A new study published in Nature reveals that researchers have successfully trained a large language model to perform complex reasoning tasks by integrating a novel neuro-symbolic architecture. The system, named CogNet, combines neural network pattern recognition with structured symbolic logic, allowing it to solve multi-step problems in mathematics and code debugging that traditionally stymie pure neural approaches. Initial benchmarks show CogNet outperforming existing models by 15-20% on specialized reasoning tests, though the authors caution that the system remains narrow in scope and requires significant computational resources. The development marks a step toward more reliable and interpretable AI systems capable of logical deduction. Read the full article at https://technologyreview.com/2024/05/cognet-reasoning-ai.