The approach uses the structure of the data as the training signal rather than a separate label. A model can be asked to predict a masked token, the next item in a sequence or a missing part of an input, and the answer is already in the data. This is the approach behind most large language models and many generative systems, which is why the amendment reaches it. The public summary of the draft says the note at 7.5 is expanded to warn that training a model on output from generative AI, self-supervised on synthetic data, can reduce performance over successive iterations, a phenomenon called model collapse that can worsen with repeated cycles.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.