Calibrate-Then-Delegate: Safety Monitoring with Risk and Budget Guarantees via Model Cascades
Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be escalated to a more expensive expert. Existing cascades delegate based on probe uncertainty, but uncertainty is a poor proxy for the utility of an expert call, as it ignores whether the expert would actually improve the prediction. To address this problem, we introduce Calibrate-Then-Delegate (CTD), a model-cascade approach that provides probabilistic guarantees on either computation cost or safety performance while enabling instance-level (streaming) decisions. CTD builds on a novel delegation value (DV) probe, a lightweight model operating on t...
| Authors | Edoardo Pona, Milad Kazemi, Mehran Hosseini et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 April 2026 |
| Last updated at source | 21 August 2026 |
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