Neural Foam: A Sparse, Growing Architecture for Model-to-Model Communication

We present Neural Foam, a novel neural network architecture featuring sparse connectivity and dynamic growth through a formally defined mitosis operation. Our system implements a "brain-body" separation where a semantic embedding model (brain) handles memory and retrieval while a language model (body) handles generation. We introduce Model-to-Model (M2M) communication protocols using 57 semantic clusters for routing signals between models. On our M2M benchmark (N=120 test pairs, 6 categories, balanced), we achieve 82% accuracy vs. 16.7% random baseline, with 100% accuracy on intent classification, error signals, and reasoning steps.