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This work test the reasoning capabilities of Neural Cellular Automata, networks of recurrent cells that use strictly local connectivity and asynchronous updates, and shows that NCAs are robust reasoners capable of dynamically modulating compute to recover efficiently from damage, and that they can scale to solve reason...
These results demonstrate an interactive feedback loop: environmental task demands actively shape the physical architecture of self-replication, while spontaneous replication alters the evolutionary trajectory of functional problem-solving.
This work adds a persistent hidden state to a diffusion denoiser and removes its timestep conditioning, leaving a single shared update that can be run to arbitrary depth, and develops an anytime solver that keeps improving with inference depth far beyond the rollout lengths and backpropagation window used in training.
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