WHEN SHOULD RAG WRITE? SELECTIVE MEMORY FOR CONTINUAL MEDICAL QA

Medical question answering systems can learn from answer feedback, but a source-supported claim may still be inapplicable to a later patient. We introduce EvoMedAgent, a selective admission policy for graph memory with a frozen generator. The policy couples three mechanisms: executable clinical scope that must pass before retrieval ranking, source-family calibration of assertion support, and joint admission checks for source conditions and unresolved conflicts. The policy requires patient-bound observations to remain patient-specific and general assertions to retain reuse conditions. Within a test-then-learn protocol, accepted assertions become visible to later questions. Evaluation spans six frozen medical QA benchmarks, paired online streams, and transfer to disjoint EHRNoteQA patients after memory is frozen. Relative to no update, the full policy improves MedQA AULC by 5.3 points and EHRNoteQA transfer accuracy by 3.5 points. Matched-editor comparisons and component ablations establish the contribution of admission checks, while feedback-schedule controls examine evidence availability. Together, these results show that selective admission improves later predictions with a frozen generator and explicit checks on the reuse conditions of accepted evidence.