Weekly Digest — 2026-W30

AI Coworkers that run your projects and operations

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AI Crypto Investigations/Research Agents

Agentic click tracking that shows what grows your business

Understand anything online without leaving the page.

The live AI employee inside your product, serving every user

Instant, hardware Isolated Sandboxes for AI agents

The video AI agent that understands your whole library

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The easiest way to build AI agents for all your tasks

Clone any website into clean code. Free & open source

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Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge. Yet existing skill libraries are mostly hand-written, text-centric, or derived from agent traces, leaving tutorial videos and other multimodal human resources largely underused. We present RESOURCE2SKILL, a framework that distills multimodal resources, including tutorial videos, repositories, articles, and reference artifacts, into executable skills for software agents. RESOURCE2SKILL organizes these skills as a hierarchical multimodal Skill Wiki, where each entry combines structured text, code, visual examples, metadata, and provenance. This design preserves complementary signals from different resources: videos capture temporal operations and visual effects, code captures executable tool patterns, and articles or artifacts provide conceptual and stylistic grounding. At inference time, agents retrieve and compose relevant skills from the wiki; when coverage is insufficient, the same construction operator can acquire new skills online. Across seven practical authoring domains, RESOURCE2SKILL improves average overall score by +11.9 percentage points over no-skill agents and outperforms strong harness baselines in 26 of 28 main-aggregate model-domain cells. Ablations confirm the value of multimodal skill format, hierarchical organization, source diversity, selection strategy, and online acquisition.

Graph retrieval-augmented generation (GraphRAG) enhances large language models with structured knowledge, yet existing systems construct knowledge graphs in a single extraction pass, producing noisy entities and brittle retrieval. RAGU, an open-source modular GraphRAG engine, addresses this by separating extraction from consolidation: entities and relations pass through two-stage typed extraction, DBSCAN-backed deduplication, LLM summarization, and Leiden community detection. A key insight motivates a compact extractor: the skills an in-pipeline LLM needs - comprehension, extraction, reasoning over context - are language skills that grow only weakly with model size, unlike factual world knowledge. Accordingly, we train Meno-Lite-0.1, a 7B model optimized for language skills, which outperforms Qwen2.5-32B on knowledge-graph construction (+12.5% relative harmonic mean) and matches it on English GraphRAG tasks. On GraphRAG-Bench (Medical), RAGU retrieves the most complete context at every factoid level (evidence recall up to 0.84 vs. leq0.76) and overtakes HippoRAG2 on synthesis tasks; on multi-hop factoid QA, the apparent HippoRAG2 advantage is shown to be largely an answer-format artifact. RAGU is installable via pip install graph_ragu, runs on a single GPU, and is released under MIT. The source code is publicly available at https://github.com/RaguTeam/RAGU, and the Meno-Lite-0.1 model can be obtained from https://huggingface.co/bond005/meno-lite-0.1.

We present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a wide range of mobile manipulation tasks in unseen environments out-of-the-box, and (2) efficiently adapting to novel downstream tasks with minimal fine-tuning data. We propose a two-stage training recipe consisting of pre-training and post-training. During pre-training, we imbue the model with broad and generalizable action-generation capabilities by training on over 100k hours of real-world manipulation trajectories collected via UMI devices. Crucially, we develop a scalable auto-labeling pipeline that annotates trajectory clips with natural languages describing scene state transitions, providing rich and precise conditioning for action learning. During post-training, we aim to align these capabilities with robot embodiments and imperative instructions that humans naturally use to prompt robots. Extensive experiments demonstrate strong scaling behavior. Xiaomi-Robotics-1 consistently improves with increased data scales and model sizes during pre-training. This scaling behavior directly transfers to post-training, where a stronger pre-training model yields better out-of-the-box real-robot performance in unseen environments. Furthermore, Xiaomi-Robotics-1 serves as a strong robot foundation policy that can be efficiently fine-tuned on complex, dexterous tasks with high data efficiency. Across multiple simulation benchmarks, Xiaomi-Robotics-1 outperforms state-of-the-art methods. Notably, it establishes a new state-of-the-art with a 57.6% success rate on RoboCasa365, surpassing the previous best of 46.6%. Furthermore, it achieves an average score of 20.07 on RoboDojo, significantly outperforming the prior state-of-the-art (13.07). Code and model checkpoints will be released. Project page: https://robotics.xiaomi.com/xiaomi-robotics-1.html

We present Loopie, the most powerful looped Transformer to date. The Loopie series consists of two Mixture-of-Experts (MoE) models: a 20B-parameter model with 2B active parameters and a 6Bparameter model with 0.6B active parameters. Looped Transformers have long faced a challenge: given an N-fold increase in pre-training compute, increasing the parameter count by a factor of N usually outperforms looping a model N times. Loopie addresses this challenge. Extensive ablation studies, including comparisons with a vanilla 30B-A3B model, show that Loopie substantially outperforms vanilla Transformer baselines trained with the same compute budget. Our novel post-training pipeline equips Loopie with strong reasoning abilities. At the 2025 IMO and IPhO, Loopie achieves gold-medal performance without tools.

