{"ID":2883811,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08091","arxiv_id":"2508.08091","title":"Growing Reservoirs with Developmental Graph Cellular Automata","abstract":"Developmental Graph Cellular Automata (DGCA) are a novel model for morphogenesis, capable of growing directed graphs from single-node seeds. In this paper, we show that DGCAs can be trained to grow reservoirs. Reservoirs are grown with two types of targets: task-driven (using the NARMA family of tasks) and task-independent (using reservoir metrics). Results show that DGCAs are able to grow into a variety of specialized, life-like structures capable of effectively solving benchmark tasks, statistically outperforming `typical' reservoirs on the same task. Overall, these lay the foundation for the development of DGCA systems that produce plastic reservoirs and for modeling functional, adaptive morphogenesis.","short_abstract":"Developmental Graph Cellular Automata (DGCA) are a novel model for morphogenesis, capable of growing directed graphs from single-node seeds. In this paper, we show that DGCAs can be trained to grow reservoirs. Reservoirs are grown with two types of targets: task-driven (using the NARMA family of tasks) and task-indepen...","url_abs":"https://arxiv.org/abs/2508.08091","url_pdf":"https://arxiv.org/pdf/2508.08091v1","authors":"[\"Matias Barandiaran\",\"James Stovold\"]","published":"2025-08-11T15:32:01Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.AI\"]","methods":"[]","has_code":false}