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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.06570 |
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| _version_ | 1866910014107549696 |
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| author | M3 Team Dou, Chengfeng Yang, Fan Li, Fei Jia, Jiyuan Ju, Qiang Wang, Shuai Li, Tianpeng Zeng, Xiangrong Zhou, Yijie Zhang, Hongda Tai, Jinyang Sun, Linzhuang Guo, Peidong Mo, Yichuan Wang, Xiaochuan Cui, Hengfu Zhang, Zhishou |
| author_facet | M3 Team Dou, Chengfeng Yang, Fan Li, Fei Jia, Jiyuan Ju, Qiang Wang, Shuai Li, Tianpeng Zeng, Xiangrong Zhou, Yijie Zhang, Hongda Tai, Jinyang Sun, Linzhuang Guo, Peidong Mo, Yichuan Wang, Xiaochuan Cui, Hengfu Zhang, Zhishou |
| contents | We introduce Baichuan-M3, a medical-enhanced large language model engineered to shift the paradigm from passive question-answering to active, clinical-grade decision support. Addressing the limitations of existing systems in open-ended consultations, Baichuan-M3 utilizes a specialized training pipeline to model the systematic workflow of a physician. Key capabilities include: (i) proactive information acquisition to resolve ambiguity; (ii) long-horizon reasoning that unifies scattered evidence into coherent diagnoses; and (iii) adaptive hallucination suppression to ensure factual reliability. Empirical evaluations demonstrate that Baichuan-M3 achieves state-of-the-art results on HealthBench, the newly introduced HealthBench-Hallu and ScanBench, significantly outperforming GPT-5.2 in clinical inquiry, advisory and safety. The models are publicly available at https://huggingface.co/collections/baichuan-inc/baichuan-m3. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06570 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making M3 Team Dou, Chengfeng Yang, Fan Li, Fei Jia, Jiyuan Ju, Qiang Wang, Shuai Li, Tianpeng Zeng, Xiangrong Zhou, Yijie Zhang, Hongda Tai, Jinyang Sun, Linzhuang Guo, Peidong Mo, Yichuan Wang, Xiaochuan Cui, Hengfu Zhang, Zhishou Computation and Language We introduce Baichuan-M3, a medical-enhanced large language model engineered to shift the paradigm from passive question-answering to active, clinical-grade decision support. Addressing the limitations of existing systems in open-ended consultations, Baichuan-M3 utilizes a specialized training pipeline to model the systematic workflow of a physician. Key capabilities include: (i) proactive information acquisition to resolve ambiguity; (ii) long-horizon reasoning that unifies scattered evidence into coherent diagnoses; and (iii) adaptive hallucination suppression to ensure factual reliability. Empirical evaluations demonstrate that Baichuan-M3 achieves state-of-the-art results on HealthBench, the newly introduced HealthBench-Hallu and ScanBench, significantly outperforming GPT-5.2 in clinical inquiry, advisory and safety. The models are publicly available at https://huggingface.co/collections/baichuan-inc/baichuan-m3. |
| title | Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.06570 |