From a machine learning layman's point of view but with some experience with modeling, it's hard to see this as a discovery. Model decomposition and model reduc

From a machine learning layman's point of view but with some experience with modeling, it's hard to see this as a discovery. Model decomposition and model reduction techniques are very basic concepts in mathematical modeling, and decomposing models in modes with high participation is a very basic technique, which boils down to finding linear combinations of basis that are more expressive. This is even less surprising given LLMs are applied to models with a known hierarchical structure and symmetry. Can anyone say exactly what's novel in these findings? From a layman's point of view, this sounds like announcing the invention of gunpowder.