World Models have emerged as a powerful paradigm for learning compact, predictive representations of environment dynamics, enabling agents to reason, plan, and generalize beyond direct experience. Despite recent interest in World Models, most available implementations remain publication-specific, severely limiting their reusability, increasing the risk of bugs, and reducing evaluation standardization. To mitigate these issues, we introduce stable-worldmodel (SWM), a modular, tested, and documented world-model research ecosystem that provides efficient data-collection tools, standardized environments, planning algorithms, and baseline implementations. In addition, each environment in SWM enables controllable factors of variation, including visual and physical properties, to support robustness and continual learning research. Finally, we demonstrate the utility of SWM by using it to study zero-shot robustness in DINO-WM.

翻译：世界模型已成为学习环境动态的紧凑、预测性表示的一种强大范式，使智能体能够进行推理、规划并泛化至直接经验之外。尽管世界模型近期备受关注，但大多数现有实现仍局限于特定出版物，严重限制了其可复用性，增加了错误风险，并降低了评估标准化程度。为缓解这些问题，我们引入了stable-worldmodel（SWM），这是一个模块化、经过测试且文档完善的世界建模研究生态系统，提供高效的数据收集工具、标准化环境、规划算法及基线实现。此外，SWM中的每个环境都支持可控的变化因素，包括视觉与物理属性，以支撑鲁棒性与持续学习研究。最后，我们通过使用SWM研究DINO-WM中的零样本鲁棒性，展示了其实用价值。