MedCache解决了临床智能体如何处理跨时间、跨专科患者记录的难题，比传统方法更准确高效。
MedCache是一种混合框架，构建时效性患者记忆，将证据组织为重叠专科视图，将查询路由到相关记忆，并自适应调用一个或多个专科医生。该框架在多访问、多专科患者记录基准上表现优异，显著提升推理准确性和记忆效率。研究显示时间有效性比保留更多历史更重要，专科分解记忆可减少上下文但可能隐藏共享证据。MedCache在强单智能体和多智能体基线上表现更好，并能跨模型骨干和数据集泛化。
MedCache: Efficient and Temporally Valid Memory for Longitudinal Clinical Agents
Longitudinal clinical agents must maintain an evolving patient state from evidence distributed across visits, time points, and specialties. However, how agent memory should be designed for this setting remains unclear. We introduce a benchmark of multi-visit, multi-specialty patient records that evaluates long-context evidence retrieval, cross-time evidence aggregation, and cross-specialty clinical reasoning. Using this benchmark, we systematically study four memory design choices: curation, organization, retrieval, and memory-augmented reasoning. We find that temporal validity is more important than simply retaining more history; specialty-factorized memory reduces context but can hide shared evidence; and multiple agents help when specialists must reason together, not merely when evidence comes from multiple memories. Guided by these findings, we propose \textit{MedCache}, a hybrid framework that constructs temporally valid patient memory, organizes evidence into overlapping specialty views, routes each query to relevant memories, and adaptively invokes one or multiple specialists. Experiments show that MedCache improves reasoning accuracy and memory efficiency over strong single-agent and multi-agent baselines, while generalizing across model backbones and external datasets.