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When two phones try to create a shared secret key from random properties of radio waves, interference makes their measurements imprecise. It's like two people looking at a reflection in a fogged-up window: each sees a blurry silhouette, and there's no way to make out a common picture.

That's where an autoencoder comes in — a trainable program that removes noise, like a cloth wiping the glass. Using methods reminiscent of spectroscopy, it separates signal from interference, leaving only clean data identical for both devices.

Compared to conventional schemes, this approach slashes errors and yields keys that pass randomness tests with flying colors. A surprising bonus: you can extract more key material than before, as if the wiped window reveals a whole landscape.