Fudan University（复旦大学）

Multi-hop Reasoning via Early Knowledge Alignment

通过早期知识对齐实现多跳推理

Yuxin Wang, Shicheng Fang, Bo Wang, Qi Luo, Xuanjing Huang, Yining Zheng, Xipeng Qiu

机构 * Computer Science, Fudan University（复旦大学计算机科学系） ； Institute of Modern Languages and Linguistics, Fudan University（复旦大学现代语言与语言学研究所） ； Shanghai SII（上海SII） 纠错

AI总结 EKA通过早期知识对齐提升多跳推理效率，增强检索精度和系统性能。

Comments 16 pages

Test-time generative augmentation for medical image segmentation

测试时生成增强用于医学图像分割

Xiao Ma, Yuhui Tao, Zetian Zhang, Yuhan Zhang, Xi Wang, Sheng Zhang, Zexuan Ji, Yizhe Zhang, Qiang Chen, Guang Yang

机构 * organization= School of Computer Science ； Engineering, Nanjing University of Science ； organization= Bioengineering Department ； Imperial-X, Imperial College London , city= London , postcode= W12 7SL , country= UK ； organization= Digital Medical Research Center, School of Basic Medical Sciences, Fudan University , city= Shanghai , country= China ； organization= Shanghai Key Laboratory of MICCAI , city= Shanghai , country= China ； organization= School of Biomedical Engineering, Shenzhen University , city= Shenzhen , country= China ； organization= Department of Computer Science ； Engineering, The Hong Kong University of Science ； Engineering, The Chinese University of Hong Kong , city= Hong Kong , country= China ； Lung Institute, Imperial College London , city= London , postcode= SW7 2AZ , country= UK ； organization= Cardiovascular Research Centre, Royal Brompton Hospital , city= London , postcode= SW3 6NP , country= UK ； organization= School of Biomedical Engineering \& Imaging Sciences, King's College London , city= London , postcode= WC2R 2LS , country= UK 纠错

AI总结 本研究提出TTGA方法，通过生成模型在测试时增强医学图像分割，提升分割精度并提供像素级误差估计。

Comments Accepted for publication in Medical Image Analysis (MedIA). Finalized version. Vol. 109, March 2026

Journal ref Medical Image Analysis, Vol. 109, 103902, 2026