1、2025-8-19QuarkMed Medical Foundation ModelTechnical ReportAo Li1,Bin Yan1,Bingfeng Cai1,Chenxi Li1,Cunzhong Zhao1,Fugen Yao1,Gaoqiang Liu1,Guanjun Jiang1,Jian Xu1,Liang Dong1,Liansheng Sun1,Rongshen Zhang1,Xiaolei Gui1,Xin Liu1,Xin Shang1,Yao Wu1,YuCao1,Zhenxin Ma1and Zhuang Jia11Quark Medical Team,

2、Alibaba GroupRecent advancements in large language models have significantly accelerated their adoption in health-care applications,including AI-powered medical consultations,diagnostic report assistance,and medicalsearch tools.However,medical tasks often demand highly specialized knowledge,professi

3、onal accuracy,and customization capabilities,necessitating a robust and reliable foundation model.QuarkMed ad-dresses these needs by leveraging curated medical data processing,medical-content Retrieval-AugmentedGeneration(RAG),and a large-scale,verifiable reinforcement learning pipeline to develop a

4、 high-performance medical foundation model.The model achieved 70%accuracy on the Chinese Medi-cal Licensing Examination,demonstrating strong generalization across diverse medical benchmarks.QuarkMed offers a powerful yet versatile personal medical AI solution,already serving over millions ofusers at

5、 https:/.1.IntroductionThe advent of large language models(LLMs)has marked a pivotal moment in artificial intelligence,demonstrat-ing remarkable capabilities in understanding and generating human-like text across a multitude of domains.This progress has catalyzed significant interest in their applic

6、ation to specialized fields,particularly medicine,where they hold the potential to revolutionize medical information retrieval,enhance early diagnostic accuracy,and support personalized health care requirements.However,the medical domain presents unique and formidable challenges 47.Unlike general-do