AI RESEARCH
TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC
arXiv CS.CL
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ArXi:2604.12216v1 Announce Type: cross The widespread use of Large Language Models (LLMs) in text generation has raised increasing concerns about intellectual property disputes. Watermarking techniques, which embed meta information into AI-generated content (AIGC), have the potential to serve as judicial evidence. However, existing methods rely on statistical signals in token distributions, leading to inherently probabilistic detection and reduced reliability, especially in multi-bit encoding (e.g., ). Moreover, such methods