Unverified50% confidenceFactExact time
Traditional 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.
1
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50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
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Related Claims
UnverifiedAt reViT inference time, the dynamic combination of the FFN at each depth requires computing the mixing weights online, i.e. online routing and merging.72% similarUnverifiedMoE replaces the dense feed-forward layers (FFN) in traditional Transformers with multiple expert sub-networks, and uses a router to dynamically select a small number of experts to perform computation for each token.64% similarUnverifiedMoR-MLLM is built on the Mixture-of-Recursions (MoR) framework.63% similarVerifiedThe MoE architecture uses a router to dynamically select the Top-K experts for computation for each token, allowing the total parameter count to scale while keeping the number of activated parameters per inference constant.62% 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.62% similar
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