Abstract

The necessity of robust predictive models in real-world applications has led to the emergence of world models capable of representing complex environmental dynamics. By learning the underlying physics and causal relationships, these models are particularly suited for domains where real-world data acquisition is constrained by extreme safety risks and high costs. This is prominently evident in autonomous driving, where reasoning models must navigate “long-tail” edge cases—including rare accident scenarios—that are inherently scarce in empirical datasets.

Similarly, as smart factory ecosystems evolve, the demand for automated safety monitoring to address risks such as fires, falls, and cargo instability has grown significantly. However, the development of robust safety systems remains severely hindered by a fundamental bottleneck: the scarcity of actual accident data, as deliberately creating these dangerous scenarios for data collection is practically infeasible.