Large language models (LLMs) have revolutionized how we interact with technology, exhibiting impressive abilities in understanding and generating human-like text. However, scaling these models to unlock their full potential has been computationally expensive. Mixture-of-Experts (MoE) models offer a solution by activating only specific parts of the model for different tasks, making it possible to train larger models with limited resources. But even MoE models face a challenge: effective knowledge sharing among their expert modules. Think of it like a team of specialists working independently—they might be experts in their respective fields, but they lack the shared understanding necessary for truly collaborative problem-solving. Researchers have developed a novel approach called CartesianMoE to address this bottleneck. Inspired by a technique used to find shared knowledge in datasets, CartesianMoE boosts knowledge sharing among experts in a multiplicative way. Existing MoE models share knowledge additively, like combining individual reports. CartesianMoE, however, takes a more integrated approach. Imagine the specialists now merging their expertise to co-create a more comprehensive and insightful solution. This is akin to how CartesianMoE combines specialized knowledge from different “sub-experts” to form a more robust and knowledgeable expert. Tests show that CartesianMoE outperforms existing MoE models in predicting the next word in a sequence (perplexity) and performing downstream tasks like question answering and common sense reasoning. Notably, CartesianMoE demonstrates better stability, even when some expert modules are unavailable. This robustness is crucial for real-world applications where models must handle unexpected situations. The implications of this research are significant. CartesianMoE offers a more efficient and robust way to scale LLMs, paving the way for even larger and more capable AI models. While further exploration into multi-layered Cartesian products is on the horizon, the current results demonstrate a promising step forward in building more collaborative and intelligent AI systems. This breakthrough could lead to LLMs that reason more like humans, offering a deeper understanding of complex issues, solving challenging problems, and ultimately, driving innovation across diverse fields.