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 w

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ethics.ai (16 June 2026), “WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning,” evidence record 870, https://ethics.ai/record/870 (originally published by arXiv).

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