OrangeBot.AI Digest — 2026-06-21

Let your agents read, send, and react to email autonomously

E-2 visa business plans, drafted by an AI

Draft, restructure & tighten wording from panel inside Word

Manage your backups from Claude, Cursor, and VS Code

Foundation models for agentic coding and long-horizon work

Run your AI agents over any app, even games.

Give every customer their own Hermes or OpenClaw agent

a fast, glassy clipboard manager for macOS, Windows & Linux

A notes app that lives in your MacBook's notch.

Surf like it's 1999

AI video studio: text & image to video, with real control

AI support agent, help center & changelogs in a single inbox

Build a room of ambient sound that lives in your notch

a small hosted memory layer for AI agents

Work across 20+ apps in Slack with multiplayer collaboration

While 10B-level industrial foundation models have pushed the boundaries of image inpainting, their prohibitive computational costs severely hinder practical deployment. Constructing a highly optimized task-specific specialist offers a promising solution; however, extreme structural compression inevitably triggers a severe representation bottleneck. To conquer this, we propose Moebius, a highly efficient lightweight inpainting framework. We systematically reconstruct the diffusion backbone by introducing the Local-λ Mix Interaction (LλMI) block. Comprising Local-λ and Interactive-λ modules, it elegantly summarizes spatial contexts and global semantic priors into fixed-size linear matrices, preserving complex latent interactions while drastically shedding parameters. Furthermore, to unlock the full representational capacity of this highly compact architecture, we synergistically pair it with an adaptive multi-granularity distillation strategy. Operating strictly within the latent space to avoid expensive pixel-space decoding, this strategy dynamically balances multiple gradient-based losses to achieve high-fidelity alignment. Extensive experiments across natural and portrait benchmarks demonstrate that this optimal synergy enables Moebius to rival or even surpass the generation quality of the 10B-level industrial generalist FLUX.1-Fill-Dev. Remarkably, Moebius achieves this using less than 2\% of the parameters (0.22B vs. 11.9B) while delivering a >15times acceleration in total inference time, setting a new efficiency standard for high-fidelity inpainting. Project page at https://hustvl.github.io/Moebius.

Dexterous interaction with articulated objects is important for household, assistive, and humanoid manipulation, where multi-finger hands can provide compliant contact patterns beyond parallel-jaw grasping. However, articulated-object manipulation differs from static-object manipulation: the target part cannot be directly actuated, and its motion must emerge through sustained physical hand--handle contact. This makes the transition from object-centric articulated generation to hand-driven dexterous hand--object interaction non-trivial, since geometric trajectory replay or open-loop execution does not model the contact dynamics required to move the articulated part. Moreover, policies trained only for task completion under fixed dynamics can overfit nominal contact loads, especially without tactile or force feedback, and may degrade when the contact load changes. To address these challenges, we present DragMesh-2, a contact-driven framework for dexterous interaction with articulated objects that extends articulated interaction from object-centric generation to hand-driven dexterous hand--object interaction, where articulated motion must arise through physical contact. We further propose PICA, a physically informed contact-aware training mechanism that injects physical signals into policy learning without tactile or force feedback, improving robustness and task success under changing contact loads. Finally, we conduct systematic evaluation across multiple damping conditions and articulated-object categories to study robustness under contact-load variation, and provide a pure-geometry dexterous interaction resource to support future loco-manipulation and humanoid hand--object interaction research. Across seven GAPartNet objects, DragMesh-2 achieves stronger robustness under contact-load variation than the compared methods while maintaining high task success across damping conditions.

LiveCodeBench (LCB) has recently become a widely adopted benchmark for evaluating large language models (LLMs) on code-generation tasks. By curating competitive programming problems, constantly adding fresh problems to the set, and filtering them by release dates, LCB provides contamination-aware evaluation and offers a holistic view of coding capability. However, LCB remains restricted to Python, leaving open the question of whether LLMs can generalize across the diverse programming languages required in real-world software engineering. We introduce Multi-LCB, a benchmark for evaluating LLMs across twelve programming languages, including Python. Multi-LCB transforms Python tasks from the LCB dataset into equivalent tasks in other languages while preserving LCB's contamination controls and evaluation protocol. Because it is fully compatible with the original LCB format, Multi-LCB will automatically track future LCB updates, enabling systematic assessment of cross-language code generation competence and requiring models to sustain performance well beyond Python. We evaluated 24 LLMs for instruction and reasoning on Multi-LCB, uncovering evidence of Python overfitting, language-specific contamination, and substantial disparities in multilingual performance. Our results establish Multi-LCB as a rigorous new benchmark for multi-programming-language code evaluation, directly addressing LCB's primary limitation and exposing critical gaps in current LLM capabilities.

Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reusable skills are acquired only after explicit instructions. We study Playful Agentic Robot Learning, where an embodied coding agent uses self-directed play as a continual skill-learning stage before downstream tasks arrive. We introduce RATs, Robotics Agent Teams designed for play-time skill acquisition. During play, RATs proposes novel yet learnable exploratory tasks, plans and executes robot-code policies, verifies intermediate progress, diagnoses failures, retries with dense, step-level feedback, and distills successful executions into a persistent code skill library. At test time, the agent reuses relevant skills from this frozen library to help solve new tasks. Experiments in LIBERO-PRO and MolmoSpaces show that play-learned skills improve held-out downstream tasks over no-play and random-play baselines, with 20.6 and 17.0 percentage-point gains over CaP-Agent0 on LIBERO-PRO and MolmoSpaces, respectively. Moreover, the learned skills can be plugged into other inference-time Code-as-Policy agents by simply retrieving them into the context, improving RoboSuite and real-world transfer by 8.9 and 8.8 points, respectively, without finetuning the underlying model.

Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely remain tied to static, stateless inference from isolated visual observations. We introduce \textsc{S-Agent}, a spatial tool-use agentic paradigm for understanding and reasoning over continuous multi-view images and videos. By formulating spatial reasoning as spatio-temporal evidence accumulation rather than isolated frame-level prediction, S-Agent reshapes spatial perception into scene-centric understanding beyond frame-centric recognition. Specifically, S-Agent casts the VLM as a semantic planner that decides what evidence is needed, while a hierarchy of spatial tools and experts grounds objects in 2D, lifts them into 3D geometric evidence, and aggregates this evidence into high-level spatial knowledge (e.g., counting, measurement, orientation, and relative position). Additionally, a temporal memory mechanism, including Scene Memory for maintaining the evolving scene state and Agent Memory for accumulating reasoning context, enables evidence integration across frames and reasoning steps. Comprehensive experiments on multi-view and video spatial reasoning benchmarks show that S-Agent consistently improves both open-source and closed-source VLMs in a training-free manner. Beyond inference-time augmentation, supervised fine-tuning (SFT) on S-Agent-generated spatial trajectories S-300K yields S-Agent-8B, a compact spatial agent that significantly surpasses similar-scale baselines (e.g., Qwen3-VL-8B) and performs comparably to advanced closed-source models (e.g., GPT-5.4 and Gemini 3).

Agent benchmarks are growing fast, but no single benchmark touches more than four or five of the dimensions that deployment exposes. This paper aggregates the largest coordinated deep-dive of one MCP-based industrial-agent benchmark to date: fourteen parallel implementation studies covering new asset classes (including a multi-modal visual extension), alternative orchestrations, retrieval strategies, reasoning modes, infrastructure optimizations, and evaluation-methodology probes. Consolidating those studies with seven prior agent benchmarks, we argue that aggregate-score leaderboards systematically underspecify deployed-agent evaluation. Rankings derived from aggregate scores do not transfer to out-of-distribution settings; recent public-to-hidden competition retrospectives provide direct empirical evidence of this rank instability. We propose ranking configurations by predictive validity, the correlation between in-sample and out-of-sample rank, rather than in-sample mean, and report a twelve-tier measurement apparatus that exposes the deployment-relevant dimensions HELM and its agent-era successors collapse. The position is operationalized through three falsifiable out-of-distribution criteria with explicit thresholds; existing evidence partly supports it but is too thin to confirm. We close with a pre-registered pilot design and a field-level vision for what the next generation of agentic benchmarks should report.

