AromaGen generates real-time custom aromas from free-form text or visual inputs via multimodal LLM mapping to 12 odorants, matching or exceeding human mixtures after iterative refinement in a 26-person study.
arXiv preprint arXiv:2604.00002 (2026) 18 E
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SCENT uses VLM-generated scene descriptions as a semantic bridge to align electronic-nose signals with visual and textual embeddings, improving cross-modal smell retrieval and enabling object-context odor disentanglement.
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AromaGen: Interactive Generation of Rich Olfactory Experiences with Multimodal Language Models
AromaGen generates real-time custom aromas from free-form text or visual inputs via multimodal LLM mapping to 12 odorants, matching or exceeding human mixtures after iterative refinement in a 26-person study.
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What Images Cannot Say: Language-Guided Olfactory Representation Learning
SCENT uses VLM-generated scene descriptions as a semantic bridge to align electronic-nose signals with visual and textual embeddings, improving cross-modal smell retrieval and enabling object-context odor disentanglement.