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This work asks whether a learned per-query gate can skip the reranker where it will not help, using only features available before the LLM call with an executable fallback with an executable fallback.
It is proved that no single ANN score feature achieves best predictive performance across all failure regimes; the dominant feature differs between datasets (query length on MuSiQue, hop-1 concentration on HoVer), and a constructive witness pair shows each is necessary in one regime and non-contributory in the other.
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