arXiv:2606.16605v1 Announce Type: new Abstract: 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
As AI models, particularly world models, are increasingly deployed in real-world, safety-critical applications, the need for robust adversarial testing becomes paramount to ensure reliable operation.
This benchmark addresses a crucial gap in evaluating the adversarial robustness of world models, directly impacting their trustworthiness and viability for high-stakes engineering and agentic systems.
The introduction of ARB4WM provides a unified framework for systematic adversarial robustness testing, which will likely accelerate the development of more secure and reliable AI agents and robotic systems.
- · AI Safety Researchers
- · Robotics Developers
- · Agentic Systems Companies
- · Defence Contractors
- · Developers of Undifferentiated, Brittle AI Models
- · Sectors Reliant on Unsecured AI Deployment
Increased focus on adversarial training and robust model design for world models will become a standard practice.
Safer and more dependable AI-powered autonomous systems will emerge, accelerating adoption in sensitive industries.
The benchmark could become a de facto standard, influencing regulatory discussions and certification processes for AI in safety-critical domains.
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Read at arXiv cs.AI