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.
Sniff ai: Is my ’spicy’ your ’spicy’? exploring llm’s perceptual alignment with human smell experiences
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
NOSE aligns molecular, receptor, and linguistic modalities in a shared embedding space via tri-modal orthogonal contrastive learning and weak positive samples, achieving SOTA performance and zero-shot generalization on olfactory tasks.
A simulation-to-real navigation policy enables a quadrotor to locate an odor source using only basic olfaction sensors and optional vision, validated in indoor real-world flights.
citing papers explorer
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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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NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning
NOSE aligns molecular, receptor, and linguistic modalities in a shared embedding space via tri-modal orthogonal contrastive learning and weak positive samples, achieving SOTA performance and zero-shot generalization on olfactory tasks.
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Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation
A simulation-to-real navigation policy enables a quadrotor to locate an odor source using only basic olfaction sensors and optional vision, validated in indoor real-world flights.