The field of cellular automata and morphogenesis is moving towards increasing stability and complexity in artificial organisms. Researchers are exploring new methods to improve the stability of neural cellular automata, allowing for the growth of more complex and specialized structures. This is achieved through the introduction of identity layers and constraints during training, enabling the emergence of social interaction at a cellular level. Developmental graph cellular automata are also being used to grow reservoirs, capable of solving benchmark tasks and outperforming traditional reservoirs. Noteworthy papers include: Identity Increases Stability in Neural Cellular Automata, which presents a method for improving the stability of NCA-grown organisms. Growing Reservoirs with Developmental Graph Cellular Automata, which shows that DGCAs can be trained to grow reservoirs that statistically outperform typical reservoirs.