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。
26X / Twitter 内容
17播客条目
0含全文字幕播客
30arXiv 论文
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X / Twitter
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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From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Gen...
Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines typically optimize task-specific datasets in isolation.
Multi-Agent AI System for Radiology Report Structuring and Quality Assurance wit...
Purpose: To develop and evaluate a locally deployed multi-agent AI system for radiology report structuring and quality assurance.
On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecif...
Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have shown great promise in recent literature.
TokEval: A Tokenizer Evaluation Suite
Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities.
The concentration game: Bayesian updating, regret, and information
We give a two-player zero-sum repeated game between a learner and nature whose value identity generates Bayesian updating and an exact accounting of exponential-weights regret at once, and supplies the comparator-class variational form that a wide class of concentration phenomena share. The terminal payoff is the most a comparator can gain at fixed relative...
Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Recepti...
本条暂无可读中文摘要，请通过下方链接查看原文。