Weekly Digest — 2026-W22

Make your own personal website with bento tiles

MCP-native self-updating context layer for your AI

The coding-agent harness you can make your own

AI that learns how you work and turns it into software

AI music engine with granular stem control

Use the right LLMs in your apps. Setup fallbacks. Be happy.

Fast, accurate STT for production-grade voice agents

AI coding platform built specifically for the Python stack

Tell Scribe what to say. It writes the rest.

API for agentic multi-document processing

Launch and run any compute job on AWS with 1 command

Turn talking, pics, or scribbles into clean, structured data

Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should instead be trained as the external state of a frozen agent, with the same discipline that makes weight-space optimization reproducible. SkillOpt is, to our knowledge, the first systematic controllable text-space optimizer for agent skills: a separate optimizer model turns scored rollouts into bounded add/delete/replace edits on a single skill document, and an edit is accepted only when it strictly improves a held-out validation score. A textual learning-rate budget, rejected-edit buffer, and epoch-wise slow/meta update make skill training stable while adding zero inference-time model calls at deployment. Across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex, Claude Code), SkillOpt is best or tied on all 52 evaluated (model, benchmark, harness) cells and beats every per-cell competitor among human, one-shot LLM, Trace2Skill, TextGrad, GEPA, and EvoSkill skills. On GPT-5.5 it lifts the average no-skill accuracy by +23.5 points in direct chat, by +24.8 inside the Codex agentic loop, and by +19.1 inside Claude Code. Transfer experiments further show that optimized skill artifacts retain value when moved across model scales, between Codex and Claude Code execution environments, and to a nearby math benchmark without further optimization.

Diffusion Transformers (DiTs) have become a de facto backbone of modern visual generation, and nearly every major axis of their design -- tokenization, attention, conditioning, objectives, and latent autoencoders -- has been extensively revisited. The residual stream that governs how information accumulates across layers, however, has been directly inherited from the original Transformer. In this paper, we present a systematic empirical analysis of cross-layer information flow in DiTs, jointly along depth and denoising timestep, and identify three concrete symptoms of traditional residual addition, namely monotonic forward magnitude inflation, sharp backward gradient decay, and pronounced block-wise redundancy. Motivated by this diagnosis, we propose Diffusion-Adaptive Routing (DAR), a drop-in residual replacement that performs learnable, timestep-adaptive, and non-incremental aggregation over the history of sublayer outputs. Moreover, the proposed DAR is compatible with many modern Transformer enhancement methods, such as REPA. On ImageNet 256times256, DAR improves SiT-XL/2 by 2.11 FID (7.56 vs.\ 9.67) and matches the baseline's converged quality with 8.75times fewer training iterations. Stacked on top of REPA, it yields a 2times training acceleration in the early stage, suggesting cross-layer information routing as an underexplored design axis in diffusion modeling, one that operates orthogonally to existing representation-alignment objectives. Beyond pretraining, DAR can also be applied during the fine-tuning stage of large-scale T2I models and preserves high-frequency details during Distribution Matching Distillation.

We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with more than 6B parameters across various benchmarks, while requiring significantly less training compute. For example, Lens requires only about 19.3% of the training compute used by Z-Image. The training efficiency of Lens stems from two key strategies beyond its compact model size. First, we maximize data information density per training batch by (i) training on Lens-800M, a dataset of 800M densely captioned image-text pairs whose captions are generated by GPT-4.1 and contain approximately 109 words on average, providing richer semantic supervision than conventional short captions, and (ii) constructing each batch from images with multiple resolutions and diverse aspect ratios, thereby enlarging the effective visual coverage of each optimization step. Second, we improve convergence speed through careful architectural choices, including adopting a semantic VAE that provides better latent representations and employing a strong language encoder that accelerates optimization while enabling multilingual generalization from English-only training data. After pre-training, we apply RL with taxonomy-driven prompts (Lens-RL-8K) and structured reward rubrics to suppress artifacts and improve visual quality, a reasoner module with training-free system prompt search to better align user requests with the model, and distillation-based acceleration for 4-step inference. Through efficient training and systematic optimization, Lens generalizes to arbitrary aspect ratios from 1:2 to 2:1 and resolutions up to 1440^2, and supports prompts in several commonly used languages. Thanks to its compact size, Lens generates a 1024^2 image in 3.15 seconds on a single NVIDIA H100 GPU, while its distilled turbo version performs 4-step generation in 0.84 seconds.

