AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work argues that answers are not necessary for identifying ambiguity: they are often redundant, add avoidable cost, and can mislead through epistemic leakage, and suggests that ambiguity-induced aleatoric uncertainty is better estimated from the interpretation space than from the response space.
ADAS is proposed, a training-free reranking rule that leaves the base sampler's stopping rule unchanged and greedily discounts each token-wise confidence score according to its attention to already selected positions, weighted by their prediction uncertainty.
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