“Traditional AI systems process information like trains running on fixed tracks—they follow predetermined pathways from input to output. The LRM, in contrast, operates more like a car that can choose its route based on traffic conditions. When it encounters a problem, it creates a dynamic network of potential pathways through which information can flow. This process closely resembles what cognitive scientists call the “generate and test” strategy in human problem-solving. Research by Pat Langley and Herbert Simon demonstrated how successful problem solvers use what they called the “generate and test” strategy—creating multiple potential solutions and systematically evaluating them against known constraints.103 The LRM’s approach revealed the essence of systematic hypothesis testing, a skill that sets it apart in the AI landscape. When faced with uncertainty, it didn’t lock onto a single solution but instead generated multiple alternatives, echoing the “divergent thinking” seen in human creative problem-solving.104 It then meticulously checked each possibility against the constraints of the task,”