Is Escalation Worth It? On the Depth of LLM Cascades
LLM cascades, in which a cheap model defers to an expensive one on low-confidence queries, are widely used to reduce inference cost. Given a pool of models, a practitioner must decide how many models to include and where to set each deferral threshold. We derive first-order optimality conditions showing that, at an optimum, the ratio of expected accuracy gain to expected downstream cost is equal across deferral boundaries. A local search based on these conditions closely matches exhaustive search. We also derive an identity that decomposes the accuracy gain of score-based escalation over random escalation into two AUROC terms. Across five benchmarks and nine deferral scores, with model se...
| Authors | Dylan Bouchard |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 5 October 2026 |
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