2026 年 10 月 16 日 星期五THE DAILY EDITION
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AI for Science

从数学到生命科学,观察人工智能如何改变研究。

共 16 篇报道时间均为北京时间
研究前沿

视觉持续学习方法能否迁移到蛋白质研究?

arXiv:2610.18879v1 Announce Type: new Abstract: Protein function annotation operates over an expanding label space, making class-incremental learning (CIL) a natural framework for incorporating newly discovered functions without full retraining. Yet existing CIL methods have been developed largely in vision and have not been systematically evaluated over frozen protein representations.

研究前沿

VETNET:为 AI 驱动的兽医研究构建基准数据集

arXiv:2610.14149v1 Announce Type: new Abstract: VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and veterinary medicine through standardized, high-quality datasets and multi-institutional collaboration across academic and non-academic sectors.

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