Large Language Models (LLMs) are increasingly capable of complex tasks, but how well do they actually retain and utilize information, especially when distractions are present? Researchers are exploring this question through innovative testing methods that go beyond typical benchmarks. The challenge isn't just about memorization, but also how LLMs operationalize information—turning learned facts into actionable decisions. Think of it like a robot waiter who needs to remember not to recommend a dish with a certain ingredient, even while juggling multiple customer requests and friendly conversations. To test this, researchers created scenarios with carefully controlled distractions, like a talkative customer showing endless photos. The results revealed some interesting limitations in current LLMs. When faced with a large amount of historical context (think a long shift for the robot waiter) and a distractor right before a decision needs to be made, the LLMs struggled to choose the correct action. Even worse, in some complex cases, their performance dipped below random chance, suggesting the distractions significantly skewed their decision-making. This research sheds light on how LLMs handle information flow and how distracting elements can disrupt reasoning. By understanding these limitations, we can develop strategies to improve LLM reliability and make AI assistants and agents truly helpful in real-world situations.