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。
0X / Twitter 内容
12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PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantiz...
Mixture-of-Experts (MoE) is a popular class of large language models (LLMs), offering high efficiency and accuracy.
A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynam...
To address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack that leverages stochastic analog processes in physical hardware.
Cluster-Aware Matching via Laplacian Optimal Transport
In many applications of matching, the point clouds to be matched are not merely unstructured sets of points but rather samples from distributions with an intrinsic cluster structure.
Physics-enhanced reinforcement learning for real-time optimal control of dynamic...
Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems.
Evaluating Open-Weight LLMs for Generating Structured Threat Information for Aut...
Connected and Autonomous Vehicles (CAVs) rely on interconnected software and hardware components, including sensors, Electronic Control Units, in-vehicle infotainment systems, and telematics units, where vulnerabilities can compromise assets, users, and vehicle operations. These vulnerabilities are commonly documented as plain text in the Common Vulnerabilit...
When Does Muon Help Agentic Reinforcement Learning?
Muon is competitive with AdamW in large-scale pre-training, but its value for reinforcement-learning (RL) post-training remains unclear.