Large language models (LLMs) have shown amazing abilities to reason and plan, breaking down complex problems into steps like solving math problems or writing code. But they often stumble due to a kind of “AI short-sightedness” – focusing on the immediate next step without considering the bigger picture. Think of it like planning a road trip by only looking at the next mile marker, ignoring the overall route. This “myopia” leads to errors that could have been avoided with a little foresight. This new research digs into this problem, revealing that LLMs often get stuck in these local optima, making mistakes early on that derail the entire process. For example, an LLM tasked with cooking might grab a spice bottle instead of salt simply because it's closer, completely forgetting the initial instructions. The researchers show that even advanced LLMs like Llama-3 frequently lack this global awareness, especially when their solutions go wrong. To tackle this, they've developed a clever technique called Predictive-Decoding. It's like giving the LLM a preview of the road ahead. By sampling multiple possible future trajectories and evaluating their outcomes, Predictive-Decoding helps the LLM choose actions that contribute to the overall goal, not just the next step. This approach significantly boosts performance across a range of tasks, from math and coding to simulated agent-based scenarios where an AI interacts with an environment. Impressively, it achieves this without needing extra training data and while using computing power more efficiently than previous methods. This research suggests a shift in how we think about LLM reasoning. It's not enough for AIs to be good at next-step prediction; they need to develop a sense of global awareness, anticipating long-term consequences to truly unlock their problem-solving potential. This work provides a promising path toward building less myopic, more effective AI systems.