A world model creates a surrogate world to train a controller and predict
safety violations by learning the internal dynamic model of systems. However,
the existing world models rely solely on statistical learning of how
observations change in response to actions, lacking precise quantification of
how accurate the surrogate dynamics are, which poses a significant challenge in
safety-critical systems. To address this challenge, we propose foundation world
models that embed observations into meaningful and causally latent
representations. This enables the surrogate dynamics to directly predict causal
future states by leveraging a training-free large language model. In two common
benchmarks, this novel model outperforms standard world models in the safety
prediction task and has a performance comparable to supervised learning despite
not using any data. We evaluate its performance with a more specialized and
system-relevant metric by comparing estimated states instead of aggregating
observation-wide error.

提出了基础世界模型，通过将观察结果转化为有意义且因果潜在的表示，使代理动力学可以直接预测因果未来状态，并且在安全预测任务中表现优于标准世界模型，与监督学习相比具有可比拟的性能，而且不需要使用任何数据。