Tensor-Accelerated Eager Multi-Resolution Grids for Evolving Large-Scale Substrates

What the math gives to ML

The paper's transferable contribution is a reformulation of irregular adaptive computation as static tensors plus boolean masks. A complete multiresolution coordinate set lets all candidate locations and genomes be evaluated by one compiled batched function, while hierarchical variance filtering preserves adaptive refinement without recursive control flow. This pattern can accelerate CPPN-based topology discovery, adaptive neural fields, sparse graph construction, and other models that currently perform per-example dynamic traversal. The most practical transfer is eager candidate generation combined with masked refinement and chunked streaming when the finest grid exceeds device memory.