nom nom: Metabolic Worldbuilding through Neural Cellular Automata

This paper presents nom nom, a navigable online artwork and computational platform that uses Neural Cellular Automata (NCA) to construct a continuously evolving world. Independently pretrained NCA divisions inhabit a shared high-resolution environment, where learned local dynamics are coupled with territorial competition, population recovery, NCA-native creatures, and weather processes that directly modify the cellular state space. A Gemini-powered observer, Gaia, periodically interprets the world, narrates its development, and may intervene through a bounded vocabulary of weather events. We describe this system-level approach as metabolic worldbuilding: the maintenance of generative variation through recurring processes of growth, competition, displacement, mutation, damage, and recovery within a bounded computational world. Through the architecture and 30-day online deployment of nom nom, we examine how generative, regulatory, perturbative, and observational capacities can be distributed across learned models, explicit mechanisms, an multimodal model-based observer, and artist-defined constraints. Audiences encounter the resulting world as a living landscape whose territories, creatures, disturbances, and narrated histories continue to change over time.