Strong world-model agents frequently contain weak world models. We study this agent-world-model gap by reproducing five visual world-model agents in Atari Pong: DreamerV3, DIAMOND, TWISTER, Simulus, and STORM, with performance comparable to the reported results, and independently evaluating their frozen world models. First, closed-loop rollout diagnosis qualitatively inspects visual trajectories generated by each frozen model under an independently trained policy. All five models exhibit clear visual or dynamical failures, including ball disappearance, incorrect motion, and invalid ball-paddle interactions. Second, under native zero-shot model-based reinforcement learning (MBRL), a new policy is trained entirely within the frozen model from scratch using the agent's native RL procedure, without real-environment training. When evaluated in the real environment, these policies substantially underperform the reproduced agents: DreamerV3 (-5.5 to -20.9), DIAMOND (19.7 to -9.6), TWISTER (17.7 to -13.3), Simulus (20.8 to -11.6), and STORM (18.7 to -21.0), where -21 is the minimum Pong return. This gap also extends broadly across Atari100K. Motivated by the ball-related rollout failures in Pong, we propose Concept-Guided Spatial Regularization (CGSReg), an auxiliary reconstruction loss on task-critical concept regions. We evaluate it under a more challenging pixel-space zero-shot MBRL setting, where policies learn directly from images generated by the frozen world model. Ball-region CGSReg improves pixel-space zero-shot MBRL in DreamerV3, DIAMOND, TWISTER, and Simulus, and also improves closed-loop rollouts in the first three; STORM shows no clear improvement.

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