AI 中文总结
本文提出生理世界模型（PWM），引入人体状态转换标记，构建含六项基准任务的框架，用于建模人体生理状态响应现实因素的变化，助力个性化健康管理等场景。
AI 中文摘要
如今，连续多模态传感技术可对人体生理状态进行日常监测，而非仅在临床就诊时进行观测。然而，多数健康人工智能系统仅用于识别当前状态、评估风险或分析单个生物标志物，并未直接建模生理状态如何响应现实世界事件、行为、情境及干预措施而发生变化。本文提出了生理世界模型（Physiological World Model, PWM），这是一种用于学习整个人体层面状态变化的事件条件框架。我们引入了人体状态转换标记（HumanState Transition Token），这是一种结构化的、带有质量评分的单元，可将事件发生前的生理状态与事件或动作、相关情境、干预信息、事件后的生理轨迹、观测结果及数据质量相连接。我们描述了从状态表示到有界干预规划的四个能力级别，以及四种数据采集与验证协议。此外，我们提出了六项基准任务，涵盖人体状态表示、多时间尺度预测、个体响应预测、替代干预模拟、有界规划以及分布偏移下的可靠性。该框架为个性化健康管理、行为干预设计及临床医生监督下的决策支持提供了可行路径，同时明确区分了预测与因果推断，并清晰呈现了不确定性、安全性、治理机制及使用限制。
英文摘要
Continuous multimodal sensing now allows human physiology to be observed throughout daily life rather than only during occasional clinical visits. However, most health artificial intelligence systems are designed to recognize current states, estimate risks or analyse individual biomarkers. They do not directly model how physiological states change in response to real-world events, behaviours, contexts and interventions. Here we propose the Physiological World Model (PWM), an event-conditioned framework for learning these changes at the level of the whole person. We introduce the HumanState Transition Token, a structured, quality-scored unit that connects the physiological state before an event with the event or action, relevant context and intervention information, the physiological trajectory after the event, observed outcomes and data quality. We describe four capability levels, from state representation to bounded intervention planning, together with four data acquisition and validation protocols. We also propose six benchmark tasks covering HumanState representation, forecasting across multiple timescales, individualized response prediction, simulation of alternative interventions, bounded planning and reliability under distribution shift. Together, this framework provides a practical path towards personalized health management, behavioural intervention design and clinician-supervised decision support, while clearly separating prediction from causal inference and making uncertainty, safety, governance and limits of use explicit.
