Paper Overview
Research area: NLP Authors: Ji Soo Lee, Xilun Chen, Pierce Chuang, Ashish Shenoy, Jason Wei, Dohwan Ko, Hyunwoo J. Kim, Benoit Corda Release date: 2026-09-04 arXiv: 2609.05405
Abstract
Recent advances in wearable sensing enable continuous monitoring of physiological and behavioral signals, yet existing benchmarks rarely evaluate whether AI systems can reason over a real user's longitudinal wearable record. We introduce WearableQA, a benchmark comprising 4,084 10-option multiple-choice questions constructed from the wearable time series, blood biomarkers, and demographics of 200 real users, each with up to 500 days of daily measurements. WearableQA preserves authentic wearable distributions that include device noise and inter-individual variability. To evaluate distinct reasoning capabilities, we introduce 16 question types organized along two complementary axes: data versus health reasoning, which distinguishes computation over longitudinal measurements from physiological interpretation.
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Key Details
- Scale: 4,084 multiple-choice questions, each with 10 answer options
- Data sources: wearable time series, blood biomarkers, and demographics from 200 real users
- Longitudinal coverage: up to 500 days of daily measurements per user
- Realism: preserves authentic distributions including device noise and inter-individual variability
- Evaluation design: 16 question types along two axes — data reasoning vs. health reasoning