Where Does Harmful Replacement Come From? Coverage and Switching in Frozen Model Routing

Adding a frozen expert gives a router another possible answer, but also another way to replace a correct incumbent answer with an incorrect one. We introduce a diagnostic protocol for this trade-off when two new candidates are available. Holding a fixed support budget of 64 candidate evaluations per intervention, a pair-fixed policy uses one candidate on every eligible request, whereas a per-request policy chooses between the two candidates separately for each request. Across two development assets, per-request choice increases net accuracy gain relative to the incumbent by 0.182 percentage points (pp) compared with pair-fixed choice, while increasing harmful-replacement mass by 0.201 pp. An exact request-wise decomposition attributes the difference to coverage, where one policy returns a candidate and the other retains the incumbent, and switching, where both return candidates but return different ones. A controlled $2\times 2$experiment shows that these decisions interact: expanding candidate use can help or hurt depending on which candidate is returned on the added requests. A held-out text-model asset and an independently constructed score reproduce the interaction and the sign reversal of the coverage effect, whereas switching-harm reduction is not stable across assets or score constructions.