Calibration and Risk-Budgeted Decisions with System One Models

Abstract:LLM workflows and agents rely on a set of binary decisions like allow/block an input, or discard/retain a retrieved document. When human deferral is unavailable, uncertain cases must still receive an action. We study System One decision scores in this setting by defaulting to a conservative fallback when uncertain, and controlling the false omission risk within the subset of inputs assigned the utility-maximizing action. Across six datasets covering moderation, safety, and reranking, we compare general System One models with task baselines and a local open-weights fine-tuned System One model. We show that naive baselines for threshold selection exceed the target risk in roughly half of settings with defined test risk, compared with 7.8\% for Learn Then Test (LTT), which leverages finite-sample statistical guarantees. Our results show why relying on a probability-like interface is insufficient for deployment decisions and how risk-budgeted selection trades useful action against observed error.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2610.19811 [stat.ML]

(or arXiv:2610.19811v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2610.19811 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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