Cross-domain structural pattern
Loss scales inversely with depth due to functionally similar layers reducing error through ensemble averaging rather than compositional learning. Residual architecture bias combined with incompatible target functions produces inefficient yet robust regime. Improving efficiency requires architectural innovations encouraging compositional depth usage instead of redundant layer averaging.
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