tenghao Huang, Dong Hee Lee, John Sweeney, Jiatong Shi, Emily Steliotes, Matthew Lange, Jonathan May, Muhao Chen

Book

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2

Pages

5493-5504