visual-learning

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.