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。
15X / Twitter 内容
19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3D-Aware VLMs with Implicit and Explicit Geometries
Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tasks that require fine-grained spatial understanding and reasoning.
Expanding Flow Maps
Flow-based generative models have enabled remarkable progress in fast and controllable generation across continuous and discrete state spaces, yet existing parameterizations are constrained to fixed dimensions or fixed sequence lengths.
GraphVid: Interactive Graph-Controllable Video Generation
Controllable video generation remains challenging due to the difficulty of specifying precise multi-object interactions using text prompts or motion-control inputs that primarily constrain pixel movement.
Barzilai-Borwein Fails Superlinear Convergence on an Open Set of Quadratics for...
Barzilai--Borwein (BB) method has shown strong practical performance in continuous optimization, yet its convergence dynamics remains poorly understood.
Synthetic data generation framework for quality control automation in gravure pr...
Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection.
Surprisal Theory is Tautological (without Rational Grounding)
Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some language model.