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。
17X / 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Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and E...
Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires.
The Seriality Gap in Video Diffusion Models
When one ball strikes another, then another, video models should predict the consequences of each bounce.
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Sca...
Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains. We present TerraZero, a procedural driving simulator and self-play training stack....
PalmClaw: A Native On-Device Agent Framework for Mobile Phones
Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next action.
A Shortcut to Statistically Steady-State Turbulence with Flow Matching
Many nonlinear physical systems exhibit an initial transient phase in which perturbations grow before nonlinear interactions lead to a statistically steady state.
Audio-Native Speech Recognition with a Frozen Discrete-Diffusion Language Model
Automatic speech recognition is dominated by autoregressive decoders that emit one token at a time.