Weekly Digest — 2026-W27

Are you in AI answers? Find out and fix it in minutes

Bookkeeping assistant that runs receipt workflows end-to-end

Convert leads into customers with AI conversations

AI to turn vertical UGC into widescreen ads

Lightweight PDF & EPUB reader in your browser

Social media growth agents that build your momentum

Ready-made AI analytics skills for your business data

Predict anything with AI

Build with coding agents from anywhere

An AI co-pilot and dashboard built for dispatcher speed

Email-native assistant that learns how you work

Manage tasks and calendars from Claude, ChatGPT or Cursor

Video generation models have emerged as a promising paradigm for embodied world simulation. However, both general-domain video generators and robot-specific data fine-tuned models can still produce physically implausible manipulations, including discontinuous motion trajectories and inconsistent robot-object interactions, which limits their reliability as world simulators. Through extensive experiments, we find that such physical instability mainly arises from two factors: deformation of moving objects and implausible spatio-temporal correlations among interacting entities, particularly during contact. Building on this observation, we propose PhysisForcing, a scalable training framework that strengthens physical consistency by focusing supervision on physics-informative regions through joint optimization of pixel-level and semantic-level features. The framework consists of a pixel-level trajectory alignment loss, which supervises DiT features using reference point trajectories, and a semantic-level relational alignment loss, which aligns DiT features with inter-region relations extracted from a frozen video understanding encoder. Extensive experiments on R-Bench, PAI-Bench, and EZS-Bench show that PhysisForcing consistently improves embodied video generation over strong baselines, improving the Wan2.2-I2V-A14B and Cosmos3-Nano base models on R-Bench by 22.3\% and 9.2\% (7.1\% and 3.7\% over vanilla finetuning), with the Cosmos3-Nano variant attaining the best overall score. Beyond generation, as a world model under the WorldArena action-planner protocol it raises the closed-loop success rate from 16.0\% to 24.0\% and further improves downstream policy success, indicating that physically aligned video models yield stronger representations for robotic manipulation.

We study whether we can learn novel manipulation skills from human actions to a bi-manual robot with parallel grippers. Human action data is cheap, abundant, and diverse, making it one of the most promising resources for scaling up robot learning. Yet transferring skills from humans to robots remains hard: most prior work treats humans as just another bi-manual 6DoF embodiment, where hand-pose estimates are noisy and the contact patterns of human fingers differ fundamentally from those of a parallel gripper. We argue that learning rotation-inclusive action signals from human data is therefore sub-optimal, and instead propose a bridging action representation: the relative wrist translation within the initial head-camera frame, an action space shared by humans and robots. To handle the potential absence of certain action components in different embodiments, we build a π_0-like vision-language-action model with interleaved action tokens and attention masking. On a suite of novel bi-manual manipulation tasks, our bridging action transfers human manipulation knowledge to robots far more effectively than noisy 6DoF human actions and scales with the amount of human data.

We introduce an axiomatic evaluation framework for latent thought representations in LLMs, comprising metrics that are independent of downstream benchmark scores and reveal representational failures that benchmark accuracy masks. Existing evaluations conflate representation quality with model capacity. Therefore, failures cannot be attributed to the representation rather than to the model that processes it. We formalize four functional axioms (Causality, Minimality, Separability, and Stability) and define a quantitative measure for each, computed directly on the representation independently of downstream accuracy. We audit open-weight LLMs across 23 reasoning tasks (e.g., Spatial Reasoning, Factual QA). We find that no candidate satisfies all four axioms simultaneously, that the representations distinguish task type reliably but cannot distinguish between two questions within the same task, and that the representations encode little information beyond what is already present in the input embedding. The failure is consistent across dense, reasoning-distilled, and RL-trained model families, indicating that the gap is structural rather than a property of model size or training procedure.

