This research introduces a novel watermarking framework for Large Language Models that improves content traceability while maintaining high text quality.

• Uses cumulative entropy thresholds to balance watermark strength with text quality
• Provides robust detection against various removal attacks
• Works as a test-time solution compatible with existing watermarking techniques
• Enables traceable AI content without compromising generation quality

This advancement addresses critical security concerns about LLM misuse by making AI-generated content identifiable while preserving natural text flow—essential for responsible AI deployment in business contexts.

Entropy-Guided Watermarking for LLMs: A Test-Time Framework for Robust and Traceable Text Generation