The exponential growth of global academic output has confronted researchers and AI agents with an unprecedented ``information explosion,'' where fragmented and unstructured knowledge organization impedes deep interdisciplinary integration. Current academic retrieval tools predominantly rely on superficial keyword matching or vector-space semantic retrieval, which lack the topological reasoning capabilities required to navigate complex logical connections. Agentic deep-research-based frameworks are often prone to logical hallucinations and consuming high inference costs. To bridge this gap, in this report, we introduce SciAtlas, a large-scale, multi-disciplinary, heterogeneous academic resource knowledge graph designed as a panoramic scientific evolution network. By integrating over 43M papers from 26 disciplines, and a total of 157M entities and 3B triplets, SciAtlas provides a structured topological cognitive substrate that dismantles disciplinary barriers and furnishes AI agents with a global perspective. Furthermore, we develop a neuro-symbolic retrieval algorithm featuring tri-path collaborative recall and graph reranking, achieving a seamless transition from simple semantic matching to deterministic association discovery. We also present key application directions of SciAtlas, including literature review, automated research trend synthesis, idea positioning, and academic trajectory exploration, to demonstrate that SciAtlas can serve as an effective ``cognitive map'' to empower the full loop of automated scientific research while significantly reducing reasoning costs. We have released the interfaces for KG retrieval and various downstream tasks in our GitHub repo.

Unified audio-language modeling has emerged as a prominent trend in modern speech systems, promising to bring the reasoning capabilities of large language models to auditory tasks. However, existing unified foundations often struggle to match the depth of specialized systems across automatic speech recognition (ASR), text-to-speech synthesis (TTS), and realtime spoken interaction. Bridging this gap remains an open challenge. This report presents StepAudio 2.5, a unified audio-language foundation model that matches or exceeds specialized systems across all three capabilities. Rather than treating these tasks as architecturally distinct, we operate on the premise that once text and audio share a multimodal representational space, task specialization becomes a matter of operational regimes: data construction, optimization targets, and decoding constraints. Guided by this insight, we advance the post-training paradigm from standard supervised learning to task-tailored Reinforcement Learning from Human Feedback (RLHF), using it as the primary mechanism to define complex optimization targets. We leverage this RLHF-centric alignment, alongside specialized decoding, to shape a shared backbone into three distinct operational modes. Concretely, the ASR branch advances transcription efficiency via verifiable multi-token decoding; the TTS branch achieves controllable, expressive synthesis through preference-based RLHF and context-rich supervision; and the Realtime branch realizes low-latency, persona-consistent dialogue via generative reward modeling within an RLHF framework. On standard benchmarks, StepAudio 2.5 achieves state-of-the-art results across ASR, TTS, and Realtime, demonstrating that a singular audio-language foundation can successfully internalize the distinct deployment objectives of speech understanding, generation, and live interaction.

We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understanding solely from textual prompts. Unlike existing approaches that require explicit visual prompts, such as masks or points, SWIM leverages mask supervision only during training to guide cross-modal attention, allowing the model to automatically attend to the user-specified object at inference. Our cross-attention analysis of pretrained multimodal large languagemodels (MLLMs) reveals a systematic discrepancy: Attribute words produce sharp, localized activations in the visual modality, whereas object nouns yield diffuse and scattered patterns due to semantic reference bias and distributed high-level representations. To address this misalignment, we construct NL-Refer, an enriched dataset, in which each object mask is paired with a precise natural language referring expression. SWIM extracts multi-layer cross-attention maps from object nouns and enforces spatial consistency with ground-truth masks. Experimental results demonstrate that SWIM substantially improves text-visual alignment and achieves superior performance over visual-prompt-based methods on fine-grained object understanding benchmarks. The code and data are available at https://github.com/HumanMLLM/SWIM{https://github.com/HumanMLLM/SWIM}.

