6 citations · 21 across the 12 of their papers we have counts for
12 papers
Learning transferable human physiology from two million hours of sleep with SleepFM-2
Rahul Thapa, Christopher Sun, William Theodor Lehn-Schioler +14
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 mo…
Agentic AI-enabled discovery across large-scale sleep physiology
Rahul Thapa, Umaer Hanif, Robin Guillard +10
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 s…
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Structured Scaling of AI Discovery Across Diverse Scientific Domains
Haotian Ye, Haowei Lin, Jingyi Tang +30
Scientific discovery often requires many cycles of proposing, testing, and refining candidate solutions. Language models can increasingly participate in these loops, but simply gen…
Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Models
Magnus Ruud Kjaer, Rahul Thapa, Gauri Ganjoo +7
Polysomnography (PSG), the gold standard test for sleep analysis, generates vast amounts of multimodal clinical data, presenting an opportunity to leverage self-supervised represen…
Disentangling Reasoning and Knowledge in Medical Large Language Models
Rahul Thapa, Qingyang Wu, Kevin Wu +11
Medical reasoning in large language models (LLMs) aims to emulate clinicians' diagnostic thinking, but current benchmarks such as MedQA-USMLE, MedMCQA, and PubMedQA often mix reaso…
How Well Can General Vision-Language Models Learn Medicine By Watching Public Educational Videos?
Rahul Thapa, Andrew Li, Qingyang Wu +8
Publicly available biomedical videos, such as those on YouTube, serve as valuable educational resources for medical students. Unlike standard machine learning datasets, these video…
MedCaseReasoning: Evaluating and learning diagnostic reasoning from clinical case reports
Kevin Wu, Eric Wu, Rahul Thapa +7
Doctors and patients alike increasingly use Large Language Models (LLMs) to diagnose clinical cases. However, unlike domains such as math or coding, where correctness can be object…
SMIR: Efficient Synthetic Data Pipeline To Improve Multi-Image Reasoning
Andrew Li, Rahul Thapa, Rahul Chalamala +3
Vision-Language Models (VLMs) excel at understanding single images, aided by high-quality instruction datasets. However, multi-image reasoning remains underexplored in the open-sou…
OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning
Pan Lu, Bowen Chen, Sheng Liu +3
Solving complex reasoning tasks may involve visual understanding, domain knowledge retrieval, numerical calculation, and multi-step reasoning. Existing methods augment large langua…
Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language Models
Rahul Thapa, Kezhen Chen, Ian Covert +4
Recent advances in vision-language models (VLMs) have demonstrated the advantages of processing images at higher resolutions and utilizing multi-crop features to preserve native re…
SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals
Rahul Thapa, Bryan He, Magnus Ruud Kjaer +4
Sleep is a complex physiological process evaluated through various modalities recording electrical brain, cardiac, and respiratory activities. We curate a large polysomnography dat…