A new study introduces a method to predict compositional errors in LLMs by analyzing their representational geometry. This approach identifies scenarios where models are likely to fail, enabling developers to create targeted stress tests and improve model performance. The findings could enhance active learning strategies in real-world applications, making AI systems more robust. This advancement is crucial as it addresses the challenges of deploying LLMs in complex tasks.