从学习倦怠与学习投入双重视角，分析药学类本科生的学习状态，为《物理化学》课程教学改革提供实证依据。采用学习倦怠量表和学习投入量表对某高校药学类本科生进行调查，分别回收有效问卷 1010份和 877份。运用SPSS进行信效度检验、K-means聚类分析、差异检验、相关性分析及多元线性回归分析。结果显示，学生可分为积极投入型、中庸平衡型、消极倦怠型；男生学业疏离与学习活力均显著高于女生；大二学生学习投入显著低于大三、大四；有考研意向者学习投入显著更高；专业认同度与数理基础共同解释学习投入的显著变异。药学类本科生学习状态存在明显异质性，大二是学习投入低谷期，专业认同与数理基础是关键预测因素。建议《物理化学》课程实施分层教学、强化专业认同、关注大二学生及数理薄弱者。

Perioperative medicine is a meeting ground of multiple disciplines, with anesthesiology lying at the center. It is expanding rapidly in temporal frames, disciplinary contents, and objects of study. Key trends include the use of artificial intelligence for precise risk profiling rather than simple scoring, drug repurposing and reassessment guided by biomarkers and advanced monitoring technologies, and the recognition that the perioperative period extends well beyond discharge, affecting long-term outcomes like cognitive decline and renal injury. In 2025, the Chinese Journal of Anesthesiology identified ten scientific challenges in anesthesiology and perioperative medicine, establishing perioperative brain health as a national research priority.

Foundation model-based multi-agent systems are moving into production, yet work on runtime reliability and serving efficiency is still fragmented across diagnostics and infrastructure. This review synthesizes 137 references, 45 systems, and 8 benchmarks through a unified runtime-stack perspective. We organize diagnostics into failure localization, observability, safety, and benchmarks, and infrastructure into memory management, prefix-aware scheduling, cache lifecycle, and power management. The central synthesis is that both layers act on overlapping runtime state but optimize different objectives. Diagnostics benefits from retaining traces, context, and replayable state, whereas serving infrastructure reclaims or compresses state to satisfy memory, latency, and throughput budgets. We term this interaction the diagnostics–infrastructure retention tension. We further identify three workload characteristics—sparse activation, invocation distance, and inter-agent memory sharing—that shape agent-aware serving, and use explicit evidence tiers to separate full-text-verified results from abstract-level or contextual evidence. Rather than ranking heterogeneous systems, we derive testable co-design directions: retention-aware eviction, failure-aware scheduling, budgeted observability, and infrastructure-triggered diagnostics. This framing positions runtime state retention as a shared systems resource and clarifies where current multi-agent diagnostics and serving research remain disconnected.