Smart Cellular Bricks: Towards Collective Intelligence for the Physical World

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Collections of hundreds of physical 3D cellular bricks, simple modular hardware units that run identical local Neural Cellular Automata without any global knowledge, collaboratively infer their overall shape class.

（＊日本語は英文の後に）

At Sakana AI, a recurring theme in our research is collective intelligence: the observation that sophisticated, robust behavior can arise from many simple parts following local rules, with no central controller, as it does in a colony, a tissue, or a brain. Until now, we have explored this idea in simulated systems, such as getting several frontier models to reason together and build on one another’s attempts, coordinating them so that many models act as one, or having agents with partial, overlapping views negotiate along their shared boundaries to converge on a globally consistent solution.

Today, we are happy to share that a paper extending this line of work into physical hardware has been accepted for publication in Nature Communications. The system is a collection of simple cubic bricks, each running the same small neural network and communicating only with the bricks it is physically connected to. No brick is told its position or which shape it is part of, yet from these purely local exchanges the collective converges on the correct global shape and can locate where modules are missing or damaged.

For us, this is a first step toward taking our work on collective intelligence beyond software and into the physical world. We wanted to ask whether the same decentralized principles hold up when communication is noisy and modules fail. They largely do. Hundreds of bricks classify a range of 3D shapes, still recognize shapes with variations they were not trained on, and keep converging correctly even when a fraction of their modules go silent. Using the same framework, the collective can also flag structural damage and guide a step-by-step recovery.

The work is a collaboration between researchers at IT University of Copenhagen , Sakana AI , and Autodesk .

Read the full paper in Nature Communications: https://www.doi.org/10.1038/s41467-026-75166-7

Explore the code on GitHub: https://github.com/rmorenoga/cube3D

Introduction

Many biological systems exhibit a remarkable capacity to determine their own anatomical structure. Through local communication and self-organization, groups of cells can assess whether they have correctly formed a target shape, such as an organ, and can actively remodel body parts following injury. A salamander can regenerate a damaged tail that transforms into a functional leg, and simple organisms like Hydra and Planaria can fully restore their morphology regardless of which part is lost. This ability to classify general anatomical features, rather than match a fixed target shape, is what enables variability among individuals while enhancing the robustness of the process. The overall function and design of an organ may be consistent across a species, even as its specific shape, size, or scale differs from one individual to the next.

Artificial systems composed of many physically distributed modules that can autonomously infer their structural class, without centralized control, would represent a significant step toward more adaptable, intelligent artificial collectives. Such systems could enable powerful applications in smart materials and reconfigurable robotics, where global knowledge must emerge from local sensing and communication. Motivated by the scalability and resilience of biological collective intelligence, we introduce a fully decentralized system in which hundreds of physically embodied “cellular” bricks collectively classify their global shape and detect local damage, with no central controller and no module ever knowing its own position.

Neural cellular automata for shape classification. (A) A cellular brick module. (B) Bricks assembled into four different shapes. (C) Each cell takes in local information from its connecting neighbors and its own hidden channels; information is aggregated locally, enabling the object to recognize its particular shape over multiple iterations. (D) The local update rules are encoded with a neural cellular automaton, a deep neural network.

The collective intelligence algorithm we developed builds on the framework of differentiable Neural Cellular Automata (NCA) and self-classifying collective systems, extended to operate in 3D and implemented on physical modular hardware. NCAs generalize traditional cellular automata, in which the local update rules are typically hand-crafted, by instead learning these rules. Unlike traditional CAs that operate with discrete cell states, NCAs use continuous-valued cell states, enabling end-to-end differentiability and compatibility with gradient descent. On a high level, each cell in our system is tasked with determining which type of shape it is a part of, based solely on communication with...