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Minbin Huang

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#artificial intelligence Preprint May 2026

UniPool: Learning Expert-to-Layer Ownership from Brief Global Access

This work studies whether a brief global-access phase can learn a compatible expert-to-layer allocation that is then executed privately, and proposes UniPool-Lock, which gives every layer its own router over one global pool, balances aggregate pool usage, and scores experts with a scale-stable NormRouter.

Minbin Huang, Han Shi, Chuan-Yang Zheng et al. · 1 citation
Preprint Aug 2026

TradingMoE: Routing the Right Experts in Evolving Markets

This work introduces a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys and proposes TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts.

Chang Zhou, Xing-Tong Yu, Minbin Huang et al. · 0 citations

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