Large Language Models (LLMs) have made incredible strides in understanding and generating text. But what happens when you give them eyes and ears? Recent research has focused on multimodal LLMs, which can process not just text, but also audio and video. It sounds amazing – AI that truly perceives the world like we do! But a new study reveals a surprising problem: these multimodal LLMs sometimes hallucinate, perceiving things that aren't actually there. The study introduces AVHBench, a cleverly designed benchmark to test how well audio-visual LLMs truly understand what they're seeing and hearing. Imagine an AI watching a silent video of a cat and claiming it hears meowing. Or playing an audio clip of a dog barking and the AI insists it sees a dog on screen, even if the screen is blank. These are the kinds of hallucinations researchers are discovering. Turns out, simply adding audio and video inputs to an LLM doesn't guarantee accurate perception. In fact, these LLMs often perform better with just one input (either audio *or* video) or even just text descriptions. Why the struggle? The research suggests that current LLMs haven’t yet mastered how to effectively process and integrate multiple sensory inputs. They struggle to link audio cues to visual events, leading to these perceptual errors. The good news? Researchers are already working on solutions. Early experiments show that by improving how audio features are aligned with the LLM’s understanding and by fine-tuning the model with carefully annotated data, these hallucinations can be reduced. AVHBench offers valuable insights into the challenges of multimodal AI perception, paving the way for more robust and reliable models that can truly see and hear the world around them. This is a crucial step towards building AI that can seamlessly interact with and understand our multimodal world, potentially leading to breakthroughs in robotics, virtual assistants, and much more.