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AI watermarks are invisible markers embedded into text generated by large language models (LLMs). Their purpose is to help identify whether a piece of text was produced by an AI system rather than written by a human.

AI watermark removal and AI content detection are two separate processes that address different parts of how large language models (LLMs) generate and mark text. Although both relate to identifying whether text was written by an AI system, they work in fundamentally different ways.

Token distribution in AI watermarking refers to the intentional manipulation of token probability patterns within LLM-generated text to embed a hidden, statistically detectable signal.

AI detectors attempt to estimate whether a piece of text was generated by a large language model (LLM). They rely on statistical patterns, token entropy, and stylistic signals—but these signals are approximate and unreliable.

Short texts often cannot be reliably analyzed for AI detection or watermark identification because they lack the minimum amount of linguistic data required for statistical evaluation.