mlsanityData to 7 Oct 2026

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

arXiv 2604.01650 ↗First posted 2 Apr 2026cs.HC cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

Smell's connections with food, memory, and social experience have motivated researchers to bring olfaction into interactive systems. Multimodal AI opens new possibilities for generating smells from natural language, yet it remains unclear whether pretrained models encode sufficient olfactory knowledge to translate language into perceptible compositions. We present AromaGen, an AI-powered 12-channel wearable olfactory system that maps free-form natural-language descriptions to 12 base odorants selected to cover a semantically derived olfactory space, while supporting iterative refinement through natural-language feedback. In a between-subjects study (N=60) across 50 real-world benchmark smells, participants distinguished AromaGen-generated target smells above chance in a three-alternative forced-choice task, with retrieval-augmented AI composition performing comparably to human-expert composition and zero-shot AI composition. Natural-language feedback significantly improved the perceived similarity of both AI- and human-expert compositions. Our findings demonstrate the feasibility of using pretrained multimodal AI, with human aroma composition data, to generate perceptible olfactory experiences from natural language.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 7 of its 91 references that are in the map. First posted on arXiv 2 Apr 2026.

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7 of 91 references.

References · 7

  1. 2019Multimodal Transformer for Unaligned Multimodal Language Sequencescited by 637
  2. 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 256
  3. 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AIcited by 175
  4. 2024Agent AI: Surveying the Horizons of Multimodal Interactioncited by 167
  5. 2025SmellNet: A Large-scale Dataset for Real-world Smell Recognitioncited by 9
  6. 2024Sniff AI: Is My 'Spicy' Your 'Spicy'? Exploring LLM's Perceptual Alignment with Human Smell Experiencescited by 7
  7. 2026Benchmark for Assessing Olfactory Perception of Large Language Modelscited by 3

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  1. 2026What Images Cannot Say: Language-Guided Olfactory Representation Learning
  2. 2026Before Smelling the Video: A Two-Stage Pipeline for Interpretable Video-to-Scent Plans ↗
  3. 2026FoodSense: A Multisensory Food Dataset and Benchmark for Predicting Taste, Smell, Texture, and Sound from Images
  4. 2025Diffusion Graph Neural Networks and Dataset for Robust Olfactory Navigation in Hazard Robotics
  5. 2025Does Visual Grounding Enhance the Understanding of Embodied Knowledge in Large Language Models?
  6. 2026The Unwritten Benchmark: A New Challenge for Multimodal Machine Learning in Abstract Perceptual Reasoning
  7. 2024FoodPuzzle: Developing Large Language Model Agents as Flavor Scientists
  8. 2025Chain of Questions: Guiding Multimodal Curiosity in Language Models