EvoTD: Decoupled Memory Evolution for Specialty Triage and Diagnostic Inference

Clinical practice requires clinicians to synthesize heterogeneous specialty-specific knowledge before making diagnostic decisions. Medical question answering has therefore become a widely employed task for such knowledge synthesis, owing to its controllable evaluation. However, most prior approaches either treat specialty triage as a one-time preprocessing step without leveraging it during diagnostic inference or update notes without enforcing consistency with the selected specialty-specific context. To this end, we propose EvoTD, a decoupled memory evolution framework for specialty triage and diagnostic inference. Specifically, DualEvo is first designed to maintain separate specialty-boundary patches and notes, preventing diagnostic-experience updates from altering specialty triage. Then, SkillRoute adaptively selects deterministic boundary rules, graph-based scoring, and LLM reasoning to construct specialty-specific context via case evidence. To improve the reliability of memory evolution, we develop ReliHarness to synthesize entity-indexed notes from predictions and harness their usage through warmup, single-pass pre-injection, and harmful-memory quarantine. Comprehensive experiments show that EvoTD improves over direct prompting by 7.1 and 6.1 points (3-seed means) on NEJMQA and MMLU-Pro Health, respectively.