Conformal Predictive Monitoring for Multi-Modal Scenarios

• URL: http://arxiv.org/abs/2509.01338v1
• Date: Mon, 01 Sep 2025 10:19:00 GMT
• Title: Conformal Predictive Monitoring for Multi-Modal Scenarios
• Authors: Francesca Cairoli, Luca Bortolussi, Jyotirmoy V. Deshmukh, Lars Lindemann, Nicola Paoletti,
• Abstract summary: We present GenQPM, a method that approximates the probabilistic and multi-modal system dynamics without requiring explicit model access.<n>We demonstrate the effectiveness of GenQPM on a benchmark of agent navigation and autonomous driving tasks.
• Score: 6.726718522458479
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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