arXiv:2606. 15217v1 Announce Type: cross Abstract: Offline model-based optimization (MBO) proposes candidates by optimizing a surrogate trained on a fixed historical dataset.
By Seungjin Choi
The paper introduces a selection‑safe conformal early‑exit method that calibrates a single split‑conformal quantile using a terminal‑path score derived from prefix maxima. This approach guarantees coverage for any data‑dependent stopping policy that preserves terminal‑set containment, without requiring an α/L correction. Empirical results on eight encoder backbones and CIFAR‑100 show that pathwise selection achieves higher macro coverage (0.901 vs. 0.814) with smaller set sizes and lower latency fractions compared to naive layer‑wise selection, and remains robust to deployment‑time budget changes.
By Adria Binte Habib, Ibne Farabi Shihab
The paper introduces Adaptive Conformal Inference, an online calibration method that replaces static quantile estimation in hardware‑aware neural architecture search (NAS). By providing distribution‑free guarantees that at most a fraction δ of candidates are wrongly discarded, the method maintains steerable coverage across arbitrary sequences of reinforcement‑learning proposals. Experiments on three neural‑network families show that it tracks requested coverage levels within ~10⁻³ while pruning 25–50% of evaluations without accuracy loss, outperforming static calibration and a Gaussian‑process baseline.
By Pedro Brandimarte, Nerea Aranjuelo, Marcos Nieto, Oihana Otaegui
arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.
By Rahul Vaze
arXiv:2608. 01130v1 Announce Type: new Abstract: A broad range of models face the mismatch where they are updated through trajectory losses but are evaluated by downstream task reward.
By Yuyang Shen
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