Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selection

The paper argues that partition-level scores used in decomposed algorithm selection overestimate what a deployable selector can achieve. It introduces the deployment‑fidelity gap, G(R), which measures the difference between partition‑level and end‑to‑end utility, and derives conditions under which a partition choice is truly deployment‑optimal. Experiments on five public benchmarks show that every decomposed pipeline has a positive gap, with some cases where the partition advantage disappears or reverses when evaluated end‑to‑end, highlighting the need to report both partition and end‑to‑end scores.

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