Sakana AI

Sakana AI

Sakana AI researchers developed a system where hundreds of simple physical cubic bricks, each running an identical small neural network, collectively infer their overall 3D shape through only local communication with neighboring bricks. In hardware experiments, the system achieved 100% accuracy across four distinct shapes ranging from 26 to 197 bricks, converging to correct consensus in fewer than 60 update cycles. The approach demonstrates robust distributed shape classification that works even with damaged modules, detects structural inconsistencies, and can regrow missing bricks, advancing toward adaptive physical collective intelligence systems.