这篇论文解决了大模型看不懂可穿戴数据的问题。WEQA框架比基线的准确率高出24%，还通过了医学专家盲测，做医疗AI的同学值得看看。
论文提出WEQA，一个查询自适应智能体框架，统一LLM推理与可穿戴分析及建模工具。它采用LLM控制器合成执行计划，动态路由查询至传感器分析与预训练模型组合，并借助外部知识进行响应审计。研究团队构建了涵盖四个公开可穿戴数据集、三个健康领域的基准。实验显示，WEQA比LLM和智能体基线准确率高出24%。一项由12名医学专家和8名用户参与的盲测表明，其在有用性和临床合理性上有显著提升。
WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning
Language models are remarkably capable at medical question answering, in some cases surpassing the accuracy of general physicians. However, answering questions about wearable health data remains challenging and understudied, as these ubiquitous sensors produce continuous, high-dimensional, and longitudinal data, which is non-trivial to align with text-centric distributions in LLM pretraining. The diversity of sensor modalities and user intents cannot be effectively handled by a fixed reasoning workflow or a single pretrained foundation model. To address these challenges, we propose WEQA, a query-adaptive agent framework that unifies LLM reasoning with specialized wearable analytical and modeling tools. An LLM controller is employed to synthesize execution plans and dynamically route each query to the appropriate combination of sensor analysis and pretrained models, and perform grounded response auditing with external knowledge. We also curate a benchmark spanning four open wearable datasets comprising analytic and predictive tasks in three different health domains. Experiments show that our framework is 24% more accurate than LLM and agentic baselines, and a blinded study with 12 medical experts and 8 users shows substantial gains in usefulness and clinical soundness.