Imagine a world where AI models could seamlessly integrate their expertise, dynamically adapting to complex, real-time challenges. This isn't science fiction; it's the promise of Mixture of Expert (MoE) models. However, traditional MoEs face limitations in time-sensitive scenarios where data constantly evolves, like predicting stock market trends. A groundbreaking research paper, "Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models," introduces a novel approach called MoE-F to revolutionize how we combine LLMs. Unlike static MoEs, MoE-F uses a dynamic filtering mechanism that continuously refines the weighting of expert LLMs based on their ongoing performance. This is akin to having a team of specialized AI analysts, each contributing their unique insights, with MoE-F acting as a conductor, optimizing the blend of their predictions as new data emerges. The core of MoE-F lies in its innovative use of stochastic filtering. By treating expert selection as a hidden Markov process, the algorithm can track the latent expertise that best captures the data's evolution. This allows MoE-F to predict the optimal combination of LLMs for the next step, leveraging the wisdom of the crowd while adapting to rapid changes. The results are impressive. In a real-world test of predicting financial market movements, MoE-F significantly outperformed individual LLMs, achieving a remarkable 17% absolute improvement in F1 score. This suggests that combining LLMs dynamically can unlock a new level of predictive power, particularly in volatile and ever-changing environments. The implications extend beyond finance. MoE-F could be instrumental in any domain that requires real-time predictions from complex systems, such as weather forecasting, healthcare diagnostics, or even personalized learning. While the current research focuses on LLM experts, future work aims to incorporate diverse model types, like neural Stochastic Differential Equations (SDEs), to capture both large-scale trends and intricate market mechanics. This will further refine the adaptive power of MoE-F, opening doors to more accurate and nuanced predictions. The ability to combine and refine expert AI predictions dynamically holds immense potential for improving decision-making in countless fields. MoE-F represents a significant step toward realizing this potential, offering a glimpse into a future where AI systems can truly work together, learning and adapting as a team.