2026-05-28原文

Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

This survey explores how Mixture-of-Experts (MoE) effectively addresses multimodal learning challenges from three perspectives: efficient engine, representation learner, and adapter, while identifying research gaps.

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Key points

MoE enables scalable multimodal modeling by decoupling computational cost from parameter growth.
MoE integrates complementary expert knowledge for enriched alignment and interaction representations.
MoE provides modular mechanisms for imperfect data scenarios like modality imbalance and missing modality.
The survey highlights gaps in interpretable routing, expert communication, modality integration, and lifelong multimodal learning.

Why it matters

This matters because moE enables scalable multimodal modeling by decoupling computational cost from parameter growth.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

[2605.27431] Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

[Submitted on 22 May 2026]

Title:Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

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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 effectively resolve multimodal challenges?} We approach this from three key perspectives: (1) \textbf{MoE as an Efficient Multimodal Engine:} enabling scalable multimodal modeling by decoupling computational cost from parameter growth and mitigating modality redundancy through selective expert activation; (2) \textbf{MoE as a Multimodal Representation Learner:} integrating complementary multi-opinion expert knowledge to enrich alignment and interaction representations; and (3) \textbf{MoE as a Multimodal Adapter:} providing a modular and flexible mechanism to model imperfect data scenarios such as modality imbalance and missing modality. Through our extensive literature review, we identify critical research gaps, including interpretable routing, expert communication, modality integration, and lifelong multimodal learning. We position this survey as a foundation for future research toward interpretable and sustainable multimodal Mixture-of-Experts system.

Comments: This survey paper has just been accepted by IJCAI 2026. Results were released by 30 April 2026. As I could not find a particular place to drop the acceptance email. I have upload the acceptance email alongside the LaTeX files of the paper, named as this http URL

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as: arXiv:2605.27431 [cs.LG]

(or arXiv:2605.27431v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.27431

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Liangwei Zheng [view email] [v1] Fri, 22 May 2026 05:01:21 UTC (232 KB)

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