AINeutralarXiv – CS AI · 10h ago6/10
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Effective Explanations Support Planning Under Uncertainty
Researchers propose a computational model that evaluates explanations by converting them into executable action plans through large language models and planning agents. Across four experiments with 1,200 explanations, higher-scored explanations correlate with improved navigation performance and user helpfulness judgments, demonstrating that explanation quality can be measured by practical outcomes under uncertainty.