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English(EN) InfiMed2: A Generalist Medical Multimodal Foundation Model from Contextual Evidence and Stability-Aware Supervision

InfiMed2模型在医疗基准测试中达到SOTA，超越Qwen3.5-9B

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一系列新的医疗多模态基础模型InfiMed2，有4B和27B参数两种尺寸。这些模型采用阶段感知数据处理设计，利用包含临床知识和生物医学视觉证据的55.68B token语料库。训练流程包括适配视觉编码器、构建广泛的医学知识以及专注于基于证据的数据混合。在监督微调方面，InfiMed2采用答案稳定性和正确性约束选择等技术来生成更具信息量的解释。经过强化学习进一步优化的4B模型在五个基准测试中的平均准确率为66.73%，优于Qwen3.5-9B模型；而27B模型达到了73.72%，为该领域的开放权重模型树立了新标杆。 AI

影响 为开放权重医疗多模态模型树立了新标杆，有望加速人工智能驱动的医疗保健领域的研究和开发。

排序理由 该条目描述了一篇详细介绍新型AI模型开发和性能的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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InfiMed2模型在医疗基准测试中达到SOTA，超越Qwen3.5-9B

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该条目描述了一篇详细介绍新型AI模型开发和性能的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Guanghao Zhu, Zeyu Liu, Zhitian Hou, Pengkai Wang, Zhijie Sang, Shuo Cai, Yang Yu, Yuanyi Wang, Yanggan Gu, Congkai Xie, Jianmin Wu, Hongxia Yang ·

    InfiMed2：一种来自上下文证据和稳定性感知监督的通用医疗多模态基础模型

    arXiv:2609.34798v2 Announce Type: replace Abstract: Recent medical multimodal models have benefited from larger corpora, broader modality coverage, and stronger reasoning-oriented training, yet effective data design across continued pretraining (CPT) and post-training remains cha…

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