Advances in radiance fields have enabled photorealistic novel view synthesis. In several domains, large-scale real-world datasets have been developed to support comprehensive benchmarking and to facilitate progress beyond scene-specific reconstruction. However, for distractor-free radiance fields, a large-scale dataset with clean and cluttered images per scene remains lacking, limiting the development. To address this gap, we introduce DF3DV-1K, a large-scale real-world dataset comprising 1,048 scenes, each providing clean and cluttered image sets for benchmarking. In total, the dataset contains 89,924 images captured using consumer cameras to mimic casual capture, spanning 128 distractor types and 161 scene themes across indoor and outdoor environments. A curated subset of 41 scenes, DF3DV-41, is systematically designed to evaluate the robustness of distractor-free radiance field methods under challenging scenarios. Using DF3DV-1K, we benchmark nine recent distractor-free radiance field methods and 3D Gaussian Splatting, identifying the most robust methods and the most challenging scenarios. Beyond benchmarking, we demonstrate an application of DF3DV-1K by fine-tuning a diffusion-based 2D enhancer to improve radiance field methods, achieving average improvements of 0.96 dB PSNR and 0.057 LPIPS on the held-out set (e.g., DF3DV-41) and the On-the-go dataset. We hope DF3DV-1K facilitates the development of distractor-free vision and promotes progress beyond scene-specific approaches. The dataset and leaderboard are available at https://johnnylu305.github.io/df3dv1k_web/.

Style-content dual-reference generation aims to synthesize an image that preserves the structure and semantics of a content reference while adopting the style of a separate style reference.Despite recent progress, this setting remains challenging because models must balance content fidelity, style alignment, and instruction following avoiding semantic leakage from the style reference.A key bottleneck is the lack of large-scale triplet data with clean content-style separation and broad long-tail style coverage.In this work, we propose FreeStyle, a scalable dual-reference generation framework based on community LoRA mining.We treat community LoRAs as compositional anchors for style and content, and design a rigorous generation and filtering pipeline to construct large-scale Style-Reference and Content-Reference triplets across multiple base models.To address content leakage, we adopt a two-stage curriculum with stage-specific disentanglement mechanisms: an attention-level enrichment constraint that suppresses style-reference leakage in the style-transfer stage, and a frequency-aware RoPE modulation strategy that targets positional-correspondence-based leakage in the harder dual-reference stage.We also introduce a benchmark covering both style-reference and dual-reference generation, with evaluations on style similarity, content preservation, aesthetics, instruction following, and leakage rejection. The benchmark incorporates a style-invariant Content Alignment Score (CAS) and introduces a calibrated VLM-based Rejection Score for evaluating generation reliability and leakage suppression.Extensive experiments show that our model achieves a strong balance among style alignment, content preservation, and leakage suppression.

Conditional diffusion and flow models routinely fail to satisfy the very constraints that define their task. For instance, a depth-conditioned model often produces images whose re-extracted depth disagrees with the input, even though the forward operator--the depth predictor defining the constraint--is available during both training and inference. Existing approaches generally fall into two categories: supervised models that treat the conditioning signal as a static cue and ignore alignment information at inference, and guidance-based methods that consult it through hand-tuned linear updates, typically trading fidelity to the condition against the plausibility of the generated sample. We argue that the fundamental gap in both paradigms is that the model is never trained to utilize its own alignment error. We introduce FlowBender, a closed-loop framework that treats this error as a first-class input, training the network to learn a correction policy conditioned on inference-time feedback. At each step, an unguided look-ahead pass estimates the clean signal, a task-specific deviation is computed via the forward operator, and a refinement pass consumes this signal to produce a corrected velocity. We propose several variants of FlowBender, including a gradient-based formulation for differentiable operators and a zero-order variant for non-differentiable settings such as JPEG compression. For efficient sampling, we introduce a prior-step shortcut that enables closed-loop correction at a minimal additional computational cost. Across image-to-image translation, restoration, and 3D mesh texturing, FlowBender consistently outperforms standard supervised baselines, alignment-loss-augmented training, and state-of-the-art inference-time guidance, improving fidelity and plausibility simultaneously rather than trading them against each other. Project page: https://flow-bender.github.io/

Creating 3D visual illusions, a single 3D mesh that reveals entirely different semantics from various viewing angles, is a fascinating but tough challenge. Existing optimization-based methods are slow and can produce oversaturated colors. In contrast, naive stitching approaches fail to produce geometrically coherent objects. This results in visible unnatural seams and semantic leaks. In this paper, we present a fast and training-free framework for generating text-driven 3D visual illusions. Our approach decouples the generation into two stages. First, we propose a cross-space dual-branch denoising process. This process dynamically decodes 3D latents into voxel space for CLIP-guided orientation alignment and Signed Distance Field (SDF) blending, which ensures seamless geometric fusion. Second, we introduce a view-conditioned texture synthesis module that projects and aggregates view-specific 2D diffusion priors onto the fused geometry. Extensive experiments demonstrate that our method generates highly realistic, dual-semantic 3D illusions in just 3-5 minutes. It significantly outperforms existing methods in geometric integrity, semantic recognizability, and efficiency. Project page: https://siang1105.github.io/JanusMesh.github.io/

