Threshold-Aware Conformal Routing (TACR) is a method that decides whether to use a fast surrogate model or a full simulator based on whether the surrogate’s conformal interval lies entirely on one side of a fixed decision threshold. TACR learns input‑dependent interval widths that are tighter near the threshold, preserving distribution‑free coverage while reducing the number of simulator calls. Experiments on scientific and engineering datasets show TACR cuts simulator deferrals by 14‑75% compared to standard conformal prediction, and by an additional 10‑24% over a variant without threshold‑aware weighting.
arXiv:2606. 15217v1 Announce Type: cross Abstract: Offline model-based optimization (MBO) proposes candidates by optimizing a surrogate trained on a fixed historical dataset.
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.
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.
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.
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.
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.
arXiv:2606. 09923v1 Announce Type: cross Abstract: Neural operators such as the Fourier Neural Operator (FNO) have emerged as powerful surrogates for solving partial differential equations (PDEs), achieving speedups of several orders of magnitude over traditional numerical solvers.
arXiv:2606. 02876v1 Announce Type: new Abstract: Randomized smoothing (RS) uses a smoothed classifier to provide architecture-agnostic certificates of $\ell_2$ classification robustness, but its dependence on per-input Monte Carlo (MC) sampling undermines its use in real-time systems.
arXiv:2606. 10734v1 Announce Type: new Abstract: Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model training with the conformal goal of producing efficient (i.
arXiv:2608.23638v1 Announce Type: cross Abstract: Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration...
CopRoute introduces a dependence-aware conformal routing method for language models that orders models by the number of new queries each correctly answers beyond those already covered, selecting the minimal cutoff that satisfies a target all‑miss risk. This approach accounts for model failure dependencies—such as shared training data or architecture—that traditional risk‑controlled policies ignore. Experiments on RouterBench and RouterEval show CopRoute achieves fewer certified inference calls than state‑of‑the‑art methods, reducing calls by up to 79 % in some cases while maintaining the same risk level.
arXiv:2512. 23075v5 Announce Type: replace-cross Abstract: Policy gradient methods for Large Language Models optimize a policy $\pi_\theta$ via a surrogate objective computed from samples of a rollout policy $\pi_{\text{roll}}$.