01457nas a2200145   4500008004100000245005500041210005400096260001300150520097800163100001801141700001701159700002401176700002001200856009101220       2026                        eng d00aReasoning in Large Language Models: RAG and Beyond0 aReasoning in Large Language Models RAG and Beyond  bElsevier3 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/reasoning-large-language-models-rag-and-beyond