LLMs hallucinate. Everyone knows it. Most solutions involve better prompting, retrieval-augmented generation, or fine-tuning. All of these try to fix the problem inside the language model. What if you used a camera to catch the LLM lying? That’s SENSE - a real-time framework that takes an LLM’s claims about the world, checks them against live visual input using computer vision, and flags contradictions before they reach the user. Not prompt engineering. Not RAG. A live visual audit loop. The Core Problem Imagine a robot or a smart assistant telling you “Hey! There is a red vase on the desk.” The LLM generated that description. But is it actually true? Is there really a red vase? Is it on the desk or somewhere else entirely? Traditional hallucination mitigation can’t answer this question because it only lives in text space. SENSE answers it by looking at the actual scene.…