# Longitudinal Personal Health Benchmark

Medical large language models answer single encounters well but struggle to accumulate and govern experience across encounters. HealthJev separates shared safety rules and medical knowledge from a private longitudinal memory, and a self-trained lightweight decision model, JevGate, screens candidate lessons before they enter memory at a median 86 ms per decision. On a longitudinal benchmark of 90 simulated daily interactions for 20 users built from real MIMIC-IV trajectories, it scored a mean 0.668, versus 0.634 for MemoryBank, 0.593 for Mem0, 0.510 for ReAct and 0.502 for Memp; across six biomedical tasks it gained 13.3 percentage points on average over the base condition.

Medical large language models answer single encounters well but struggle to accumulate and govern experience across encounters. HealthJev separates shared safety rules and medical knowledge from a private longitudinal memory, and a self-trained lightweight decision model, JevGate, screens candidate lessons before they enter memory at a median 86 ms per decision. On a longitudinal benchmark of 90 simulated daily interactions for 20 users built from real MIMIC-IV trajectories, it scored a mean 0.668, versus 0.634 for MemoryBank, 0.593 for Mem0, 0.510 for ReAct and 0.502 for Memp; across six biomedical tasks it gained 13.3 percentage points on average over the base condition.