arXiv — cs.AI preprintsInternational5 October 2026
Learning transferable human physiology from two million hours of sleep with SleepFM-2
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2609.06849v2 Announce Type: replace Abstract: Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation model developed and evaluated on 282,511 polysomnography recordings from 26 cohorts, including 235,865 used for pretraining. These data span more than two million hours of multimodal physiology. Compared with SleepFM, SleepFM-2 improves disease prediction and sleep scoring, supports arousal, limb movement and respiratory event detection, and transfers to wearable sens
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- MintFlow: Minimal Trajectory Intervention for Constrained Flow Matching5 October 2026
- Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses5 October 2026
- The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?5 October 2026
- Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation5 October 2026
- Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents5 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.