reViT Uses Recurrent Transformers with Depth-Programmed Experts
October 7, 2026
reViT employs a single Transformer block applied recurrently to match the accuracy of full-depth vision encoders at similar FLOPs. It utilizes a shared expert bank and a continuous normalized-depth coordinate to program a resampleable trajectory through FFN parameter space.
HOW THIS AFFECTS YOU
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builderThis provides a path to high-accuracy vision models with much lower inference FLOP requirements.
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researcherYou can explore weight-space merging and recurrent architectures to achieve depth-specific transformations.