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原文摘要

We introduce OpenMarkX, an extensible platform for watermarking open-source model and agent outputs under standard inference. It takes models, compatible methods, private keys, and evaluation settings, and produces watermarked weights, detection results, and quality and robustness reports. Its 16 text-watermark configurations are complemented by agent, vision-language, image, and physics-field workflows. Our proposed method, OpenMark, addresses learnability and robustness through joint model--policy learning and perturbation-aware training. On several open-source LLMs, OpenMark achieves 0.92 TPR at 1% FPR while reducing perplexity by 20.5% on average relative to the strongest prior method, and remains more detectable after such modifications, e.g., retaining 0.57 TPR at 5% FPR after 0.5-ratio model merging, compared with at most 0.34 for prior methods.