The research area is witnessing a significant shift towards integrating advanced computational techniques with traditional combinatorial methods to address complex query complexities. A notable trend is the development of frameworks that leverage matrix multiplication and information theory to generalize and enhance existing notions of query width, leading to improved algorithm complexities for Boolean queries. Additionally, there is a growing focus on output-sensitive evaluation methods for regular path queries, which aim to optimize performance based on the size of the query output. The introduction of new models, such as the u-query model, is expanding the theoretical understanding of query complexity by incorporating unknowns and establishing relationships with standard query models. Furthermore, advancements in parameterised complexity and graph representations are providing novel approaches to consistent query answering and repair counting, with potential implications for practical implementations. The resilience of regular path queries is also being explored, with a classification of complexity emerging for different language classes, highlighting both tractable and hard cases. Overall, the field is progressing towards more efficient, generalized, and theoretically grounded solutions for complex query problems.