Ethan Baron

Posterior inference over phylogenetic trees has depended on likelihoods, which become intractable once real processes such as insertions, deletions or site selection are modeled. This family of conditional generative models builds a tree step by step, bottom-up, middle-out or top-down, and is trained with a discrete diffusion objective; on real and synthetic data it recovers MCMC posteriors more closely than the prior simulation-based method Phyloformer 2, matches IQ-Tree on simple data, and is reliably more accurate on more realistic complex models.

Posterior inference over phylogenetic trees has depended on likelihoods, which become intractable once real processes such as insertions, deletions or site selection are modeled. This family of conditional generative models builds a tree step by step, bottom-up, middle-out or top-down, and is trained with a discrete diffusion objective; on real and synthetic data it recovers MCMC posteriors more closely than the prior simulation-based method Phyloformer 2, matches IQ-Tree on simple data, and is reliably more accurate on more realistic complex models.