Imagine an AI trying to understand not just the "what" of an event, but the "why" and "how" behind changes in reality. That's the challenge posed by metaphysical reasoning—a complex form of thinking that goes beyond surface-level observations to explore the possibilities and limitations of actions and their consequences. A groundbreaking new research paper introduces MARS (Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset), a benchmark designed to test whether Large Language Models (LLMs) can truly grasp these nuanced concepts. Researchers have framed this challenge as a three-step process. First, can AI identify implausible or "metaphysical" events, like a person jumping off a building and flying? This step tests the AI’s ability to differentiate between realistic and fantastical scenarios. Second, can AI predict the outcomes of both plausible and implausible events? This step assesses whether the AI can reason through consequences, even in hypothetical situations. Finally, can AI figure out what changes are needed to turn an impossible scenario into a possible one? For example, what would it take for someone to jump off a building safely? This final step evaluates the AI's capacity for problem-solving and understanding causal relationships. The results of testing various LLMs with MARS are revealing. Even the most advanced models struggle with these tasks, especially in distinguishing the realistic from the unreal. Interestingly, models pre-trained on large datasets of conceptual knowledge perform better, suggesting that exposure to diverse concepts is crucial for this type of reasoning. Further analysis reveals LLMs have specific weaknesses in reasoning about time, space, and numbers. They also tend to get confused by irrelevant details or even hallucinate information not present in the original text. This research highlights the significant hurdles that still exist in developing truly "conscious" AI. While LLMs have made impressive strides in various reasoning tasks, their ability to grasp metaphysical concepts remains limited. MARS provides a crucial benchmark for future research, paving the way for developing more sophisticated AI capable of understanding not just the world as it is, but also the world as it could be.