The paper introduces a distribution‑free certification layer that can be applied to any crash‑severity prediction model without modifying the model itself. It provides guarantees for ordinal outcomes, per‑class validity, transfer of coverage to unobserved severities, and one‑sided certificates under deployment shift, all grounded in a functional of the true data law. The framework is evaluated on 5.2 million Texas records, demonstrating a model‑independent lower bound on set width for vulnerable road users and is released as an open‑source package with theorem‑level tests.
By Amir Rafe, Subasish Das
arXiv:2606. 29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by 7.
By Varun Kotte
arXiv:2608. 07913v1 Announce Type: cross Abstract: Selective-risk certificates promise that accepted outputs meet a declared error target.
By Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma
arXiv:2609. 18622v1 Announce Type: new Abstract: Classifiers can make identical predictions yet require labels to compare their selective performance: confidence ranks weight the same errors differently.
By Tetsuji Kuboyama
arXiv:2606. 29484v1 Announce Type: cross Abstract: Modern deepfake detectors are rarely consumed as bare classifiers.
By Md Anas Biswas
The paper introduces a label‑free method called AURCC for selecting the best foundational model for medical image classification when the target domain lacks labels. AURCC uses a pseudo‑label discrepancy computed by the SUDO framework to score models without fine‑tuning. Experiments on chest X‑ray data across three inter‑hospital shifts show that AURCC closely matches the true model ranking, outperforming simple source‑accuracy baselines especially when source data are limited.
By Juan I\~naki Larrea, Lucas Mansilla, Enzo Ferrante
In high-stakes healthcare applications, machine learning models are frequently trained on data from one patient population and deployed on another, creating a distribution shift that degrades both acc...
arXiv:2606. 04035v1 Announce Type: cross Abstract: We present a systematic study of domain-dependent safety behavior in open-weight LLMs: 7 standardized experiments across 7 ethical domains, testing 5 models (12B--70B) in 4,200 interactions with dual-judge validation.
By Zacharie Bugaud
Classifiers can make identical predictions yet require labels to compare their selective performance: confidence ranks weight the same errors differently. We quantify this requirement for the area und...
arXiv:2609.39934v1 Announce Type: cross
Abstract: Checkpoint selection in domain generalization often relies on source-validation accuracy, yet the selected checkpoint need not provide reliable proba...
By Jinshi Liu, Jiahao Li, Pan Liu, Yanfeng Li, Rui Qian, Zhao Tong, Yue Sun, Tao Tan
The paper introduces a method to certify selective prediction in machine learning systems by computing the availability of safety gates through exact-binomial inversion and dynamic programming. It demonstrates that a truth-informed planner can significantly improve mean coverage over naive approaches, and that reallocating error budgets further enhances coverage across diverse applications such as LLM tool‑calling, content moderation, lesion classification, and recommendation. The study highlights the importance of planning and finite‑sample estimation in ensuring reliable, granular deployment of selective predictors.
By Parivesh Priye, Yufeng Wang, Haibin Ling, Michael Chaykowsky
The study examines whether accuracy gaps between source and target tasks can certify the failure of scalar recalibration maps for large language model judges. Across thirteen judges, two generators, eight domains, and 1,176 transfers, the accuracy gap only provides a weak lower bound on target calibration error and can predict opposite outcomes. Even with a finite‑sample lower certificate, the method shows low power (0.13) and does not reliably indicate when recalibration will fail.
By Fariya Afrin, Ibne Farabi Shihab