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University of Hull
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Uncertainty in LLM outputs can be effectively quantified and managed in multi-agent actuarial systems, leading to more reliable risk assessments.
Synthetic images can be safely integrated into training processes, but only if their quality is rigorously assessed against real data.
Bayesian uncertainty propagation reveals critical failure points in multi-hop reasoning, outperforming traditional methods in complex scenarios.
Forget Fitzpatrick scores: lesion-skin contrast is the real culprit behind skin lesion segmentation errors, not overall skin tone.