OrangeBot.AI Digest — 2026-07-09

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Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through scientific principles and physical constraints, from stereochemistry and bonding to symmetry, energetics and periodic order. However, applying artificial intelligence to this process presents a joint challenge of representation and reasoning: models must preserve domain-native structural information while showing how specific evidence supports predictions under these constraints. Here we introduce SciReasoner, a multimodal scientific foundation model for native structural reasoning across proteins, small molecules and inorganic crystals. SciReasoner discretizes coordinates, topologies and periodic connectivities into a unified structure-aware vocabulary, treating structural tokens as addressable evidence units during reasoning. In homology-controlled Gene Ontology prediction, SciReasoner improves Cellular Component annotation for low-homology and orphan-like proteins, increasing F_{max} from 0.42 to 0.55. In chemistry, it raises single-step retrosynthesis accuracy from 0.63 to 0.72 while generating fragment-level disconnection and precursor-verification traces. In materials science, its representations separate elemental and compound phases and resolve high- and low-band-gap regimes. Across 86 benchmarks, SciReasoner achieves state-of-the-art performance on 67 tasks. Double-blind expert evaluation rates its reasoning traces as preferred or at least comparable to those of a frontier large language model in 98% of cases. By making structure an inspectable substrate for reasoning under scientific constraints, SciReasoner connects accurate prediction with interpretable scientific inference.

Mainstream Vision-Language-Action (VLA) models predict actions primarily from the current observation under a Markovian assumption, thus struggling with long-horizon, temporally dependent tasks. Existing memory-augmented VLAs either expand the observation window or retrieve history from the memory bank as auxiliary policy-side context. However, they leave memory outside the native latent embedding space of VLA reasoning, preventing historical experience from being fluidly interleaved with multimodal reasoning and action formation. To this end, we introduce LaMem-VLA, a latent-memory-native framework that reconstructs historical experience into latent memory tokens and directly interweaves them with VLA reasoning. At its core, LaMem-VLA introduces four coordinated components: (i) a curator that organizes historical experience into two complementary short-term and long-term memory vaults; (ii) a seeker that queries both vaults using the multimodal cognition to retrieve context-relevant evidence; (iii) a condenser that reconstructs the retrieved evidence into compact short-term and long-term latent memory tokens; and (iv) a weaver that injects these memory tokens with the current observation and instruction into one continuous embedding sequence. By representing, retrieving, and consuming historical experience entirely in the same continuous latent space, LaMem-VLA enables memory to directly participate in VLA reasoning and guide action generation under a bounded context. Extensive experiments on SimplerEnv and LIBERO demonstrate the superiority of our LaMem-VLA.

Despite the recent promise in robot control, video generative models suffer from a domain mismatch due to their primary focus on content creation. For example, their design inherently prioritizes visual fidelity and creativity over computational efficiency and physical realism. In this work, we present LingBot-Video, a DiT-based video pretraining paradigm specifically tailored for embodied intelligence. From the architecture perspective, we adopt the Mixture-of-Experts (MoE), instead of dense, framework to achieve a better trade-off between modeling capacity and inference efficiency, and manage to scale it up from scratch. From the data perspective, we construct a data profiling engine that augments standard internet videos with extensive robot-oriented footage, encompassing manipulation, navigation, and egocentric perspectives, to equip the base model with an intrinsic understanding of actions and world dynamics. From the training perspective, we develop a multi-dimensional reward system to enforce the alignment regarding physical rationality and task completion, going beyond standard criteria such as aesthetics, prompt-following, and motion consistency. Comprehensive evaluations validate its performance and efficiency as a video foundation model. We contribute LingBot-Video as the inaugural large-scale, open-source MoE video foundation model to the community, in a pioneering effort to bridge digital creativity and physical actuation.

