Synchronization-Free Algebraic Fingerprints for Large Language Models: From Autoregressive to Diffusion Models

2026-07-18

AI Summary

This work addresses the vulnerability of existing large language model watermarking schemes to common text edits—such as insertions, deletions, or reordering—which often disrupt synchronization and degrade robustness. To overcome this limitation, the paper proposes a synchronization-free algebraic fingerprinting mechanism that embeds identity information through self-contained binary congruence constraints derived from adjacent token pairs within local contexts. By integrating cryptographic hashing, Reed–Solomon error-correcting codes, and algebraic decoding algorithms—and modeling the perturbation process as a binary symmetric channel—the method inherently resists textual alterations without relying on block-based synchronization. Theoretical analysis demonstrates that identity information can be reliably recovered even under high tampering rates with minimal redundancy, while supporting variable-length payloads, thereby significantly enhancing both the robustness and practicality of linguistic watermarks.

Abstract

Large Language Models (LLMs) have created an urgent need for reliable watermarking methods that enable attribution of generated text while remaining robust to editing and paraphrasing. We propose a novel synchronization-free watermarking scheme in which every watermark consists of a single binary congruence generated from a pair of neighbouring tokens. For each token pair, a cryptographic hash determines an evaluation point of a Reed--Solomon polynomial representing the secret identity, while the parity of the polynomial evaluation determines the watermark bit embedded into the second token of the pair. Since each congruence is self-contained and depends only on the local token pair, the proposed construction is naturally resistant to insertions, deletions, and token reordering. We analyse the recovery problem from an algebraic perspective, discuss several decoding algorithms suitable for different identity sizes, and model watermark corruption as a Binary Symmetric Channel. The analysis shows that reliable recovery requires only a small redundancy even for relatively high token corruption rates. Unlike existing block-based watermarking schemes, the proposed method avoids synchronization problems while providing a flexible framework for embedding both short and long secret identities.

Problem

• watermarking
• Large Language Models
• text attribution
• robustness
• synchronization-free

Innovation

• synchronization-free watermarking
• algebraic fingerprints
• Reed-Solomon codes
• large language models
• text attribution