Our workshop focuses on improving reasoning in neural networks, particularly the challenges and strategies for achieving System-2 reasoning in transformer-like models. The workshop addresses issues like distinguishing memorization from rule-based learning, understanding, syntactic generalization, and compositionality. The workshop also covers the importance of understanding how systematic models are in their decisions for AI safety, integrating neural networks with symbolic reasoning, and developing new architectures for enhanced reasoning capabilities. We have (tentatively) confirmed a distinguished group of speakers and panelists who are some of the most influential figures in recent literature on reasoning. Considering how important these topics are today and our distinguished lineup of speakers, we expect \textbf{more than 500 participants to the workshop}.