睡眠医学和生理信号分析的研究者终于有了一个无需大量标注数据就能学到通用表征的基础模型——Hypnos 用下一词预测解决了多模态生理数据的学习难题，做睡眠分期或房颤检测的团队可以直接用它生成嵌入，大幅降低标注成本。
该研究提出 Hypnos，一个多模态睡眠基础模型，使用来自 2 万多次夜间多导睡眠监测的 8 种传感模态（如 EEG、ECG、呼吸信号）训练。与现有使用掩码重建或对比学习的方法不同，Hypnos 采用下一词预测作为自监督目标，通过残差向量量化将每种模态离散化为 token 流，并用自回归 RQ-Transformer 并行预测所有模态的下一个 token。在睡眠阶段分类任务中，Hypnos 仅用 1% 的标注数据就达到了强监督基线的性能，还能泛化到日间生理信号，在检测房颤上超越专门的 ECG 基础模型。结果表明，下一词预测是多模态生理信号表征学习的有效且可扩展的自监督目标。
Next-Token Prediction Learns Generalisable Representations of Sleep Physiology
Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains. Existing models have typically been trained with masked-reconstruction or contrastive objectives. However, masked reconstruction may be poorly suited to the stochastic nature of these signals, while contrastive approaches rely on positive-pair definitions despite the semantic invariances of physiological signals being poorly understood. In this work, we show that next-token prediction is a simple and scalable alternative. We develop Hypnos, a multi-modal sleep foundation model trained using eight different sensing modalities (e.g. EEG, ECG, respiratory signals) drawn from over 20,000 overnight polysomnography recordings. We tokenize each modality into streams of discrete tokens using residual vector quantization, then train a large auto-regressive RQ-Transformer to jointly predict the next token across all modalities in parallel. After training, Hypnos can be applied to continuous streams of sensor data from any subset of supported modalities, generating embeddings for downstream tasks. Across a range of benchmarks, Hypnos significantly outperforms existing foundation models. In sleep stage classification, we match the performance of strong supervised baselines on held-out test sets whilst using \(100\times\) less labelled data. Hypnos even generalises to daytime physiology, surpassing a dedicated ECG foundation model at detecting atrial fibrillation. Our results demonstrate that next-token prediction is a strong self-supervised objective for representation learning from multi-modal physiological signals.