Conformal Predictive Monitoring for Multi-Modal Scenarios

2025/09/01 by Francesca Cairoli, Cairoli, Francesca, Luca Bortolussi +7
Computer Science · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.2509.01338

openalex publication_date 2025/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider the problem of quantitative predictive monitoring (QPM) of stochastic systems, i.e., predicting at runtime the degree of satisfaction of a desired temporal logic property from the current state of the system. Since computational efficiency is key to enable timely intervention against predicted violations, several state-of-the-art QPM approaches rely on fast machine-learning surrogates to provide prediction intervals for the satisfaction values, using conformal inference to offer statistical guarantees. However, these QPM methods suffer when the monitored agent exhibits multi-modal dynamics, whereby certain modes may yield high satisfaction values while others critically violate the property. Existing QPM methods are mode-agnostic and so would yield overly conservative and uninformative intervals that lack meaningful mode-specific satisfaction information. To address this problem, we present GenQPM, a method that leverages deep generative models, specifically score-based diffusion models, to reliably approximate the probabilistic and multi-modal system dynamics without requiring explicit model access. GenQPM employs a mode classifier to partition the predicted trajectories by dynamical mode. For each mode, we then apply conformal inference to produce statistically valid, mode-specific prediction intervals. We demonstrate the effectiveness of GenQPM on a benchmark of agent navigation and autonomous driving tasks, resulting in prediction intervals that are significantly more informative (less conservative) than mode-agnostic baselines.

Citations

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Related

• Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes
• Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees
• Multi-Agent Reachability Calibration with Conformal Prediction
• Conformal Prediction Intervals for Markov Decision Process Trajectories
• Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model