Imagine a robot trying to navigate a complex warehouse, or a self-driving car making split-second decisions in traffic. These scenarios require more than just pattern recognition—they demand advanced reasoning about actions and their consequences. A new research paper introduces ActionReasoningBench, a challenging benchmark designed to push the limits of current AI capabilities in this critical area. The benchmark tests how well Large Language Models (LLMs) can reason about actions and their effects, like predicting what happens when a robot picks up an object or a car changes lanes. It focuses on six key aspects of reasoning about action, ranging from basic state tracking (like knowing where objects are) to more complex tasks like understanding numerical relationships and handling unexpected consequences (what researchers call ramifications). The results reveal that LLMs, despite their impressive abilities in language, still struggle with these types of reasoning tasks. While they perform moderately well on basic problems, they often stumble with the kind of nuanced, commonsense reasoning humans take for granted. For instance, imagine telling an LLM, "The robot picks up the red block. The green block was on top of the red block." Current LLMs might struggle to infer that the green block is no longer on the red block and is now on the table (or floor). These kinds of ramifications, or indirect effects of actions, pose a major challenge for current LLMs. Even state-of-the-art models like GPT-4 struggle with ramification problems, showing just how difficult these reasoning tasks are. This research highlights the need for new approaches in AI development that go beyond just memorizing patterns and move towards true understanding of cause and effect in dynamic environments. The ActionReasoningBench provides researchers with a valuable tool for measuring progress towards this goal.