🚀 AI 成長日報

2026-07-29 | 每日 AI 知識更新

🇺🇸 歐美 AI 熱門新聞

🇨🇳 中國 AI 新聞

科氪 | 定义AI睡眠健康新赛道 东莞数字人体与智慧睡眠创新联合体落地慕思

7月27日，东莞市数字人体与智慧睡眠创新联合体揭牌暨工作推进会在慕思健康睡眠股份有限公司（以下简称“慕思”）总部举行。本次工作推进会由东莞市科学技术局、厚街镇人民政府指导，慕思、广东华中科技大学工业技术研究院联合主办，东莞市相关职能部门、顶尖高校、三甲医疗机构、产业链核心企业、投资机构及主流媒体代表约300人参会。 推进会同步举行聘任与授牌仪式，正式聘任中科院院士、华中科技大学副校长尹周平为东莞市数字人体与智慧睡眠创新联合体首席科学家，聘任慕思集团CTO陈文泽为行政总师。 氪星晚报｜Meta宣布与贝莱德达成战略合作，将在埃尔帕索开发数据中心；“国科超导”完成超亿元融资；我国启动人工智能大模型IPv6能力提升专项行动

大公司： YouTube与NBC环球达成合作协议，将为美用户提供流媒体捆绑服务 7月27日，美国流媒体平台YouTube与媒体巨头NBC环球（NBCUniversal）宣布深化战略合作。根据双方达成的协议，自2027年初起，美国本土的YouTube Premium订阅用户将无需支付额外费用，直接获取NBC环球旗下流媒体平台Peacock Premium的会员权益。（新浪财经） Meta宣布与贝莱德达成战略合作，将在埃尔帕索开发数据中心

36kr.com ·

Kando AI完成数千万元种子轮融资，要做“决策领域的Cursor”｜涌现新项目

文｜邓咏仪 编辑｜张雨忻 吴秉哲每天至少复盘一次。 作为北大计算机博士，他同时也是一个高频的投资者。每天盘后，他会回顾当天的判断——哪些预案执行了，哪些被盘面的新信息打乱了，哪些潜意识的决策事后被验证是对的。 这个习惯持续了很多年。但有一个问题是，大部分复盘都没被系统性地沉淀下来。 “你的认知是一种资产，但现在所有东西都停留在人脑里。”吴秉哲说。他和Kando AI CEO毛书翰同为活跃的二级市场投资者。两个人的共性是，都在高频地做不确定性环境下的决策，而且也意识到，这些决策背后的认知模式，从未被数字化。 2026年6月，两人正式创立了Kando AI，希望让AI从真实决策及其结果中持续学习，

36kr.com ·

氪星晚报｜美国大型企业告别裁员潮重启招聘；英伟达、微软、IBM等数十家企业成立新联盟，旨在共同保障AI安全；《光伏行业成本核算模型通则》发布，引导行业有序竞争

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 ·

📄 國際新發表 AI 論文

Concept-based Visual Counterfactual Explanations with Diffusion Models

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-

SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series

Authors: · Published:

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

QFoldAgent: An Autonomous Quantum Optimization Multi-Agent System for Protein Structure Prediction

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

Same Question, Different Answers: Evaluating LLM Reliability Beyond Accuracy

Authors: · Published:

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

DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs

Authors: · Published:

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

🔓 開源論壇熱門 Top 10

🤗 Hugging Face 竄起模型

zai-org/GLM-5.2

🏷️ transformers, safetensors, glm_moe_dsa · ❤️ 4593

🎬 YouTube AI 熱門影片

🎤 AI 教父/大神訪談

📺 中國 Bilibili AI 熱門