The field of artificial intelligence is witnessing a significant shift towards the development of more robust and formalized foundations. Recent research has focused on establishing a stronger connection between logic and machine learning, with a particular emphasis on providing a semantic framework for deep learning. This has led to the development of novel logical systems and the exploration of new areas of application, such as access security and explorability in pushdown automata. Notably, researchers are making progress in characterizing the expressive power of various logical systems, including the development of new axiomatizations and the investigation of the size of interpolants in modal logics. Some particularly noteworthy papers in this area include: The paper on Neurosymbolic Deep Learning Semantics, which introduces a framework for semantic encoding and characterizes the common ingredients of various existing approaches. The paper on Explorability in Pushdown Automata, which studies explorability as a measure of nondeterminism in pushdown automata and shows that it induces an infinite hierarchy of expressiveness.