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This paper proposes a path-space formulation of prediction in AI world models, treating the distribution over future trajectories as the fundamental predictive object. It shows that prediction, planning, and uncertainty emerge as operations on a single action functional, and demonstrates that attention asymmetry in learned models correlates with irreversibility in the data.