Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts
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 s...
| Authors | Hahyeon Choi, Nojun Kwak |
|---|---|
| Primary category | cs.LG |
| Published | 5 May 2026 |
| Last updated at source | 6 May 2026 |
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