Asymmetry of verification highlights the disparity between the ease of verifying solutions and the complexity of solving problems, particularly in AI and reinforcement learning. The article discusses examples of tasks with varying degrees of verification difficulty and introduces the verifier's rule, which states that tasks that are easy to verify will be readily solved by AI. It also explores implications for future AI developments and connections to concepts like P = NP.

A model-agnostic verification-and-refinement pipeline was developed to improve the performance of large language models on International Mathematical Olympiad (IMO) problems, achieving an accuracy of approximately 85.7% on the 2025 competition. This approach significantly outperformed the baseline accuracies of the models Gemini 2.5 Pro, Grok-4, and GPT-5, highlighting the importance of effective methodologies alongside powerful base models for solving complex mathematical tasks.