We present LingBot-World 2.0 (also known as LingBot-World-Infinity), an advanced iteration of LingBot-World featuring four distinct upgrades. (1) Our model achieves an unbounded interaction horizon while maintaining consistent output quality, benefiting from a carefully crafted causal pretraining paradigm. (2) Through distilling a real-time variant from the base model, our system guarantees rapid response time, sufficient to drive 720p video streams at 60 fps. (3) Compared to the previous version, this update introduces highly diverse interactive elements, comprising a broader spectrum of actions (e.g., attacking, archery, spell-casting, and shooting) alongside a richer variety of text-driven events. (4) We pioneer the integration of an agentic harness within the domain of world modeling, wherein a pilot agent is tasked with planning and executing character behaviors, while a director agent is responsible for synthesizing novel environmental elements as the scene progresses. Additionally, to facilitate a shared experience, we develop an interface that permits multiple players to simultaneously immerse themselves in this vivid world simulator. We pair our primary 14B model with a lightweight 1.3B counterpart, which supports effortless deployment on a single GPU.

Generalist robot manipulation policies have advanced rapidly, yet existing benchmarks remain limited in systematically evaluating their capabilities. Many rely on simple, short-horizon, or skill-narrow tasks with limited capability coverage, and are often conducted only in simulation or only in the real world. Simulation enables scalable feedback but misses physical deployment challenges, while real-world evaluation is costly, time-consuming, and difficult to reproduce. We introduce RoboDojo, a unified sim-and-real benchmark for comprehensive evaluation of generalist robot manipulation policies. RoboDojo includes 42 simulation tasks and 18 real-world tasks covering diverse and complementary manipulation capabilities. The simulation benchmark evaluates five dimensions: generalization, memory, precision, long-horizon execution, and open-vocabulary instruction following, while the real-world benchmark exposes policies to challenging physical-world deployment conditions. RoboDojo supports scalable evaluation through heterogeneous parallel simulation in Isaac Sim and provides RoboDojo-RealEval, a reproducible real-world evaluation system with remote cloud access, standardized hardware, scene reset, evaluation protocol, and deployment interface. Together with XPolicyLab, policies can be integrated once and evaluated across simulation and real-world settings with minimal adaptation. We integrate 30 policies into XPolicyLab and evaluate them on RoboDojo, establishing a public leaderboard and systematic analysis of current policy performance. The website is available at http://robodojo-benchmark.com/.

Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-interleaved, which is inefficient for long-horizon agentic tasks. Recently, asynchronous RL has emerged as a more efficient alternative by updating the model as rollouts arrive. However, existing asynchronous RL systems often emphasize throughput, while leaving training stability and task effectiveness largely underexplored. For example, a key challenge is that group-wise sampling in the widely-used GRPO framework does not naturally fit asynchronous agentic training. In this paper, we present Single-rollout Asynchronous Optimization (SAO) to address the stability and off-policy challenges in asynchronous RL. To reduce off-policy effects and improve generalization, we replace group-wise sampling with single-rollout sampling, that is, using one rollout per prompt. We further improve this single-rollout strategy with practical value-model training designs. To improve optimization stability, we introduce a strict double-side token-level clipping strategy. SAO is able to train stably for one thousand steps and consistently outperform GRPO and its variants on agentic coding and reasoning benchmarks, such as SWE-Bench Verified, BeyondAIME, and IMOAnswerBench. We also demonstrate that single-rollout RL is particularly effective in a simulated online learning setting, where the model must adapt to changing evolving environments. To this end, SAO is successfully deployed in the agentic RL pipeline for training the open GLM-5.2 model (750B-A40B).

Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention models fall behind in long-context recall compared to softmax-attention-based transformer architectures. Increasing the state size of linear attention improves recall performance but at the cost of higher FLOPs. In this work, we introduce Sparse Delta Memory (SDM), an architecture that scales the hidden state of gated linear RNNs to orders of magnitude higher capacity using a sparse addressing scheme. SDM extends the Gated DeltaNet architecture by replacing the dense key-value outer product with sparse reads and writes to a large explicit memory. We show that, under an isoFLOP constraint and with an identical number of parameters, a higher state memory capacity significantly improves performance on in-context learning and long-context retrieval tasks. Moreover, by learning the initial state of the SDM memory and therefore using it as a parametric memory, we show that the model further improves on a wide range of common-knowledge and reasoning tasks.

Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, but current systems still rely on researcher intuition to choose where information is stored, how observations are processed, and how model calls are connected. Agent Architecture Search (AAS) automates such design for text-domain agents, but has not been systematically evaluated on perceptual embodied agents through simulator rollouts. We study this transfer. We introduce AgentCanvas, a typed-graph runtime that hosts embodied executors as editable node-and-wire programs with simulator-aware execution and episode-level logs, and KDLoop, a coding-agent search procedure that cycles through proposal, critique, experiment, and distillation, with triggered reflection after stalls. We evaluate three AAS variants across four embodied executors spanning vision-language navigation, embodied question answering, and language-conditioned manipulation. The resulting 3x4 matrix shows that architecture-level search can produce deployable and directional success-rate gains on embodied tasks, while one apparent high-scoring candidate is rejected as leak-bearing. At the same time, the experiments expose constraints that are muted in text-domain AAS: optimization signals can be masked by rollout noise, search can become trapped in local edit basins, and episode-level credit assignment only partially emerges even when detailed logs are available. These results characterize both the promise and the current limits of automated architecture search for embodied agents.

Humans can navigate an unfamiliar city and gradually form a coherent spatial mental map spanning tens of square kilometers. Can AI build spatial representations at a comparable scale? Although recent foundation models have advanced scene reconstruction and embodied intelligence, scaling to entire cities remains an open challenge, primarily due to the lack of city-scale data. To bridge the gap, we introduce WildCity, a real-world multimodal dataset collected by autonomous fleets traversing complex urban environments. Our dataset includes 18 trajectories, each averaging 83.7 kilometers in length, and preserves the core challenges of in-the-wild perception, e.g., dynamic objects, lighting variations, and imperfect camera poses. We further establish an urban-tailored reconstruction baseline and convert the reconstructed environments into a closed-loop simulator. Beyond the dataset and baseline, we systematically analyze the key challenges on the path to simulation-ready urban digital twins: scalability, extrapolation, and uncertainty. Ultimately, WildCity aims to catalyze progress not only in city-scale rendering, but more broadly in the pursuit of AI that can perceive, remember, and reason across space at a scale comparable to human cognition. Project page: https://han-xiangyu.github.io/Wild-City/

Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry. Tactile sensing provides these complementary signals, yet tactile data remain costly to collect and hard to generalize across sensors, robots, and tasks. We introduce OmniTacTune, a policy-agnostic real-world RL pipeline that adapts tactile feedback to pretrained visual policies through residual correction. OmniTacTune uses a two-stage design: it first bootstraps tactile-aware learning from autonomous base-policy rollouts, then learns a lightweight tactile residual policy through online interaction. Extensive experiments show that OmniTacTune generalizes across diverse contact-rich tasks, visual base policies, and tactile representations. Across four real-world contact-rich tasks, it improves visual base policies from 5-40% success to 85-100% within 40-80 minutes, demonstrating an efficient path for adapting tactile feedback to scalable visual robot policies. Project page: https://colinyu1.github.io/omnitactune-site/

We present AgentLens, a production-assessed benchmark for interactive code agents. Most code-agent benchmarks reduce a run to a single bit -- did the task pass? -- but the people who actually use these agents experience the entire trajectory: how the agent follows instructions, uses its tools, verifies its own work, recovers from mistakes, and talks to them along the way. AgentLens evaluates that whole trajectory. It pairs formal verification, where an objective check exists, with LLM-written trajectory reviews and side-by-side comparisons, so that each run yields a readable explanation of why the score is what it is. This makes AgentLens useful for more than ranking models: we use it to diagnose model behavior, compare successive versions of our own agent, and catch product regressions in a nightly evaluation pipeline. We release the benchmark as open source at https://github.com/agent-lens/agent-lens-bench.

