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This paper presents a new framework for model merging that casts the problem as a convex quadratic program over residual updates, minimizing a squared-output calibration objective. It subsumes existing heuristic methods and provides a closed-form diagnostic to predict merge quality, showing consistent gains on language and vision benchmarks.