[2604.01650] AromaGen: Interactive Generation of Rich Olfactory Experiences with Multimodal Language Models

Computer Science > Human-Computer Interaction arXiv:2604.01650 (cs) [Submitted on 2 Apr 2026 (v1), last revised 23 Apr 2026 (this version, v2)] Title:AromaGen: Interactive Generation of Rich Olfactory Experiences with Multimodal Language Models Authors:Yunge Wen, Awu Chen, Jianing Yu, Jas Brooks, Hiroshi Ishii, Paul Pu Liang View a PDF of the paper titled AromaGen: Interactive Generation of Rich Olfactory Experiences with Multimodal Language Models, by Yunge Wen and 5 other authors View PDF HTML (experimental) Abstract:Smell's deep connection with food, memory, and social experience has long motivated researchers to bring olfaction into interactive systems. Yet most olfactory interfaces remain limited to fixed scent cartridges and pre-defined generation patterns, and the scarcity of large-scale olfactory datasets has further constrained AI-based approaches. We present AromaGen, an AI-powered wearable interface capable of real-time, general-purpose aroma generation from free-form text or visual inputs. AromaGen is powered by a multimodal LLM that leverages latent olfactory knowledge to map semantic inputs to structured mixtures of 12 carefully selected base odorants, released through a neck-worn dispenser. Users can iteratively refine generated aromas through natural language feedback via in-context learning. Through a controlled user study ($N = 26$), AromaGen matches human-composed mixtures in zero-shot generation and significantly surpasses them after iterative refinemen...