Yining Qian

No potential conflict of interest was reported by the author(s). The authors confirm that the data supporting the findings of this study are available within the article.

Papers Published on Jan 22 · Submitted by AngLv on Jan 23 Abstract Mixture-of-Experts (MoE) models mostly use a router to assign tokens to specific expert modules, activating only partial parameters and often outperforming dense models. We argue that the separation between the router's decision-making and the experts' execution is a critical yet overlooked issue, leading to suboptimal expert selection and ineffective learning.

Abstract Decoding the language of DNA sequences is a fundamental problem in genome research. Mainstream pre-trained models like DNABERT-2 and Nucleotide Transformer have demonstrated remarkable achievements across a spectrum of DNA analysis tasks.