The panel surveys what constitutes a world model and argues for interaction-driven, action-conditioned models, often in latent space. Speakers emphasize intrinsic motivation and artificial curiosity for exploration, compact and manipulable causal representations, and evolutionary approaches aiming for antifragility and handling unknown unknowns. They discuss using language as memory, multimodal and multi-timescale predictions, and extracting algorithmic information from foundation models to plan via subgoals. Benchmarking is deemed multi-axial, spanning video prediction, trainability, and facilitation of exploration, and tightly coupled with agent learning. Research directions include causal discovery, efficient latent losses, data selection, specialized domain models, and open-source foundation world models.