Hyper-Connections (HC) expand the residual stream of Transformers into N parallel streams, providing a form of memory scaling beyond model width and depth. Manifold-Constrained HC (mHC) stabilizes this formulation at scale. The large gains from N{=}1 to N{=}4 suggest residual-stream expansion as a promising scaling axis. However, existing HC-family methods typically stop at N{=}4. Our experiments reveal why: scaling mHC beyond this point yields diminishing performance gains and rapidly increasing training cost. We attribute this limitation to two bottlenecks: insufficient write-back information for an expanding number of streams and residual-mixing generation whose cost scales cubically with N. To address both bottlenecks, we propose xHC (Expanded Hyper-Connections), the first HC-family method to achieve meaningful expansion beyond N{=}4. xHC combines temporal feature augmentation for richer write-back with a sparse residual-stream architecture that updates only k=4 of the N=16 streams while retaining dense access to the full residual state. Across 18B and 28B MoE models, xHC delivers strong and consistent downstream improvements. On an 18B MoE model, xHC improves the average downstream score by 4.0 points over mHC, while adding only modest training FLOPs over the vanilla baseline. Scaling-law experiments show that the vanilla and mHC require 1.50times and 1.19times the compute of xHC, respectively, to reach the same loss. Practical large-N training also requires controlling memory traffic from the expanded residual state. We therefore introduce xHC-Flash, which reduces the per-sublayer memory traffic from 73.5C to 40C, comparable to the 34C required by mHC at N{=}4, while retaining the gains of full xHC. Together, xHC and xHC-Flash make large-N residual-stream expansion effective and practical for LLM pre-training.

Healthcare spans high-stakes communication, expert reasoning, and workflow execution, yet specialized LLMs that cover these use cases together remain limited. A healthcare model must handle patient consultation, clinical reasoning over text and images, interactive diagnosis, and electronic health record (EHR) tool use. These capabilities fail in different ways, and a narrow update for one task can degrade another. We present Cura 1T, a healthcare-specialized LLM trained through a human-gated self-evolution loop. In each evolution round, a training agent plans a target capability, trains the model, evaluates benchmark trajectories, and refines the data mixture from observed failures. This data-centered loop improves the model through targeted synthetic and curated examples rather than a single generic medical-data update. Across the healthcare evaluation suite, Cura 1T ranks at or near the top among frontier baselines, while remaining competitive on out-of-domain reasoning and agentic benchmarks.

Video multimodal large language models (MLLMs) can describe what happens in a video, but rarely identify when the supporting evidence occurs. We study generalist video temporal grounding, in which one model predicts a variable-cardinality set of evidence intervals across video lengths, domains, query forms, and viewpoints. Existing training strategies are misaligned with this set-valued task: long-video labels often rely on brittle one-pass annotation, while reinforcement-learning rewards either fail to distinguish non-overlapping predictions or require fragile segment matching. TimeLens2 treats temporal evidence as an interval set throughout supervision and optimization. TimeLens2-93K constructs reliable multi-span supervision through caption-derived proposals, independent localization, cross-agent consensus, semantic verification, and boundary refinement. Our temporal Wasserstein reward computes exact one-dimensional \(W_1\) between uniform distributions over merged interval supports, providing dense, matching-free feedback under unequal cardinalities and equivalent fragmentation; temporal IoU complements it with precise-overlap feedback. Across seven benchmarks, TimeLens2-2B outperforms all size-matched baselines on every benchmark, while the 4B and 8B variants achieve state-of-the-art performance, surpassing open-source models with up to 397B parameters. The 2B, 4B, and 8B variants improve over their Qwen3-VL backbones by 14.2, 13.0, and 18.1 mIoU points, respectively.

