It's not really exaggeration. It's doing something similar to abliteration, but more. First it finds the part of the weights that does a thing. But then the difference is, while abliteration deletes stuff, this is adding/changing things. It does this by doing semantic analysis of the weights, changing a little bit, see what happens. It's not super complex or advanced; it's essentially a form of statistical analysis and experiment.

This means we might not need to fine-tune with thousands of passes and tons of GPU. We could just tell a frontier model to edit some weights and iterate until there's better results. This could cut the cost of fine-tunes by 100x. Makes no difference to the normal end-user, but pretty huge improvement for LLM research and model tuning.