01698nas a2200169   4500008004100000245007400041210006900115260001300184520108200197100002101279700002401300700001701324700003001341700002601371700002001397856011101417       2026                        eng d00aNeurosymbolic Hidden Neuron Analysis in Convolutional Neural Networks0 aNeurosymbolic Hidden Neuron Analysis in Convolutional Neural Net  bElsevier3 a<p class="rtejustify">This tutorial introduces a step-by-step, deductive pipeline for making the inner workings of neural networks more transparent by assigning human-understandable concepts to hidden neuron activations. The approach automatically maps neuron behavior to symbolic concepts drawn from structured knowledge sources and attaches an error margin to each label, providing a measure of confidence in its precision. While demonstrated in detail on the ADE20k scene dataset---including single-concept neurons, multiple neurons contributing to the same concept, and multi-concept neurons---the method is also applied to the SUN2012 dataset and adapted for a text classification task, highlighting its generalizability across modalities. The chapter is designed to be practical and educational, focusing on a replicable methodology that readers can adapt to varied applications. Through worked examples, visualizations, and evaluation strategies, the tutorial offers a clear, reusable framework for concept-based neuron analysis in both vision and language models.</p>
1 aDalal, Abhilekha1 aSarma, Moumita, Sen1 aDas, Avishek1 aAkkamahadevi, Samatha, E.1 aVasserman, Eugene, Y.1 aHitzler, Pascal  uhttps://daselab.cs.ksu.edu/publications/neurosymbolic-hidden-neuron-analysis-convolutional-neural-networks01457nas 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