Large Language Models (LLMs) unlocked new possibilities in automated code writing, becoming the backbone of most code completion tools. While LLMs excel in mainstream languages, they often lack support for the so-called low-resource languages where training data is scarce. As a result, these languages lag behind in the quality of code completion tooling available to their communities. A concrete example is Pharo, a Smalltalk-inspired language whose IDE currently offers only single-token completion. In this work, we report on our experience bringing LLM-based code completion to Pharo. First, we describe an end-to-end pipeline that combines Pharo-specific data curation, continued pre-training and fine-tuning of open code LLMs. Second, we introduce a set of Pharo code completion benchmarks designed to evaluate whether models (i) learn Pharo's syntax and (ii) accurately complete masked Pharo code from real-world GitHub repositories. Third, we show empirically that Pharo-specialized models substantially outperform their original base checkpoints and also exceed the accuracy of substantially larger code LLMs on Pharo completion. Overall, our case study demonstrates the feasibility of bringing strong LLM-based code completion to low-resource programming languages, with models small enough to provide ``real-time'' in-IDE support.

Pixel-wise Earth-observation (EO) foundation models are now achieving state-of-the-art performance via generated spatial embeddings. However, how these models scale and how best to spend a pretraining budget remain poorly understood. We present the largest controlled scaling study for EO to date: 395 training runs on 1,024 GH200 superchips within a fixed pixel-wise Barlow Twins family, each evaluated on 15 downstream tasks. We find that pretraining loss barely predicts downstream performance (|Pearson r| < 0.2), so selecting models by loss wastes a large share of the compute. We also find that, as the training budget grows, the encoder and the data should grow together while the projector stays fixed, which gives a simple rule for allocating compute. Using this rule, we train a family of pixel-wise models (0.5B and 1B, with a 2B model in training) and distill them into compact students for embeddings-as-data deployment. The 21-million-parameter distilled TESSERA v2-1B-M in aggregate outperforms all open and proprietary models tested, some of which are orders of magnitude larger. These students produce Matryoshka representations that are inexpensive to serve: a 16-dimensional prefix keeps 92% of the full 128-dimensional performance at 1/8 of the storage. Upon completion of training we plan to release v2 global embeddings covering 2017-2025. Together, these results give a concrete, empirically grounded recipe for scaling pixel-wise EO foundation models: train large encoders, select by downstream performance, and distil into flexible student models. All code will be released at https://github.com/ucam-eo/tessera.

Long-horizon failure in world models is conventionally attributed to compounding error, a generic framing that does not distinguish what kind of error compounds. We propose a kinematic-vs-dynamic reframing: world models tend to imagine kinematically rather than dynamically. We operationalize this as the imagined Kinematic-Consistency Error, a per-step diagnostic that measures how far a rollout departs from a closed-form kinematic null, paired with a perturbation protocol that tests whether iKCE responds when physical conditions cross a regime boundary. We instantiate the diagnostic on a released DreamerV3 checkpoint trained on DMC walker-walk, where imagined iKCE runs roughly two orders of magnitude above that of matched real-physics rollouts. Across a friction sweep that crosses the gait-collapse boundary, the model's iKCE stays statistically flat even as the trained policy's reward collapses through the same range, providing the kinematic-not-dynamic signature. The diagnostic distinguishes kinematic from dynamic imagination at horizons longer than the embodiment's gait period.

Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolateral oblique (MLO) views, which provide a more complete characterization of breast abnormalities. However, existing multi-view learning approaches typically rely on feature-level aggregation or single-stage cross-attention, which can entangle view-specific and shared representations and restrict interaction to limited network depths. To address these limitations, we propose a token-centric dual-view learning framework that unifies prompt-based adaptation and cross-view fusion within a frozen vision transformer backbone. The framework reformulates inter-view interaction as structured token-level communication, where dedicated fusion tokens explicitly encode bidirectional information exchange between CC and MLO views via cross-attention, serving as intermediate carriers of cross-view dependencies rather than relying on direct feature fusion. Unlike conventional methods that apply fusion at a single layer, fusion modules are inserted at multiple transformer depths, enabling progressive and repeated interaction across the encoder hierarchy. Fusion tokens are reintegrated into the token sequence and refined by subsequent transformer layers, facilitating hierarchical propagation of complementary information while preserving view-specific structure. Experiments on VinDr-Mammo and CMMD datasets demonstrate consistent improvements over linear probing, prompt-only adaptation, and conventional fusion baselines. On the VinDr-Mammo BI-RADS classification task, the framework achieves 50.40% F1-score and 0.8090 AUC, including a 0.10 AUC improvement over a dual-view fusion baseline in the binary setting. Ablation studies further validate the effectiveness of token-based fusion and multi-depth interaction design.

