Recent years have seen a flurry of generative nucleotide models, mostly of limited utility. In this paper, we use the functional representation of DNA as a complex, composite function on the plane of evolution to extend the theoretical unification of ecological and evolutionary change to the problem of synthetic DNA models. Through experiments on synthetic and real DNA sequences we show that next-token prediction decoder transformer architectures are limited in their capacity to learn such functions.
