We generated 13 phylogenetic trees using our input MSA dataset for the latent space phylogenetic analysis. Each tree consisted of approximately 100 randomly sampled leaf nodes from the preprocessed MSA. Using our fully automated in-house ancestral sequence reconstruction tool FireProtASR , we obtained an average of 10 levels in each phylogenetic tree. To explore the relationship between the tree branches and the latent space, we mapped each individual branch, along with its reconstructed ancestral sequences, into the latent space. We then measured the correlation between the depth of a node (i.e., the distance between the root and the node) and the distance of the corresponding ancestral node's latent embedding from the origin of the latent space, following an approach similar to . In addition, we sought to gain insights into the reconstruction strategy by evaluating the direction in which the tree branches were mapped in the latent space. Specifically, we calculated the vector representing the first principal component of each branch and computed the dot product with the vector pointing from the embedding of the leaf node sequence to the origin of the latent space. This analysis allowed us to compare the trajectory directions of the different tree branches, and we reasoned that the straight evolutionary strategy would generate ancestral-like sequences.