混合专家模型搬上太空网络，分布式推理的硬核尝试，论文新思路值得关注
arXiv:2605.00515v2 Announce Type: replace-cross Abstract: Leveraging continuous solar energy harvesting at high efficiency, space data centers are env…
最新研究：后训练MoE模型通过自蒸馏跳过一半专家，无需从头预训练，显著降低计算量。
arXiv:2605.18643v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) scales language models efficiently through sparse expert activation, and its …
用混合专家模型引导功能网络先验，脑解码准确性与可解释性双提升
arXiv:2605.19279v1 Announce Type: new Abstract: Visual image reconstruction from functional Magnetic Resonance Imaging (fMRI) is a fundamental task in…
22位作者联合研究，探索用大语言模型替代人类专家评估试题难度的方法与效果
arXiv:2605.18562v1 Announce Type: cross Abstract: Accurate estimates of item difficulty are essential for valid assessment and effective adaptive lear…
全新方法利用MoE正交生长，大幅节省LLM预训练成本，突破沉没成本陷阱。
arXiv:2510.08008v2 Announce Type: replace Abstract: As the computational demands for pre-training Large Language Models (LLMs) continue to surge, the …