Reinforcement Learning has become a standard paradigm for aligning Large Language Models with human intent and task requirements. While Group Relative Policy Optimization offers an efficient, value-model-free alternative to Proximal Policy Optimization, adapting it to real-world multi-reward settings remains challenging. Standard scalarization practices, such as Reward Combination and Advantage Combination, suffer from significant drawbacks: Reward Combination frequently generates advantages with excessively large squared magnitudes that lead to training instability, while Advantage Combination relies on static hyperparameters and ignores cross-objective correlations. To address these limitations, we propose Dynamic Variance-adaptive Advantage Optimization (DVAO), which dynamically adjusts combination weights based on the empirical reward variance of each objective within a rollout group, effectively up-weighting objectives with a stronger learning signal while suppressing noisy ones. We mathematically prove that DVAO maintains bounded advantage magnitudes for stable training and introduces a self-adaptive cross-objective regularization mechanism. Extensive experiments on mathematical reasoning and tool-use benchmarks using Qwen3 and Qwen2.5 models demonstrate that DVAO significantly outperforms baseline methods, achieving a superior multi-objective Pareto frontier and robust training stability.

Interactive world models are advancing rapidly, yet existing benchmarks cover only part of the required competencies, leaving no unified standard for systematic evaluation. To fill this gap, we introduce WBench, a comprehensive multi-turn benchmark for interactive world model evaluation along five dimensions, namely video quality, setting adherence, interaction adherence, consistency, and physics compliance. WBench contains 289 test cases and 1,058 interaction turns, where each case specifies a world setting and a multi-turn interaction sequence, covering diverse scenes, styles, subjects, and both first- and third-person perspectives, together with four interaction types, including navigation, subject action, event editing, and perspective switching. For navigation, WBench unifies text, 6-DoF pose, and discrete-action control, enabling evaluation of models with different native input interfaces. Evaluation uses 22 automatic sub-metrics that combine specialist vision models with large multimodal models, and all metrics are validated against human judgments. Across 20 state-of-the-art models, we find that no single model performs strongly across all dimensions. We provide detailed diagnostic insights into the characteristic strengths, weaknesses, and open challenges of each model. Code and data are available at https://github.com/meituan-longcat/WBench.

As personal agents evolve to handle complex, user-centric tasks, static plain-text chat is rapidly becoming a bottleneck. Generative UI emerges as the necessary new interface layer, dynamically synthesizing the right controls, options, and state from the interaction context in real time. We present Macaron-A2UI, a model for Generative UI in personal agents. Our goal is to move beyond text-only interaction by enabling agents to generate natural language together with lightweight, executable UI actions for information collection, preference refinement, confirmation, and multi-goal organization. We build a large-scale Generative UI corpus from heterogeneous dialogue sources, introduce A2UI-Bench for controlled evaluation, and train 30B, 235B and 754B models with parameter-efficient LoRA-based supervised fine-tuning followed by reward-driven reinforcement learning. The best Macaron-A2UI model reaches 75.6 overall on A2UI-Bench without explicit schema hints, surpassing the strongest full-schema frontier baseline. We release the models, benchmark, and evaluation protocol to support future work on Generative UI for personal agents.

