Imagine an AI describing a picture. It confidently points out details that simply aren't there—a classic case of an AI 'hallucination.' This isn't just a quirky bug; it's a major roadblock for deploying AI in tasks where accuracy is paramount, like medical image analysis or self-driving cars. New research from Stanford tackles this problem by focusing on how AI processes visual information. They've discovered that these hallucinations often stem from instability in how AI models 'see' images. Even small, inconsequential changes to an image can lead to wildly different interpretations, triggering false details in the AI’s description. To counter this, the researchers developed a clever technique called Visual and Textual Intervention (VTI). Essentially, they nudge the AI's understanding of the image towards a more stable representation by pre-computing the impact of image variations and then applying those corrections during image processing. This approach doesn't require retraining the entire model, making it a practical fix. The team also found that hallucinations can originate in the language part of the AI, where it generates descriptions. So, VTI also stabilizes the text generation process, ensuring that the AI's words match what it actually sees. Tests on various visual tasks showed significant reductions in hallucinations across the board, suggesting VTI could be a key step towards making AI vision more reliable. This research sheds light on a fundamental challenge in AI vision and provides a promising path towards more robust and trustworthy AI systems.