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

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 \textbf{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/DM), and code generation (HumanEval) demonstrate state-of-the-art performance: ARGH-Mark achieves 96.2\% detection F1 under 30\% deletion attacks—surpassing MPAC by 21.9\%—while maintaining negligible quality degradation ($\Delta_{\text{KL}} = 0.07$ vs. 0.21 baseline). The framework reduces detection latency to 40ms (48$\times$ faster than hash-seed methods) and boosts bit recovery by 17.9$\times$ at BER=0.2 through Hamming correction. Crucially, watermarked text preserves 99.3\% of baseline utility across all tasks. Theoretically, we establish linear-time detection complexity and quantifiable robustness bounds, proving that ARGH-Mark achieves a new Pareto frontier in the watermarking design space. By open-sourcing both implementation and the \textsc{WatErMark} benchmark with 10K adversarial samples, this work advances trustworthy deployment of generative AI in alignment-critical applications.