Conformal Selective Acting (CSA) fills a gap in conformal methods by providing per-round, pathwise-valid selective risk bounds for adaptive RLVR LLM streams under predictable updates and isotonic calibration.
Selective Conformal Risk Control
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abstract
Reliable uncertainty quantification is essential for deploying machine learning systems in high-stakes domains. Conformal prediction provides distribution-free coverage guarantees but often produces overly large prediction sets, limiting its practical utility. To address this issue, we propose \textit{Selective Conformal Risk Control} (SCRC), a unified framework that integrates conformal prediction with selective classification. The framework formulates uncertainty control as a two-stage problem: the first stage selects confident samples for prediction, and the second stage applies conformal risk control on the selected subset to construct calibrated prediction sets. We develop two algorithms under this framework. The first, SCRC-T, preserves exchangeability by computing thresholds jointly over calibration and test samples, offering exact finite-sample guarantees. The second, SCRC-I, is a calibration-only variant that provides PAC-style probabilistic guarantees while being more computational efficient. Experiments on two public datasets show that both methods achieve the target coverage and risk levels, with nearly identical performance, while SCRC-I exhibits slightly more conservative risk control but superior computational practicality. Our results demonstrate that selective conformal risk control offers an effective and efficient path toward compact, reliable uncertainty quantification.
years
2026 5representative citing papers
A joint finite-sample certificate for adaptive selective conformal risk control that treats selected risk as a ratio and couples empirical-Bernstein, Clopper-Pearson, and closeness bounds.
PCAA introduces a runtime-neutral governance model for heterogeneous agent systems based on action certificates with five checkpoints, externality awareness, and enforceability classes.
A step-function score transformation I_w = w·1{s≥w} reduces the estimated-coverage gap in Backward Conformal Prediction from about 4.2% to 1.1% on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
CCSS-IX is a context-conditioned structured simulator for wastewater digital twins that uses adaptive expert mixing and self-falsifying conformal decision rules to reduce unsafe actions while maintaining low prediction error on real plant and benchmark data.
citing papers explorer
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Conformal Selective Acting: Anytime-Valid Risk Control for RLVR-Trained LLMs
Conformal Selective Acting (CSA) fills a gap in conformal methods by providing per-round, pathwise-valid selective risk bounds for adaptive RLVR LLM streams under predictable updates and isotonic calibration.
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A Joint Finite-Sample Certificate for Adaptive Selective Conformal Risk Control
A joint finite-sample certificate for adaptive selective conformal risk control that treats selected risk as a ratio and couples empirical-Bernstein, Clopper-Pearson, and closeness bounds.
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Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems
PCAA introduces a runtime-neutral governance model for heterogeneous agent systems based on action certificates with five checkpoints, externality awareness, and enforceability classes.
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Improving Backward Conformal Prediction via Non-Conformity Score Transformation
A step-function score transformation I_w = w·1{s≥w} reduces the estimated-coverage gap in Backward Conformal Prediction from about 4.2% to 1.1% on CIFAR-10, CIFAR-100, and Tiny-ImageNet.
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Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support
CCSS-IX is a context-conditioned structured simulator for wastewater digital twins that uses adaptive expert mixing and self-falsifying conformal decision rules to reduce unsafe actions while maintaining low prediction error on real plant and benchmark data.