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 refinement, achieving a median similarity of 8/10 to real food aromas and reducing perceived artificiality to levels comparable to real food. AromaGen is a step towards real-world interactive aroma generation, opening new possibilities for communication, wellbeing, and immersive technologies.

翻译：气味与食物、记忆及社交体验的深层联系，长期促使研究人员将嗅觉引入交互系统。然而，多数嗅觉界面仍局限于固定香氛盒与预设生成模式，且大规模嗅觉数据集的匮乏进一步限制了基于人工智能的方法。我们提出AromaGen——一种由人工智能驱动的可穿戴式界面，能够基于自由文本或视觉输入实时生成通用型气味。AromaGen由多模态大语言模型驱动，该模型利用潜在嗅觉知识将语义输入映射为由12种精选基础气味剂组成的结构化混合物，并通过颈戴式释放器输出。用户可通过自然语言反馈，借助上下文学习迭代式优化生成的气味。通过一项受控用户研究（$N=26$），AromaGen在零样本生成中达到与人工调配混合物相当的水平，并在迭代优化后显著超越后者，与真实食物气味的相似度中位数达到8/10，且人工感知度降至与真实食物相当的水平。AromaGen是迈向真实世界交互式气味生成的一步，为通信、健康及沉浸式技术开辟了新可能。