ARGH-Mark: Anchor-Synchronized Watermarking with Hamming Correction for Robust and Quality-Preserving LLM Attribution

Abstract

The proliferation of large language models has intensified demands for reliable content attribution, yet existing watermarking techniques face a fundamental trilemma: they cannot simultaneously optimize for robustness against attacks, minimal text quality degradation, and detection efficiency. To resolve this challenge, we propose ARGH-Mark, a novel watermarking framework that integrates three synergistic innovations: (1) Anchor-synchronized phase recovery for maintaining detection integrity under insertion/deletion attacks, (2) RG-balanced vocabulary modulation that dynamically partitions lexicons via contextual hashing to preserve generation quality, and (3) Hamming-based error correction enabling single-bit error rectification through algebraic coding. Comprehensive evaluations across question answering (ELI5), summarization (CNN/DailyMail), and text generation (C4) demonstrate state-of-the-art performance: the proposed ARGH-Mark framework achieves near-perfect match rate and bit accuracy across diverse configurations, while preserving the quality of the generated text. It significantly reduces detection latency, enabling real-time extraction, and maintains high robustness against token tampering attacks through integrated Hamming error correction, ensuring reliable attribution in adversarial settings. ARGH-Mark achieves a new Pareto frontier in the watermarking design space and advances trustworthy deployment of generative AI in alignment-critical applications.