The Universal Sparse Tensor (UST) decouples a tensor's sparsity from its actual memory layout for greater flexibility and performance. A tensor format DSL (Domain Specific Language) describes how the sparse tensor should be represented. Type polymorphism on a small set of base operations defines the vast space of instances for these operations. This talk demonstrates how the UST can be used to perform an exhaustive state space search over all CUDA kernels for PyTorch operations, accounting for many different iteration orderings and sparse storage formats. When integrated with heuristic pruning techniques and potential agent-assisted optimization, this methodology facilitates the acceleration of sparsity in a manner that was previously unattainable for most model developers.