用新方法动态决定何时调用专家模型，既省成本又保安全。

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 the same internal representations as the safety probe that predicts the benefit of replacing the probe score with the expert score after an expert call. CTD calibrates a threshold on the DV signal using held-out data and multiple hypothesis testing, yielding finite-sample guarantees while optimising the complementary objective. Evaluated on four safety datasets using learned-attention probes, CTD improves over uncertainty-based delegation over much of the budget range, avoids unnecessary expert calls, and extracts positive-value subsets even from groups where the expert score is worse than the probe score on average. Ablations across four probe architectures show that these findings are not specific to any particular architecture.