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Gradient-free few-shot adaptation emerges from decentralized cellular dynamics, achieving state-of-the-art OOD transfer by updating a spatial program through local neighborhood interactions.
An in-silico substrate in which every pixel of a two-channel cellular-automata grid carries a tiny neural network (an agent) that senses its Moore neighborhood is studied, which exposes a clear phenotype-genotype diversity trade-off.
