想检验你的世界模型扛不扛揍？ARB4WM这个新基准专门测视觉扰动下的鲁棒性，比单看动作空间全面多了。
ARB4WM提出一个统一的评估框架，用于测试世界模型在视觉扰动下的对抗鲁棒性。该框架定义了五个白盒损失目标，涵盖策略、价值和潜在动力学三个层面。在MetaWorld和DeepMind Control Suite的20个任务上评估了四种Dreamer-style代理。结果显示，针对值估计、潜在表示和RSSM动力学的攻击与直接策略破坏同等有害，早期或频繁扰动尤其严重。代码已开源并提供使用接口。
ARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control
World models are widely used in robotic and agentic engineering control systems due to their ability to learn latent dynamics for planning and decision-making. As these systems are increasingly deployed in safety-critical settings, understanding their robustness under adversarial conditions has become essential. However, existing evaluations lack a unified benchmark for testing adversarial threats across the policy, value, and latent-dynamics levels of world-model agents. To fill this gap, we present ARB4WM, a unified evaluation framework for pre-deployment robustness and risk assessment of world-model agents under visual perturbations. ARB4WM defines five white-box loss objectives across these three levels and studies their effects when combined with single-step or multi-step perturbation strategies and temporal attack modes, including full-frame, half-sequence, and sparse-frame exposure. Specifically, we evaluate four Dreamer-style agents across 20 tasks from MetaWorld and the DeepMind Control Suite under different loss objectives, perturbation strategies, and temporal attack modes. Results show that attacks targeting value estimation, latent representations, and RSSM dynamics can be as damaging as direct policy disruption, and that early or frequent perturbations are especially harmful, while input-level defenses provide limited recovery under adaptive attacks. These findings suggest that safety, risk, and reliability assessment for world models should cover multiple component-oriented attack objectives and temporal exposure protocols rather than relying solely on action-space robustness. Source code is available at https://github.com/zaoanguai/ARB4WM.