We develop efficient algorithms for non-uniformly sampling over directed acyclic graph structures, and use these along with results from online learning, to develop efficient algorithms for agnostically-learning Bayes nets in KL divergence.

LLMs show a utilitarian boost in moral judgment when reasoning in groups, similar to humans, but driven by distinct model-specific mechanisms, highlighting key considerations for multi-agent alignment and moral reasoning.