GlucoFM decomposes CGM traces into dual state-event streams, pretrains on 109k hours of unlabeled data, and reports superior subject-disjoint performance on seven clinical tasks across four cohorts.
HEARTS: Benchmarking llm reasoning on health time series.arXiv preprint arXiv:2603.06638, 2026
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TimeSRL uses semantic abstractions from time-series data optimized via reinforcement learning to achieve better cross-dataset generalization than standard ML or LLM baselines in mental health prediction.
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GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoring
GlucoFM decomposes CGM traces into dual state-event streams, pretrains on 109k hours of unlabeled data, and reports superior subject-disjoint performance on seven clinical tasks across four cohorts.
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TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health
TimeSRL uses semantic abstractions from time-series data optimized via reinforcement learning to achieve better cross-dataset generalization than standard ML or LLM baselines in mental health prediction.