Autonomous agents are moving from tools into a layer of social infrastructure: they browse, purchase, deploy software, manage systems, and increasingly interact with one another. As these systems scale, the bottleneck shifts away from raw model capability toward coordination. Agents need to form reliable relationships, organize multi-agent work, exchange value, support an AI economy, and stay safe and accountable under real-world oversight. This paper introduces the Foundation Protocol (FP), a graph-first coordination layer for an emerging human-AI society. FP unifies heterogeneous entities, including agents, tools, resources, humans, institutions, and organizations, and supports native multi-party organization and event-based collaboration. It also provides economic primitives for metering, receipts, and settlement, and treats policy, provenance, and audit as first-class concerns. FP is designed to wrap and bridge existing protocols rather than replace them, enabling incremental adoption while reducing integration and governance overhead. The aim is to keep autonomous agency composable while keeping accountability non-negotiable, so that coordination itself can become shared infrastructure for a human-AI society that is open, pluralistic, and governable.

Sparse-view 3D reconstruction is increasingly addressed with feed-forward splatting networks that predict explicit primitives directly from images. Yet most existing methods remain centered on Gaussian primitives and expose surfaces only indirectly: extracting a usable mesh for downstream simulation, physics reasoning, or embodied interaction still requires expensive post-hoc steps that break the feed-forward promise. This limitation is especially pronounced in pose-free settings, where scene structure and camera parameters must be estimated jointly from sparse observations. We present TriSplat, a feed-forward reconstruction network that represents scenes with oriented triangle primitives and directly exports simulation-ready mesh scenes from a single forward pass. Given input images, the network predicts local 3D point maps, triangle attributes, camera poses, and optional intrinsics. Rather than regressing triangle orientation as an unconstrained latent variable, our approach constructs geometry normals from the predicted point maps, refines them with an image-conditioned normal head, and converts them into stable local frames for triangle parameterization. A mono-normal bootstrap schedule further stabilizes early training, while opacity and blur scheduling progressively sharpens the learned surface representation for direct mesh extraction. Experiments on RealEstate10K and DL3DV show that this representation produces more geometry-faithful reconstructions than Gaussian feed-forward baselines while maintaining competitive novel-view rendering quality. Because the rendering primitives are themselves surface triangles, the output can be directly ingested by physics engines, collision detectors, and standard rendering pipelines without any conversion, making it a practical simulation-ready solution for feed-forward 3D scene reconstruction.

Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders and frozen language backbones with output heads, recent efforts have shifted the paradigm toward native multimodal modeling (NMM) with the intrinsic integration of modalities for superior multimodal performance. Despite its potential, the design space of native architectures remains insufficiently defined. In this paper, we present the community with a formalized roadmap for this transition. Specifically, we formally define the architectural nativity, distinguishing mid-fusion and early-fusion from non-native paradigms. We further organize the existing native models through the lens of input-output duality into three categories: (i) Multi-to-Text for cross-modal comprehension with text-only output; (ii) Multi-to-Target for scenario-oriented generation, e.g., image, audio and video generation, and (iii) Multi-to-Multi for unified modeling with symmetric input-output. We deliver a comprehensive and industrial-grade investigation into the transition toward the definitive NMM framework, where understanding and generation seamlessly coexist within a unified transformer paradigm. We systematically unpack the end-to-end pipeline from industrial perspectives from architectural coordination, massive data curation, to full-stack training recipes, inference & deployment, and the comprehensive evaluation for truly native modeling.

Lakshmi Varanasi / Business Insider : X says it is cracking down on large accounts that have been gaming its revenue-sharing program by “programmatically reuploading content from smaller accounts” —  X is introducing new strategies to rein in its copycat economy.  —  Elon Musk's social media platform is now cracking …

Reuters : Iranian state media reports that President Masoud Pezeshkian has issued an order to reopen international internet access after a near-90-day blackout —  Iran's President Masoud Pezeshkian has issued an order to reopen international internet access, Iranian state media reported on Monday …

Reuters : Report: the EU plans to fine Google a high triple-digit million euro amount as part of a 2025 probe over concerns it favors its own services in search results —  The European Union is planning to fine Alphabet's (GOOGL.O) Google a high triple-digit million euro amount as part of an antitrust investigation …

New York Times : A look at the UK's AI Safety Institute, whose researchers probe AI models for safety gaps, as its work becomes a blueprint for other governments' AI policies —  The government's A.I. Security Institute, staffed by alumni from OpenAI and Google, is becoming a model for countries grappling with A.I.'s emerging risks.