We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) to improve both the visual quality and instruction-following capability of the Qwen-Image-2.0 diffusion model. To provide reliable reward signals, we construct task-specific composite reward models by fine-tuning vision-language models with a pointwise scoring paradigm and chain-of-thought reasoning. For text-to-image generation, the reward models cover alignment, aesthetics, and portrait fidelity dimensions. For image editing tasks, the reward system addresses instruction-following accuracy and face identity preservation. Building on this reward system, we develop a scalable GRPO-based RL training framework, incorporating a hybrid classifier-free guidance (CFG) strategy to preserve pre-trained knowledge, prompt curation via intra-group reward range filtering, and per-category reward weight calibration. To merge the task-specialized RL policies for T2I and editing, we propose on-policy distillation as the final training stage, which consolidates multiple teachers into a single student model through trajectory-level velocity matching. Extensive evaluation shows that Qwen-Image-2.0-RL achieves 57.84 overall score on Qwen-Image-Bench (+2.61 over the base model), Elo ratings of 1193 in text-to-image arena (+78) and 1349 in image edit arena (+93), demonstrating consistent gains in aesthetic quality, prompt adherence, and editing accuracy.

Language models (LMs) represent tokens using embedding matrices that scale linearly with the vocabulary size. To constrain the parameter footprint, prior work proposes hashing many tokens into a single vector within encoder-only models. While this offers parameter efficiency, many-to-one collisions prevent its use in causal LMs. In this paper, we propose MultiHashFormer, a new framework that allows hash-based autoregression. Each token is represented as a unique hash signature, a short sequence of discrete hash IDs, generated by multiple independent hash functions. A Hash Encoder compresses this signature into a single latent vector for processing by a Transformer decoder. Then, a Hash Decoder generates the hash signature of the next token, which is then mapped back to text. We evaluate our approach at the 100M, 1B and 3B parameter scales, demonstrating that MultiHashFormer consistently outperforms standard Transformer LMs across multiple benchmarks. Furthermore, we show that our model handles multilingual vocabulary expansion with a constant parameter footprint without any modifications.

Vision-language models (VLMs) are increasingly deployed in consumer, medical, financial, and enterprise applications. This broad deployment expands the safety surface: risks can arise from multimodal question answering, assistant responses, and cross-modal composition, while moderation policies may vary across products, regions, and deployment stages. Most existing guardrails either rely on fixed taxonomies or target only a narrow set of interaction settings, which limits their adaptability when safety rules change at deployment time. We present SingGuard, a policy-adaptive multimodal guardrail model family for safety assessment in multimodal conversations. SingGuard treats the active policy as a runtime input: given natural-language rules, it checks the target content against the active policy rule by rule and predicts both the safety label and the triggered rule. To balance efficiency and interpretability, SingGuard supports fast, hybrid, and slow inference regimes along a fast-to-slow reasoning spectrum, ranging from direct safety judgments to policy-grounded deliberation. We further optimize this behavior with fast--slow decoupled reinforcement learning. We also introduce SingGuard-Bench, a multimodal guardrail benchmark with 56{,}340 examples spanning 80+ fine-grained risk types across multimodal QA, adversarial attack, and dynamic-rule evaluation settings, including cross-modal joint-risk cases where each modality is harmless in isolation but their composition implies unsafe intent. Across six benchmark families (35 datasets), SingGuard achieves state-of-the-art average F1 in every family. Dynamic-rule evaluation further shows improved policy-following accuracy from 0.6465 to 0.7415 under runtime policy shifts. Our code is available at https://github.com/inclusionAI/Sing-Guard.

LLM agents are expected to act over multiple turns, using search, browsing interfaces, and terminal tools to complete user goals. Yet not every goal is well specified or achievable in the available environment. In such cases, a reliable agent should recognize that further interaction is unlikely to help and abstain from additional tool calls. We define Agentic Abstention, the problem of deciding when an agent should stop acting under uncertainty. Unlike standard LLM abstention, which is usually evaluated as a single-turn answer-or-abstain decision, agentic abstention is a sequential decision problem: an agent can answer, abstain, or gather more information at each turn, and the need to abstain may only become clear after interacting with the environment. We study this problem across web shopping, terminal environments, and question answering, evaluating 13 LLM-as-agent systems and 2 agent scaffolds on more than 28,000 tasks. Our results show that the main challenge is not only whether agents can abstain, but also when they abstain. Some agents never abstain when they should, while others do so only after many unnecessary interactions. This gap is especially large on tasks where the instruction appears feasible until the environment reveals otherwise (e.g., no valid result matches the instruction). We further find that model scale, reasoning, and agent scaffolding affect abstention in different ways, where larger or more capable models sometimes perform worse at timely abstention. Finally, we introduce CONVOLVE, a context engineering method for improving agentic abstention that distills full interaction trajectories into reusable stopping rules. On WebShop, CONVOLVE substantially improves timely abstention without updating model parameters, raising Llama-3.3-70B's timely recall rate from 26.7 to 57.4. Our dataset and code are available at https://lhannnn.github.io/agentic-abstention

