AI image generators have made incredible strides, conjuring up stunning visuals from mere text prompts. But how do we truly gauge their capabilities and uncover hidden flaws? Researchers have developed a clever new framework called DyEval, a dynamic and interactive system that pushes these AI artists to their limits. Imagine a continuous feedback loop where humans and AI collaborate. DyEval uses a large language model (LLM) to generate increasingly complex text prompts based on how the image generator performs. Users assess the generated images, flagging successes and failures. This feedback helps refine the next round of prompts, creating a progressively challenging test. DyEval isn't just about finding errors; it's about understanding *why* they happen. When an image fails to meet expectations, a special module dives into the text prompt, isolating the specific words or phrases that tripped up the AI. Think of it as a detective searching for clues within the text. This process helps uncover surprising weaknesses. For instance, many generators struggle with possessive pronouns like "his" or "her," revealing a gap in their contextual understanding. Similarly, cultural nuances and implicit relationships often pose a challenge. The research reveals consistent patterns across different AI models. While they excel at generating objects and static attributes like color and shape, dynamic actions, and complex relationships often lead to errors. This innovative testing approach isn't just for researchers. By understanding these limitations, developers can refine their models, creating more robust and reliable AI image generators. DyEval represents a significant step towards a future where AI-generated visuals are not just impressive but also consistently accurate and contextually relevant.