Data centers may face temporary power cuts to prevent blackouts on largest US grid
The decision arrives as the breakneck pace of data center construction has grid operators scrambling to generate power.
techcrunch.com ·2026-07-29 | 每日 AI 知識更新
The decision arrives as the breakneck pace of data center construction has grid operators scrambling to generate power.
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36kr.com ·大公司: 汇丰控股将在新加坡设全球人工智能卓越中心,并招聘超100名AI专家 汇丰控股7月27日宣布,将于今年下半年在新加坡设立全球人工智能卓越中心(CoE),旨在开发可扩展至集团全球网络的AI能力,并计划招聘100多名AI专家。声明称,该卓越中心初期将专注于提升客户财富管理体验、引入智能化的财资解决方案以及开发AI赋能的数字支付。(界面) 小米MiMo-V2.5登OpenRouter全球周榜、月榜双第一
36kr.com ·36氪获悉,立昂技术公告,公司发布2026年度以简易程序向特定对象发行股票预案,拟募集资金总额不超过2.55亿元,扣除发行费用后用于立昂云数据人工智能算力资源池项目及补充流动资金。
36kr.com ·Authors: · Published:
arXiv:2607.22544v1 Announce Type: new Abstract: Visual counterfactual explanations aim to answer "what minimal change to this image would flip the model's prediction?", and are increasingly important as vision models are deployed in safety-critical domains (e.g., medicine). Existing diffusion-based methods can produce realistic edits, but they rely on external classifiers that must work reliably on noisy images, which makes them fragile and hard to deploy for robust explanations. We introduce C-
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arXiv:2607.22548v1 Announce Type: new Abstract: Data centers and their compute nodes require accurate and flexible digital twins capable of modeling the complex interplay of workloads, environmental parameters, and physical metrics. Current machine learning approaches for HPC and its telemetry typically rely on a static subset of anonymous, fixed-position sensor variables tailored to single tasks. Consequently, these models become obsolete when target tasks change or sensor metrics vary. We prop
Authors: · Published:
arXiv:2607.22549v1 Announce Type: new Abstract: Hybrid quantum-classical protein structure prediction depends strongly on Hamiltonian penalty weights, yet existing lattice-based workflows typically fix these coefficients by hand and evaluate only very short fragments in simulation. We present QFoldAgent, a closed-loop multi-agent framework for 5-residue tetrahedral-lattice folding in which a design agent proposes sequence-conditioned penalties, a VQE-based quantum-classical pipeline optimizes th
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arXiv:2607.22554v1 Announce Type: new Abstract: Large language models (LLMs) often achieve strong accuracy on benchmarks, yet it remains unclear how reliably they apply this knowledge when the same question is phrased in different but equivalent ways. In this work, we study how model answers change under meaning-preserving paraphrases across factual question answering and mathematical reasoning tasks. Across four benchmarks and 13 models, we find that model outputs frequently depend on the exact
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arXiv:2607.22555v1 Announce Type: new Abstract: Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with explanations. Frontier LLMs are strong generalists, but single-shot prompting often yields brittle diagnostic reasoning. We present the DeepLens Diagnosis Agent, a five-stage harnessing pipeline (combining model capabilities with disciplined process constraints) centered on a small medical reasoning mode
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