01500nas a2200133   4500008004100000245008100041210006900122520097800191100001801169700001701187700002401204700002001228856011801248       2026                        eng d00aSymbolic Reasoning Methods in Neural Architectures and Large Language Models0 aSymbolic Reasoning Methods in Neural Architectures and Large Lan3 a<p>This chapter presents a comprehensive overview of contemporary approaches that integrate neural networks and large language models (LLMs) with classical symbolic reasoning. We review the evolution of methods designed to embed logical inference within neural architectures and explore recent advances in prompting strategies, hybrid reasoning frameworks, retrieval-augmented learning, and reinforcement-based reasoning optimization methods. We discuss the symbolic foundations of logical reasoning and then analyze how neural and LLM-based methods have progressively evolved to emulate, extend, and optimize symbolic reasoning across diverse tasks. Finally, we explore emerging neurosymbolic paradigms that unify neural and symbolic reasoning to achieve interpretable, scalable, and generalizable intelligence. Our analysis underscores the growing importance of neurosymbolic AI as a foundational direction for developing reliable and explainable reasoning systems.</p>
1 aBarua, Adrita1 aDas, Avishek1 aSarma, Moumita, Sen1 aHitzler, Pascal  uhttps://daselab.cs.ksu.edu/publications/symbolic-reasoning-methods-neural-architectures-and-large-language-models