让大模型医生提前预警风险，看清担忧积累过程。
Modeling Clinical Concern Trajectories in Language Model Agents
- 用一阶与二阶动态建模风险累积，生成连续担忧信号。
- 二阶动态使担忧曲线平滑可预测，比传统方法早12小时暴露风险。
- 适合医疗辅助系统，提升人机协同决策透明度。
在临床场景中部署的大语言模型（LLM）代理常表现出突变、阈值驱动的行为，难以在风险升级前提供预警信号。而真实临床中，医生是基于逐渐升高的担忧采取行动。我们研究了显式状态动态是否能在不赋予代理临床决策权的前提下，揭示风险积累的早期信号。提出一种轻量级代理架构：通过整合无记忆的临床风险编码器，利用一阶与二阶动态随时间演进，生成连续的升级压力信号。在合成病房场景中，无状态代理呈现陡峭的升级悬崖，而二阶动态则产生平滑且具备前瞻性的担忧轨迹，尽管升级时间相似。这些轨迹能提前暴露持续的不安，支持人工介入监控与更及时干预。结果表明，显式状态动态可提升LLM代理的临床可解释性，不仅显示何时达到阈值，更揭示担忧已持续多久。
原文摘要 · Abstract (English)
Large language model (LLM) agents deployed in clinical settings often exhibit abrupt, threshold-driven behavior, offering little visibility into accumulating risk prior to escalation. In real-world care, however, clinicians act on gradually rising concern rather than instantaneous triggers. We study whether explicit state dynamics can expose such pre-escalation signals without delegating clinical authority to the agent. We introduce a lightweight agent architecture in which a memoryless clinical risk encoder is integrated over time using first- and second-order dynamics to produce a continuous escalation pressure signal. Across synthetic ward scenarios, stateless agents exhibit sharp escalation cliffs, while second-order dynamics produce smooth, anticipatory concern trajectories despite similar escalation timing. These trajectories surface sustained unease prior to escalation, enabling human-in-the-loop monitoring and more informed intervention. Our results suggest that explicit state dynamics can make LLM agents more clinically legible by revealing how long concern has been rising, not just when thresholds are crossed.
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