Lily Mae Lazarus / Fortune : Mercor acquires Deeptune, which builds reinforcement learning environments for AI agents, three months after CEO Brendan Foody backed Deeptune's $43M Series A —  Brendan Foody started Mercor when he was 19 years old.  Now he's 23, worth billions on paper, and running one of the fastest-growing companies in AI history.

Wall Street Journal : Sources: Netflix executives are increasingly worried about declining engagement and are exploring adding live TV and bundling streaming services like Peacock —  Streamer is rethinking some of its core strategies to compete with rivals  —  Top Netflix executives who gathered …

Hannah Lang / Reuters : Coinbase's chief legal officer, Paul Grewal, is stepping down after six years; VP of legal Molly Abraham moves into his role with the title of general counsel —  Coinbase (COIN.O)'s Chief Legal Officer Paul Grewal is stepping down after six years at the U.S. crypto giant where he fought off …

Andrew Ackerman / Washington Post : Federal Reserve Chair Kevin Warsh names Marc Andreessen and Xbox CEO Asha Sharma to lead a task force on the economic impact of new technologies, including AI —  The billionaire investor will help shape how the Federal Reserve assesses a technology his firm has bet heavily on.

Sarah Perez / TechCrunch : Google says it will automatically add a disclosure to ads that are made with its AI advertising tools, expanding the disclosure beyond election ads to all ads —  Google is rolling out a new feature aimed at helping people understand when an ad they're seeing was made using AI technology.

Duncan Riley / SiliconANGLE : PitchBook: US venture funding hit $412.7B in H1 2026, up 30% on all of 2025, with AI startup funding accounting for 86%, or $355.9B; Q2 saw seven $1B+ rounds —  U.S. venture capital deal value hit $412.7 billion in the first half of 2026, nearly 30% more than investors put to work in all of last year …

Maxwell Zeff / Wired : Anthropic, set to add usage-based billing for Fable 5 on July 12, aims to return the model to Claude's subscription plans “when sufficient capacity allows” —  Claude subscribers must soon pay usage-based fees to access Anthropic's best consumer AI model—a sign that the golden era of AI subscriptions is ending.

Sam Clark / Politico : The EU Parliament advances a bill letting tech companies scan for CSAM, reviving a proposal rejected in March, with an exemption for E2EE services like WhatsApp —  Top-level pressure and obscure procedures breathe new life into a controversial proposal to allow scanning for online child sexual abuse material.

Zac Hall / 9to5Mac : OpenAI is discontinuing ChatGPT Atlas, its standalone desktop browser, in favor of the new ChatGPT desktop app —  ChatGPT Atlas, OpenAI's standalone desktop browser, is being sunset in favor of the new ChatGPT desktop app.  —  OpenAI released an all-new ChatGPT desktop app today …

Bloomberg : Sources: AI contractor marketplace Mercor is discussing raising new funds at a roughly $20B valuation, less than a year after raising money at a $10B valuation —  Mercor, a startup that helps improve artificial intelligence models with rich specialized data, is discussing raising new funds …

Zac Hall / 9to5Mac : OpenAI merges Codex and ChatGPT desktop apps for Mac and Windows under a new ChatGPT desktop app, allowing users to switch between Codex, Chat, and Work —  OpenAI held its second livestream this week today at 10 am PT. The video teased that the company was “introducing the next chapter for ChatGPT” today.

OpenAI : GPT-5.6 Sol costs $5 per 1M input tokens and $30 per 1M output tokens, GPT-5.6 Terra costs $2.50 and $15, and GPT-5.6 Luna costs $1 and $6 —  More intelligence from every token, stronger performance per dollar, and more capability on demand for your hardest work.