This paper introduces EvolvingWorld, a framework and benchmark for character and world co-evolution in interactive literary worlds. Existing systems either treat interactive literary simulation as static persona imitation or isolated scene generation, failing to capture how characters and worlds evolve together over time. To address this, EvolvingWorld models literary simulation as a long-horizon process where characters interact, scenes progress, and character and world states are persistently updated. Unlike prior systems relying on fixed schemas, EvolvingWorld adopts an open-schema framework to support simulation across diverse literary worlds. The framework consists of two coupled modules: a Character Agent for multi-character role-play and persistent profile evolution, and an LLM-based World Model for global and location/entity-level state maintenance and scene progression. Based on this architecture, we formulate 7 trainable tasks for scene initialization, interaction generation, and state update. We construct a dataset from 57 books, producing 138,596 supervised training samples and 222 snapshots for testing. Furthermore, we introduce a trajectory-level LLM-as-Judge evaluation protocol spanning 10 dimensions and 20 metrics. Experiments show that EvolvingWorld can improve long-horizon simulation by effectively maintaining persistent, coherent character and world development.

Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools. DeepSearch-World contains 420K multi-hop QA tasks constructed from entity-level random walks and supports key agentic cognitive behaviors useful for self-evolving, including progress verification, grounded reflection, and failure recovery. DeepSearch-Evolve iteratively performs trajectory generation, filtering, data mixing, and fine-tuning to train stronger agents. Without distillation from more capable models, DeepSearch-World-9B achieves competitive performance compared with open-source agents, reaching 31.2% on BrowseComp, 61.5% on GAIA, and 93.4% on HotpotQA, showing that verifiable environments enable scalable self-evolution for long-horizon web agents. We will release the environment, 420K training pool, validation set, model, and code to facilitate future research on self-improving deep search agents.

Pruning long context for coding agents has been a vital technology for efficient context management. While existing context pruning methods such as SWE-Pruner realize this by attaching a separate code classifier, we find the agent itself encodes internal representations indicating the relevance of code context when reading tool output. Based on this finding, we propose SWE-Pruner Pro, which prunes tool outputs directly inside the agent. Concretely, a small head turns the agent's own internal representations into a keep-or-prune label for each line, with a length-aware embedding keyed to each tool output's line count. Across two open-weight backbones and four multi-turn benchmarks, SWE-Pruner Pro saves up to 39% of prompt and completion tokens while preserving task quality, with bounded inference overhead. Notably, on MiMo-V2-Flash SWE-Pruner Pro additionally raises the SWE-Bench Verified resolve rate by +3.8% and the long-context Oolong accuracy by +2.2 points.

Human-object centric video personalization (HOCVP) is a core task within subject-driven video generation. However, existing methods suffer from two key limitations. First, most approaches focusing on inter-subject personalization still struggle to strike a balance between high subject fidelity and accurate interaction patterns between humans and diverse objects, especially when objects represent abstract concepts such as logos. Second, while intra-subject references (e.g., OCR maps, multi-view inputs) are expected to enhance subject fidelity, most existing works lack mechanisms to understand such latent correspondence. To address both challenges, we propose HOMIE, an HOCVP framework that tackles both inter- and intra-subject input settings in a unified manner. Compared to previous approaches, HOMIE proposes a better MLLM integration strategy to extract knowledge of reference-level relationships without compromising the controllability of text encoders or incurring costly re-alignment. Specifically, we introduce global multimodal guidance within self-attention to better align MLLM-derived semantic features with VAE tokens. Furthermore, we propose modality-reference embedding to differentiate tokens from MLLM features and VAE tokens and associate intra-subject reference image tokens. Extensive experiments validate that our method achieves state-of-the-art performance across various HOCVP tasks. Project Page: https://yiyangcai.github.io/homie-page.github.io/

Modern video generation models are increasingly hailed as emerging world models with an internalized grasp of physical law. Yet existing benchmarks largely evaluate physical plausibility only at the output level, without verifying whether the model arrives there through a faithful, law-grounded reasoning process. We introduce Apple-PI, the first benchmark that anchors video-model evaluation explicitly in physical laws. Apple-PI comprises three components. 1) Orchard: a dataset of 400 videos covering ten canonical tasks in classical mechanics. It separates single-law tasks for confounder-free diagnosis from multi-law tasks for probing generalization. 2) Benchmark Protocol: a three-stage protocol based on scientific reasoning, including Perception, Formulation, and Deduction. It uses chain-of-frames prompting on infographic-annotated first frames, treating the generated video as the model's visible reasoning trace. 3) Evaluation Suite: a hybrid evaluation suite that combines MLLM-based subjective scoring with physics-law-grounded objective measures. This enables stage-resolved diagnosis of not only whether a model fails, but where it fails. Benchmarking 11 models shows that current video models remain far from reliable law-grounded world simulators, with the best video model scoring only 0.473. Our stage-, pillar-, and source-resolved analyses further expose a Perception-to-Formulation-to-Deduction bottleneck, weak multi-law state transfer, and a persistent Sim-to-Real gap. These findings position Apple-PI as a diagnostic foundation for guiding future video models toward world models with law-grounded physical intelligence.

