We often marvel at how smoothly AI chatbots like ChatGPT can answer questions, sometimes even crafting eloquent prose. But beneath the surface, a fundamental question lingers: can these impressive language models *actually* reason? New research challenges the notion that today’s AI truly grasps logic, revealing how heavily these bots rely on context and background knowledge to solve problems, rather than pure deductive or abductive reasoning. In the paper "Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities," researchers from Rutgers University and Microsoft dissect AI’s reasoning abilities by testing large language models (LLMs) with both abstract logic puzzles and real-world scenarios that embed the same logical structure. They discovered a fascinating discrepancy. While larger models sometimes excel at abstract tasks, even they stumble when that same logic is placed within different contexts. Smaller models, on the other hand, heavily lean on the context for clues, often outperforming their abstract reasoning on real-world scenarios. This suggests that AI’s apparent skill in logic might stem more from pattern recognition within specific contexts than from genuine reasoning abilities. This reliance on context raises some critical questions. How much can we trust AI's problem-solving when it depends so heavily on its training data? The study shows that an AI trained on a variety of domains like "Culture and the Arts" or "Technology and Applied Sciences" could excel in its training fields but fail in unfamiliar areas. This dependence limits their adaptability and may also skew decision-making in fields where the context varies widely. The research suggests a new direction for developing more robust AI reasoning. Instead of focusing solely on abstract logical tasks, future training needs to emphasize handling the nuances of real-world context, allowing the AI to disentangle core logic from surrounding information and apply reasoning skills more flexibly.