Inked in the Code: Can Watermarks Save LLMs from Deepfake Dystopia?
TL;DR for operators BiMark is a proposed watermarking method for large language models that tries to solve a practical trilemma: keep generated text quality intact, detect the watermark without access to the original model, and embed more than a yes/no signal.1 The important part is not that it “detects AI text.” That is the shallow version, beloved by procurement decks and policy panels that have never met a paraphraser. The more useful claim is that BiMark can encode provenance-like metadata—model identity, timestamp, source label, policy context—inside the token sampling process, then recover that information later using statistical evidence and the right secret key. ...
