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该论文提出了一种针对小型临床表格数据的混合注意力网络+逻辑回归（HAN+LR）集成模型，用于在鲜胚移植前预测IVF/ICSI活产，在616个周期的数据集上取得0.710的AUC-ROC，优于TabPFN和文本序列化语言模型基线，并提出对滋养层细胞差异化加权的胚胎形态编码方法。
This paper introduces the Multi-Scale Feature Attention Network (MSFAN), a deep learning architecture for classifying 12 types of polymers using THz Dual-Comb Spectroscopy, achieving 85.2% accuracy and outperforming state-of-the-art models.
Cactus-Compute releases Needle, a 26M parameter distilled model from Gemini 3.1, using a pure attention architecture optimized for on-device inference and local fine-tuning.