Objective-Channel Incompatibility in Model Fitting: A Structural Analysis of Multi-Objective Non-Transitivity

This technical note examines structural incompatibilities that arise in multi-objective model fitting when different objective channels impose partially non-transitive constraints on parameter optimization. The analysis shows that even when local fits remain internally consistent, simultaneous global optimization across heterogeneous objective channels may become structurally unstable or non-commutative. In such cases, increased precision does not guarantee convergence toward a unified solution, but may instead amplify residual migration between objective layers. The note does not propose new physical models, learning algorithms, or optimization procedures. Its purpose is strictly diagnostic: to characterize conditions under which multi-objective fitting ceases to admit globally coherent closure, despite stable local performance. The framework applies broadly to statistical inference, cosmological parameter fitting, machine learning evaluation, and other composite-model environments where partially overlapping objective structures coexist. No ontological or domain-specific claims are introduced.