Tristan Bove / Fortune : Study: rate of fabricated references in biomedical papers has grown 12x+ since 2023; in early 2026, one in 277 papers had at least one non-existent reference —  It was a process that had become routine for Maxim Topaz.  —  The associate professor at Columbia University's School of Nursing …

Check Point Research : How Iranian threat actor Nimbus Manticore used techniques like AI-assisted malware development and SEO poisoning to target companies during the US-Iran war —  Key Findings  — The Iranian, IRGC affiliated, threat actor Nimbus Manticore resurfaced during Operation Epic Fury …

Axios : Sources: Bond Capital is leading a new investment for AI startup Suno, which would value it at ~$5B, up from $2.45B last fall; Suno is expected to raise $250M+ —  Bond Capital is leading a new investment for AI music startup Suno, which would be valued at around $5 billion, Axios Pro has learned.

Lawrence Abrams / BleepingComputer : Charter confirms a data breach after ShinyHunters claimed to steal 40M customer records from Charter's Salesforce instance and threatened to leak the data —  U.S. telecommunications giant Charter Communications has confirmed it suffered a data breach after the ShinyHunters extortion group threatened …

Samantha Subin / CNBC : Micron hit a $1T market value for the first time on Tuesday as shares jumped 19%, driven by demand for its memory chips in the AI race —  Micron topped a $1 trillion market value for the first time on Tuesday as shares popped 19%, driven by insatiable artificial intelligence demand for its memory chips.

Jeremy Kahn / Fortune : London-based Perceptic, which says its end-to-end AI platform for drug development is being used by top pharmaceuticals, raised a $12M seed led by Accel —  A trio of former Palantir executives who helped spearhead that company's Life Sciences practice have founded a startup called Perceptic …

Nilay Patel / The Verge : Q&A with Sundar Pichai about reshaping the information ecosystem with Search changes, putting AI agents in everything, when AI will replace him as CEO, and more —  Today, I'm talking with Google and Alphabet CEO Sundar Pichai, in a conversation we recorded just after the Google I/O developer conference.

Tom Warren / The Verge : Nvidia officially retires its GeForce Control Panel app after 20 years, following the porting of all of its major features to the Nvidia app —  Nvidia has now ported across all of the major Control Panel features to its Nvidia app. … Nvidia announced more than two years ago that it was working …

欧洲刑警组织披露，他们黑进了被网络犯罪分子使用的 VPN 服务“First VPN”，访问了用户数据库，识别了数千用户身份。First VPN 的网站已经显示被执法部门扣押的信息，它过去曾在俄语网络犯罪论坛上打广告，宣称能隐藏用户的 IP 地址，加密所有通信，不记录任何日志。它还声称将拒绝与司法机关合作，其服务不受任何司法管辖，且不会存储任何用户数据。First VPN 的活动始于 2014 年，在 27 个国家/地区提供了 32 个出口节点服务器。至少有 25 个勒索软件组织利用了其基础设施进行网络侦察和入侵。警方搜查了该服务管理员在乌克兰的住所，拆除了 33 台服务器。

对英伟达、AMD、Google 和亚马逊四家公司的 AI 芯片的分析显示，HBM 内存芯片成本占到了 AI 芯片组件成本的三分之二（63%），逻辑芯片占 13%，先进封装占 15%，辅助组件占 9% 。四家公司在 HBM 上的支出从 2024 年的约 120 亿美元增至 2025 年的 320 亿美元，增速远超其它芯片组件。随着内存芯片供应持续紧张且价格上涨，HBM 在 2026 年的市场份额可能会进一步扩大。超大规模数据中心运营商在其资本支出预期中已经预见到这一点：微软 2026 财年 1900 亿美元的资本支出预期中，约有 250 亿美元来自组件价格上涨；Meta 将其 2026 年资本支出预期上调了 100 亿美元，理由同样是组件价格上涨。