Streaming video editing has made rapid progress, yet practical deployment is still limited by two core issues: maintaining stable backgrounds and non-edited regions over time, and achieving the low latency required for real-time interactive scenarios. Meanwhile, recent streaming video generation methods are mostly developed for synthesis and cannot be directly applied to editing due to the strict preservation requirement and region-specific control. In this work, we present a novel streaming video editing framework that performs causal, frame-by-frame editing with strong content preservation and real-time responsiveness. Our key design is a three-stage distillation pipeline that progressively transfers editing capability from a powerful bidirectional foundation model to an efficient unidirectional streaming editor, enabling stable long-horizon edits without sacrificing visual fidelity. To further support real-time deployment, we introduce an AR-oriented mask cache that reuses region-related computation across frames, substantially reducing redundant processing and accelerating inference. Finally, we establish a dedicated benchmark for streaming video editing. Extensive evaluations demonstrate that our method achieves state-of-the-art visual quality among streaming baselines while drastically boosting inference speed to 12.66 FPS, making it suitable for interactive and augmented reality applications.

We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities. To support this goal, we build a long-horizon knowledge-action infrastructure that connects external knowledge, actions, observations, and verifier outcomes, producing agentic trajectories with an average length of 45K tokens. Based on this, we train Agents-A1 with a three-stage recipe. First, we perform full-domain supervised fine-tuning to align the base model with broad agentic behaviors. Second, we train domain-level teacher models to capture specialized expertise in each domain. Third, we propose a multi-teacher domain-routed on-policy distillation with salient vocabulary alignment to improve knowledge transfer efficiency across different domains, unifying six heterogeneous domains into one deployable student model. Agents-A1 achieves strong and broad performance for long-horizon agent benchmarks. Compared with 1T-parameter model such as Kimi-K2.6 and DeepSeek-V4-pro, Agents-A1 achieves leading results on SEAL-0 (56.4), IFBench (80.6), HiPhO (46.4), FrontierScience-Olympiad (79.0), and MolBench-Bind (56.8), and remains highly competitive on SciCode (44.3), HLE (47.6) and BrowseComp (75.5). We hope this work provides the community with a practical path for scaling the horizon using a 35B agent that can reach or match the performance of 1T models on long-horizon tasks.

As large language models and harness frameworks continue to advance, agents operating in terminals are increasingly capable of performing a broader range of general computer-use tasks beyond coding. However, existing benchmarks do not adequately evaluate general-purpose terminal computer-use agents (TUAs): general computer-use benchmarks primarily target graphical user interfaces (GUIs), whereas terminal-based benchmarks largely emphasize technical and programming-centric workflows historically native to the shell. We introduce TUA-Bench, a general-purpose benchmark for terminal-use agents. TUA-Bench includes 120 real-world tasks across five task families, covering routine digital activities-including document editing, email management, and live-web information seeking-as well as scientific and engineering workflows co-designed with PhD-level domain experts that require specialized software. This breadth distinguishes TUA-Bench from prior shell-focused or domain-specific benchmarks. Each task is manually designed, runs in a real terminal with a deterministic setup script, and is evaluated by an execution-based scoring protocol. We find that the strongest frontier agent, Claude Code with Claude Opus 4.8 max reasoning effort, achieves 65.8% overall performance, with substantial gaps across both tracks. By providing a broad and realistic evaluation of terminal-use capabilities, TUA-Bench aims to accelerate the transition from narrow, task-specific assistants to general-purpose agents capable of operating reliably across diverse digital environments.

To reduce memory consumption during LLM inference, a handful of methods have been proposed for KV cache pruning. While these techniques can accomplish lossless memory reduction on many datasets, they often hinge on an under-emphasized condition: an input/domain-specific threshold for KV cache budget needs to be pre-determined to achieve the optimal performance. However, such input-sensitive design may be considerably limited in real-world scenarios, as open-domain inputs span diverse domains, lengths and difficulty levels, without clear boundaries for threshold selection. As a result, the dependence of such input-sensitive threshold can be a fundamental limitation that causes large degradation on arbitrary inputs. In this work, we propose a new objective that lifts the threshold constraints for robust KV compression, advocating for "threshold-free" methods that adaptively adjust budget allocation while preserving full-cache performance. We then propose a novel method, ReFreeKV, serving as the first instantiation of this objective. Extensive experiments across 13 datasets with diverse context lengths, task types, and model sizes demonstrate its efficacy and efficiency. Our code is publicly released at https://github.com/Patrick-Ni/ReFreeKV.

Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry. Research communities across disciplines are increasingly evaluating tabular foundation models on diverse datasets and tasks. However, these task- and discipline-specific evaluations remain largely inaccessible to model researchers because benchmark software and evaluation protocols are fragmented. As a result, model researchers rely on standard benchmarks, which are mostly defined for tasks where tabular foundation models already excel. The most challenging scenarios are excluded, limiting meaningful progress in the field by focusing on marginal improvements on IID data rather than on broader, more demanding challenges. To overcome this, we introduce BeyondArena, the first unified holistic benchmark for tabular data that supports diverse task types (IID, temporal, grouped), across sample size and feature dimensionality scales, with diverse feature types (with text, with high cardinality) from a broad range of disciplines. To enable unified benchmarking beyond standard benchmarks, we introduce Data Foundry, a Python framework and metadata schema for curating tabular datasets for predictive machine learning. Our results across 11 models and 142 curated datasets show that existing tabular foundation models excel on tiny- to medium-sized IID data, while traditional tree-based and deep learning models still dominate on non-IID, large, and high-dimensional datasets. BeyondArena guides model research for the most demanding challenges in tabular data, enabling progress towards truly foundational tabular models.

Murray Stassen / Music Business Worldwide : Tidal adopts an AI policy that blocks wholly AI-generated music from earning royalties and removes AI-generated music that impersonates artists —  TIDAL is introducing an AI policy that will automatically tag wholly AI-generated music in its app and block it from earning royalties.

Jaspreet Singh / Reuters : Digital Realty says it plans to acquire a majority stake in three fully leased Northern Virginia data centers from Blackstone-managed funds in a $7.8B deal —  Digital Realty (DLR.N) said on Monday it would acquire a majority stake in three fully leased Northern Virginia data centers …

Debby Wu / Bloomberg : Source: Taiwanese authorities raided Super Micro's Taiwan office as part of a probe into the alleged smuggling of Nvidia chips to China; SMCI closed down 8.1% —  Super Micro Computer Inc.'s offices in Taiwan were raided by government authorities on Monday, widening an investigation …

Bloomberg : As a new law bars DOD from working with companies whose lobbyists also represent blacklisted entities, DC lobbying firms drop companies like Alibaba and Tencent —  Washington's most powerful lobbying firms are rushing to cut ties with Chinese tech giants including Alibaba Group Holding Ltd …

Sean O'Kane / TechCrunch : Waymo and Uber quietly ended their partnership in Phoenix in May; Uber says it is readying the launch of a separate autonomous vehicle partnership in the city —  Waymo robotaxis are no longer available on Uber's ride-hail app in Phoenix, Arizona, ending a nearly three-year partnership in the city …

Reuters : Sources: component and supplier lists, and photos of iPhone 18 Pro models, are among files a ransomware group stole from Apple's Indian supplier Tata —  Sensitive lists of components and suppliers, and photos of Apple's upcoming iPhone 18 Pro models are part of files posted on the dark web …

Reuters : Filing: President Trump reports $1.4B+ in income from his family's crypto ventures in 2025, including $500M+ from WLF and $635M from the sale of his $TRUMP coin —  U.S. President Donald Trump reported more than $1.4 billion in income from his family's crypto ventures last year …

Bloomberg : Sources: TikTok settles a lawsuit before a second California trial over social media harm to minors for an undisclosed sum; Meta and Snap remain defendants —  TikTok is finalizing a settlement related to a lawsuit claiming that the world's largest social media platforms are addictive to minors …

Bobby Allyn / NPR : Sources: Mark Zuckerberg met with Kalshi CEO Tarek Mansour about a possible takeover last year, before directing Meta employees to build a prediction market app —  Before Meta CEO Mark Zuckerberg directed employees to build a standalone prediction market app, he proposed buying Kalshi …

Tim Fernholz / TechCrunch : Omen AI, which provides real-time coolant health monitoring for data centers, raised a $31M Series A led by Nava Ventures, bringing its total funding to $41.5M —  The AI-driven demand for compute power has data centers looking to squeeze more from every rack of GPUs.  One consequence?

