Virtual Encoders in Multimodal Transformers
Multimodal language models traditionally rely on dedicated perceptual encoders to construct task-usable representations. More integrated architectures have recently emerged, which instead expose the shared transformer to lightly projected patches, audio frames, or discrete visual tokens. Where does this encoding happen when such representations are not provided? We find that the transformer can internalize this missing computation, constructing task-usable perceptual representations within its own early-to-middle layers before the downstream language model. We call this computational structure a Virtual Encoder. Across linear probing, similarities to perceptual encoders, and causal analys...
| Authors | Katsuya Ogata, Yuta Nakashima |
|---|---|
| Primary category | cs.CV |
| Published | 22 September 2026 |
| Last updated at source | 24 September 2026 |
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