Blake Brittain / Reuters : Judge approves Anthropic's $1.5B settlement with authors, the first major US case of its kind to settle; some authors opted out and have other ongoing lawsuits —  A federal judge in San Francisco on Monday signed off on artificial intelligence company Anthropic's landmark $1.5 billion settlement …

Bloomberg : Source: Ancestry.com pitches a risky $1.75B term loan at 4 to 4.25 percentage points above the benchmark to refinance debt from its 2020 Blackstone acquisition —  Ancestry.com Inc. is seeking to refinance debt raised to fund its 2020 acquisition by Blackstone Group Inc. funds …

Bloomberg : Snap reaches a deal to settle the second case set for trial over social media harm to minors, following TikTok and YouTube, leaving Meta as the only defendant —  Snap Inc. said it has reached a tentative agreement to settle the second case set for trial over claims that the world's largest …

Sarah Perez / TechCrunch : X relaunches its Android app after a year-long rewrite from scratch; Nikita Bier calls it “one of the largest engineering projects” in the company's history —  Nearly a year ago, Elon Musk-owned X announced it would begin rebuilding the Android version of its app …

Cheng Ting-Fang / Nikkei Asia : Taiwan indicts a former TSMC executive for allegedly stealing trade secrets, in what authorities say is the first National Security Act case involving China —  TAIPEI — Taiwanese prosecutors on Monday indicted a former TSMC deputy manager for allegedly stealing trade secrets involving …

Marina Temkin / TechCrunch : Natural, which aims to help companies let their AI agents make autonomous payments, raised a $30M Series A led by Forerunner, bringing its total raised to $40M —  AI agents are starting to execute more sophisticated tasks, such as identifying vendors that can deliver freight, comparing prices …

Sara Fischer / Axios : OpenAI renews its investment in the American Journalism Project by committing an additional $5M in funding and $3M in tech credits over the next two years —  OpenAI is renewing its investment in the American Journalism Project by committing an additional $5 million in funding and $3 million …

Wall Street Journal : OpenAI adds Nubank founder and CEO David Vélez and BNY CEO Robin Vince to its board of directors, as the company moves toward a public listing —  AI company names David Vélez of Nubank and Robin Vince of BNY as directors  —  OpenAI is adding David Vélez …

OpenAI : OpenAI says its models chained vulnerabilities across its research environment and Hugging Face's infrastructure to find solutions for the ExploitGym benchmark —  Last week, Hugging Face disclosed a new kind of security incident(opens in a new window) after they detected and contained …

Sam Sabin / Axios : Cisco releases Antares-350M and Antares-1B, two open-weight AI models to find known vulnerabilities within a codebase, and plans to release Antares-3B soon —  - Cisco says the models performed similarly to much larger models, including OpenAI's GPT-5.5 and Z.ai's GLM-5.2 …

Juby Babu / Reuters : Super Micro says Q4 orders have topped $60B and expects gross margins of 15% to 17%, up from its prior forecast of 8.2% to 8.4%; SMCI jumps 13%+ after hours —  Super Micro Computer (SMCI.O) said on Tuesday it has received orders in excess of $60 billion in the fourth quarter …

Mark Landler / New York Times : France becomes the first European country to pass a law banning social media access for users under 15, potentially going into effect as soon as September 1 —  Its new law, the first of its kind enacted by a European country, will ban access to some accounts for anyone under 15.

