The field of automated reasoning and knowledge graph querying is rapidly advancing with innovative solutions to long-standing challenges. Researchers are developing more efficient and scalable methods for analyzing complex dependencies and querying large knowledge graphs. One notable direction is the use of pseudo-Boolean encodings and novel compilation methods to enable faster and more accurate reasoning. Another area of focus is the development of approximate algorithms and symbolic search frameworks to tackle the complexity of cyclic queries and large knowledge graphs. Additionally, new semiring semantics and weighted rewriting techniques are being explored to analyze and prove properties of abstract reduction systems. Noteworthy papers in this area include: Pseudo-Boolean d-DNNF Compilation for Expressive Feature Modeling Constructs, which proposes a novel pseudo-Boolean encoding and compilation method for feature models. Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering, which introduces a symbolic search framework to reduce the computational load of neuro-symbolic search. Other notable papers, such as Weighted Rewriting: Semiring Semantics for Abstract Reduction Systems and A Fine-Grained Complexity View on Propositional Abduction -- Algorithms and Lower Bounds, are also making significant contributions to the field.