EvoMedAgent: Auditable Reasoning and Failure-Driven Memory Evolution for Medication Recommendation

Medication decisions often involve multiple conditions, ongoing therapies, and patient-specific safety constraints. Effective medication recommendations therefore must go beyond generating clinically plausible outputs to support auditable and safe real-world use. This calls for two complementary capabilities that, however, are often overlooked in existing methods: (1) auditability, enabling clinicians to trace how patient evidence and competing treatment needs lead to individual decisions; and (2) correctability, enabling the system to identify recurrent recommendation errors in comparable clinical contexts, derive targeted corrections, and validate them before updating for safe decisions.We introduce EvoMedAgent, an agentic medication recommender that couples an auditable reasoning pipeline with failure-driven memory evolution. The pipeline makes medication selection auditable through evidence-grounded unit decisions, patient-level reconciliation, and independent review, linking these judgments to each final medication in a reasoning trace. Using this trace and recorded medication sets, the evolution process attributes observed errors, proposes clinically scoped corrections, and validates them before incorporating them into memory. Extensive experiments show that EvoMedAgent achieves substantial improvements over state-of-the-art baselines on real-world datasets, with ablations supporting both mechanisms, and human and LLM evaluations supporting improved auditability.Our code is available at https://anonymous.4open.science/r/EvoMedAgent/.