World Action Models (WAMs) commonly rely on video generation to bridge visual world modeling and robot control. However, video-based WAMs face three coupled limitations: dense multi-frame future tokens make inference costly, full video prediction spends capacity on action-irrelevant temporal and appearance details, and long-horizon future imagination may introduce errors that mislead action prediction. These issues raise a simple question: Does world action model really need video generation? We propose ImageWAM, a simple WAM framework that repurposes pretrained image editing models for robot action prediction. In contrast to video generation, image editing provides a better-matched prior: it only needs to model a target-frame transformation, focuses on action-relevant current-to-target visual differences, and grounds task instructions to localized visual changes through edit pretraining. In practice, ImageWAM does not decode the target frame at inference time; instead, it conditions a flow-matching action expert on the KV caches produced by image-editing denoising, using them as a compact world-action context. ImageWAM outperforms standard VLA baselines and matching competitive WAMs without additional policy pretraining across different simulator and real-world experiments. It also reduces FLOPs to 1/6 and latency to 1/4 of video-based WAMs. Attention analysis further shows that editing caches focus on task-relevant change regions, supporting image editing as an effective alternative to video-based world-action modeling.

World models are increasingly regarded as a decisive step toward artificial general intelligence, yet modeling the physical world demands more than rendering convincing frames on demand: it requires an internal world state that keeps evolving over time, decoupled from observation, so that objects endure and events run to their conclusions whether or not a camera is watching, much as the moon holds to its orbit when no one is looking. This requirement is a blind spot of existing benchmarks, which reward surface properties such as fidelity, motion, and camera controllability while never asking whether a generated world keeps evolving once it is unobserved. We introduce WRBench, the first systematic diagnostic benchmark that treats camera motion as an intervention on observability and resolves evaluation into a human-calibrated chain that asks whether the camera executes the requested interaction, whether the scene stays continuous and identifiable while in view, and whether a returning target remains consistent with the event that was set in motion. Across 9{,}600 videos from 23 models spanning four control paradigms, one finding proves stubborn: current systems maintain the observed world as a tracking shot, resuming a returning target in the state at which it was abandoned rather than advancing the event while it went unseen. Because this failure recurs across control paradigms, model families, and increments of scale, robust world-state evolution does not follow from cleaner imagery, tighter control, richer geometric priors, or sheer parameter count We therefore argue that the stability of the physical state kernel and the consistency of worldlines under viewpoint intervention should become first-class objectives of world-model design, so that a world model captures how the world will unfold rather than how the next frame appears.

Large language models (LLMs) often fail when answering requires identifying a small but decisive piece of evidence within a long or complex context, such as a single line in a tool trace or a subtle detail in an image. We propose ContextRL, a context-aware reinforcement learning (RL) method that improves long-horizon reasoning and multimodal performance through an indirect auxiliary objective. Instead of supervising only the final answer, ContextRL presents the model with a query, an answer, and two highly similar contexts, and rewards it for selecting the context that supports the query--answer pair, thereby encouraging fine-grained grounding. We construct contrastive context data in two domains: for coding agents, trajectories serve as contexts, yielding 1k pairs built via condition filtering; for multimodal reasoning, images serve as contexts, yielding 7K pairs built via generative editing and similarity search. ContextRL achieves average gains of +2.2% over standard GRPO on 5 long-horizon benchmarks, and +1.8% across 12 diverse visual question answering benchmarks. To disentangle the effect of the proposed objective from that of additional data, we compare against data-augmentation baselines that repurpose the same contrastive contexts as standard query--context--answer examples. These baselines provide little to no improvement, showing that the gains arise from the proposed context-selection objective rather than from the contrastive data alone.

