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This research addresses a critical reliability gap in Large Language Model multi-agent systems by introducing a framework that tracks how errors propagate through agent communication graphs. By combining local uncertainty estimates with inherited risks from upstream messages, PropUQ-MAS significantly improves the accuracy of reliability assessments in complex, interconnected workflows. This advancement is essential for deploying robust autonomous agent teams where isolated failures can cascade into systemic breakdowns.
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PropUQ-MAS: Propagation-Aware Uncertainty Quantification for LLM Multi-Agent Systems

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.22130v1 Announce Type: cross Abstract: LLM-based multi-agent systems (MAS) solve complex tasks through communication among role-specialized agents. However, inter-agent dependencies introduce reliability risks beyond isolated agent failures. For instance, errors in intermediate messages could be inherited and amplified by downstream agents. Existing uncertainty quantification (UQ) methods mainly target isolated responses or single-agent reasoning, and therefore fail to capture uncertainty propagation in MAS. To this end, we propose PropUQ-MAS, 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. Extensive experiments demonstrate that PropUQ-MAS consistently improves UQ in MAS, with average relative gains of +6.10% in AUROC and +47.58% in PRR.

Abstract:LLM-based multi-agent systems (MAS) solve complex tasks through communication among role-specialized agents. However, inter-agent dependencies introduce reliability risks beyond isolated agent failures. For instance, errors in intermediate messages could be inherited and amplified by downstream agents. Existing uncertainty quantification (UQ) methods mainly target isolated responses or single-agent reasoning, and therefore fail to capture uncertainty propagation in MAS. To this end, we propose PropUQ-MAS, 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. Extensive experiments demonstrate that PropUQ-MAS consistently improves UQ in MAS, with average relative gains of +6.10% in AUROC and +47.58% in PRR.

Multiagent Systems (cs.MA); Computation and Language (cs.CL)

arXiv:2608.22130 [cs.MA]

(or arXiv:2608.22130v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2608.22130 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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