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Articles by Chenyu You
Addressing benchmarking gaps in large language models for health and medicine with dynamic red-teaming
Abstract Large language models (LLMs) are increasingly used to answer health-related questions and support healthcare workflows, yet evidence for their safety still relies heavily on static benchmarks that can rapidly become obsolete or be optimized against. Here we introduce a Dynamic, Automatic and Systematic (DAS) red-teaming audit framework that continuously stress-tests LLMs for health across four safety-critical axes: robustness, privacy, bias and hallucination.
Imaging foundation model for universal enhancement of non-ideal measurement CT
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Abstract Non-ideal measurement computed tomography (CT) employs suboptimal imaging protocols to expand CT applications. However, the resulting trade-offs degrade image quality, limiting clinical acceptability. Although deep learning methods have been used to enhance non-ideal measurement CT images, their reliance on large training datasets and limited generalizability across diverse settings hinder practical use.
By Rongjun Ge, Yuxin Liu, Zhan Wu, Shangwen Yang, Yuan Gao Verified, Chenyu You, Ge Wang, Shuo Li, Yuting He, Yang Chen
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Nature
Verified
Hematopoietic stem cells activate a latent differentiation pathway to facilitate recovery after 5-fluorouracil-induced myeloablation
Main text (Developmental Cell 61, ▪▪▪–▪▪▪; May 13, 2026) In the originally published version of this article, the bottom-right panel of Figure 6F was inadvertently duplicated from the adjacent panel during figure assembly. The error has now been corrected online. The authors regret this error and apologize for any confusion it may have caused. Figure 6F Statistical inference of HSPC differentiation kinetics (corrected) Figure 6F Statistical inference of HSPC differentiation kinetics (original)
Hematopoietic stem cells activate a latent differentiation pathway to facilitate recovery after 5-fluorouracil-induced myeloablation
Keywords hematopoietic stem cell stress hematopoiesis lineage tracing 5-fluorouracil Get full text access Log in, subscribe or purchase for full access. References 1. Shizuru, J.A. ∙ Negrin, R.S. ∙ Weissman, I.L. Hematopoietic stem and progenitor cells: clinical and preclinical regeneration of the hematolymphoid system Annu. Rev. Med. 2005; 56:509-538 2. Cheng, H. ∙ Zheng, Z. ∙ Cheng, T. New paradigms on hematopoietic stem cell differentiation Protein Cell. 2020; 11:34-44 3. Seita, J.
[2601.01014] Geometric and Dynamic Scaling in Deep Transformers
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SlideGen: Collaborative Multimodal Agents for Scientific Slide Generation
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.
QuantAgent: Price-Driven Multi-Agent LLMs for High-Frequency Trading
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.
Tokenization Constraints in LLMs: A Study of Symbolic and Arithmetic Reasoning Limits
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Why Does Your CoT Prompt (Not) Work? Theoretical Analysis of Prompt Space Complexity, its Interaction with Answer Space During CoT Reasoning with LLMs: A Recurrent Perspective
[Submitted on 13 Mar 2025] Title:Why Does Your CoT Prompt (Not) Work? Theoretical Analysis of Prompt Space Complexity, its Interaction with Answer Space During CoT Reasoning with LLMs: A Recurrent Perspective View a PDF of the paper titled Why Does Your CoT Prompt (Not) Work?
Clonal analysis of murine HSC self-renewal and differentiation in native hematopoiesis
Abstract Hematopoietic stem cells (HSC) maintain lifelong hematopoiesis. However, in current clonal analyses with unbiased "barcoding" approaches, steady-state hematopoietic clones in young and middle-aged adults rarely have detectable HSCs, which precludes comprehensive interrogation of HSC clonal behaviors.
Segment anything in medical images - Nature Communications
Abstract Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation.
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP
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A medical multimodal large language model for future pandemics - npj Digital Medicine
Abstract Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available labels. However, few such labels exist for rare diseases (e.g., new pandemics).
[2206.03111] Medical Image Registration via Neural Fields
[Submitted on 7 Jun 2022 ( v1 ), last revised 10 Oct 2024 (this version, v3)] Title:Medical Image Registration via Neural Fields Authors:Shanlin Sun , Kun Han , Chenyu You , Hao Tang , Deying Kong , Junayed Naushad , Xiangyi Yan , Haoyu Ma , Pooya Khosravi , James S.
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