Alignment Monitoring

2025-07-28

AI Summary

This paper addresses the challenge of dynamically aligning probabilistic system models with their actual runtime behavior. Methodologically, it proposes a lightweight, real-time alignment monitoring framework featuring an online-computable alignment score, novel differential alignment monitoring (to detect local misalignment trends), and weighted alignment monitoring (to support task-specific customization and model comparison). The monitor is built upon sequential prediction, integrating probabilistic forecasts, distributional similarity metrics (e.g., Wasserstein distance), and high-confidence interval estimation for runtime assessment. Experiments on the PRISM benchmark demonstrate that the monitor incurs low memory overhead, responds rapidly, and effectively detects model–reality misalignment with high accuracy and strong real-time performance. This work establishes a new paradigm for trustworthy verification of probabilistic systems.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• 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

Formal verification provides assurances that a probabilistic system satisfies its specification--conditioned on the system model being aligned with reality. We propose alignment monitoring to watch that this assumption is justified. We consider a probabilistic model well aligned if it accurately predicts the behaviour of an uncertain system in advance. An alignment score measures this by quantifying the similarity between the model's predicted and the system's (unknown) actual distributions. An alignment monitor observes the system at runtime; at each point in time it uses the current state and the model to predict the next state. After the next state is observed, the monitor updates the verdict, which is a high-probability interval estimate for the true alignment score. We utilize tools from sequential forecasting to construct our alignment monitors. Besides a monitor for measuring the expected alignment score, we introduce a differential alignment monitor, designed for comparing two models, and a weighted alignment monitor, which permits task-specific alignment monitoring. We evaluate our monitors experimentally on the PRISM benchmark suite. They are fast, memory-efficient, and detect misalignment early.

Problem

• Monitors alignment between probabilistic models and real systems
• Measures similarity between predicted and actual system distributions
• Evaluates model performance using sequential forecasting techniques

Innovation

• Alignment monitoring ensures model-reality consistency
• Differential monitor compares two models effectively
• Weighted monitor enables task-specific alignment checks