The field of computational complexity and probabilistic models is moving in a direction that emphasizes the development of new techniques and tools for analyzing and understanding complex systems. Recent research has focused on the study of algebraic pseudorandomness, internal effectful forcing, and the counting power of transformers with no positional encodings. These advances have led to a deeper understanding of the limitations and possibilities of various computational models, including arithmetic circuits, automata, and Markov chains. Notably, the development of new logical relations and fibrational models has enabled the construction of more expressive and flexible models for reasoning about probabilistic systems. Furthermore, the study of energy games, Galois energy games, and probabilistic bisimilarity has led to new insights into the behavior of complex systems and the development of more efficient algorithms for analyzing them. Overall, the field is experiencing a surge of innovation, driven by the intersection of theoretical computer science, mathematics, and probability theory. Some noteworthy papers in this area include: NoPE: The Counting Power of Transformers with No Positional Encodings, which shows that transformers without positional encodings can still express complex counting properties. Robust Probabilistic Bisimilarity for Labelled Markov Chains, which introduces a new notion of robust probabilistic bisimilarity that ensures continuity of the probabilistic bisimilarity distance function.