Axios : OpenAI broadly releases GPT-5.6, and launches ChatGPT Work, an AI agent that can gather context across apps and files to create documents, on Mac and Windows —  - Sol is the most powerful version, while Luna is designed for speed. … - A new “ultra” mode within Sol allows the system …

Beatrice Nolan / Fortune : Microsoft President Brad Smith says the US now has AI “regulation without transparent or complete rules”, and adds that “without rules, businesses can't plan” —  Microsoft President Brad Smith has weighed in on U.S. policy on AI, saying that the Trump administration's …

John Reynolds / Tech.eu : Kraken Technology, which designs and builds autonomous maritime platforms, such as uncrewed subsurface vessels, raised a $175M Series B at a $1B valuation —  It says it will use the funding to develop its uncrewed surface vessels while expanding manufacturing facilities.

根据发表在《Frontiers in Psychology》期刊上的一项研究，父母对屏幕和智能手机的上瘾可能会对孩子的发育和心理造成长期的负面影响。研究显示，对设备管理不当的看护者可能会加剧“不安全依恋”，使得人际关系变得更加焦虑和回避。这项研究基于美国 600 名 12-17 岁的未成年人的调查，儿童表示他们感到被盯着屏幕的父母边缘化或忽视。研究人员表示，缺乏安全依恋的孩子可能会缺乏自信或表现出较低的自我意识；在人际关系和亲密关系方面表现出困难；并不愿意承担取得成功所必需的风险。

拟议中的集体诉讼披露了一起使用 xAI 的 Grok 生成数千张 CSAM（child sex abuse materials）图像的案件。 一名女孩的继父利用一张继女在 11 岁时拍摄的照片使用 Grok 生成了 7000 张 CSAM 图像。诉讼称 Grok 在整个过程中没有标记任何有害行为。Grok 的儿童安全系统只在这名男子输入“gang rape”后才介入，向 NCMEC 发送了举报，NCMEC 随后向执法部门报告了这起案件。法律规定 CSAM 被标记后需要共享 IP 等用户信息。但 xAI 被指控拒绝合作。最终警方通过搜查令扣押了继父的设备随后将其逮捕。继父还被指控网上出售这些 CSAM 材料，换取其他儿童性犯罪者的 CSAM 材料。该男子获准保释两天后便开枪自杀，而他的继女则饱受焦虑和抑郁的折磨，诉讼称她的生活在一夜之间被摧毁了，生活变成了一场噩梦。

广西南宁横州市爆发了严重洪灾，网上视频显示，救援人员借助无人机，转移被困群众。视频中的无人机是深圳大疆的农业无人机 FlyCart 100，其载重量为 85 公斤，能悬挂起正常体重的民众。这其实并非第一次用无人机救灾民。去年中国就有农业无人机从洪水中救出被困男子的案例。无人机已经在深圳等城市广泛用于外卖和快递。去年 3 月，中国民用航空局批准亿航智能和合肥合翼航空两家公司开展商用载人无人机服务。

根据发表在《Morbidity and Mortality Weekly Report》期刊上的一项研究，美国部分地区居民多达三成携带导致红肉过敏的抗体。这一比例远超此前的预计，意味着红肉过敏的美国人比以前认为的多得多。该过敏反应被称为α-半乳糖综合征（alpha-gal syndrome），其特点是发作缓慢，通常在餐后 2-6 小时之间出现，使得很难将过敏反应与食物联系起来。症状包括荨麻疹、恶心、呕吐、腹部绞痛、腹泻，或严重过敏反应如呼吸困难、喉咙紧缩、舌头或嘴唇肿胀、头晕、脉搏微弱和血压下降等迹象。研究人员分析了美国 10 个州 3000 份献血样本，结果显示田纳西州、阿肯色州等五个州的抗体比例最高达到 31.2%，平均 24%。

