MedMemoryBench: Benchmarking Agent Memory in Personalized Healthcare
Read the original on arXiv AI →MedMemoryBench is a new benchmark designed to evaluate memory mechanisms in personalized healthcare agents, addressing the shortcomings of existing open‑domain conversation benchmarks. It uses a human‑agent collaborative pipeline to generate realistic, long‑horizon medical trajectories from synthetic patient archetypes, producing a dataset of roughly 2,000 sessions and 16,000 interaction turns. The benchmark introduces an "evaluate‑while‑constructing" streaming assessment protocol that mirrors dynamic memory accumulation in production, and it formally studies memory saturation—how continuous information influx can degrade retrieval and reasoning. The results highlight significant bottlenecks in current architectures, especially in complex medical reasoning and noise resilience, underscoring the need for more robust, production‑ready medical agents.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.