Embodied world models (WMs) are implicit learning systems that predict the next future latent state directly from observations and actions. Unique from other end-to-end learning systems, the dynamics predictor of WMs can simulate the environment's response to alternative actions without physical deployment nor pre-specifying states of the dynamical system. Current metrics focus on the quality of model outputs, e.g., clarity of reconstructions or task success, but fail to assess the fidelity of modeling consequences of counterfactual actions. Inspecting the forced response to action perturbations through sensitivity analysis is one way to audit how well WMs can predict the consequences of counterfactual actions. Deploying WMs on hardware will require new evaluation frameworks to analyze the tradeoffs between state error and accurate sensitivity prediction.

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