Ben Schoon / 9to5Google : Google shuts down the Tenor API, affecting GIF pickers on platforms like Discord, WhatsApp, and Bluesky; Nikita Bier says X has migrated elsewhere —  GIFs are a huge part of the modern web, with Tenor being one of the libraries that powers a variety of apps including Twitter/X. Google acquired Tenor …

Zac Hall / 9to5Mac : Claude Sonnet 5 costs $2 per 1M input tokens and $10 per 1M output tokens through August 31, after which prices rise to $3 and $15, respectively —  Anthropic is upgrading Claude Sonnet, replacing Sonnet 4.6 from February with Sonnet 5 as the best medium-sized model.

世界各地的学生正使用 AI 驱动的智能眼镜在考试中作弊，而各国也开始加强对考生眼镜的检查。本月初的中国高考有逾千万学生参加，政府要求检查所有学生的眼镜。英格兰考试监管机构负责人警告，AI 眼镜和智能耳机等设备可能会加剧考试作弊现象。韩国报告了首起利用 AI 眼镜作弊的案例。专家担心相关个案可能预示着智能眼镜作弊正成为更为普遍的问题。研究过智能眼镜应用的 Thomas Corbin 说，“如果我们看到有些案例被报道，那么肯定还有更多案例没有被报道。”

福特公司是众多拥抱 AI 的公司之一，该公司在质检等运营环节部署了 AI。福特 CEO Jim Farley 去年 6 月表示，AI 将让大量白领落在后面。首席运营官 Kumar Galhotra 去年 10 月表示公司在整个工业系统部署 AI。福特汽车硬件工程副总裁 Charles Poon 如今表示 AI 驱动的质量检查未能达到预期。自动化工具缺少资深技术人员的训练和知识，他表示资深技术人员在公司能利用其知识改进技术前已离开了公司。这些员工已重新聘用，负责训练系统并指导年轻员工。

超新星爆炸将碳、氮、氧等组成生命的元素散布到宇宙中。这些粒子成为宇宙射线在宇宙中跋涉数百万年后，如今正不断撞击地球大气。搭载于国际太空站上的阿尔法磁谱仪（AMS-02）所收集数据的新发现为我们揭开了这些粒子的奥秘。AMS-02 自 2011 年启用以来，已累积收集超过 230 兆个宇宙射线事件。研究团队深入分析了周期表上介于氦与铁之间的 20 种元素，惊人地发现这些宇宙射线并非随机分布，而是可以归纳为四个独特的类别：两种「主要宇宙射线」（源自深空未受破坏的原始射线）及两种「次要宇宙射线」（在旅途中与星际气体碰撞产生的混合粒子）。粒子中质子数的奇偶性（奇数或偶数）与其演化路径密切相关，显示恒星内部的元素合成机制深刻影响了它们在太空中的行为。新发现直接挑战了现行的宇宙射线模型。目前科学家尚无法完全解释这些观测结果，这意味着现有理论可能遗漏了某些关键物理机制。

波音 747 曾是美国实力、发明、进步和平民主义的象征。如今它却成为所有这些价值观衰落的缩影。从 1970 年第一架 747 投入使用，到 2023 年停产该机型，波音公司共生产了 1574 架飞机，包括两架至今仍在服役的空军一号。类似 20 世纪的大多数科技创新，747 项目也是由军方推动的。1960 年代初，波音应政府要求设计一款大型军用运输机。洛克希德赢得了竞标生产了 C-5 银河运输机。波音失利之后放手让工程师在此基础上研发出最大的商用飞机。747 的用途远不止客运和货运。NASA 曾利用一架改装过的 747 将航天飞机运送到肯尼迪航天中心。美国的这两大象征似乎预示着 20 世纪的进步永无止境。但它们终究会落幕。航天飞机项目于 2011 年终止，而 747 飞机也逐渐从天空消失。如今亲眼目睹 747 变得越来越难，尤其是在美国。Atlas Air 和 Kalitta 仍在运营部分波音 747。汉莎航空运营着波音 747 客运航班最多的航线，大韩航空仍在运营 747 的国际航线，中国、伊朗和俄罗斯用它执飞类似巴士的国内航线。

