Agents Scale Sleep Discovery
From MyClaw Newsletter · subscribed via aiste.ulozaite@gmail.com · original ↗ · unsubscribe
Researchers built AI Sleep Co-Scientist, an expert-guided system that analyzed roughly 124,000 sleep studies and more than 50 TB of physiological signals. By automating labor-intensive preprocessing, hypothesis development, and statistical analysis while keeping humans in review.
document.documentElement.classList.add(‘js’); [2607.25175] Agentic AI-enabled discovery across large-scale sleep physiology
Search arXiv
Press Enter to search · Advanced search
Computer Science > Multiagent Systems
arXiv:2607.25175 (cs)
[Submitted on 28 Jul 2026 (v1), last revised 29 Jul 2026 (this version, v2)]
Title:Agentic AI-enabled discovery across large-scale sleep physiology
Authors:Rahul Thapa, Umaer Hanif, Robin Guillard, Andreas Brink-Kjaer, Adrien Specht, Matteo Saibene, Magnus Ruud Kjaer, Harrison G. Zhang, Federico Bianchi, Elisabeth Roxane M. Heremans, Eric C. Landsness, Emmanuel Mignot, James Zou
View a PDF of the paper titled Agentic AI-enabled discovery across large-scale sleep physiology, by Rahul Thapa and 12 other authors
Abstract:Sleep occupies roughly one-third of human life, yet many aspects of its physiology remain poorly understood. Large polysomnography (PSG) datasets offer new opportunities to study sleep and its links to disease, but extracting insight from these recordings requires substantial expert effort and remains difficult for general-purpose AI systems. We developed AI Sleep Co-Scientist, an expert-guided environment in which human scientists direct specialist agents for hypothesis development, signal preprocessing, and statistical analysis, reviewing intermediate outputs. Each reported result is linked to the executable code that produced it. Across four cohorts of approximately 124,000 PSG recordings and more than 50 TB of raw signals, we conducted five case studies spanning how sleep physiology relates to future disease, how it distinguishes clinical phenotypes, and how sleep is organized and regulated. Diminished network-level physiological coupling during sleep was associated with incident Parkinson’s disease (HR 1.48) and Alzheimer’s disease (HR 1.38). A physiologically structured late-fusion sleep-age model outperformed an unconstrained early-fusion approach, and its age residual was associated with incident disease across multiple organ systems. Arousal dynamics characterized comorbid insomnia and sleep apnoea as an intermediate phenotype skewed towards obstructive sleep apnoea, distinguished by prolonged post-arousal wakefulness. Rapid eye movement (REM) bout duration tracked preceding non-REM sleep more closely than intervening wakefulness. Transient-oscillation analysis identified a fast-sigma deficit and excess centrofrontal theta activity in narcolepsy type 1. Together, these findings connect sleep to disease risk, clinical classification, and its own regulation, and show how agentic AI can support large-scale, multimodal discovery.
Subjects:
Multiagent Systems (cs.MA)
Cite as:
arXiv:2607.25175 [cs.MA]
(or arXiv:2607.25175v2 [cs.MA] for this version)
https://doi.org/10.48550/arXiv.2607.25175
Focus to learn more
arXiv-issued DOI via DataCite
Submission history
From: Rahul Thapa [view email]
[v1] Tue, 28 Jul 2026 00:59:08 UTC (6,067 KB)
[v2] Wed, 29 Jul 2026 05:10:06 UTC (6,067 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Agentic AI-enabled discovery across large-scale sleep physiology, by Rahul Thapa and 12 other authors
view licenseCurrent browse context:
cs.MA
| < prev | next > |
| new | recent | 2026-07 |
Change to browse by:
References & Citations
export BibTeX citation Loading…
BibTeX formatted citation
×
loading…
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
- Author
- Venue
- Institution
- Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs.
| Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) |
mathjaxToggle();
We gratefully acknowledge support from our major funders, member institutions, , and all contributors.
About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)
Major funding support from