过去几个月惠普笔记本电脑用户通过论坛等报告在更新 BIOS 之后设备出现了问题，包括设备无法启动、风扇噪音异常以及蓝屏死机等等。一名移动工作站 ZBook Ultra G1a 的用户称更新 BIOS 之后设备在启动过程中卡住。受影响的产品包括 ZBook Ultra G1a，存在问题的 BIOS 版本号 01.04.03 和 01.04.05；EliteBook X G1a，存在问题的 BIOS 版本号 01.03.11 和 01.05.00。惠普表示它正对此展开调查，建议受影响的用户联系其技术支持团队。这不是第一次惠普设备因为存在问题的 BIOS 更新而导致设备故障。

俄罗斯政府已推迟对使 VPN 的移动互联网用户收费的计划。俄罗斯数字发展部在三月表示将打击 VPN 的使用。它最初要求移动网络运营商从 5 月 1 日起对每月国际数据流量超 15GB 的用户收费。但由于追踪 VPN 使用和计费方面存在困难，该期限已推迟至 6 月 1 日。该收费计划可能会再次被推迟，可能会在 9 月底国家杜马和地方选举之后实施。原因是一个功能完整的国际流量支付系统需要三到四个月才能建成。在这项政策推行前，俄罗斯的移动互联网频繁发生中断事件。

根据 PNAS 期刊上的一项研究，政治情绪的生理反应和日常经历的普通情绪不同。研究人员邀请近 1000 名美国参与者使用名为 emBODY 的身体映射工具，绘制出感受到的普通情绪和政治情绪的身体部位。研究发现，政治情绪有着独特的身体反应模式。举例来说，政治抑郁会引发身体更广泛、更强烈的感受，而非普通抑郁的麻木感。这意味着政治绝望会激励人行动而不是对一切漠然。政治厌恶感与普通厌恶感也不同。病原体引起的厌恶感如呕吐反应会在胃部和喉咙强烈感受到，而政治厌恶感则更像是愤怒。这意味着政治将厌恶感转化为一种更具道德感和愤怒感的情绪，改变了对政治厌恶感的思考方式。研究还发现不同意识形态的人体验的政治情绪存在差异。倾向于民主党的参与者相比倾向共和党的参与者，对愤怒、焦虑、抑郁和厌恶等负面政治情绪的身体感受更为强烈。

几十年来，降低空气阻力的一大原则是表面必须光滑。日本东北大学研究团队率先证明，仅仅应用分布式微粗糙度（distributed micro-roughness 或 DMR），就能将空气阻力降低达 43.6%。DMR 是一种肉眼无法分辨的、极其微小且不规则的表面粗糙度。研究团队利用 1m-MSBS 系统精确测量了光滑表面和 DMR 涂层表面的阻力系数，结果显示 DMR 涂层表面的阻力系数低于光滑表面。

2021 年美国德州通过法案禁止孕妇在妊娠约 6 周后堕胎。2022 年美国最高法院在 Dobbs v. Jackson Women’s Health Organization 一案中裁决宪法未赋予公民堕胎权，因此推翻了 1973 年的 Roe v. Wade 案。截至 2026 年初美国有 13 个州全面禁止堕胎，7 个州禁止孕妇妊娠 22 周后堕胎。严格堕胎禁令被认为会增加妊娠相关死亡率。发表在《American Journal of Public Health》期刊上的一项研究调查了严格堕胎禁令对孕妇健康的影响。结果显示，在 14 个严格禁止堕胎和禁止妊娠 6 周后堕胎的州，妊娠相关死亡比预期高 9.2%。

