Eager Masked Multiresolution Discovery

Integrate this at the topology-discovery or sparse-coordinate-generation stage of a CPPN-based network, neural field, adaptive CNN, or sparse graph generator; leave the CPPN architecture unchanged for the first experiment. First precompute tensors coords[d] for levels d=0,...,D, together with integer tensors parent_of_child[d] and children_of_parent[d]. Coordinates should have shape [N_d, 2]; mappings should use int32. For a population of genomes, evaluate the CPPN with a compiled batched call having shape [population, N_d, coordinate_features]. Reshape each level's child outputs to [population, N_parent, 4, K], calculate mean_child, then calculate V = mean((child_output - mean_child)^2, dim=(child, output)). Propagate active[d+1] = active[d][parent_of_child[d]] & (V[d] > tau[d]), forcing the root active. Apply the resulting mask to instantiate nodes, edges, sparse tokens, or neural-field samples. A practical threshold is tau[d] = alpha * median(V[d]); alternatively retain the top fixed fraction of parent cells to impose a compute budget. For large depths, evaluate coordinates in fixed-size chunks and write masks or CPPN outputs to host memory rather than materializing every activation on the GPU. The mathematical quantities to reproduce exactly are the coordinate grid, parent-child hierarchy, batched CPPN evaluation, variance score, and hierarchical mask; threshold schedule, chunk size, and CPPN-output decoding are tunable engineering choices. Start with XOR or a 2-D image-coordinate neural field, population 256, and depths 4–7. Compare against recursive traversal using identical CPPN parameters and thresholds. Record generation time, peak GPU memory, discovered-node count, and solve rate. Success means at least 5x lower topology-evaluation time at equal node count, no loss in solve rate, and stable scaling with population size without recompiling once per genome.