Conformal Cascade: Distribution-Free Accuracy Guarantees for Multi-Tier LLM Inference
Large language model (LLM) cascades reduce inference cost by routing easy queries to a small model and deferring hard queries to a larger one. Production cascades govern this deferral through a confidence threshold, but LLM confidence scores are miscalibrated, the threshold must be tuned per model pair and per domain, and no setting yields a formal bound on cascade accuracy. We introduce \textbf{Conformal Cascade} (CC), a multi-tier inference framework that uses conformal prediction set size as the deferral rule: accept when the calibrated set collapses to a single answer, defer otherwise. The procedure delivers a distribution-free, finite-sample accuracy guarantee. By a per-tier union bo...
| Authors | Yifan Dou, Shikan Lian, Shibo Li |
|---|---|
| Primary category | cs.LG |
| Published | 27 July 2026 |
| Last updated at source | 31 July 2026 |
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