Model Ecosystem Pruning via Joint Behavioral Coverage

An individual redundancy audit asks whether a model can be replaced by its peers. It leaves open whether several such models can be removed together, because their replacements may depend on one another. We study model ecosystem pruning: finding a common retained subset and convex replacement weights that reproduce every original endpoint's declared response within a tolerance. Joint behavioral coverage preserves a family of responses rather than the performance of one aggregate predictor. We characterize the fixed convex aggregations it protects and derive a dependency-aware composition condition that bounds error accumulation along replacement chains. Experiments use 20 open-weight LLM judges on 3,724 comparison pairs under seven prompt configurations. At a predeclared tolerance of 0.20, three judges pass individual removal tests, but their joint removal fails in all five data splits. Separate exact analyses require all 20 judges at tolerances up to 0.12 for the fitting objective. Coverage-based selection improves reconstruction over accuracy-based selection, and backward-initialized 2-swap reaches the empirical optimum in 59 of 60 small search instances. Under benchmark shift, refitting weights recovers most of the gap to selecting a new subset. Joint substitutability, rather than individual redundancy alone, determines which ecosystem removals preserve the declared behavior.