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航天员也分“工种”？央视详解驾驶员、飞行工程师、载荷专家三大类别
IT之家 5 月 24 日消息，据央视新闻报道，目前我国航天员分为航天驾驶员、航天飞行工程师和载荷专家三个类别，三种类型的航天员在选拔来源、职责分工和训练要求上各有侧重，共同保障空间站各项任务的顺利实施。 在三种类别中，最为公众所熟悉的是航天驾驶员。他们通常是飞船操纵与控制的核心岗位，在轨期间的工作…
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SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networks
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…
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摩根大通未来将扩招 AI 专家、缩招传统银行家，迎战人工智能时代
摩根大通CEO放话：AI专家扩招、传统银行家缩编，未来可能三天半工作制，金融业用工大洗牌来了。
IT之家 5 月 21 日消息，据彭博社 21 日（今天）报道，摩根大通 CEO 杰米 · 戴蒙明确指出，公司未来有望 增加 AI 专家的招聘比例 ，同时 缩减传统银行家的岗位规模 ，以应对 AI 技术的加速应用。“长远来看，AI 技术肯定会削减摩根大通的整体用工需求。未来金融圈会涌现出各种全新岗位…
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The AI people have been right a lot
盘点那些年AI专家们的神准预言，看看他们如何一次次押中技术走向。
Article URL: https://dylanmatthews.substack.com/p/the-ai-people-have-been-right-a-lot Comments URL: https://news.ycombinator.com/item?id=48211361 Poin…
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Post-Trained MoE Can Skip Half Experts via Self-Distillation
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 …
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FPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding
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…
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T-FIX: Text-Based Explanations with Features Interpretable to eXperts
用文本特征生成专家可理解的解释，提升AI决策透明度。
arXiv:2511.04070v3 Announce Type: replace Abstract: As LLMs are deployed in knowledge-intensive settings (e.g., surgery, astronomy, therapy), users ar…
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Estimating Item Difficulty with Large Language Models as Experts
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…
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L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts
创新MoE路由方法，结合低秩分解与Lipschitz控制提升专家专化性和模型性能。
arXiv:2601.21349v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models scale neural networks by conditionally activating a small su…
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Mixture of Experts for Low-Resource LLMs
揭秘低资源语言大模型中的专家路由：对比Transformer与Mamba混合架构的MoE表现。
arXiv:2605.17598v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable efficient model scaling, yet expert routing behavior acr…
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Composition of Memory Experts for Diffusion World Models
快速访问arXiv上这篇融合记忆专家与扩散世界模型的前沿论文，一键获取AI研究最新突破与全文PDF。
arXiv:2605.18813v1 Announce Type: new Abstract: World models aim to predict plausible futures consistent with past observations, a capability central …
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Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts
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 …
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Getting started with AI: Advice from the experts at Vercel Ship
Vercel Ship 上谷歌、Groq、OpenAI 专家分享企业应用 AI 的实战见解与入门建议。
At our annual end-user conference, Vercel Ship , we hosted a panel discussion on AI for enterprise teams featuring Paige Bailey (Google), Sunny Madra …