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.
SleepLM: Natural-Language Intelligence for Human Sleep
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abstract
We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural language. Despite the critical role of sleep, learning-based sleep analysis systems operate in closed label spaces (e.g., predefined stages or events) and fail to describe, query, or generalize to novel sleep phenomena. SleepLM bridges natural language and multimodal polysomnography, enabling language-grounded representations of sleep physiology. To support this alignment, we introduce a multilevel sleep caption generation pipeline that enables the curation of the first large-scale sleep-text dataset, comprising over 100K hours of data from more than 10,000 individuals. Furthermore, we present a unified pretraining objective that combines contrastive alignment, caption generation, and signal reconstruction to better capture physiological fidelity and cross-modal interactions. Extensive experiments on real-world sleep understanding tasks verify that SleepLM outperforms state-of-the-art in zero-shot and few-shot learning, cross-modal retrieval, and sleep captioning. Importantly, SleepLM also exhibits intriguing capabilities including language-guided event localization, targeted insight generation, and zero-shot generalization to unseen tasks. All code and data will be open-sourced.
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2026 3verdicts
UNVERDICTED 3representative citing papers
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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.
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WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning
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