NVIDIA (Jensen Huang / AI infrastructure)
你收到的每一条AI回复，最初都始于一个电子⚡
黄仁勋近期接受红杉资本采访时将AI基础设施比作五层蛋糕：在芯片、数据中心、模型之下的最底层是能源。智能时代的核心约束并非算力、软件，而是电力。英伟达将AI工厂称作当代发电机，输入电子、输出智能token，这场人类史上最大规模基建已投数千亿美元，仍需数万亿跟进，核心问题是电网能否跟上AI扩容节奏⬆️
AI Signal Daily Digest
AI Signal 日报
追踪 AI 一线声音：做事的人、写代码的人、下注的人。今天重点从 GitHub 源池拉取 X / Twitter、播客和 arXiv，保留原文链接，页面只放可快速阅读的卡片摘要。
9X / Twitter 内容
16播客条目
0含全文字幕播客
30arXiv 论文
提示：summaries feed generated_at is older than 30 hours。
X / Twitter
保留原文链接，按信号强度筛选播客
优先使用 transcript，有链接才纳入今天暂无可展示的新内容
源池已读取，但这一栏没有通过筛选的高质量中文摘要。
论文
arXiv feed 可能略旧，保留论文链接ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understa...
Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge: models must absorb knowledge from heterogeneous 2D and 3D medical images, and evaluation protocols must align with radiologists' clinical practice and provide an accurate, fi...
Certified Parallel-in-Time Sinkhorn for Dynamic Entropic Optimal Transport
Dynamic applications, including optimal-transport Flow Matching, repeatedly solve related entropic optimal transport problems, yet conventional distributed Sinkhorn processes frames sequentially and synchronizes after every iteration.
Learning Distributions from Multiple Data Providers
Motivated by learning from heterogeneous and overlapping data providers, we study a stylized model of distribution learning from restricted conditional samples.
Rethinking Classifier-Free Guidance in On-Policy Diffusion Distillation
On-policy distillation (OPD) adapts diffusion models by querying a teacher along trajectories generated by the current student, but how it should behave under classifier-free guidance (CFG), a default component of modern diffusion systems, remains poorly understood. Existing OPD methods naturally extend velocity matching to the CFG-composed prediction, direc...
KANEx: Translating Kolmogorov-Arnold Networks' Interpretability to Medical Expla...
Computer vision models have become highly effective for medical applications, yet their black-box nature continues to undermine clinician trust.
Global Convergence of DGM and PINN Algorithms for Solving Nonlinear PDEs
The Deep Galerkin Method (DGM) and Physics Informed Neural Networks (PINNs) have become widely-used methods for solving partial differential equations (PDEs) in the rapidly growing field of scientific machine learning.