Exact Lindbladian Dynamics from Conformal Embeddings and Topological Defects in Conformal Field Theory

What the math gives to ML

The paper provides two concrete solvability mechanisms: linear Majorana jump operators produce a triangular hierarchy on reduced even-Majorana monomials, while Verlinde-line jumps diagonalize primary-sector dynamics and cause analytically controlled dephasing. The most transferable construction is the triangular hierarchy, which can become a structured state-space or recurrent neural layer whose multiscale features evolve by a lower-triangular generator and can therefore be integrated recursively or exactly. Its quantitative signature is stronger than a generic stability claim: if the diagonal decay rates are negative, perturbations decay as a polynomial in time multiplied by an exponential, with the polynomial degree fixed by hierarchy depth.