A recent study published on arXiv explores the problem of learning k-CNF formulas from uniform random solutions, focusing on the local lemma regime. The authors revisit Valiant's algorithm, demonstrating its effectiveness in this specific learning context. Their approach leverages uniform random solutions as the data source and is grounded in a theoretical framework that connects to Boolean Markov random fields. The findings suggest that under certain conditions, Valiant's algorithm can successfully learn these formulas, marking a positive step forward in the field. Moreover, the study highlights the potential for significant advancements in understanding and applying Boolean Markov random fields. This work contributes to ongoing research efforts in machine learning and theoretical computer science, particularly in domains where learning from random solutions is relevant. The article situates itself within a broader context of recent developments in AI and learning theory.