医学界完成了一项前沿手术—— 一名男性通过移植自己童年取出并冷冻保存16年的睾丸组织，重新恢复了精子生成能力。这批睾丸组织样本是他在接受化疗前采集冷冻的。化疗会严重损伤生殖功能，导致不育。这一里程碑式成果有望开启全新的人类不育治疗浪潮。2008 年比利时布鲁塞尔自由大学医院的医生为一名患病的 10 岁男孩摘除部分睾丸组织并冷冻保存。他当时罹患镰状细胞贫血，即将接受化疗，为后续造血干细胞移植做准备。10 余年后，这名男孩成年，希望拥有自己的后代。医护人员对其进行了两年监测，确认他已完全无法正常生成精子。随后，团队将冷冻保存 16 年的11个睾丸组织片段，分别移植至其剩余睾丸内部及阴囊皮下。移植组织在成年男性的激素环境与生理条件下培育生长一年。一年后，研究团队取出移植组织并开展检测分析。检测结果显示，多个移植组织中均发现生精干细胞，且存在活跃的精子生成迹象。在其中一个移植样本中，研究人员成功检出一枚成熟精子。

Mozilla 释出了 Firefox 153.0。它是 ESR 版本，将提供至少 15 个月的支持。Firefox 153.0 主要新特性包括：Vulkan 视频解码支持，Linux 版支持 Video Acceleration API (VA-API) ，增加对 JPEG-XL 图像格式的支持，Windows 版支持 HDR 视频播放，PDF 功能增强，等等。

黑客入侵了罗马尼亚的土地管理机构 Agenția Națională de Cadastru și Publicitate Imobiliară（ANCPI)，在勒索失败之后删除了整个土地登记数据库以及备份，导致全国房地产市场瘫痪，官方应用和网站已离线一周。公证员无法记录新的交易，公民也无法获取所有权证明或详细的土地记录。黑客是在 7 月 14 日删库的，一天后 ANCPI 部分被盗数据在黑客论坛上出售，泄露的数据包括员工凭证、内部文件以及该机构的 IT 网络详细信息。ANCPI 表示正在重建系统。虽然黑客声称删除了备份，但 ANCPI 表示它还有备份副本。

体细胞突变随年龄增长而积累，能导致细胞死亡。根据发表在《npj Aging》期刊上的一项研究，科学家开发出一种方法量化体细胞突变对人类寿命的影响。研究只考虑体细胞突变，没有考虑其它任何衰老特征。研究发现，肝脏等增殖性组织通过细胞更新能在数千年内维持功能，而神经元和心肌细胞等有丝分裂后细胞则是长寿的重要瓶颈。综合多个器官预测的人类寿命中位数是 146-194 岁，约为目前人类寿命的两倍。这意味着体细胞突变是衰老的重要驱动因素，但无法单独解释目前的人类寿命，驱动衰老的还有其它因素。

法国和意大利研究人员发现，有了 AI 之后人们愈来愈不愿说不知道了，AI 抑制了人们的批判性思维，使得人们更容易重复 AI 生成的虚假信息。研究人员将参与者分成两组，一组必须在没有 AI 的帮助下回答问题，另一组可以向 AI 寻求建议。结果发现没有 AI 帮助时 44% 的人会回答说不知道答案，而有了 AI 的帮助后只有 3% 的人这样做。研究人员让参与者使用了一个更可能出错的模型 Step 3.5 Flash。结果显示，不使用 AI 正确率为 27%，使用 AI 后降至了 9%，原本会给出正确答案的人在听取了 AI 建议后给出了错误答案。尽管正确率下降了，但参与者的自信心却增加了。

Google 逾 4500 名员工联名写信给首席执行官皮查伊，要求公司给员工更明确的裁员保障。员工提出的要求包括：保证遣散费、在强制裁员前先推出自愿离职计划，以及取消据称按配额评定绩效的制度。请愿书由 Alphabet 员工工会发起，已送到 Google 总部。工会主席、软件工程师考尔（Parul Koul）说，Google 市值已达到约 4 万亿美元，公司在取得空前成功的同时仍不断裁员，说明管理层把利润放在员工之上。请愿书要求，Google 在所有产品部门裁员前，先让员工选择是否自愿离职，并保证被裁员工能拿到遣散待遇，或允许他们把遣散费换成更长时间的带薪休假。员工也要求停止按固定比例评出低绩效员工的做法，认为这种制度可能变成裁员工具。考尔说，员工到皮查伊办公室递交请愿书时，大门紧闭，管理层没有正式回应。他们最后把请愿书交给一名职员，对方承诺会转交给皮查伊。

去年美国在全球大规模征收关税，任天堂据此提高了 Switch 2 等相关设备和配件的售价。今年 2 月美国最高法院裁决征收关税违法，任天堂随后起诉美国政府要求退还关税。美国玩家则跟着提起了一项拟议中的集体诉讼，认为任天堂应该将美国关税退款退给消费者。任天堂周一请求法庭驳回该诉讼，称它无法律义务将美国关税退款退给消费者。任天堂的观点十分简单，任天堂或其零售合作伙伴设定了最终价格，而买家是自愿支付的。

