This research introduces a theoretical framework that optimizes both watermarking and detection to identify AI-generated text with mathematical precision.

• Establishes universal minimum error rates for watermark detection
• Creates a distribution-adaptive approach that minimizes text distortion while maximizing detection accuracy
• Provides robust defense against adversarial attacks on watermarking systems
• Balances security needs with text quality preservation

For security professionals, this framework offers a theoretically sound method to authenticate content origin in an era of increasingly sophisticated AI text generation, helping combat misinformation and unauthorized AI content use.

Theoretically Grounded Framework for LLM Watermarking: A Distribution-Adaptive Approach