arXiv AI By Cheng Gui, Yihua Shao, Lei Li, Jingcai Guo, Maolin Wang

Whether or Where? Tail Calibration Signals Finite-Budget Reward Hacking in Best-of-N Selection

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arXiv Machine Learning
Oct 19

Coverage Cliffs in Off-Policy Quantile Certification

The paper introduces TailCert, a method for certifying an upper tail quantile of a policy’s performance using logged contextual‑bandit data. It builds a PAC‑Bayes Bernstein lower‑confidence envelope over a threshold grid and all data‑dependent posteriors, returning an atom‑safe upper confidence bound on the selected posterior’s pooled p‑quantile. The authors provide finite‑sample guarantees, analyze sample complexity on a rare‑action class, and demonstrate the method’s effectiveness through simulations.

By Pauline Bourigault, Emmanuelle Bourigault
arXiv Statistics ML
5d ago

Confidence Is Not a Promise: A Reference Audit of Typed Decision Models

The paper evaluates how well typed decision models, which output probability distributions, actually provide reliable guarantees. By comparing open-weight Laya and Jev 1.13 across 190 evaluations, the study finds that point‑estimate thresholds frequently break promised accuracy, while a Beta lower‑bound rule and Learn‑then‑Test approach maintain guarantees more consistently. The work also explores calibration across languages, the impact of noise, data requirements for fine‑tuning, and the limitations of confidence in predicting outcomes or providing faithful explanations.

By Pedro Carvalho Brom