Shifted Poisson unfoldings and quantum anomalies

What the math gives to ML

The paper develops a homotopy-theoretic notion of a controller for parameter-dependent shifted Poisson structures: a flat splitting lifts base tangent directions to transverse symmetries while remaining invisible to the deformation problem. Its transferable asset is the distinction between arbitrary parameter variation and genuinely flat transport, together with curvature tests for path-independent comparison. A neural-network adaptation is to treat task, domain, or conditioning variables as a base manifold and learn a connection on adapter weights whose curvature is explicitly penalized. This could make adaptation along different task paths agree and improve interpolation between sparsely observed tasks.