Researchers developed CAPT, a method that lets newer general-purpose language models borrow clinical knowledge from older specialized medical models without requiring expensive retraining. The approach uses contrastive decoding to selectively inject clinical terminology and reasoning patterns while keeping the newer model's superior language abilities, and it works even when the models use different vocabularies. In tests on clinical tasks, CAPT beat existing ensemble methods by significant margins and produced outputs physicians rated as more accurate and clinically appropriate.

• CAPT lets a new general-purpose LLM borrow clinical knowledge from an old specialized medical model at inference time, no retraining needed, even when they use different tokenizers/vocabularies
• It beat UniTE by 17.6% and proxy tuning by 41.4% on average across six clinical classification and generation tasks
• Token-level analysis confirmed the mechanism works as designed: clinical terms (drugs, symptoms, procedures) come from the legacy clinical model while structure/fluency comes from the new general model
• Physicians rated CAPT's outputs as more accurate and clinically appropriate in case studies

Researchers developed CAPT, a method that combines a new general-purpose language model with an older clinical model without requiring retraining. The technique uses contrastive decoding to blend clinical knowledge into the newer model while keeping its reasoning abilities intact, and it outperformed existing ensemble methods by 17-41% on clinical tasks. This approach helps hospitals with limited computing resources adopt newer models without the expensive process of retraining them on clinical data.

• CAPT combines a new general-purpose LLM with an old clinical model at inference time via contrastive decoding, no retraining needed, even across mismatched vocabularies.
• It beat UniTE by 17.6% and standard proxy tuning by 41.4% on average across six clinical tasks.
• Physician review and token-level analysis showed more clinically specific language, fewer context errors, and better actionable recommendations.
• Lets resource-constrained hospitals adopt newer models without expensive retraining cycles on clinical data.