A universal emulator for planar Ising lattices

What the math gives to ML

The paper's transferable asset is a hardware/software separation: one fixed sparse transition operator is reused for many target structures, while small binary masks specify which local interactions are active. This suggests compiling a family of graph, routing, or sparse-attention modules into one shared host supergraph, replacing per-architecture sparse kernels with a single optimized kernel plus masks. The strongest initial test is not thermodynamic prediction itself, but whether fixed host sparsity improves batching, compilation reuse, and memory locality for many graph instances or dynamically changing sparse patterns without changing the learned operator parameters.