Represent an original neural-network block and a proposed rewritten block as constrained optimization formulations over inputs and trainable parameters, then certify that the rewrite preserves feasibility and the ordering of losses over a bounded domain. This gives a compiler or pruning pipeline a formal reject/accept gate instead of relying only on numerical regression tests.
Replace a fixed weighted sum of normalized neural-network objectives with a differentiable fuzzy scalarizer that assigns every criterion to desirable, tolerable, and undesirable regions. Explicit output consequents turn these semantic classes into a scalar training loss, while localized memberships reduce flat plateaus and make the optimizer distinguish genuine preference minima from arbitrary ties.