Reasoning System
Reasoning systems aim to build artificial intelligence capable of performing complex logical deductions and decision-making, mirroring human cognitive abilities. Current research emphasizes improving the reliability and efficiency of these systems, focusing on hybrid architectures that combine neural networks with symbolic reasoning methods, such as those employing chain-of-thought prompting, knowledge graphs, and various forms of logic-based reasoning. These advancements are crucial for enhancing the trustworthiness and explainability of AI systems across diverse applications, from medical diagnosis and legal reasoning to scientific discovery and robotics.
Papers
TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering
Jian Wu, Yicheng Xu, Yan Gao, Jian-Guang Lou, Börje F. Karlsson, Manabu Okumura
Newton-Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
Lingbing Guo, Weiqing Wang, Zhuo Chen, Ningyu Zhang, Zequn Sun, Yixuan Lai, Qiang Zhang, Huajun Chen