A 30-year-old open problem in complexity theory resolved by GPT-5.6 Pro (zenodo.org)

In a remarkable breakthrough, researchers have utilized the advanced capabilities of GPT-5.6 Pro to resolve a 30-year-old open problem in complexity theory, significantly impacting the AI and machine learning community. This long-standing question revolved around the ability to separate complexity classes, particularly P versus NP, which has implications for cryptography, optimization problems, and algorithmic efficiency. The use of a generative model to tackle such a foundational challenge highlights the growing intersection of AI technology and theoretical computer science. The implications of this resolution are profound, as it not only enhances our understanding of computational limits but also opens new avenues for practical applications of AI in solving complex, real-world problems. Key technical details shared indicate that GPT-5.6 Pro employed innovative strategies in symbolic reasoning and pattern recognition, which enabled it to navigate and provide insights into the intricate relationships between various complexity classes. This advancement showcases the potential of AI to contribute to theoretical domains, ultimately influencing future research trajectories in both AI and complexity theory.