The illusion of readiness: Stress testing large frontier models on multimodal medical benchmarks
Gu, Yu, Fu, Jingjing, Liu, Xiaodong, et al.
preprint2025
arXiv arXiv:2509.18234
View paper →
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Yu Gu
where models meet meaning .Making intelligence useful — in health, science, and beyond
About
I'm Yu Gu (also Aiden Gu; Chinese: 顾禹). I design large language models, multimodal foundation models, and agentic reasoning frameworks. Currently a Principal Applied Scientist at Microsoft Research and Health & Life Sciences, working at the intersection of AI, healthcare, and scientific discovery.
My work has been published in Nature/Science/Cell and major AI conferences (ICLR/NeurIPS/CVPR etc.). Previously, I co-founded an AI startup that grew to a Series C acquisition.
Featured
Selected highlights from recent research.
Magma: A Foundation Model for Multimodal AI Agents
Jianwei Yang, Reuben Tan, Qianhui Wu, et al.
preprint2025
CVPR
View paper →
Biomedjourney: Counterfactual biomedical image generation by instruction-learning from multimodal patient journeys
Gu, Yu, Yang, Jianwei, Usuyama, Naoto, et al.
preprint2024
arXiv arXiv:2310.10765
View paper →
A whole-slide foundation model for digital pathology from real-world data
Xu, Hanwen, Usuyama, Naoto, Bagga, Jaspreet, et al.
journal2024
Nature
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A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities
Zhao, Theodore*, Gu, Yu*, Yang, Jianwei, et al.
journal2024
Nature methods
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Domain-specific language model pretraining for biomedical natural language processing
Gu, Yu, Tinn, Robert, Cheng, Hao, et al.
journal2022
ACM
View paper →
Publications
Research papers, conference proceedings, and scholarly contributions.
X-reasoner: Towards generalizable reasoning across modalities and domains
Liu, Qianchu, Zhang, Sheng, ... et al.
arXiv arXiv:2505.03981 (2025)
preprint2025
Research
Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale
Wong, Cliff, Preston, Sam, ... et al.
arXiv arXiv:2502.00943 (2025)
preprint2025
Research
The illusion of readiness: Stress testing large frontier models on multimodal medical benchmarks
Gu, Yu, Fu, Jingjing, ... et al.
arXiv arXiv:2509.18234 (2025)
preprint2025
Research
QuantRad: Advancing Quantitative Reliability in Radiology Report Generation with Cascaded Decoders
Jin, Ying, Codella, Noel C, ... Hwang, Jenq-Neng
arXiv arXiv:x (2025)
preprint2025
Research
QRad: Enhancing Radiology Report Generation by Captioning-to-VQA Reframing
Jin, Ying, Codella, Noel C, ... Hwang, Jenq-Neng
NeurIPS - The Second Workshop on GenAI for Health: Potential, Trust, and Policy Compliance (2025)
conference2025
Research
Research
Areas of focus and ongoing directions.
Large Language Models
active2020–Present
Building and adapting domain-specific LLMs for biomedical NLP and real-world healthcare tasks, including pretraining, fine-tuning, and evaluation.
Publications:
Domain-specific language model pretraining for biomedical natural language processingFine-tuning large neural language models for biomedical natural language processingUniversalner: Targeted distillation from large language models for open named entity recognitionScaling clinical trial matching using large language models: a case study in oncology
LLMPretrainingBiomedical NLPEvaluation
Multimodal Foundation Models
active2022–Present
Designing and stress-testing vision-language foundation models across medical imaging and multimodal benchmarks at scale.
Publications:
Magma: A Foundation Model for Multimodal AI AgentsA foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalitiesA whole-slide foundation model for digital pathology from real-world dataBiomedjourney: Counterfactual biomedical image generation by instruction-learning from multimodal patient journeysBiomedParse: a biomedical foundation model for image parsing of everything everywhere all at onceThe illusion of readiness: Stress testing large frontier models on multimodal medical benchmarks
MultimodalFoundation ModelsVision-LanguageMedical Imaging
Reasoning
active2024–Present
Advancing generalizable reasoning across modalities and domains, and targeted distillation for robust information extraction.
Publications:
ReasoningGeneralizationDistillation
Agents
active2024–Present
Developing multimodal AI agents and workflows that orchestrate tools and reasoning to act in complex real-world settings.
Publications:
AgentsTool UsePlanningMultimodal
News & Updates
Recent publications, presentations, and milestones across research and collaborations.
📄
The Illusion of Readiness: Stress Testing Large Frontier Models on Multimodal Medical Benchmarks
September 18, 2025The Illusion of Readiness: Stress Testing Large Frontier Models on Multimodal Medical Benchmarks. In just the first week, the paper has sparked meaningful conversation — highlighted by Eric Topol, shared across Health AI communities, and prompting outreach from Science and Health AI leaders looking for what comes next.
Related: The illusion of readiness: Stress testing large frontier models on multimodal medical benchmarks →🎤
Announcing QRad
September 2, 2025We're excited to announce QRad, accepted to NeurIPS - The Second Workshop on GenAI for Health: Potential, Trust, and Policy Compliance, a new project that enhances radiology report generation by captioning-to-VQA reframing.
Related: QRad: Enhancing Radiology Report Generation by Captioning-to-VQA Reframing →🎤
BiomedParse topped the CVPR 2025 3D Biomedical Image Segmentation Challenge! Our model delivered best-in-class performance across 42 tasks spanning CT, MRI, PET, ultrasound, and microscopy. check out the announcement
Related: A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities →7 more updates
Connect
Models, teams, and a dream — often in that order.