The field of temporal network analysis and phylogenetic inference is witnessing significant developments, with a focus on improving the efficiency and scalability of algorithms for analyzing dynamic networks and reconstructing phylogenies. Researchers are exploring novel approaches to temporal network analysis, including the use of core times and adaptive weight vectors, to enhance the accuracy and robustness of phylogenetic inference. Notably, advancements in trie-building algorithms are enabling high-throughput phyloanalysis of large-scale digital evolution experiments. Furthermore, revisited algorithms for temporal k-core queries are achieving substantial speedups, making them highly scalable for long-term temporal analysis. Noteworthy papers include: Improving MSA Estimation through Adaptive Weight Vectors in MOEA/D, which introduces a novel variant of MOEA/D that adaptively adjusts subproblem weight vectors to improve the exploration-exploitation trade-off. A Scalable Trie Building Algorithm for High-Throughput Phyloanalysis of Wafer-Scale Digital Evolution Experiments, which details an improved trie-building algorithm that achieves a 300-fold speedup versus existing state-of-the-art approaches. Temporal k-Core Query, Revisited, which introduces a novel algorithm CoreT that dynamically records the earliest timestamp at which each vertex or edge enters a k-core, enabling substantial pruning of redundant computations.