Multi-step LLM pipelines fail through interactions among retrieval, reasoning, and formatting steps, so prompt-only optimization can miss bottlenecks in the chain. We present FAPO (Fully Autonomous Prompt Optimization), a framework that lets Claude Code optimize an LLM pipeline inside a standardized codebase. FAPO evaluates a pipeline, inspects intermediate steps, diagnoses failures, proposes scoped changes, and validates variants repeatedly to optimize against a score function. It first tries prompt edits and, only when prompt optimization appears insufficient, changes chain structure within the permitted scope when attribution identifies a structural bottleneck. Across six benchmarks and three task models, FAPO beats the baseline GEPA in 15 of 18 model-benchmark comparisons. In 11 model-benchmark comparisons, FAPO wins with non-overlapping mean pm trial-standard-deviation ranges, and the mean FAPO-GEPA gain is +14.1 pp. In the six HoVer and IFBench comparisons where prompt-first search escalated to structural changes, FAPO wins all six with a mean gain of +33.8 pp. FAPO also improves performance on security tasks: on CTIBench-RCM, a security CVE-to-CWE task, prompt-only FAPO lifts test accuracy by +4.0 pp on GPT-5, +7.1 pp on Foundation-Sec-8B-Instruct, and +2.0 pp on Foundation-Sec-8B-Reasoning. These results position FAPO as a state-of-the-art pipeline optimization technique for both general-purpose and security-focused tasks.

Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence. Although emerging coding agents can generate code to automate algorithm search, their successes remain largely confined in digital environments. We conjecture that the missing abstraction to automate robotics research is a repeatable feedback loop for real-world policy improvement: reset the scene, execute a policy, verify the outcome, and refine the next iteration. To bridge this gap, we introduce ENPIRE, a harness framework for coding agents that instantiates this physical feedback routine with four core modules: an Environment module (EN) for automatic reset and verification, a Policy Improvement module (PI) that launches policy refinement, a Rollout module (R) to evaluate policies with one or multiple physical robots operating in parallel, and an Evolution module (E) in which coding agents analyze logs, consult literature, improve training infrastructure and algorithm code to address failure modes. This closed-loop system transforms real-world manipulation learning into a controllable optimization procedure, minimizing human effort while allowing fair ablations across training recipe and agent variants. Powered by ENPIRE, frontier coding agents can autonomously train a policy to achieve a 99% success rate on challenging, dexterous manipulation tasks, such as organizing a pin box, fastening a zip tie, and tool use, a process that further accelerates when we dispatch an agent team on a robot fleet. Our results suggest a practical and scalable path toward deploying coding agents to autonomously advancing robotics in the physical world.

Marilen Martin / Bloomberg : As Europe falls behind the US and China in consumer AI, its engineering companies and AI startups are turning to industrial AI applications to boost efficiency —  Pressure to become more efficient has Europe racing to bring artificial intelligence to the shop floor.

Caroline Petrow-Cohen / Los Angeles Times : SF-based Humble Robotics, which is developing an electric, self-driving cabless freight truck with 200 miles range and 55 mph max speed, raised $24M —  - 7 min Click here to listen to this article  — San Francisco startup Humble Robotics has raised $24 million to build an electric …

Kieran Smith / Financial Times : Sources: Virgin Media O2 and VodafoneThree have deployed tech to disable phones stolen from their stores, after phone makers resisted broader antitheft measures —  Virgin Media O2 and VodafoneThree's move comes after Apple and Samsung resisted calls to adopt broader measures

Sophie Shulman / CTech : Sources: Anduril has begun exploring the possibility of establishing operations in Israel and is in talks to recruit a local manager —  Palmer Luckey's $61 billion startup considers local operations, partnerships, and investments in Israeli startups.  —  Anduril, the world's largest …

Mark Gurman / Bloomberg : Sources: John Ternus gets ready to put his firm imprint on Apple's industrial design team, which has lost a true seat at Apple's exec table over the past decade —  Also: The 2027 iPhone and AirPods plans.  —  The No. 1 priority for new Apple CEO John Ternus should be revamping …

Bloomberg : SoftBank says it is struggling to find startups in Latin America ready for major investments and has completed only two new deals over the past two years —  SoftBank Group Corp. is struggling to find startups in Latin America ready for major investments, underscoring how sharply the tech boom …

Wall Street Journal : Investigation: Polymarket is paying creators to make deceptive videos about winning bets, targeting users in the US, where its primary crypto platform is banned —  The prediction market has flooded social media with deceptive videos by paid creators, according to a Wall Street Journal investigation

Financial Times : Inside Palantir's fight to save its £330M, seven-year contract with NHS England, as public and political pressure grows to end the deal via a 2027 break clause —  Critics question how the tech giant won a showpiece contract.  It complains about the politicisation of procurement.

