Thermal pseudo-transitions in a frustrated spin-pseudospin sawtooth chain

What the math gives to ML

The paper provides an exact transfer-matrix mechanism for thermal pseudo-transitions in a frustrated chain: two nearly competing dominant eigenvalues exchange character without producing a true finite-temperature singularity. This creates sharp but continuous peaks in entropy and specific heat, while the physical correlation length can be controlled by a different subleading eigenvalue. A transferable neural-network construction is to estimate an effective transfer operator over recurrent states, residual layers, or optimizer states and use its eigenvalue gap and mode entropy to trigger adaptive damping. The key falsifiable prediction is a finite response peak near an avoided eigenvalue crossing, together with a memory scale determined by the relevant subleading eigenvalue.