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arXiv Machine Learning By Pranuthi Tenali, Sahil Sidheekh, Saurabh Mathur, Vijayalakshmi Saravanan, Erik Blasch, Kristian Kersting, Sriraam Natarajan

Reliable Fusion of Conflicting Experts

Read the original on arXiv Machine Learning →

The paper introduces a probabilistic‑circuit framework for fusing opinions from multiple black‑box experts in noisy, conflict‑prone environments. It dynamically assigns context‑specific credibility to each expert, allowing reliable aggregation without needing access to their internal models or retraining. Experiments on multiple‑choice question answering with large language models show that this method outperforms individual models and static ensemble baselines, consistently improving predictive accuracy and decision reliability under disagreement.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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