arXiv:2605.27431v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across diverse modalities and tasks. Despite its growing success, a comprehensive and systematic review on the MoE metho addressing multimodal challenges remains lacking. Existing surveys tend to evaluate either multimodal learning or MoE independently from method taxonomy, overlooking the unique interplay between them. This survey fills that gap by answering a central question: \textit{How does MoE effectivel
The proliferation of multimodal AI systems and the increasing recognition of Mixture-of-Experts as a scalable solution drive the timely need for a comprehensive review of their interplay.
A strategic reader should care because improved multimodal learning through MoE can accelerate AI development across various applications, impacting diverse industries and even national AI capabilities.
This survey provides a systematic framework for understanding how Mixture-of-Experts addresses multimodal challenges, potentially streamlining research and development in this critical area of AI.
- · AI researchers
- · Multimodal AI developers
- · Generative AI platforms
- · Big tech investing in AI
- · AI models without scalable architectures
- · Compute-constrained AI development
The survey clarifies methodologies for effective multimodal AI, potentially leading to more robust and versatile AI applications.
Accelerated development in multimodal AI could enable more sophisticated AI agents capable of understanding and interacting with complex real-world data.
Generalized and efficient multimodal AI might reduce dependency on specialized data pipelines, benefiting nations striving for independent AI infrastructure.
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