Going back to the demonstrated power of LES over GES in the proof-of-principle ML2 model based on the κMP2 reference calls for developing robust energy density generators at higher levels of theory. This will be a crucial objective in our future work, which can be supported by several existing wavefunction-based frameworks . This will enable us to further capitalize on the lessons from this work by integrating ML2 and MLS2 from the following planned steps. First, with energy densities at a higher level of theory, we can implement LES-based training for MLS2, further enhancing its accuracy and transferability. Second, by replacing e κMP2 c,os (r) and e κMP2 c,ss (r) of Eq. 11 with their ML2-based surrogates, we can bypass the need for these two more expensive quantities in practical calculations after the resulting ML DFA is trained. Finally, the demonstrated use of LES opens up possibilities for future exploration, including its applicability beyond DFAs, such as in ML force fields.