ESA 欧几里得太空望远镜（Euclid）发射升空后，除了肩负绘制宇宙三维结构、探究暗物质与暗能量本质的主要任务外，也展现搜寻早期宇宙天体的强大能力。最新研究利用欧几里得首批巡天资料，成功发现 34 个红移大于 6.5 的星体，其中 27 个位于红移 7 以上，并确认 EUCL J172902.75+641018.1 的红移高达 7.77，成为目前已知距离最遥远的类星体。类星体是由星系中心超大质量黑洞吸积大量气体所产生的极高亮度天体，其亮度甚至可超越整个宿主星系，因此能作为探测早期宇宙的重要灯塔。

由于内存危机，2026 年二季度 PC 出货量 6820 万台同比下滑 5%。IDC 警告如果这一情况继续下去小型供应商可能会倒闭。联想等大型供应商因为能提前与内存制造商协商供货，因此状况还比较好。联想最近公布的财报显示，其 PC 和智能设备业务收入增长了 26%。IDC 警告称，随着苹果、戴尔、惠普和联想等行业巨头利用其规模优势确保内存供应并挤压小型竞争对手，厂商整合的风险在增加。行业巨头已做好从小型竞争对手手中夺取市场份额的准备，可能会迫使实力较弱的企业进行合并或退出市场。

OpenMandriva 项目发生一起破坏事件，开发者出于透明原则公布了事件经过：有多个新人加入了项目团队，其中之一是 Mumble 项目的 Davide Beatrici，他比较知名因此获得了团队的信任，获得了管理员权限。同时加入的还有 Beatrici 的朋友。但他的朋友在发行版的 Matrix 聊天室对其他人进行了攻击和辱骂，导致其他人退出。此人最终被踢出。在朋友被踢出之后 Beatrici 也离开了项目。鉴于此开发者切断了他维护的镜像连接。但此举却激怒了 Beatrici，他滥用其管理权限删除了 GitHub 上的部分库，在 cooker 库中发布了一个空包，导致所有 gnome 和 cosmic 软件包失效，破坏使用 gnome 或 cosmic 的用户的系统。OpenMandriva 项目目前正致力于恢复已删除的库，恢复失效包的功能。项目披露这一情况旨在提醒开源社区注意 Davide Beatrici。

Linux Mint 发行版项目开发的 Cinnamon 是少数仍然不支持 Wayland 只兼容 X11 的桌面环境。但这种情况即将发生改变。Linux Mint 项目宣布，他们在支持 Wayland 上投入了大量精力，如今 Wayland 支持已经相当稳定，足以媲美 X11，因此 Wayland 支持不再被视为实验性，下个版本的 Cinnamon 将同时支持 Wayland 和 X11。Cinnamon 的下一个版本 v6.8 将包含在计划于圣诞节发布的 Linux Mint 新版本中。

研究人员分析了 1999-2023 年近 8700 名美国居民的健康数据，这些居民包括 20 岁以下的青少年和 20 岁以上的成年人。结果显示：成年人的肥胖率从 30% 增至 41%，重度肥胖率从 5% 增至 10%，腹部脂肪堆积过多的腹部肥胖率从 48% 增至 61%；青少年的总体肥胖率上升了 30%，重度肥胖率上升 50%，腹部肥胖率增长了三倍。分析还发现，肥胖率存在​​性别差异：女性重度肥胖率（13%）和腹部肥胖率（70%）都高于男性（分别为 7% 和 51%）。研究人员指出，这种差异可能是由于女性一生中激素水平的变化比男性更为显著。此外，非拉丁裔黑人的肥胖率高于其他人群。

柏林地方法院周三裁定一名 32 岁的原籍中国的男子犯有协助实施三起严重强奸及严重性胁迫罪，并判处该男子总计五年监禁。这名男子是一个交流有关强奸被麻醉女性信息的聊天群组的八名成员之一。该被告（现已获得医学博士学位）在群组内多次就如何麻醉女性提供医学建议。2024 年 1 月 7 日，一名女性在美因河畔法兰克福遭另一名涉案男子强奸。在此前一天，被告在明知对方有强奸意图的情况下，提供了关于麻醉的具体建议，而该男子随后采纳了这些建议。法庭还认定被告在 2020 年和 2021 年期间曾三次性虐待其未婚妻。这些行为发生在北京的一家酒店客房内，受害女性在这些事件中同样处于被麻醉状态。主审法官将这些罪行定性为严重犯罪。他指出，这些行为体现了极端的厌女倾向，因为涉案女性被贬低为满足性欲的客体。

