Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation | ScienceToStartup | ScienceToStartup

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References (34)

[1]

Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts

2025Naibin Gu, Zhenyu Zhang et al.

[2]

DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts

2025Yuchen Feng, Bowen Shen et al.

[3]

Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking

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"An approach to increase the scalability of Mixture-of-Experts models by transforming depth into virtual width using a universal expert pool."

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Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation

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References (34)

Founder's Pitch

"An approach to increase the scalability of Mixture-of-Experts models by transforming depth into virtual width using a universal expert pool."

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Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation

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References (34)

Founder's Pitch

"An approach to increase the scalability of Mixture-of-Experts models by transforming depth into virtual width using a universal expert pool."

Commercial Viability Breakdown

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