Molecular Olfaction Architecture (MOA): A Conceptual Framework for Olfactory Perception in Large Language Models

Large language models (LLMs) have achieved multimodal perception across vision, audio, and text. Olfaction remains the only major human sensory channel without a corresponding digital input modality for LLMs. This paper proposes the Molecular Olfaction Architecture (MOA), a conceptual framework in which a molecular detection layer identifies volatile organic compounds (VOCs) and passes them as structured input to an LLM, which performs semantic reasoning to produce a natural language description of the detected scent. An informal proof-of-concept demonstrates the viability of the reasoning layer. Limitations and directions for future empirical validation are discussed.