AdaptNC: Adaptive Nonconformity Scores for Uncertainty-Aware Autonomous Systems in Dynamic Environments

2026-02-02

AI Summary

In dynamic environments, distribution shifts often cause conventional conformal prediction methods to produce overly conservative and volumetrically inefficient uncertainty regions, struggling to balance coverage accuracy with tightness. To address this, this work proposes AdaptNC, a framework that, for the first time, enables joint online adaptation of nonconformity scoring functions and conformal thresholds. AdaptNC employs an adaptive reweighting mechanism to optimize the scoring function and incorporates a replay buffer strategy to mitigate coverage fluctuations induced by score switching. Evaluated on robotic benchmarks involving multi-agent policy switches, abrupt environmental changes, and sensor degradation, AdaptNC significantly reduces prediction region volume while rigorously maintaining the target coverage, outperforming existing approaches that adapt only the threshold.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Sequential Decision Making
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Rigorous uncertainty quantification is essential for the safe deployment of autonomous systems in unconstrained environments. Conformal Prediction (CP) provides a distribution-free framework for this task, yet its standard formulations rely on exchangeability assumptions that are violated by the distribution shifts inherent in real-world robotics. Existing online CP methods maintain target coverage by adaptively scaling the conformal threshold, but typically employ a static nonconformity score function. We show that this fixed geometry leads to highly conservative, volume-inefficient prediction regions when environments undergo structural shifts. To address this, we propose \textbf{AdaptNC}, a framework for the joint online adaptation of both the nonconformity score parameters and the conformal threshold. AdaptNC leverages an adaptive reweighting scheme to optimize score functions, and introduces a replay buffer mechanism to mitigate the coverage instability that occurs during score transitions. We evaluate AdaptNC on diverse robotic benchmarks involving multi-agent policy changes, environmental changes and sensor degradation. Our results demonstrate that AdaptNC significantly reduces prediction region volume compared to state-of-the-art threshold-only baselines while maintaining target coverage levels.

Problem

• Conformal Prediction
• distribution shifts
• nonconformity score
• autonomous systems
• uncertainty quantification

Innovation

• Adaptive Nonconformity Scores
• Conformal Prediction
• Online Adaptation
• Distribution Shift
• Uncertainty Quantification