Mariana Catacci / CNN : Brazil takes its National Civil Defense warning platform offline after suspected hackers send an unauthorized alert to mobile phones in several states —  An unauthorized alert bearing a mysterious message that was sent to cell phones in several states across Brazil on Saturday morning …

Gregory Zuckerman / Wall Street Journal : A look at Jane Street's push to supercharge trading with AI and become a major AI investor; it invested $1B in CoreWeave in April and has a stake in Anthropic —  The firm has surged from a handful of staffers to 3,500 with plans to recruit more than 500 employees this year.

Dana Goldstein / New York Times : A look at “humanizer” and “autotyper” apps that help students evade AI-detection software by slowly auto-typing essays and making AI text sound less robotic —  Big tech companies and small start-ups are using social media to hype new tools that allow students to trick teachers and A.I. detectors.

Aisha Down / The Guardian : A speculative scenario titled “Europe 2031” projects economic and political instability in the EU if it fails to keep pace with the US and China in the AI race —  A speculative thought-experiment warns the continent could pay a heavy price for lagging behind the US

Wall Street Journal : How the success of AI-related companies in South Korea, Taiwan, and Japan is driving stock gains, bigger bonuses, and a retail investing frenzy in Asian markets —  Global success of AI-related companies in South Korea, Taiwan and Japan stokes market fever  —  Na Se-bin has lost all sense of the value of money.

Ella Creamer / The Guardian : Granta says it will stop publishing short story contest winners or join publishing partnerships it doesn't control after AI use allegations against a winner —  Literary magazine will no longer engage in ‘external publishing partnerships’ after Commonwealth prize furore

Angela Cullen / Bloomberg : Claude Guillemot, co-founder of Ubisoft and chairman of gaming hardware company Guillemot Corporation, died at 69 after a plane crash in France —  Claude Guillemot, who co-founded French video-game publisher Ubisoft Entertainment SA with his brothers in 1986, has died, according to the company.

芬兰图书馆不只是提供图书借阅，而是维系重要的社会功能。其它国家的公共图书馆在消失，而芬兰还在新建图书馆。美国在 2008-2019 年间关闭了 766 家公共图书馆，英国在 2016-2023 年间逾 180 家图书馆关闭或转交给志愿团体运营。芬兰人口约 560 万，有逾 700 家图书馆，除了借阅图书，图书馆出借的最大物品是空间：可免费预定房间用于会面、学习、进行政治讨论或创作音乐。赫尔辛基市中心的 Oodi 图书馆在 2019 年被评为全球最佳新建图书馆，它提供了缝纫机、网球拍和游泳池通行证的借用服务。这种借用文化源于芬兰的实用主义，可追溯到过去的农业时代，当时的人们经常共享农机。今天的城市居民居住在小房子里，他们可能一年只需要用到一次缝纫机，那么为什么要买呢？他们可以在图书馆免费使用通过税款采购的缝纫机。根据政府报告，55% 的芬兰人每月至少去一次图书馆。数据显示芬兰人平均每年使用图书馆 9.1 次。而英国人平均每年访问图书馆约 2.5 次。美国人平均每年访问图书馆 2.4 次，欧盟平均约 3.5 次。根据芬兰的图书馆法，公共图书馆必须促进民主、言论自由和积极的公民意识。其它北欧国家也有类似的政策。2025 年芬兰在公共图书馆上的支出近 3.71 亿欧元，人均支出 65.78 欧元，而英国人均支出 10 英镑，美国人均支出 45 美元。芬兰图书馆员还能帮助用户处理各类在线事务，从税务和银行账户到养老金和数字健康记录，他们还提供简历和求职申请方面的帮助。一项针对芬兰图书馆的研究得出结论：图书馆发挥着至关重要的包容性基础设施的作用。图书馆是少数可以静静待着而无需消费的公共空间。

Google 将要求用户在摄像头前挥手以证明自己是人类而不是机器人。它提供的区分机器人和人类检测服务 reCAPTCHA 引入了手势验证。Google 表示，在手势验证期间它会分析用户在执行各种操作或手势时的一段或多段手部视频，系统会处理视频以提取手部关键点的坐标数据，其中包括 21 个指关节关键点坐标。Google 声称，视频绝不会与用户的身份相关联，并且会在验证流程结束后删除。系统绝不会录制音频。