FBI 逮捕了一名 21 岁的佛罗里达居民 Zyaire Dontaevious Zamarion Wilkins，他涉嫌与同伙通过在 Steam 游戏中植入恶意程序去窃取玩家的加密货币。该团伙在近两年时间内通过在至少八款 Steam 游戏中植入恶意程序，感染了约 8000 台电脑，在 2024 年 5 月至 2026 年 2 月间从约 80 个加密货币钱包中窃取了价值至少 22 万美元的加密货币。被植入恶意程序的 Steam 游戏包括 BlockBlasters、Dashverse、Lunara 和 PirateFi。其中仅 BlockBlasters 一款游戏就从 261 至 478 名受害者手中窃取了价值约 15 万美元的加密货币，受害者包括了 Twitch 主播 RastalandTV，他被盗走了 3.2 万美元，他正接受癌症治疗，这笔钱是观看者捐赠的。

在生成式 AI 时代，流行公民科学数据库如 iNaturalist 和 Macaulay Library 也免不了被 AI slop 入侵。研究人员在《自然》上报告，他们在记录野生动物物种的流行公民数据库内发现了数百张 AI 生成的虚假图像。问题的真实规模尚不清楚，因为有许多假图可能未被发现。文章作者 Alexander Lees 博士称他在 Facebook 上看到的野生动物照片基本上都是 AI 生成的。他指出公民科学数据库里彻头彻尾的假图还比较罕见，问题主要是照片上传者会用 AI 美化下图像，结果生成式 AI 给图像添加了不存在的内容。iNaturalist 上逾 6.1 亿张图像只有 1400 张被标记为可能使用 AI。iNaturalist 社区支持总监 Tony Iwane 认为大多数假图并非是恶意的，他同时呼吁用户保持警惕，因为信息的准确性至关重要。

WordPress 上周释出补丁修复了两个高危漏洞，它督促运行 WordPress 软件的网站立即更新，漏洞是如此严重以至于 WordPress 在条件允许的情况下启用了强制更新。安全公司随后警告，黑客正利用漏洞入侵未更新的 WordPress 网站。WordPress 是最流行的博客软件和内容管理平台，存在漏洞的 WordPress 网站数量可能多达数千万。存在漏洞的 WordPress 版本为 v6.9.0-6.9.4，以及 v7.0.0-7.0.1。根据 WordPress 的官方统计，逾 4 亿个网站运行存在漏洞的 WordPress 版本，但其中一部分网站可能已经打上了补丁。组合利用两个漏洞，黑客能远程控制存在漏洞的网站。

欧盟委员会对阿里巴巴旗下面向消费者的电商平台 AliExpress 处以 5.5 亿欧元的罚款，原因是它在打击非法商品销售上做得不够——这是根据 Digital Services Act（DSA）开出的最高罚单。布鲁塞尔去年曾警告 AliExpress，它在阻止平台上的卖家兜售非法商品以及确保其推荐引擎不推广这些非法商品上做得不够。欧盟委员会根据 DSA 要求其整改，并详细列出了需要采取的措施。一年后，欧盟委员会认定 AliExpress 的措施不足，宣布处以巨额罚款。委员会认为，AliExpress 未能妥善评估其是否有足够的人员审查潜在的非法产品，也未能充分评估其推荐和广告系统如何加剧非法产品的扩散。DSA 允许对商家处以全球营业额最高 6% 的罚款，阿里巴巴集团截至 3 月 31 日的财年收入为 1480 亿美元。布鲁塞尔原本可要求近 90 亿美元的罚款。

加州大学圣地亚哥的科学家警告地球海洋和淡水系统中的水体溶解氧迅速减少，可能会给地球带来短期内无法逆转的变化。研究人员认为水体溶解氧也应纳入地球限度理论框架，该框架突出了人类行为对地球系统的冲击的限度，若超过该限度，自然环境将丧失正常的自我调节能力，现代人类所处的相对稳定的全新世将会结束。现有的地球限度理论包含了 9 个限度：气候变化、海洋酸化、大气平流层臭氧消耗、生物地质化学循环中氮循环和磷循环、全球淡水使用、土地系统变化、生物圈完整性的侵蚀、化学污染和空气污染。水体溶解氧迅速减少的原因包括人为全球暖化、富营养污染、深层水体的运动和通气过程，等等。