Jul 2026· Lecture notes in computer science· pp. 736-748· 0 citations· 24 references
Computer Science
TL;DR
This paper investigates how multi-agent systems (MAS-based on large language models) can support actuarial risk modelling, with a particular focus on uncertainty quantification, and proposes a novel approach to uncertainty propagation using token-level log-probabilities and a Bayesian Network.
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.
UQ methods developed for single generations should be revalidated at the trajectory level, with careful attention to the consistency measurement, aggregator choice, and computational budget.
A semantic-uncertainty-guided orchestration approach, HASSUM is introduced as a general framework for uncertainty-aware coordination in multi-agent systems and suggests that semantic uncertainty is a practical and general-purpose signal for improving robustness and trustworthiness in agentic AI systems.
John Knowlton, Aritra Guha, Risto Miikkulainen· 0 citations
Experimental results show that AgentLocate consistently outperforms existing failure localization methods in identifying both responsible agents and failure steps, while remaining efficient in terms of token usage and running time.
Yu Xia, Anjun Gao, Yueyang Quan et al.· 0 citations
PropUQ-MAS is proposed, an error propagation-aware UQ framework that represents MAS execution as a communication-structured graph and estimates each step's reliability by combining local uncertainty with uncertainty inherited from upstream messages.
Yaokun Liu, Yifan Liu, D. Zhang et al.· 0 citations
Inspired by Shapiro et al. [74], we consider a stochastic optimal control (SOC) and Markov decision process (MDP) under simultaneous epistemic and aleatoric uncertainties using Bayesian composite risk (BCR) measures. The proposed BCR-SOC/MDP model evaluates the risk of stagewise cost via a two-layer framework: the inner risk measure tackles aleatoric uncertainty conditional on a latent environment parameter, while the outer risk measure deals with the epistemic uncertainty of the inner risk under the Bayesian posterior. The resulting time-varying risk evaluation induced by Bayesian updating enables an information-adaptive risk-sensitive decision framework. Unlike [74], our policies are allowed to depend explicitly on the posterior belief, reflecting that accumulated information about epistemic uncertainty can influence the assessment of future aleatoric uncertainty and, consequently, the decision maker’s actions [79]. The new modeling paradigm subsumes several classical SOC/MDP formulations, including risk-averse and distributionally robust SOC/MDPs as well as partially observed and Bayes-adaptive MDPs, and generates so-called preference robust SOC/MDP models. Moreover, we derive conditions under which the BCR-SOC/MDP model is well-defined, show that finite-horizon BCR-SOC/MDP models can be solved via dynamic programming, and extend the analysis to the infinite-horizon case. Under standard conditions, we establish asymptotic convergence of the optimal values and optimal policies as data accumulate, and provide quantitative error bounds for several representative classes of risk measures. To enhance computational tractability, we develop a hyper-parameter discretization approach for the posterior belief space. Finally, we carry out numerical tests on a spread betting problem and an inventory control problem, demonstrating the effectiveness of the proposed model and numerical schemes.
This lecture note develops, from first principles and within a single decision-theoretic setting, the link between the decision objective and the knowledge of an agent and the form of uncertainty representation that is sufficient to act optimally.
Osvaldo Simeone· arXiv.org· 3 citations
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