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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, provides a realistic and diagnostic benchmark for evaluating LLM reasoning over real-world wearable data.
Discarding irrelevant reasoning tokens can make language models up to 3x faster at test time without sacrificing performance.
