Researchers at TU Wien have uncovered a surprising capability in large language models (LLMs): they can successfully solve complex logical problems—even though they don’t truly understand the underlying logic. The study reveals that LLMs, despite being trained on vast amounts of text rather than formal reasoning systems, are able to generate correct solutions to certain types of logical puzzles and mathematical problems through pattern recognition and statistical inference. The team at TU Wien tested LLMs on a range of tasks that require formal logical reasoning, such as syllogisms, propositional logic, and even some aspects of first-order logic. While the models were not explicitly trained on logic rules or formal systems, they often produced accurate answers, especially when prompted with structured or step-by-step reasoning formats. What makes this discovery remarkable is that the models arrive at correct conclusions without grasping the abstract principles behind them. Instead, they rely on learned patterns from their training data—where logical structures often appear in natural language, such as in scientific texts, legal documents, or philosophical arguments. The LLMs essentially mimic the appearance of reasoning, not the process. The researchers also found that performance improved significantly when models were prompted to "think step by step" or break down problems into smaller parts, a technique known as chain-of-thought prompting. This suggests that even without true understanding, LLMs can simulate logical reasoning by following linguistic patterns that resemble structured thought. This behavior raises important questions about the nature of intelligence and problem-solving in AI. It demonstrates that effective performance doesn’t always require comprehension—just the ability to produce responses that align with correct outcomes based on statistical likelihood. While this capability opens new possibilities for using LLMs in domains requiring logical reasoning—such as education, legal analysis, or software verification—it also highlights the limitations of current AI systems. They can solve problems they don’t understand, but they can also make confident yet incorrect errors when patterns in the data are misleading. The findings underscore the need for caution in relying on LLMs for high-stakes decision-making, while also pointing to new ways to harness their strengths in hybrid systems that combine AI with formal logic frameworks.