@inbook {995,
	title = {Symbolic Reasoning Methods in Neural Architectures and Large Language Models},
	booktitle = {Neuro-Symbolic AI: Bridging the Gap Between Neural Networks and Symbolic Reasoning},
	year = {2026},
	chapter = {5},
	abstract = {<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>
},
	author = {Adrita Barua and Avishek Das and Moumita Sen Sarma and Pascal Hitzler}
}
