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arxiv: 2606.09605 · v1 · pith:2SNT2E6Knew · submitted 2026-06-08 · 💻 cs.AI

Next-Token Prediction Learns Generalisable Representations of Sleep Physiology

Jonathan F. Carter , Lionel Tarassenko This is my paper

Pith reviewed 2026-06-27 16:40 UTC · model grok-4.3

classification 💻 cs.AI
keywords next-token predictionfoundation modelsleep stagingphysiological signalsself-supervised learningpolysomnographyatrial fibrillationmulti-modal representations
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The pith

Next-token prediction on tokenized multi-modal sleep signals produces embeddings that match supervised baselines with 100 times less labeled data and generalize to daytime atrial fibrillation detection.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper trains Hypnos, a foundation model, by tokenizing eight physiological modalities from over 20,000 overnight recordings and using next-token prediction to learn joint representations. This approach is presented as a scalable alternative to masked reconstruction or contrastive learning for signals whose semantic invariances remain poorly defined. On sleep stage classification the resulting embeddings reach the accuracy of strong supervised models while using far less labeled data. The same embeddings also outperform a dedicated ECG foundation model when applied to daytime recordings for atrial fibrillation detection. The work therefore claims that next-token prediction alone suffices to extract generalizable features from stochastic multi-modal physiological data.

Core claim

Hypnos tokenizes each of eight sensing modalities with residual vector quantization, then trains a large auto-regressive RQ-Transformer to predict the next token across all modalities in parallel. After pretraining, the model produces embeddings from any supported subset of modalities that match supervised sleep-stage baselines on held-out test sets while using 100 times less labeled data and that surpass a dedicated ECG foundation model at detecting atrial fibrillation in daytime physiology.

What carries the argument

An auto-regressive RQ-Transformer trained with next-token prediction on parallel streams of residual-vector-quantized tokens drawn from multiple physiological modalities.

If this is right

  • Sleep stage classification matches strong supervised baselines on held-out sets while using 100 times less labeled data.
  • The same embeddings surpass a dedicated ECG foundation model at atrial fibrillation detection from daytime recordings.
  • Embeddings can be generated from continuous streams of any supported subset of the eight modalities.
  • Next-token prediction serves as a scalable self-supervised objective for multi-modal physiological signals where positive-pair definitions are difficult to specify.
  • The approach outperforms existing foundation models across the reported benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method may transfer to other stochastic multi-modal medical signals where explicit invariance definitions are unavailable.
  • Lower labeled-data requirements could accelerate model development for rare sleep or cardiac conditions.
  • Joint next-token prediction across modalities may encode cross-signal relationships that single-modality pretraining misses.
  • Further downstream tasks such as sleep-apnea event detection would provide additional tests of the claimed generality.

Load-bearing premise

That next-token prediction on tokenized physiological streams will automatically learn the semantic invariances required for downstream generalization without any explicit positive-pair or reconstruction targets.

What would settle it

On a held-out sleep staging test set, embeddings from the next-token model fail to reach the accuracy of a supervised baseline trained on the full labeled set when only one percent of the labels are supplied, or the model underperforms the dedicated ECG foundation model on atrial fibrillation detection from daytime recordings.

Figures

Figures reproduced from arXiv: 2606.09605 by Jonathan F. Carter, Lionel Tarassenko.