两名放暑假的 15 岁少年体会到了 Waymo 在时刻监视你的含义。他们在车内喝酒，用玩具枪对着其它汽车射水凝胶珠。一名 Waymo 员工当时正远程监控着这辆车，在看到车内有疑似枪支以及正在开枪之后，骗两名青少年说 Waymo 出租车出现了机械故障，需要停车，同时致电警方说有人在开枪。汽车停在了一家购物中心的停车场，当时五名警察已在那里等候搜查。搜查的结果是他们玩的是玩具枪，子弹是水凝胶珠。两名乘客被拘留后被释放交由其父母监护。检方正在审查可能的指控，包括未成年饮酒和从事威胁性行为。

由于涌入了大量低质量的 AI 生成应用，Linux 软件仓库 Flathub 于五月底宣布停止接受此类 AI 生成应用。审核递交到 Flathub 的应用是一个吃力不讨好的工作，当审核者试图与 AI 生成应用递交者沟通时，却发现对方使用的是 AI 智能体，回复都是答非所问。一位审核者对此评论说，“纯粹是噪音和浪费时间”。从 2026 年 1 月开始，Flathub 将此类应用打上了 AI Slop 的标签。知名 Linux 开发者 Evangelos“GeopJr”Paterakis 调查了 过去半年标记为 AI slop 的 120 个应用，32 个仍在维护，88 个已被抛弃，大多数都彻底删除了，部分应用在递交到 Flathub 后就停止了维护。

长鑫存储技术（CXMT）和长江存储科技（YMTC）正新建工厂，预计到 2027 年以后，两家企业的产能都将超过目前的两倍。业界相关人士透露，长鑫高性能存储器 HBM 目前在安徽省合肥市的工厂生产，在上海市正在建设的工厂将从 2027 年开始正式生产。随着新工厂的投产等，整体产能预计增至目前的 2 倍以上。长江存储生产用于长期存储的 NAND 闪存和以 NAND 为主要零部件的固态硬盘（SSD）。目前长江存储正在湖北省武汉市建设第 3 工厂，力争将原定 2027 年的量产开始时间提前至 2026 年底。该公司还计划追加建设 2 座工厂，有分析认为全部投产之后产能将增至 2 倍以上。

微软花了三年时间将 Copilot 深入集成到 Windows 11 和 Office 中，但数据显示用户使用率并不高。在 4.5 亿 Microsoft 365 订阅服务的商业客户中，只有 4.5% 的人付费使用 Copilot，而这些付费用户只有 20% 到 30% 会每周打开 Copilot。这意味着 Copilot 的周活跃用户数仅占 Microsoft 365 总用户数的 1%。Copilot 负责人 Jacob Andreou 在一份内部备忘录中称，Copilot 必须证明自己存在的价值。值得说明的是 Copilot 是指需要额外付费的 AI 服务，而 Copilot Chat 则是 Microsoft 365 用户可免费使用的 AI 服务，它的使用率比较高。

Reddit 用户报告 LG 显示器会静默安装 Windows 广告程序。而戴尔和 Alienware 显示器被发现也存在类似的行为。分析发现，LG 显示器会通过 Microsoft Store 和 Windows Update 自动安装名为 LG Monitor App Installer 的安装程序，LG Monitor App 会开机启动，会弹出广告，比如将用户导向迈克菲的杀毒软件。LG Monitor App 无法通过 Microsoft Store 卸载，阻止其弹出广告的方法是禁止其开机启动（位于 设置>应用>启动 中）。而禁止显示器自动安装程序的方法是干脆完全禁用 Microsoft Store。