The public summary says the amendment adds a generative model definition at 6.5.3.2.10. A generative model is described as producing new data from a distribution it has learned, and the definition is said to cover both probabilistic and non-probabilistic examples. The listed families include autoencoders, variational autoencoders, generative adversarial networks, normalising flows, transformers, diffusion models, Boltzmann machines, restricted Boltzmann machines, Hopfield networks and autoregressive models. The final wording is not held. The practical effect of the public record is that the framework now names the family and gives a buyer a defensible way to say whether a system is generative rather than merely predictive.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.