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Results show that pretrained convolutional representations, when integrated through learned adaptation and fusion, can improve the visual understanding of existing VLMs without replacing their original visual encoders.
A method for distilling sequential computation by replacing spans of input tokens with collapsed representations, computed on the fly by a lightweight merge module, allowing pretrained models to operate on compressed inputs without architectural changes or re-training.
Reduced Matrix Multiplication is proposed, a training-free, input-adaptive inference method that reduces Transformer matrix products by selecting informative slices along their contraction dimensions, without modifying model weights, and it is shown that the same principle extends to multimodal vision-language inferenc...
SABRE is established as a reusable framework for constructing and refreshing VLM stress tests rather than a single fixed benchmark, and the results establish SABRE as a reusable framework for constructing and refreshing VLM stress tests rather than a single fixed benchmark.
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