← 研踪 PaperTrailarXiv:2510.23891 · v2
OpenMarkX: An Extensible Platform for Watermarking Open-Source Model and Agent Outputs
首次提交 2025-10-28 06:00
版本更新 2026-11-08 01:19
雷达观测 2026-11-10 11:14
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.
作者备注：Accepted by NeurIPS 2026
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-08 01:19 · 最新观测版本
OpenMarkX: An Extensible Platform for Watermarking Open-Source Model and Agent Outputs
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.
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