MIT researchers have achieved a significant breakthrough in artificial intelligence problem-solving using a technique called test-time training (TTT). By applying TTT to large language models, they reached an unprecedented 61.9% accuracy on the challenging Abstraction and Reasoning Corpus (ARC) benchmark, matching average human performance. This advancement demonstrates the potential of purely neuronal approaches to complex reasoning tasks, challenging assumptions about the necessity of symbolic processing in AI. The research highlights the effectiveness of adapting model parameters during inference, potentially paving the way for more flexible and capable AI systems across various domains.