Figure 1
Figure 1. Figure 1: Overview. Hypnos is a large auto-regressive RQ-transformer trained via multi-modal next￾token prediction on tokenized streams of physiological sensor data. During pre-training, cross-modal attention is restricted to randomly sampled sub-groups, improving test-time generalisation to subsets of supported modalities. After pre-training, Hypnos can be used to generate high-quality embeddings for a diverse rang… view at source ↗
Figure 2
Figure 2. Figure 2: For stochastic signals such as an ECG (left), the distribution over the future may be [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Tokenizer training. Each sig￾nal Xi is encoded with RVQ into discrete residual tokens V i . Encoder and decoder are trained jointly to reconstruct Xi . For each modality, we train tokenizers to transform the continuous raw signals into discrete tokens. We do this using an encoder-decoder architecture with a residual vector quantization (RVQ) layer, previ￾ously used by several foundation models, including f… view at source ↗
Figure 4
Figure 4. Figure 4: Hypnos training. (a) For each time t, the K discrete residual tokens (illustrated with K = 4) from modality i are combined to form an embedding mi t . (b) A Transformer backbone mixes information to produce embeddings z i t for each modality, e.g. i ∈ {A, B, C, D}. (c) For all i, t, the Depth Transformer auto-regressively predicts the next residual token V i t+1,k conditioned on z i t . 3.3 Hypnos After to… view at source ↗
Figure 5
Figure 5. Figure 5: Example cross-modal attention matrices (M = 4). Dur￾ing training, attention is restricted to random sub-groups. We split modalities into groups by sampling from a Chinese Restaurant Process [2] with concentration parameter α. Modal￾ities are assigned sequentially: modality i + 1 joins an existing group g of size ng with probability ng/(i + α) and starts a new group with probability α/(i + α). The resulting… view at source ↗
Figure 6
Figure 6. Figure 6: Few-shot sleep stage classification. We train MLP probes on each foundation model and re-train supervised baselines using varying fractions of in-domain data. Using as little as 1% of the probe-training data, Hypnos matches U-Sleep trained on the full dataset on held-out MrOS. 4.4 Transfer to external ECG benchmarks [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Autoregressive generation of physiological signals. Hypnos can be used to jointly generate physiological signals for any subset of supported modalities. Here we see that conditioned on 10 s of real context (blue), Hypnos generates plausible signals with cross-modal consistency. For example, we can observe respiration-induced amplitude modulation of R-peaks in the ECG. 4.6 Modality-masking We ablate our gro… view at source ↗
Figure 8
Figure 8. Figure 8: Scaling model size improves the performance of Hypnos. Next-token perplexity and downstream metrics all improve with model scale. Sleep stage classification, apnoea detection and arousal detection performance are reported using a linear probe on the SHHS validation set. 5 Limitations and Future Work Improving sensor generalisation We demonstrated that our method enables held-out generalisation to subsets o… view at source ↗
Figure 9
Figure 9. Figure 9: Effect of input and output residual depth on downstream performance. Linear probe metrics for unimodal EEG models on the SHHS validation set varying Kin and Kout. Performance slightly improves when increasing the number of input residual tokens but worsens when increasing the number of output residual tokens. Tokenization length In our main experiments, we designed our tokenizers to produce tokens at a rat… view at source ↗
Figure 10
Figure 10. Figure 10: Effect of token duration on downstream performance. (left) Reconstruction quality decreases as token duration is varied from 0.25 s to 5 s, i.e. the compression rate increases. However, performance is worst at high token rates (0.25 s) and saturates or regresses beyond 1 s. We adopt a 1 s token duration in all other experiments. Adversarial losses Défossez et al. [16] recently observed that removing recon… view at source ↗
Figure 11
Figure 11. Figure 11: Scaling unimodal EEG models from Tiny to Large. Next-token perplexity and downstream metrics continue to improve with model scale. Sleep stage classification, apnoea detection and arousal detection performance are reported using a linear probe on the SHHS validation set. B.3 Scaling context length A key motivation for next-token prediction is that it naturally scales to longer context lengths. To quantify… view at source ↗
Figure 12
Figure 12. Figure 12: Effect of context length on single-channel EEG models. Validation perplexity decreases and downstream probing performance improves as the training context length is increased from 128 to 4096 tokens. Sleep staging and arousal detection saturate at around 1024–2048 tokens, while age regression, CVD risk and moderate OSA detection continue to improve at the longest context lengths. (a) Validation perplexity… view at source ↗
Figure 13
Figure 13. Figure 13: Effect of context length on ECG-only models. As with EEG, perplexity and downstream metrics improve with longer context. The largest relative gains are again on summary tasks such as moderate OSA detection and CVD risk. B.4 Modality-masking ablation [PITH_FULL_IMAGE:figures/full_fig_p020_13.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript introduces Hypnos, a multi-modal foundation model for sleep physiology trained via next-token prediction. It tokenizes eight sensing modalities (EEG, ECG, respiratory signals, etc.) from over 20,000 overnight PSG recordings using residual vector quantization, then trains a large auto-regressive RQ-Transformer to jointly predict the next token across modalities. The central claims are that this objective yields representations that (i) significantly outperform existing foundation models, (ii) match strong supervised baselines on held-out sleep-stage classification while using 100× less labeled data, and (iii) generalize to daytime single-channel ECG, surpassing a dedicated ECG foundation model on atrial-fibrillation detection.

