A Signal Hidden in Plain Sight

Every token a language model generates comes from a probability distribution. LLM watermarking exploits this single fact with remarkable precision. It biases that distribution just enough to leave a statistical fingerprint. Human readers cannot see it. Trained detectors can find it with near certainty. Understanding how this actually works requires going past the marketing language entirely. It requires real probability theory, real hypothesis testing, and a careful look at what “invisible” actually means in a mathematical sense.

This article goes deep. LLM watermarking sits at the intersection of cryptography, statistics, and information theory, and it deserves treatment at that level.