# Summary Researchers have developed Cross-Architecture Proxy Tuning (CAPT), a method that lets hospitals use newer, general-purpose language models without having to retrain them on clinical data from scratch. The core problem they're solving is real: every time a better foundation model comes out, healthcare organizations either stick with older clinical models or spend massive resources retraining the new ones on medical text. CAPT sidesteps this by combining a new general-purpose model with an existing older clinical model at inference time, using contrastive decoding to blend their strengths. The trick is handling the fact that these models often have completely different vocabularies—the method routes tokens through both models intelligently, amplifying clinical signals from the older model while keeping the reasoning quality of the newer one. The results are substantial. Across six clinical tasks (classification and text generation), CAPT beat both individual models and other ensemble approaches by wide margins—17.6% better than UniTE and 41.4% better than standard proxy tuning on average. Token-level analysis and case reviews from physicians showed the method actually produces more clinically specific language, fewer context errors, and better actionable recommendations. This matters most for resource-constrained hospitals that can't afford continuous retraining cycles but want to benefit from advances in general AI. They get the latest model capabilities without the computational overhead.