Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact across research communities and industry. Nevertheless, concerns about trust, safety, interpretability and accountability of AI were raised by influential thinkers. Many have identified the need for well-founded knowledge representation and reasoning to be integrated with deep learning and for sound explainability. Neural-symbolic computing has been an active area of research for many years seeking to bring together robust learning in neural networks with reasoning and explainability via symbolic representations for network models. In this paper, we relate recent and early research results in neurosymbolic AI with the objective of identifying the key ingredients of the next wave of AI systems. We focus on research that integrates in a principled way neural network-based learning with symbolic knowledge representation and logical reasoning. The insights provided by 20 years of neural-symbolic computing are shown to shed new light onto the increasingly prominent role of trust, safety, interpretability and accountability of AI. We also identify promising directions and challenges for the next decade of AI research from the perspective of neural-symbolic systems.
NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models
Department of Computer Science, AI Institute, University of South Carolina（南卡罗来纳大学计算机科学系，人工智能研究所）；Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校计算机科学与电气工程系）；Department of Computer Science and Engineering, The Ohio State University（俄亥俄州立大学计算机科学与工程系）
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Politecnico di Milano（米兰理工大学）；ELLIS Institute Finland（芬兰ELLIS研究所）；Åbo Akademi University（奥博 Akademi 大学）；Samsung AI（三星人工智能研究院）；Delft University of Technology（代尔夫特理工大学）；Stony Brook University（石溪大学）；Politecnico di Torino（都灵理工大学）；University of Stuttgart（斯图加特大学）；Graz University of Technology（格拉茨工业大学）；Lockheed Martin, Advanced Technology Labs（洛克希德·马丁公司先进技术实验室）；BrainCreators（BrainCreators公司）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Amsterdam（阿姆斯特丹大学）；University of Edinburgh（爱丁堡大学）；UCLA（加利福尼亚大学洛杉矶分校）
Neurosymbolic Retrievers for Retrieval-augmented Generation
Dept. of CSEE University of Maryland Baltimore County, Maryland, USA（电子工程系大学马里兰大学巴尔的摩县）；Dept. of CSEE University of Maryland Baltimore County, MD, USA（电子工程系大学马里兰大学巴尔的摩县）
We discuss how over the last 30 to 50 years, Artificial Intelligence (AI) systems that focused only on data have been handicapped, and how knowledge has been critical in developing smarter, intelligent, and more effective systems. In fact, the vast progress in AI can be viewed in terms of the three waves of AI as identified by DARPA. During the first wave, handcrafted knowledge has been at the center-piece, while during the second wave, the data-driven approaches supplanted knowledge. Now we see a strong role and resurgence of knowledge fueling major breakthroughs in the third wave of AI under…
Bridging LLMs and Symbolic Reasoning in Educational QA Systems: Insights from the XAI Challenge at IJCNN 2025
URA Research Group, Ho Chi Minh City University of Technology (HCMUT), Vietnam；Ho Chi Minh City International University (HCMIU), Vietnam；University of South-Eastern Norway, Norway；Japan Advanced Institute of Science；Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400 Talence, France；University of Naples Parthenope, Italy；VNU Information Technology Institute, Vietnam National University, Vietnam；Visual Intelligence Lab, School of Computer Science \& Insight Center for Data Analyitcs, University of Galway, Ireland；Sapienza University of Rome, Italy
Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson's Disease Screening in OCT
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）；State Key Laboratory of Ophthalmology, Optometry and Vision Science, Wenzhou Medical University（温州医科大学眼视光学与视觉科学国家重点实验室）；Coventry University（考文垂大学）；School of Computer Science, University of Nottingham Ningbo China（宁波诺丁汉大学计算机学院）；School of Ophthalmology and Optometry, Wenzhou Medical University（温州医科大学眼视光学院）；Department of Electronic and Information Engineering, Changchun University（长春大学电子与信息工程学院）
University of Michigan（密歇根大学）；Lehigh University（利哈伊大学）；Portland State University（波特兰州立大学）；Santa Fe Institute（圣塔菲研究所）
The history of AI has included several "waves" of ideas. The first wave, from the mid-1950s to the 1980s, focused on logic and symbolic hand-encoded representations of knowledge, the foundations of so-called "expert systems". The second wave, starting in the 1990s, focused on statistics and machine learning, in which, instead of hand-programming rules for behavior, programmers constructed "statistical learning algorithms" that could be trained on large datasets. In the most recent wave research in AI has largely focused on deep (i.e., many-layered) neural networks, which are loosely inspired b…
