With the proliferation of the Large Language Model (LLM), the concept of World Models (WM) has recently attracted a great deal of attention in the AI research community, especially in the context of AI agents. It is arguably evolving into an essential foundation for building AI agent systems. A WM is intended to help the agent predict the future evolution of environmental states or help the agent fill in missing information so that it can plan its actions and behave safely. The safety property of WM plays a key role in their effective use in critical applications. In this work, we review and analyze the impacts of the current state-of-the-art in WM technology from the point of view of trustworthiness and safety based on a comprehensive survey and the fields of application envisaged. We provide an in-depth analysis of state-of-the-art WMs and derive technical research challenges and their impact in order to call on the research community to collaborate on improving the safety and trustworthiness of WM.

翻译：随着大型语言模型（LLM）的普及，世界模型（WM）的概念近来在人工智能研究领域，特别是在AI智能体（AI agents）的背景下，引起了广泛关注。可以说，它正逐渐演变为构建AI智能体系统的重要基础。世界模型旨在帮助智能体预测环境状态的未来演变，或帮助智能体填补缺失信息，以便其能够规划行动并安全地行为。世界模型的安全特性对其在关键应用中的有效使用起着关键作用。在本工作中，我们基于全面的综述和预期的应用领域，从可信性与安全性的角度，回顾并分析了当前最先进世界模型技术的影响。我们对最先进的世界模型进行了深入分析，并推导出技术研究挑战及其影响，以期呼吁研究界合作改进世界模型的安全性与可信性。