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The article discusses various methods for learning AI and AI agents in 2026, such as self-experimentation, free content, structured courses, and AI-assisted paths, and asks readers to describe their ideal learning setup.
This paper proposes a structured reinforcement learning framework for Bayesian persuasion in interactive driving, where a lead vehicle selectively reveals traffic information to guide connected vehicles. The method introduces MAPL and SQP algorithms, achieving 30% cost efficiency over existing methods.
QDSP is an interpretable structured learning framework for predicting death or cerebral palsy in very low birth weight infants, integrating Quota-guided Subspace Sampling and Differentiable-decision-guided Structure Perception. It outperforms baselines like XGBoost, TabNet, and TabPFN on a real cohort and external datasets, identifying clinically relevant predictors such as cPVL and birth weight.