LLMs are trained on text about the world, not the world itself. Olfaction is an interesting test case because it's one of the most ancient and direct sensory modalities. no symbolic abstraction layer, just molecular binding triggering pattern recognition.

What's compelling about pairing e-nose hardware with transformer architectures is you get that grounded perception loop you're describing. The sensor array produces high-dimensional response patterns from real physical interactions, and the model learns to classify and reason over patterns it's never been explicitly trained on—genuine novelty detection rather than interpolation over training data.

The "this is outside what I know" capability is critical for real-world deployment. A model that hallucinates a scent classification is potentially dangerous (think: fentanyl detection in law enforcement). You need calibrated uncertainty, not just a softmax score.