The article "Co-Evolving Complexity: An Adversarial Framework for Automatic MARL Curricula" addresses the ongoing challenges in creating general-purpose intelligent agents, underscoring the necessity for more complex and diverse training environments (F1). It notes that despite significant advancements in models and datasets (F3), the reliance on hand-crafted environments imposes limitations that can restrict agents' ability to acquire robust and generalizable skills (F2). This highlights a critical gap in current artificial intelligence research, where improvements in model architecture and data availability alone may not suffice without corresponding enhancements in the complexity of training scenarios. The discussion aligns with recent contextual analyses from related arXiv publications, which also emphasize the importance of evolving training environments to better prepare agents for real-world applications. Overall, the article suggests that addressing these environmental constraints is essential for advancing the development of more adaptable and capable AI systems.