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East Asian VLBI Network observations of active galactic nuclei jets: Imaging with KaVA plus Tianma plus Nanshan

by Cui, Yu-Zhu, Hada, Kazuhiro, Kino, Motoki, Sohn, Bong-Won, Park, Jongho, Ro, Hyun-Wook, Sawada-Satoh, Satoko, Jiang, Wu, Cui, Lang, Honma, Mareki, Shen, Zhi-Qiang, Tazaki, Fumie, An, Tao, Cho, Ilje, Zhao, Guang-Yao, Cheng, Xiao-Peng, Niinuma, Kotaro, Wajima, Kiyoaki, Zhang, Ying-Kang, Kawaguchi, Noriyuki, Algaba Marcos, Juan-Carlos, Koyama, Shoko, Hirota, Tomoya, Yonekura, Yoshinori, Sakai, Nobuyuki, Xia, , Jiang, Yong-Bin, Yu, Lin-Feng, Gou, Wei, Hwang, Ju-Yeon, Jiang, Yong-Chen, Sun, Yun-Xia, Jung, Dong-Kyu, Kim, Hyo-Ryoung, Kim, Jeong-Sook, Kobayashi, Hideyuki, Lee, Jee-Won, Lee, Jeong-Ae, Zhang, Hua, Li, Guang-Hui, Xu, Zhi-Qiang, Li, Peng, Oh, Jung-Hwan, Oh, Se-Jin, Oh, Chung-Sik, Oyama, Tomoaki, Roh, Duk-Gyoo, Shibata, Katsunori-M, Guo, Wen, Zhao, Rong-Bing, Zhong, Wei-Ye, Wang, Jin-Qing, Yang, Wen-Jun, Yan, Hao, Yeom, Jae-Hwan, Li, Bin, Li, Xiao-Fei, Yuan, Jian-Ping, Dong, Jian, Chen, Zhong, Akiyama, Kazunori, Asada, Keiichi, Byun, Do-Young, Hagiwara, Yoshiaki, Hodgson, Jeffrey, Jung, Tae-Hyun, Kim, Kee-Tae, Lee, Sang-Sung, Yi, Kunwoo, Liu, Qing-Hui, Liu, Xiang, Lu, Ru-Sen, Nakamura, Masanori, Trippe, Sascha, Wang, Na, Wang, Xue-Zheng, Zhang, Bo
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Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures – Benefits and Limitations

Oualid Bougzime ICB UMR 6303 CNRS, Université Marie et Louis Pasteur, UTBM, 90010 Belfort Cedex, France Samir Jabbar ICB UMR 6303 CNRS, Université Bourgogne Europe, 21078 Dijon, France Christophe Cruz ICB UMR 6303 CNRS, Université Bourgogne Europe, 21078 Dijon, France Frédéric Demoly ICB UMR 6303 CNRS, Université Marie et Louis Pasteur, UTBM, 90010 Belfort Cedex, France Institut universitaire de France (IUF), Paris, France

Abstract

Neuro-symbolic artificial intelligence (NSAI) represents a transformative approach in artificial intelligence (AI) by combining deep learning’s ability to handle large-scale and unstructured data with the structured reasoning of symbolic methods. By leveraging their complementary strengths, NSAI enhances generalization, reasoning, and scalability while addressing key challenges such as transparency and data efficiency. This paper systematically studies diverse NSAI architectures, highlighting their unique approaches to integrating neural and symbolic components. It examines the alignment of contemporary AI techniques such as retrieval-augmented generation, graph neural networks, reinforcement learning, and multi-agent systems with NSAI parad