mlsanityData to 7 Oct 2026

Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts

arXiv 2605.03348 ↗First posted 5 May 2026cs.LG cs.AIcited by 0

On the map: Mixture-of-experts → Multimodal MoE models

Abstract (arXiv)

We propose S3 (Specialization, Selection, Sparsification), a framework that rethinks multimodal learning through a structural perspective. Instead of encoding all signals into a fixed embedding, S3 decomposes multimodal inputs into semantic experts and selectively routes them for each task. Specialization forms concept-level experts in a shared latent space, Selection adapts routing for task-specific needs, and Sparsification prunes low-utility paths to yield compact, information-minimal representations. Across four MultiBench benchmarks, S3 improves accuracy and shows a consistent reverse U-shaped sparsity-performance trend, with peak performance at intermediate sparsity. These results suggest that structuring multimodal representations as selectable semantic components provides a practical and principled alternative to contrastive learning or InfoMax-driven approaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal MoE models, one of 12 directions in Mixture-of-experts. Builds on 57 of its 76 references that are in the map. First posted on arXiv 5 May 2026.

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57 of 76 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering1 Mar
  2. 2024Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts10 Oct
  3. 2024Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts18 May
  4. 2022Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts6 Jun

Other references · 53

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  6. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,848
  7. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  8. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  9. 2020Supervised Contrastive Learningcited by 2,803
  10. 2024Mixtral of Expertscited by 1,785
  11. 2021Align before Fuse: Vision and Language Representation Learning with Momentum Distillationcited by 1,612
  12. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
38 more
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  3. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,265
  4. 2022Reproducible scaling laws for contrastive language-image learningcited by 1,163
  5. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,162
  6. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
  7. 2015Deep Learning and the Information Bottleneck Principlecited by 793
  8. 2021Scaling Vision with Sparse Mixture of Expertscited by 740
  9. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
  10. 2022Mixture-of-Experts with Expert Choice Routingcited by 611
  11. 2022Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learningcited by 553
  12. 2017Look, Listen and Learncited by 452
  13. 2022DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scalecited by 400
  14. 2021BASE Layers: Simplifying Training of Large, Sparse Modelscited by 315
  15. 2016MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videoscited by 193
  16. 2019Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)cited by 173
  17. 2024Scaling Laws for Fine-Grained Mixture of Expertscited by 166
  18. 2021MultiBench: Multiscale Benchmarks for Multimodal Representation Learningcited by 143
  19. 2019UR-FUNNY: A Multimodal Language Dataset for Understanding Humorcited by 130
  20. 2022M3ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-designcited by 97
  21. 2023Scaling Vision-Language Models with Sparse Mixture of Expertscited by 88
  22. 2023Factorized Contrastive Learning: Going Beyond Multi-view Redundancycited by 73
  23. 2024Multimodal Alignment and Fusion: A Surveycited by 73
  24. 2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusioncited by 67
  25. 2024Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Modelscited by 55
  26. 2023Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learningcited by 53
  27. 2023SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalizationcited by 48
  28. 2023TaskExpert: Dynamically Assembling Multi-Task Representations with Memorial Mixture-of-Expertscited by 45
  29. 2026The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlookcited by 40
  30. 2024M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Frameworkcited by 32
  31. 2023FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Spacecited by 31
  32. 2020Disentangled Information Bottleneckcited by 24
  33. 2025Mixture of Experts Made Intrinsically Interpretablecited by 22
  34. 2024An Information Criterion for Controlled Disentanglement of Multimodal Datacited by 14
  35. 2024Monet: Mixture of Monosemantic Experts for Transformerscited by 12
  36. 2025Aligning Multimodal Representations through an Information Bottleneckcited by 12
  37. 2025A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysiscited by 5
  38. 2025Towards Interpretability Without Sacrifice: Faithful Dense Layer Decomposition with Mixture of Decoderscited by 5

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  4. 2025Understanding and Harnessing Sparsity in Unified Multimodal Models
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  6. 2026InstMoE: Adaptive Multimodal Routing with Specialized Experts
  7. 2023Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts
  8. 2024Unity by Diversity: Improved Representation Learning in Multimodal VAEs