Large language models (LLMs) like ChatGPT have impressed us with their vast knowledge, from historical facts to medical terminology. But can they actually *reason* with this knowledge? A new research paper, "CLR-Fact: Evaluating the Complex Logical Reasoning Capability of Large Language Models over Factual Knowledge," puts LLMs to the test, revealing some surprising strengths and weaknesses. The researchers built a benchmark of complex reasoning questions using two knowledge graphs—one general knowledge (FB15k-237) and one focused on biomedical facts (PrimeKG). These questions go beyond simple fact retrieval and require the LLMs to perform multi-step logical operations, like finding the intersection or union of different sets of facts. The results? LLMs are pretty good at reasoning with everyday information, achieving decent scores on the general knowledge questions. However, they struggle when faced with specialized knowledge, performing significantly worse on the biomedical questions. Think of it like this: an LLM might know that Paris is the capital of France and that France borders Belgium, but it may struggle to determine which other capital city is closest to Paris. This reveals a gap in their ability to synthesize multiple facts. Another interesting finding was the LLMs' difficulty with negation—the concept of "not." They excel at finding things that belong to a set (like "all the actors in a movie") but struggle with finding things that *don't* belong (like "all the actors who *weren't* in that movie"). This limitation in handling negative statements poses a challenge for truly complex reasoning. Finally, the research uncovered a curious asymmetry: LLMs are great at finding the union of sets (combining everything together) but surprisingly bad at intersections (finding what's common between sets). This is significant because set intersections are a fundamental building block of logical thought. The researchers found that prompting techniques like "Chain-of-Thought," where the LLM is encouraged to show its reasoning steps, can improve performance, especially for those tricky negation problems. This suggests that making reasoning explicit helps LLMs navigate complex logic. The study highlights a key area for improvement in LLM development: strengthening their grasp of logical operations, particularly with specialized knowledge and negations. As LLMs become increasingly integrated into our lives, their ability to reason effectively will be crucial for applications like medical diagnosis, legal analysis, and other areas where complex logical thinking is paramount. The next generation of LLMs will need to master these skills to truly live up to their potential.