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Researchers introduce Zero-shot World Models (ZWM), an approach that achieves visual competence comparable to state-of-the-art models while trained on minimal data (single child's visual experience) without task-specific training. This work demonstrates a path toward more data-efficient AI systems that match human developmental learning efficiency.