巴西政府称周六上午巴西多州的手机收到了一条未经授权的“极端”类别警报短信，其中包含文字 misantropi4。该单词将葡萄牙语 misantropia 的最后一个字母 a 替换为 4，这是黑客常用的做法。misantropia 的意思是厌恶人类。巴西的紧急短信系统类似美国的 AMBER Alert，允许政府官员直接向特定地理区域内的移动设备发送紧急短信。巴西政府表示其 National Civil Defense 警报平台已经下线，它认为这是一次黑客攻击，正对此展开调查。

德国联邦统计局的数据显示，尽管有大量移民补充，2025 年德国人口同比仍减少了约 11 万人，这是自 2020 年以来首次出现年度人口下降。德国去年的净移民数量为 23.5 万，但这不足以抵消死亡人数超过出生人数的缺口，2025 年德国死亡人数比出生人数多出 35.2 万人。截至 2025 年底，德国人口为 8350 万，即去年人口降幅约为 0.13%。德国上一次出现年度人口萎缩是在 2020 年，当时新冠疫情期间实施的严格旅行限制导致移民数量急剧下降。去年德国出生率也创下历史新低。与此同时人口老龄化趋势正在加速。60-79 岁年龄段的人口持续增加，去年新增 35.8 万人，而所谓“婴儿潮一代”进入退休年龄。作为纳税主力的 20-59 岁年龄段人口，其降幅远超平均水平，去年这个年龄段的人口收缩了1.0%，即减少了 40.9 万人。

2020 年 12 月现代汽车以 8.8 亿美元从软银手中收购波士顿动力 80% 的控股权，该交易对波士顿动力的估值为 11 亿美元。收购协议包含了一项卖出期权，允许软银在未来某个时候将剩余股份出售给现代汽车。现在软银行使了卖出期权，以 3.25 亿美元将其持有的剩余股份出售给现代汽车。波士顿动力已有逾三十年历史，但其机器人的商业化一直进展缓慢。四足机器人 Spot 是首个取得商业成功的产品，而其人形机器人 Atlas 要证明其商业价值仍然面临重重困难。

随着越来越多的专业人士开始在工作中依赖 AI 工具，他们来之不易的技能会不会逐渐退化？此种担忧不无道理。有证据表明，AI 驱动的技能退化现象正在医学、计算机科学等领域发生。研究人员正探讨如何在 AI 时代保留重要的人类专业知识。一项针对波兰内窥镜医生的研究表明，AI 工具会迅速削弱人类的能力。这些医生在其职业生涯至少进行 2000 例结肠镜检查。研究人员让他们使用一套 AI 系统，能实时分析结肠镜图像，标记名为腺瘤的肠道癌前期病变。一旦医生开始使用该系统，每当系统不可用时，他们的诊断能力会显著下降。在引入 AI 工具前三个月里医生在 28.4% 的结肠镜检查中发现了至少一个腺瘤。而在引入 AI 工具后三个月里，在没有 AI 辅助的情况下腺瘤检出率下降至 22.4%。

挪威首相 Jonas Gahr Stoere 周五表示，挪威将限制小学生使用生成式 AI 工具。他表示，使用 AI 会增加儿童跳过重要教育阶段的风险。儿童在学校里最重要的是学会阅读、写作和数学。挪威政府表示，6-13 岁或一至七年级学生不应使用 AI；14-16 岁初中生可在教师指导下谨慎使用 AI 工具，17-19 岁高中学生应学习如何正确使用 AI 以便为未来的学习和工作做好准备。挪威政府还表示将立法为课堂使用更多书籍提供资金以扭转平板电脑普及的趋势。新标准将从 8 月下旬的新学年开始实施。

加州亿万富翁税提案获得足够签名达到在 11 月公投的资格。亿万富翁税（California Billionaire Tax Act）将对任何身价逾 10 亿美元的加州居民一次性征收 5% 的税，该税将用于医保和教育等项目。加州是全美亿万富翁人数最多的州，总数超过 200 人，很多人在 AI 热下积累了巨额财富。该税遭到了加州亿万富翁们的强烈反对，Google 联合创始人 Sergey Brin 一人至少投入了 8200 万美元反对该税，并搬到了靠近加州的内华达州居住。Palantir 联合创始人 Peter Thiel、Google 前 CEO Eric Sc​​hmidt、加密货币亿万富翁 Chris Larsen 以及DoorDash CEO Tony Xu 等也捐出了数百万美元反对该税。英伟达 CEO 黄仁勋则对该税没有异议，称会继续居住在加州。尽管提案已获得足够的签名，但相关组织必须在 6 月 25 日之前决定是否继续推进，或者与州政府达成协议。