Significance. If the empirical claims hold after proper controls, the result would be significant: it supplies concrete evidence that a simple autoregressive objective can induce useful invariances in stochastic physiological time series without requiring contrastive positive-pair definitions or masked reconstruction. The scale (20 k+ multi-modal recordings) and the reported data-efficiency gain in sleep staging would strengthen the case for next-token prediction as a scalable pre-training strategy in healthcare signal modeling.

major comments (1)
  1. [Abstract] Abstract (generalization claim): The assertion that Hypnos 'generalises to daytime physiology, surpassing a dedicated ECG foundation model at detecting atrial fibrillation' is load-bearing for the cross-domain claim. The manuscript provides no quantification of domain shift (circadian, activity, or recording-context differences), no statement on whether the daytime evaluation used zero-shot embeddings or fine-tuning, and no comparison of the dedicated ECG model's training data volume or diversity. Without these controls it is impossible to attribute outperformance to the next-token objective or multi-modal pre-training rather than architecture or data scale.
minor comments (1)
  1. [Abstract] Abstract: No experimental details (data splits, metrics, error bars, or whether results are from linear probing vs. fine-tuning) are supplied, making it difficult for readers to assess the strength of the reported gains at first reading.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful review and for identifying areas where the generalization claim requires additional support. We address the single major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract (generalization claim): The assertion that Hypnos 'generalises to daytime physiology, surpassing a dedicated ECG foundation model at detecting atrial fibrillation' is load-bearing for the cross-domain claim. The manuscript provides no quantification of domain shift (circadian, activity, or recording-context differences), no statement on whether the daytime evaluation used zero-shot embeddings or fine-tuning, and no comparison of the dedicated ECG model's training data volume or diversity. Without these controls it is impossible to attribute outperformance to the next-token objective or multi-modal pre-training rather than architecture or data scale.

    Authors: We agree that the abstract is too concise to convey these details and that the manuscript as submitted lacks explicit quantification of domain shift and direct comparisons of the baseline model's data. In revision we will (i) expand the abstract to state that daytime AF detection uses fine-tuned embeddings on a modest amount of labeled daytime ECG, (ii) add a dedicated paragraph or short subsection that reports basic distributional statistics (e.g., heart-rate variability, signal amplitude) between the overnight PSG and daytime recordings and discusses circadian/activity differences, and (iii) include a brief comparison of the published training scale and diversity of the dedicated ECG foundation model. These additions will make the attribution to the next-token objective clearer while remaining within the scope of the existing experiments. revision: yes

Circularity Check

0 steps flagged

No circularity detected; derivation is self-contained

full rationale

The paper trains a multi-modal RQ-Transformer with a standard next-token prediction objective on tokenized overnight PSG recordings (eight modalities) and then extracts embeddings for separate downstream evaluations on held-out sleep staging and daytime ECG AF detection tasks. No equations or claims reduce the objective or results to the evaluation metrics by construction, no fitted parameters are relabeled as predictions, and no load-bearing self-citations or uniqueness theorems are invoked. The pretraining objective and downstream tasks remain independent, making the reported generalization a genuine empirical claim rather than a definitional tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no details on model architecture specifics, tokenization parameters, or training hyperparameters, so no free parameters or axioms can be identified.

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discussion (0)

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