Top500 上周公布了最新的超算榜单，深圳国家超算中心的灵晟首次亮相即登顶榜单。灵晟超算在 Linpack 测试中比排名第二的美国劳伦斯利弗莫尔国家实验室 El Capitan 超算快 22%，在 HPCG 测试中快 26%。它是首个仅靠 CPU 实现持续双精度浮点性能逾 2 Exaflops 的超算系统，美国的超算使用了 GPU 加速器。据 Chips and Cheese 根据相关幻灯片和相关 arXiv 论文报道，灵晟使用的 LX2 CPU 是基于 ARMv9.2 架构，支持 Scalable Matrix Extension(SME)指令集。相比下日本 ARM 超算富岳(Fugaku)是基于 ARMv8 架构，在今天已经相当老了。LX2 的每个核心都有 32 KB 的 L1 指令缓存和 32 KB 的 L1 数据缓存。芯片由两个计算模块（die）组成，每个模块包含四个 40 核心簇。每个簇有 2 个核心被禁用，因此每个簇有 38 个活跃核心，每个模块有 152 个活跃核心。每个簇配备 28.5 MB 的 L2 缓存，每个模块有 114 MB 的 L2 缓存，整个 LX2 封装有 304 个活跃核心和 228 MB 的总 L2 缓存。304 个核心以 1.55 GHz 运行，每个 LX2 CPU 提供 60.3 TFLOP/s 的 FP64 计算性能，功耗为 690 瓦。LX2 配备了八个“高带宽内存”，带宽为 4 TB/s（另一篇报道称 4 TB/s per chiplet，8 TB/s per socket）。所谓的高带宽内存可能不是 HBM。灵晟超算系统包含了逾 22,000 个节点和 1379 万个 CPU 核心。

Gartner 预测两年内开发者的 AI token 使用费用将超过其薪水。预测将开发者的薪水设定为每月 2000 美元，因此这并不意味着所有地区的 AI token 使用费用将会达到或超过开发者薪水，美国的开发者年薪通常高达六位数。Gartner 高级首席分析师 Nitish Tyagi 指出，在极端情况下美国开发者的 AI token 使用费用也可能会超过其薪水，有些开发者每月的 AI 支出会超过数万美元。美国部分科技公司也已经开始要求其员工控制 AI token 的使用。Tyagi 称，企业必须监管和控制 AI token 的使用，否者 AI 工具费用的增长速度可能会超过带来的生产力提升。

Claude Code 被发现会悄悄检查用户的系统时区和是否来自中国 AI 公司。对 Claude Code（2.1.196）的本地二进制文件的分析发现，它会检查系统时区是否为 Asia/Shanghai 或 Asia/Urumqi，以及是否匹配中国科技公司的域名，其中包括 baidu.com、alibaba-inc.com、alipay.com、antgroup-inc.cn、bytedance.net、kuaishou.com 、xiaohongshu.com、jd.com 和 bilibili.co 等等。此举可能是防止中国 AI 公司蒸馏其模型。

GLP-1 减肥药如 Ozempic 和 Wegovy 需要长期服用，停药后容易反弹。现在研究人员报告小鼠实验显示单次注射 DNA 指令即可实现减轻体重和控制血糖的效果，其持续时间十倍于 GLP-1 药物。新方法有助于消除重复给药的需求。人体能充当工厂合成长效抗体。新方法是基于 Weiner 实验室的肌肉 DNA 电穿孔平台（intramuscular DNA electroporation platform），患者注射一针质粒 DNA（遗传指令），然后施加电脉冲，将这些指令导入人体细胞的细胞核被读取，使细胞能持续合成抗体。Weiner 实验室使用该方法将新冠抗体指令输入到患者体内，在 1 期临床试验中患者体内抗体持续表达逾 72 周。新研究中，研究人员设计出长效肠促胰岛素激素 GLP-1 和 GIP 的 DNA 指令，其中包含了一个防止蛋白质在体内快速分解的抗体片段。在小鼠实验中，单次注射在 70 天内产生了可检测水平的肠促胰岛素，能长时间维持代谢改善效果。

