Learning Perceptual Geometry of Natural Odor Mixtures from LLM-Derived Descriptors
The relationship between odor chemistry and perception remains incompletely understood. Many existing studies focus on the perception of single molecules, whereas natural odors contain tens to hundreds of components, adding extra layer of complexity to percept formation. Here, we explore the relationship between molecular composition and perceived similarity in natural odor objects. We study this using a database of more than 1,200 volatile-compound-in-food mixtures containing more than 8,000 molecular components, and use large language models (LLMs) as a scalable surrogate for deriving mixture-level perceptual descriptions. We find that language-derived representations provide useful sup...
| Authors | Farhad Pashakhanloo, Venkatesh N. Murthy |
|---|---|
| Primary category | cs.AI |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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