arXiv:2606.24163v1 Announce Type: cross Abstract: Reliable provenance for LLM outputs requires multi-bit watermarks that remain robust under editing while maintaining strict false-positive control. Existing ECC-based LLM watermarks rely largely on hard-decision decoding, discarding token-level reliability information. We propose CORE-BREW, a Constant-hit-Rate Embedding extension of block-wise BREW for robust multi-bit watermarking. CORE-BREW calibrates the watermark channel by targeting a fixed hit rate p-star, yielding closed-form per-token log-likelihood ratios (LLRs) for principled soft-dec
The proliferation of advanced LLMs necessitates robust methods for verifying content provenance and ensuring accountability, which current watermarking techniques struggle to provide effectively.
This development offers a significant step towards reliable multi-bit watermarking for AI-generated content, crucial for intellectual property, combating misinformation, and establishing trust in LLM outputs.
The introduction of CORE-BREW shifts watermarking from hard-decision to soft-decision decoding, improving robustness against editing and enhancing the reliability of provenance tracking for LLM outputs.
- · AI developers
- · Content verification platforms
- · Intellectual property owners
- · Regulators
- · Misinformation creators
- · Entities seeking to obfuscate AI-generated content
- · Those relying on easy modification of AI outputs
- · Legacy watermarking solutions
Improved ability to trace and verify the origin of LLM-generated text, enhancing accountability.
Increased pressure on bad actors as their ability to disclaim AI content becomes more difficult, potentially reducing the spread of misinformation.
The establishment of clearer legal and ethical frameworks around AI content creation and ownership, impacting creative industries and legal systems.
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Read at arXiv cs.CL