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Can a goal-directed video world model genuinely help a robot plan, or does it mainly produce better procedural videos? This episode clarifies what WorldGuide demonstrates, how its planner–executor architecture works, and which additional experiments would be needed for physical deployment. We examine three central questions: why visual feedback produces stronger evidence than the narrow headline baseline win; why a valid text plan is not equivalent to successful execution; and what released code and checkpoints do—and do not—make reproducible. WorldGuide combines a Qwen-based planner with a HunyuanVideo executor for generated procedures such as folding, construction, and cooking. Its benchmarks evaluate visual task execution, not a physical robot platform. We place the work alongside Video Language Planning, ORCA, VideoWorld 2, and NovaPlan to distinguish generated-state feedback from feedback grounded in real observations. The discussion also covers known-task versus unseen-task generalization, noisy completion supervision, evaluation uncertainty, and configuration details that matter when reproducing results. For practitioners, the focus is a concrete adaptation path: retain the action–observation supervision structure, test progress recognition with existing robot skills, and measure whether imagined futures actually improve action selection. What happens when reality contradicts the generated plan?