在美国国会参议院周三通过一项法案阻止政府拆除 3.68 亿美元的海洋观测计划（Ocean Observatories Initiative）之后，特朗普政府表示将撤销拆除决定。海洋观测计划由逾 900 台深海仪器构成，用于监测洋流、海洋生态系统、碳吸收、热浪、渔业、沿海洪水和气候变化。每个观测站由多个锚定装置组成。这些设备测量从水面到数千英尺深处的洋流以及化学生物状况。仪器经过加固能承受深海的压力、腐蚀性海水以及可能损坏电子设备的海洋动植物。锚定装置周围的遥控机器人和滑翔机负责收集数据并将其传输到研究实验室。它于 2016 年投入运作，原计划运行 25 年，每年的运行成本为 4800 万美元。管理该项目的国家科学基金会已经宣布即日起停止移除设备，将继续运行和维护现有设备。

在正午高光、高温双重胁迫下，农作物光合作用会被大幅抑制，光能利用效率大幅下降，造成作物平均减产 30% 左右。这种作物“午睡”现象长期制约着光能利用效率与产量提升，自 1910 年被发现至今，困扰科学界长达百年。根据发表在《细胞》期刊上的一项研究，中科院等研究团队通过 10 年跨学科联合攻关，发现植物体内一种名为 MBS1 的超小蛋白可响应强光，形成凝聚体小液滴，通过类似“防护服”的保护作用过滤强光实现“防晒”。研究团队随后在海南、北京、吉林、黑龙江等地开展大田试验。测产结果显示，与底盘对照品种稻花香相比，增强表达 MBS1 的材料在光热胁迫较轻的吉林等地，增产约 10%~15%；在北京等中等光热地区，增产达 20%~30%；而在强光高热的海南，增产幅度高达 40%。这为应对全球气候变暖、实现粮食增产提供了新的基础理论与技术路径。

在内存和 SSD 之后，又一个计算机核心元件面临价格快速上涨。这一次是因为中东冲突导致生产高纯度树脂的沙特工厂停产。树脂是制造电路板（PCB）的基本原材料，位于沙特阿拉伯 Jubail 的一家工厂为陶氏化学与沙特阿美的合资企业，它供应了全球七成的高纯度 PPE 树脂。该公司于三月下旬因中东冲突关闭，4 月 6 日和 7 日还遭到导弹袭击。陶氏化学 CEO Jim Fitterling 此前表示公司预计物流和供应链恢复正常至少需要 275 天。高盛报告称 3 月到 4 月间 PCB 价格上涨了 40%。全球最大 PCB 制造商之一的胜宏科技警告中东冲突可能会推高铜和树脂的价格。替代树脂需要重新设计电路板、重新进行可靠性和性能测试，以及重新获得认证。环氧树脂原料的交货期已经从三周延长到了十五周。

在成功推出开源电子纸显示屏 DIY 工具包 Paper Monitor 和 Dev Kit 后，创业公司 Modos 准备推出一款完整的量产版显示屏。它在 Crowd Supply 上发起了众筹活动，筹款目标是 17.5 万美元，该目标已经达成，目前的金额达到了 45.6 万美元。Crowd Supply 计划推出的是 13.3 英寸、分辨率 3,200 x 2,400，支持触控，刷新率达到 60-Hz 的电子纸显示屏，其中黑白版本的众筹价格是 619 美元，彩色版本价格 719 美元。公司的两位联合创始人 Alexander Soto 和 Wenting Zhang 接受了 IEEE Spectrum 的采访。

根据发表在《科学》期刊上的一项研究，乌克兰研究人员利用相机陷阱调查了武装冲突对野生动物的影响，将 2022 年观察到的情况与 2021 年同期进行了对比。他们发现哺乳动物会通过行为调整应对武装冲突，其中包括减少夜间活动。武装冲突对卷入其中的人类是可怕的；野生动物也同样会被殃及。然而由于研究人员难以进入武装冲突地区且会面临危险，因此要理解此类冲突的影响会充满挑战。研究人员利用已运作中的相机陷阱来了解战争对野生动物的影响。他们发现冲突对该地区的哺乳动物产生了明显影响，其中包括这些动物的活动减少，尤其是在激烈冲突期间。此类影响证实，政治动荡所伤害的不仅是直接卷入其中的人类。