MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs
Authors: name: Baorong Shi, name: Bo Cui, name: Boyuan Jiang, name: Deli Yu, name: Fang Qian, name: Haihua Yang, name: Huichao Wang, name: Jiale Chen, name: Jianfei Pan, name: Jieqiong Cao, name: Jinghao Lin, name: Kai Wu, name: Lin Yang, name: Shengsheng Yao, name: Tao Chen, name: Xiaojun Xiao, name: Xiaozhong Ji, name: Xu Wang, name: Yijun He, name: Zhixiong Yang
Organization: ByteDance

We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world clinical applications. MedXIAOHE achieves state-of-the-art performance across diverse medical benchmarks and surpasses leading closed-source multimodal systems on multiple capabilities. To achieve this, we propose an entity-aware continual pretraining framework that organizes heterogeneous medical corpora to broaden knowledge coverage and reduce long-tail gaps (e.g., rare diseases). For medical expert-level reasoning and interaction, MedXIAOHE incorporates diverse medical reasoning patterns via reinforcement learning and tool-augmented agentic training, enabling multi-step diagnostic reasoning with verifiable decision traces. To improve reliability in real-world use, MedXIAOHE integrates user-preference rubrics, evidence-grounded reasoning, and low-hallucination long-form report generation, with improved adherence to medical instructions. We release this report to document our practical design choices, scaling insights, and evaluation framework, hoping to inspire further research.