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Iterated learning experiments have shown that cultural transmission reliably distorts artifacts toward the inductive biases of learners, but most of this work uses linear chains between human participants, leaving open how these dynamics play out in the networked, human-AI systems that increasingly shape cultural production. In this study, we leverage one such system, Artbreeder, which hosts daily \"remix parties\" where users iteratively build on each other's work from a single seed image, producing branching lineages of human-AI co-created images. We analyze a dataset of 130,882 images from 368 remix parties over 13 months and find that images become simpler and converge toward common thematic \"attractors\" (e.g., steampunk scenes, alien architecture). We also find tha","title":"Dynamics of collective creativity in AI art competitions","url":"https://arxiv.org/abs/2605.17141","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.17141v1 Announce Type: new \nAbstract: Creativity is a fundamental aspect of how culture evolves, yet the mechanisms by which groups produce novelty are notoriously difficult to infer from the historical record. Iterated learning experiments have shown that cultural transmission reliably distorts artifacts toward the inductive biases of learners, but most of this work uses linear chains between human participants, leaving open how these dynamics play out in the networked, human-AI systems that increasingly shape cultural production. In this study, we leverage one such system, Artbreeder, which hosts daily \"remix parties\" where users iteratively build on each other's work from a single seed image, producing branching lineages of human-AI co-created images. 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