MetaLearnNCA: Few-Shot Offline Meta-Learning via Interacting Neural Cellular Automata

Few-shot meta-learning traditionally formulates task adaptation either as analytical gradient descent through unrolled computational graphs or as metric-based distance comparisons over flattened 1D feature vectors, which either incur costly test-time backpropagation or discard native 2D spatial geometry. In this work, we propose MetaLearnNCA, a decentralized framework that achieves few-shot adaptation through the dynamical interaction of coupled Neural Cellular Automata (NCAs) without computing analytical gradients during inference. MetaLearnNCA decomposes task adaptation into an Active-NCA, which executes task inference conditioned on a continuous 2D spatial memory grid termed the spatial program, and a learned Meta-NCA, which acts as a decentralized cellular optimizer by diffusing spatial error residuals across local neighborhoods to dynamically update this program. MetaLearnNCA is competitive against canonical meta-learners in-distribution ($96.12\%$ on Omniglot) with Out-Of-Distribution transfer gains on MNIST, KMNIST, and Fashion-MNIST transfer across 10 independent testing seeds across 1-, 5-, and 10-shot regimes (e.g., surpassing Prototypical Networks by $+10.54\%$ on 10-shot MNIST and a $+3.87\%$ gain on 10-shot Fashion-MNIST over FOMAML). Our results establish that robust, gradient-free learning-to-learn can emerge from decentralized cellular dynamics on non-von Neumann substrates.