MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs
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.
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Baorong Shi
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Bo Cui
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Boyuan Jiang
6992818050fb2c0be47838b5
Deli Yu
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Fang Qian
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Haihua Yang
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Huichao Wang
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Jiale Chen
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Jianfei Pan
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Jieqiong Cao
6992818050fb2c0be47838bc
Jinghao Lin
6992818050fb2c0be47838bd
Kai Wu
6992818050fb2c0be47838be
Lin Yang
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Shengsheng Yao
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Tao Chen
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Xiaojun Xiao
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Xiaozhong Ji
claimed_verified
2026-02-16T15:36:45.325Z
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/avatars/4af5a47d78bca525c7ec985a390408a4.svg
Xiaozhong Ji
xiaozhongji
user
xiaozhongji
6992818050fb2c0be47838c3
Xu Wang
6992818050fb2c0be47838c4
Yijun He
6992818050fb2c0be47838c5
Zhixiong Yang
MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs
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.
MedXIAOHE is a medical vision-language foundation model that enhances clinical understanding through entity-aware continual pretraining, reinforcement learning, and tool-augmented agentic training for reliable diagnostic reasoning.
vision-language foundation model entity-aware continual pretraining heterogeneous medical corpora long-tail gaps reinforcement learning tool-augmented agentic training multi-step diagnostic reasoning evidence-grounded reasoning hallucination reduction medical instruction adherence