Unverified50% confidenceFactExact time
At reViT inference time, the dynamic combination of the FFN at each depth requires computing the mixing weights online, i.e. online routing and merging.
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50%
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Long-term
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10/13/2026
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UnverifiedTraditional MoE mostly dispatches at the token granularity (token-dispatch), whereas the weight-space merging adopted by reViT linearly combines the weights of multiple experts into one equivalent FFN at the parameter level according to mixing coefficients, and then executes it uniformly over all tokens.72% similarUnverifiedMixture-of-Recursions is a neural network architecture paradigm that combines weight sharing with dynamic routing, where the model shares the same set of parameters across different layers.67% similarUnverifiedSharing the underlying representations allows intermediate-layer computation results to be reused, saving FLOPs and enabling implicit semantic correlation constraints across the various judgment dimensions.63% similarUnverified贝叶斯网络通过有向无环图显式建模变量间的条件依赖关系，适合捕捉因果链条63% similarUnverifiedAfter output-feature distillation, reViT's capabilities transfer to multiple downstream tasks such as classification, segmentation, and depth prediction.63% similar
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