Proactive Routing to Interpretable Surrogates with Distribution-Free Safety Guarantees

• URL: http://arxiv.org/abs/2603.14623v1
• Date: Sun, 15 Mar 2026 21:38:43 GMT
• Title: Proactive Routing to Interpretable Surrogates with Distribution-Free Safety Guarantees
• Abstract summary: We study proactive (input-based) routing, in which a lightweight gate selects the model before either run.<n>We derive a feasibility condition linking safe routing to the base safe rate and risk budget, along with sufficient AUC thresholds ensuring that feasible routing exists.
• Score: 0.5518378568494161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model routing determines whether to use an accurate black-box model or a simpler surrogate that approximates it at lower cost or greater interpretability. In deployment settings, practitioners often wish to restrict surrogate use to inputs where its degradation relative to a reference model is controlled. We study proactive (input-based) routing, in which a lightweight gate selects the model before either runs, enabling distribution-free control of the fraction of routed inputs whose degradation exceeds a tolerance τ. The gate is trained to distinguish safe from unsafe inputs, and a routing threshold is chosen via Clopper-Pearson conformal calibration on a held-out set, guaranteeing that the routed-set violation rate is at most α with probability 1-δ. We derive a feasibility condition linking safe routing to the base safe rate π and risk budget α, along with sufficient AUC thresholds ensuring that feasible routing exists. Across 35 OpenML datasets and multiple black-box model families, gate-based conformal routing maintains controlled violation while achieving substantially higher coverage than regression conformal and naive baselines. We further show that probabilistic calibration primarily affects routing efficiency rather than distribution-free validity.

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