The field of graph algorithms and Boolean function learning is witnessing significant developments, with a focus on improving the efficiency and robustness of existing methods. Researchers are exploring new approaches to construct compact data structures for approximate distance oracles, enabling fast query answering times even in the presence of faulty edges. Additionally, there is a growing interest in learning Boolean functions from random solutions, with notable progress in reducing sample complexity. Theoretical bounds on query complexity for testing Boolean function monotonicity are also being refined, providing a better understanding of the fundamental limits of these problems. Noteworthy papers include: Fault-Tolerant Approximate Distance Oracles with a Source Set, which presents innovative constructions for fault-tolerant approximate distance oracles. Learning CNF formulas from uniform random solutions in the local lemma regime, which significantly improves sample complexity for learning Boolean Markov random fields.