Allianz Trade 公布的一份报告显示，数据中心对气候构成的危害比此前预想的更为严重。报告指出，此前人们低估了数字基础设施对气候的影响。除非采取适当措施，否则随着人工智能应用的激增，由算力需求引发的碳排放量将急剧上升。该研究估算，2025 年全球数据中心的二氧化碳排放总量达到 2.86 亿吨。这一数字比国际能源署此前研究得出的数据高出 57%。Allianz Trade 将此归因于其模型纳入了整个价值链中的间接排放（例如制造和施工环节产生的排放）以及电力传输过程中的损耗。数据显示，超过 70% 的排放源于设施的电力消耗，而约四分之一的排放则来自硬件和基础设施。数据中心对气候的危害在很大程度上取决于其地理位置。中国和美国的数据中心二氧化碳排放量合计约占全球总量的 70%。这两个国家的电力结构中，碳强度相对较高，分别为每度 384 克（美国）和 526 克（中国）。

一项为期两年、采用安慰剂对照、双盲设计的临床试验显示，高剂量 Omega-3 补充剂未能改善受试者记忆力、认知能力，也无法延缓阿尔茨海默病相关脑区的脑细胞损耗。研究共纳入 365 名 55-80 岁的受试者，他们日常很少吃鱼，而鱼类是膳食 Omega-3 的主要来源。研究人员认为受试者全部属于阿尔茨海默病患病高风险人群。近半数受试者（47%）携带 APOE4 基因，后者是目前已知的晚发性阿尔茨海默病最强遗传风险基因。受试者被随机分为两组，一组每日服用鱼油补充剂，另一组服用安慰剂。每份补充剂含有 2000 毫克二十二碳六烯酸（DHA），这是一种对大脑功能起关键作用的 Omega-3 脂肪酸。研究首要目标之一就是确认补充剂中的DHA能否真正进入大脑。研究人员通过检测包裹大脑与脊髓的脑脊液中的 DHA 含量验证了这一问题。服用 6 个月后，受试者脑脊液DHA平均提升 17%，证实该营养物质顺利抵达脑部。即便 DHA 成功进入大脑，也没有带来可量化的认知提升。研究人员分别在试验启动时与两年后对受试者的记忆、思维能力进行测评。服用 DHA 补充剂的人群，其认知测试表现与安慰剂组相比并无优势。脑部影像检测结果也印证了这一结论：鱼油补充剂无法减缓海马体萎缩。海马体是掌控记忆的核心脑区，也是评判大脑老化程度、阿尔茨海默病发病风险的标志性区域。

麦可思研究上个月发表的一项调查发现，根据 70 所本科高校最新公布的停招专业名单，日语专业共有 8 所高校停招，德语专业 5 所，翻译研究专业 5 所。麦可思称，过去多年，外语类专业曾是高校扩招的重要方向，但随着国际交流环境变化以及人工智能翻译工具快速发展，传统语言类专业的人才培养模式也开始转型。大学如何应对 AI 带来的影响？中国大学需要政府批准才能新增专业。据报道，教育部批准下学年新增 38 个专业，其中大部分侧重于科技或数字化领域。新专业包括具身智能、商业 AI、数据智能等，以及低空经济与管理、半导体设备工程、稀土科学与工程等领域。

国际清算银行（BIS）发表年度经济报告，警告 AI 泡沫破裂将增加全球经济衰退风险。报告指出，AI 投资虽能助推生产力提升，但过度投资一旦退潮反转，可能将引发金融系统混乱。根据早先的报道，亚马逊预计 2026 年资本支出 2000 亿美元，微软预计为 1900 亿美元，Google 约为 1800 亿美元，Meta 1400 亿美元。甲骨文也将投入巨资。五大数据中心运营商 2026 年的 AI 相关资本支出将超过 1 万亿美元。报告指出，“投资承诺的增长速度超过了这些公司的利润和自由现金流，导致部分公司不得不发行债券筹集额外资金。这场投资竞赛的部分原因可能是认为只有少数拥有卓越技术的企业最终能主导市场。”报告指出，“回报不尽如人意可能会引发融资突然收缩，使资本支出繁荣演变为旷日持久的投资萧条，可能对金融状况产生连锁反应。”报告还提到，电力供应、芯片短缺和电网连接瓶颈等问题引发了对“供应侧障碍”的担忧。