Meta 在 2021 年开源了缓存引擎 CacheLib，该项目旨在利用非易失性存储器作为缓存去扩展服务，以抵消不断上涨的 DRAM 成本。该项目在 2024 年 6 月之后就停止了更新，但在 2026 年 5 月 25 日 Meta 再次释出了更新——而今天由于 AI 热 DRAM 价格相比 2021 年几乎是天价。

科学家首次记录了一次非凡的鲸类迁徙壮举，证实两头座头鲸在澳大利亚东部和巴西的繁殖地之间，穿越了超过 1.4 万公里的海洋。研究人员通过对比数万张座头鲸尾鳍的图像来辨认这些鲸。每头鲸的尾鳍都有独特的斑纹，这使得研究人员能长期追踪并识别个体。2007 年，一头座头鲸在澳大利亚昆士兰州的赫维湾首次被拍到。2013年，它再次出现在同一海域，随后于 2019 年现身巴西圣保罗附近。这些繁殖地之间的最短直线距离约为1.42万公里。第二头座头鲸更令人惊叹。研究人员于2003年首次在巴西阿布洛霍斯礁群——该国主要的座头鲸繁殖地，拍摄到了它的身影。当时它正与由9头成年鲸组成的活跃群体一起游弋。22年后的2025年9月，同一头鲸被发现在澳大利亚赫维湾独自游弋。两次目击地之间的距离达 1.51 万公里，这创下了单头座头鲸已知最远迁徙距离的新纪录。研究基于19283张高质量的鲸照片，这些照片拍摄于1984年至2025年间，采集自澳大利亚东部和拉丁美洲。这些图像既来自专业研究人员，也来自通过全球鲸追踪平台“Happywhale”参与的公民科学家。

英国皇家医学院学会在递交给政府的咨询意见书中表示，社交媒体的使用与吸烟一样对年轻人的健康构成威胁。医生在接诊年轻患者时，应例行询问他们的屏幕时间和社交媒体使用情况。英国政府正在考虑的一项措施是禁止 16 岁以下儿童使用社交媒体，类似澳大利亚的做法。其它可能采取的限制包括宵禁，或禁用自动播放和无限滚动等功能。儿童精神科医生 Emily Sehmer 认为过度使用社媒的危害远甚于吸烟，因为儿童只需几秒钟就会接触到有害内容。

Uber 高管表示 AI 上支出并没有带来相应的回报。Uber COO Andrew Macdonald 上周六接受采访时表示愈来愈难以证明最大化 AI 词元花的钱是合理的。而在上个月的一次采访中 Uber CTO Praveen Neppalli Naga 告诉 The Information，该公司已经用完了 2026 年的 Claude Code 预算。Macdonald 称，通过与工程主管的交流，他认识到更高的 AI 词元使用量并没有转变为消费者功能的相应增加。他说 AI 带来的权衡成本愈来愈难以证明支出是合理的。

JAXA、早稻田大学、东京大学和庆应义塾大学的工程师团队成功完成了为五马赫高超音速飞机设计的冲压式发动机的地面燃烧试验。冲压发动机利用了发动机的前向运动来压缩空气，不使用带有可旋转叶片的压气机，它无法在空速为零的时候产生推力，需要先加速到超音速。在测试中，一架实验飞机被安装在 JAXA 角田宇宙中心的风洞中，模拟约 25 公里高空的环境条件。在五马赫的飞行速度下，机头和前缘周围的空气温度会超过 1000 摄氏度，为应对高温，工程师设计了一套先进的热防护系统，使飞机内部温度保持在接近正常工作温度的范围内，保证机载航空电子设备和控制电子设备的正常运行。JAXA 接下来计划将实验飞行器搭载在探空火箭上尝试实际飞行，它的目标是到 2040 年代实现商业高超音速客运服务。