Large language models (LLMs) have revolutionized how we interact with information, but they sometimes stumble when it comes to complex reasoning. A key challenge lies in how these models process the context they're given – the supporting text used to generate answers. Simply retrieving more relevant information doesn't always lead to better answers; sometimes, it can even make things worse. This is because LLMs exhibit "position bias," meaning they tend to overemphasize information presented earlier in the context, often overlooking crucial details buried further down. Researchers have discovered that improving the *order* of the retrieved context can dramatically impact LLM accuracy. A novel technique called Mixture-of-Intervention (MOI) tackles this challenge by cleverly rearranging the context. Instead of treating different context orderings as black boxes, MOI analyzes how the LLM's output changes when the order is varied. This allows it to disentangle the true usefulness of each piece of information from the LLM's inherent position bias. By re-ranking the context based on this "debiased utility," MOI guides the LLM to focus on the most relevant information, regardless of where it appears in the original retrieval. The results are striking: across various question-answering tasks, including those requiring multi-hop reasoning, MOI significantly boosts accuracy. This research not only highlights the importance of context management in LLM applications but also offers a practical solution for enhancing their reasoning abilities. Future research could explore how this technique can be optimized for even greater efficiency and applied to other LLM-powered tasks, like citation generation and fact verification. As LLMs become increasingly integrated into our daily lives, strategies like MOI will be essential for ensuring they provide accurate and reliable information.