ThermoToken: Thermodynamic Response-Conditioned Adaptation for RNA Secondary Structure Prediction under Family Shift

Cross-family generalization remains challenging for RNA secondary-structure prediction. RNA foundation models provide transferable sequence representations, while thermodynamic models characterize folding through sequence-dependent free energies. Existing neuro-physical approaches generally evaluate thermodynamic information at a fixed parameterization, leaving the local variation of pairing preferences around that state unobserved. We show that controlled perturbations of nearest-neighbor parameters expose how probability mass redistributes among competing folds, providing structure information beyond a single base-pair probability (BPP) map. Building on this observation, we introduce ThermoToken, which constructs a thermodynamic response representation from perturbed BPP maps and combines it with RiNALMo features and the current pairing state to iteratively revise secondary structure. Repeated updates progressively adjust pair assignments as the predicted structural configuration evolves. On strict family-held-out Archive-II evaluation, ThermoToken raises family-macro flexible F1 from 0.7193 to 0.7455, with improvements across all nine target families. More reliable prediction across RNA families can support RNA target characterization and structure-guided molecular design when annotated examples from the target family are limited.