Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks

• URL: http://arxiv.org/abs/2607.25877v1
• Date: Tue, 28 Jul 2026 15:39:43 GMT
• Title: Runtime Uncertainty Monitoring for LLM-Based Multi-Agent Systems Using Bayesian Networks
• Abstract summary: This paper investigates how multi-agent systems can support actuarial risk modelling.<n>The main contribution is a novel approach to uncertainty propagation using token-level log-probabilities and a Bayesian Network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates how multi-agent systems (MAS)-based on large language models (LLMs) can support actuarial risk modelling, with a particular focus on uncertainty quantification. Actuarial workflows represent a high-stakes decision-support setting where unreliable outputs may lead to incorrect risk assessment, unfair pricing, and regulatory non-compliance. To address uncertainty introduced by the probabilistic nature of LLMs and dependencies between agents, a multi-agent framework is proposed in which specialised agents perform data preparation, modelling, review, and explanation tasks under a central hub. The main contribution is a novel approach to uncertainty propagation using token-level log-probabilities and a Bayesian Network. Importantly, log probabilities are not treated as direct probabilities of correctness or task success. Instead, length-normalised log-probability summaries are transformed into calibrated task-level confidence estimates before incorporation into the Bayesian Network. Results show that the framework reproduces baseline actuarial performance while providing additional insight into workflow stability and runtime uncertainty propagation.

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