Smell Synthesis Technology (SST): A Digital Odor Description, an Evidence-Aware Compiler, and a Roadmap for Physical Validation

Smell Synthesis Technology (SST): A Research Framework for Digital Olfaction What if a computer could describe an odor, transmit that description, and help a future device reproduce the intended scent? Unlike images and sounds, odors depend on chemical substances, concentration, mixtures, environmental conditions, and human perception. Turning them into a reliable digital medium remains an open scientific challenge. This preprint introduces Smell Synthesis Technology (SST), a proposed device-independent computational architecture for representing, interpreting, planning, and evaluating olfactory stimuli, together with interfaces intended for future physical synthesis systems. The framework introduces a versioned .ODOR digital description format, an evidence-aware symbolic compiler, device capability assessment, calibration safeguards, separated confidence concepts, and explicit refusal conditions when supporting evidence is insufficient. The accompanying research software provides an offline reference implementation and reproducible synthetic experiments. These experiments explore closed-loop control under gain mismatch, sensor drift, residual signals, and systematic sensor bias. In one biased-sensor scenario, closed-loop control increased tracking error by 49.83% compared with open-loop control within the specified synthetic model. This counterexample illustrates why sensor feedback alone cannot establish reliable physical or perceptual performance. Research contributions include: A proposed digital representation and symbolic processing architecture for olfactory research. An evidence-aware approach to capability checking and bounded computational planning. Reproducible simulation experiments, including documented failure conditions. A claim-based evidence framework separating computational, physical, perceptual, and independent validation. Research protocols and supporting software intended to help external investigators evaluate and extend the framework. Scientific limitations: This release reports theoretical work, research software, and synthetic computational experiments. It does not demonstrate a functioning physical odor synthesizer, establish human-perceived odor equivalence, or provide chemical exposure safety certification. The architecture is presented as a proposed integration and reference implementation, not as a proven universal digital smell technology. The accompanying research archive contains source code, schemas, tests, synthetic experimental data, technical documentation, reproducibility instructions, and guidance for future laboratory investigations. This work is shared to encourage reproducibility, critical evaluation, collaboration, and independent research in digital olfaction, computational sensing, symbolic planning, and future programmable odor synthesis.