The field of algorithm analysis and combinatorial optimization is moving towards a more nuanced understanding of how algorithms behave in practice. Researchers are developing new frameworks and techniques to analyze and interpret the behavior of algorithms, such as the simplex method and neural networks, in order to better understand their strengths and weaknesses. A key direction is the integration of machine learning and optimization techniques, with a focus on developing more transparent and interpretable models. Notable papers in this area include: Mechanistic Interpretability for Neural TSP Solvers, which applies sparse autoencoders to a Transformer-based TSP solver to discover interpretable features. Probing Neural Combinatorial Optimization Models, which introduces a novel probing tool to analyze the representations and decision rationale of neural combinatorial optimization models.