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Autori principali: 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
Natura: Preprint
Pubblicazione: 2026
Soggetti:
Computation and Language
Accesso online:https://arxiv.org/abs/2602.06570
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Sommario:
  • 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.

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