TRIM: Learning to Streamline Medical Agent Workflows with Diagnostic Process Forests

Medical agents must decide when to gather more evidence and when to answer. We present TRIM (Tree-Reinforced Intervention for Medical agents), a controller that learns these decisions while keeping the host agent frozen. It can continue the pending operation, finish with an existing answer, jump to a permitted later stage, or request another operation. Diagnostic Process Forests (DPForests) index past executions by workflow position and record each action’s error rate, cost saving, and support. The controller reads these statistics alongside the current case and scores all legal actions in one forward pass. On DiagGym with 827 held-out MIMIC-IV patients, TRIM reaches 56.71% diagnostic accuracy versus 51.39% for the native agent, with 59.9% fewer examinations and 52.0% fewer model calls. Across 14 host–dataset pairs covering text, image, and multimodal tasks, SFT+GRPO reduces model calls by 32.6% on average. No pair loses accuracy or mean balanced accuracy. Savings depend on the host: repeated discussions leave room for early stopping, while workflows that produce a usable answer only in their final call change little.