[R] ZeroProofML: 'Train on Smooth, Infer on Strict' for undefined targets in scientific ML

We're sharing ZeroProofML, a small framework for scientific ML problems where the target can be genuinely undefined or non-identifiable: poles, assay censoring boundaries, kinematic locks, etc. The underlying issue is division by zero. Not as a numerical bug, but as a semantic event that shows up whenever a learned rational function hits a pole, a normalization denominator vanishes, or a physical quantity becomes non-identifiable. The motivating issue is semantic, not just numerical. A common fix for denominator pathologies is ε-regularization: replacing N/D with N/(D+ε). That often keeps.