Recent research shows that while modern AI can excel at many games, it often struggles with tasks that require understanding underlying mathematical functions. In games like Nim, current AI training approaches fail because they cannot grasp concepts like parity or other implicit rules. When winning requires this kind of intuitive reasoning, AI performance drops significantly.

These findings highlight a fundamental weakness of today’s models and suggest that future AI systems will need better mechanisms for logical reasoning and function comprehension to successfully tackle such challenges.