# Physics-Residual-Calibrated Upper Confidence Bound

For formulation discovery with few measurements and a fixed experimental budget, KolTa splits property prediction into component base contributions, role-conditioned interactions and an anchored temperature response, trained with a derivative-based physics constraint. Its acquisition rule calibrates ensemble disagreement together with the physics residual at the target temperature against out-of-fold errors. Across binary-mixture viscosity, drug solubility, ionic-liquid viscosity and simulated electrolyte conductivity, mean AULC ranks first or second on every task, and on the drug task the median queries to reach 80% recovery are about 18% fewer than the fastest comparator.

For formulation discovery with few measurements and a fixed experimental budget, KolTa splits property prediction into component base contributions, role-conditioned interactions and an anchored temperature response, trained with a derivative-based physics constraint. Its acquisition rule calibrates ensemble disagreement together with the physics residual at the target temperature against out-of-fold errors. Across binary-mixture viscosity, drug solubility, ionic-liquid viscosity and simulated electrolyte conductivity, mean AULC ranks first or second on every task, and on the drug task the median queries to reach 80% recovery are about 18% fewer than the fastest comparator.