Efficient search for excitable zero-modes in constrained systems

What the math gives to ML

The paper gives a constructive method for generating translation-invariant matrix-product states that are exact zero modes of constrained Hamiltonians. Its transferable asset is the combination of a hard global validity projector with low-bond-dimension local tensors selected from the eigendecomposition of an unconstrained local operator. In neural sequence models, this can become a structured base distribution or finite-state front-end that guarantees valid outputs and supplies an algebraic prior before a Transformer learns task-specific residuals. The strongest initial test is on synthetic exclusion languages, where validity, early optimization, and sample-efficiency predictions can be measured exactly.