Reliable Fusion of Conflicting Experts

Abstract:We study the problem of aggregating opinions from multiple black-box experts in noisy, conflict-prone settings where expert reliability varies across inputs. Static aggregation methods, such as majority voting, fail to capture this variability and often yield unreliable outcomes under disagreement. We propose a tractable, probabilistic-circuit-based fusion framework that dynamically combines expert responses using context-specific credibility estimates, enabling principled and reliable reasoning. The framework is agnostic to the underlying experts and does not require access to their internal representations or any retraining. We empirically validate our approach on multiple-choice question answering tasks using multiple LLMs as experts, comparing against individual models and static ensemble baselines. Our method consistently improves predictive performance and produces more reliable decisions under conflict, highlighting the effectiveness of context-aware credibility modeling for robust multi-expert fusion.

Machine Learning (cs.LG)

arXiv:2609.27913 [cs.LG]

(or arXiv:2609.27913v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.27913 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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